Network address shuffling is a novel moving target defense (MTD) that invalidates the address information collected by the attacker by dynamically changing or remapping the host’s network addresses. However, most network address shuffling methods are limited by the limited address space and rely on the host’s static domain name to map to its dynamic address; therefore these methods cannot effectively defend against random scanning attacks, and cannot defend against an attacker who knows the target’s domain name. In this paper, we propose a network defense method based on random domain name and address mutation (RDAM), which increases the scanning space of the attacker through a dynamic domain name method and reduces the probability that a host will be hit by an attacker scanning IP addresses using the domain name system (DNS) query list and the time window methods. Theoretical analysis and experimental results show that RDAM can defend against scanning attacks and worm propagation more effectively than general network address shuffling methods, while introducing an acceptable operational overhead. One important phase in most cyber attacks is network reconnaissance, where the attacker attempts to gather information about the potential target, including its IP address, operating system, and network services, searching for vulnerabilities to exploit and attack. It has been reported that attackers can spend an average of 45 percent of their time performing reconnaissance . TherefoIn conventional networks, the host’s network address and service port remain unchanged throughout the service lifetime, and this static service characteristic gives the attacker sufficient time to scan the target for detailed information and deploy a subsequent attack. However, traditional security systems such as firewalls, intrusion detection systems (IDS), and antivirus software are passive and incomplete in nature, and can detect only known attacks.Moving target defense (MTD)  is a divSome network address and service port shuffling methods –9 need tIn this paper, we propose a network defense method called Random Domain name and Address Mutation (RDAM) to increase the defense performance against internal and external reconnaissance and scanning. In RDAM, both the domain name and network address are changed dynamically, and the legitimate client must send a DNS request with the server’s dynamic domain name to acquire the server’s IP address. Therefore, the attacker cannot build a domain-based hit-list, and the attacker has to expend more resources when scanning the domain space because the domain space is much larger than the address space. We evaluate the effectiveness and overhead of RDAM through theoretical analysis and experiment, and the results show that RDAM can effectively defend against scanning attack and worm propagation, while introducing an acceptable operation overhead.The rest of this paper is organized as follows. Section 2 discusses related works. Section 3 describes the basic principles of RDAM. In Section 4, we describe the basic architecture and communication protocols of RDAM. Section 5 provides the evaluation results regarding the proposed method’s effectiveness and overhead. Section 6 concludes this paper.Network address shuffling techniques aim to achieve proactive network defense by dynamically changing the IP address (and service port) of the target. Currently, researchers have proposed a variety of network address shuffling techniques, in which two patterns emerge: hopping and mutation .a priori knowledge and synchronization scheme; thus, their software or systems need to be modified, which increases the deployment cost. MT6D (Moving Target IPv6 Defense) [In the hopping pattern, both communication sides need to change their addresses synchronously by shared Defense)  is a hopDefense) . MT6D caDefense) , in whicDefense)  and APODDefense)  use addrDefense)  enhancesIn the mutation pattern, address shuffling does not need to be synchronized, and the client host usually connects to the server host by a DNS request/respond or other third-party mechanisms. NASR (Network Address Space Randomization)  providesRegarding the theoretical analysis of the effectiveness of network address shuffling, Carroll et al.  presenteIn a statically configured network, hosts communicate through static IP addresses, and therefore the attacker can scan the IP address space, identify a target hit-list after receiving responses, and execute persistent attacks using the hit-list. In a network address shuffling network based on domain name mapping, the IP address of the host constantly changes, and therefore the attacker cannot maintain a target hit-list of effective IP addresses. However, the domain name of the host remains static, and therefore the attacker can maintain a domain-based hit-list to attack. In addition, limited by the limited IP address space, network address shuffling cannot effectively defend against random scanning attacks.RDAM dynamically changes the host’s IP address and domain name based on the SDN architecture. A legitimate client host must first make a DNS request before connecting to the service, and the client can connect to the service only if the correct domain name is requested. The client can retrieve the correct domain names through a security authentication server. Therefore in RDAM, the attacker cannot establish a target list of domain names or addresses, and it is difficult for the attacker to directly access a host in the network through the IP address. While the domain name space is much larger than the address space, RDAM greatly increases the difficulty to an attacker trying to scan the network based on domain names.The internal hosts in RDAM include public hosts that provide services to the external network (Internet) and private hosts that cannot be accessed by the external network. Internal hosts communicate with each other through internal addresses, while public hosts communicate with the external network through external addresses. If private hosts need to access the external network, the gateway must deploy the NAT functionality to realize the communication with the external network.In RDAM, the mutation space to be considered includes the internal address space, the external address space, and the domain name space.SR as all reserved addresses and SP as all public addresses; then the internal address space is SI ⊂ SR with a size mI = |SI| ≤ 224 + 220 + 216; the external address space is SE ⊂ SP with a size (the number of available public addresses) mE = |SE| that is very limited because of the IPv4 address space allocation strategy.Internal addresses and external addresses should be reserved addresses and public addresses in the IPv4 address space, respectively. Denote l > 1. Because domain names consist of letters (case-insensitive), numbers, and connectors (not at the beginning or end), then SD is the domain name space with a size of mD = |SD| = 362 × 37l−2. By setting the appropriate l, mD can be much larger than mE (mD ≫ mE). In particular, when l is 6, the size of the domain name space can be the same order of magnitude as the entire IPv4 address space size.We use third-level domain (3LD) names for the hosts’ domain names, where the 3LD is a random string of length nI internal hosts in the RDAM network nE ≤ nI public hosts maci as the MAC address of host hi, and ifi as the interface of the software-defined switch to which hi is connected. In particular, denote ifE as the interface of the software-defined switch to which the gateway is connected, and GMAC as the MAC address of the gateway’s interface connecting to the internal network. Then the address and domain name mutation method can be executed as follows.Suppose there are iIPi ∈ SI of host hi is the host’s real address. In order to protect communications within a physical subnet , the internal addresses of all the hosts in RDAM occupy different subnets of the internal address space so that the internal hosts cannot communicate directly through common switches. Denote L as the mask length of all subnets; then the subnet of iIPi is nI ≤ mI/2L32−.In RDAM, the internal address gIPi ≠ iIPi, and the MAC address gMACi is generated randomly. The virtual gateway can respond to some basic protocols such as address resolution protocol (ARP) and Internet control message protocols (ICMP). After the connection to the network has been established, each host will obtain the MAC address of its virtual gateway through ARP protocol for subsequent communications.In order to ensure normal communication and confuse the attacker in RDAM, each subnet hi we record its internal DNS query list hi. Therefore, if the attacker does not know the internal host's domain name, then even if the attacker knows the host’s internal address, the attacker still cannot access the host, so the internal addresses can mutate over a long period or remain unchanged.The controller can directly obtain DNS requests from internal hosts; therefore in RDAM, for each host In addition, the internal host can obtain the internal address through DHCP, and can also use a statically configured internal address; however, the subnet of the configured address cannot be repeated by other internal hosts. If hosts in the same physical subnet are configured with the same subnet, then they can directly access each other without protection. Therefore, in RDAM, all the hosts should be configured in separate subnets.si, we record its external DNS query time eIPi ∈ SE from the external network. Only within the following wini time range can external hosts access si through eIPi.Because of the recursive query, the controller may not obtain the IP address of the external host in the DNS request from the external network; therefore, it is not possible to identify the external host issuing the DNS query. To address this issue, RDAM uses the time window method to reduce the probability that the attacker can access the public host through the IP address. For each public host λi(t) of the DNS query for si at time t. It can be assumed that the time interval of external hosts issuing the DNS query for si follows an exponential distribution with parameter λi(t). Then the probability that external hosts can access si through eIPi is p. Then the time window of si is In order to calculate the time window of each public host, RDAM counts the number of times each public host is DNS-queried by an external host through its domain name to obtain the average speed si; then the next external address mutation time In the time window, the attacker could still access the public host through the external address, and therefore the external address of the public host still needs to be dynamically mutated at a high frequency. Denote hi has a dynamic domain name di ∈ SD. For the convenience of users, the domain names of all hosts are periodically changed at fixed times. Denote T0 as the first domain name mutation time, and TD as the time interval of domain name mutation; then the next domain name mutation time after time t is Each host x; then RDAM records the domain names Dx ⊂ SD that are assigned in the last x cycles. Assume φ denotes the maximum ratio of the number of allocated domain names |Dx| = xnI to the domain name space size; then the non-repeat cycle number of domain names supported by RDAM satisfies x ≤ min.Because the domain name space has sufficient size, it can guarantee that in a sufficient number of domain name mutation cycles, a domain name will not be repeated. Assume the non-repeat cycle number of the domain name mutation is In RDAM, software-defined switches modify and forward transmission control protocol (TCP) and user datagram protocol (UDP) packets according to flow tables; packets that have no matching flows in flow tables and ARP, ICMP, DNS, and DHCP packets are forwarded to the controller. The controller processes the above packets and installs the flows necessary for new connections in the software-defined switches in the path. Each connection must be associated with some fixed flows, thus ensuring end-to-end connectivity continuity.The general packet processing algorithm is presented in Algorithm 1. The flows in the algorithm are the combined description of all software-defined switches in the path, in fact the action of the flows in the source and destination switches should only contain part of the modification action, and the intermediate switches should only contain the forward action. The flow chart of Algorithm 1 is presented in Algorithm 1 Packet Processing Algorithmfor all packets p doif p is a DHCP request for hithen    iIPi, di, gIPi, gMACi, record ifi, maci, Dx∪ = {di}        generate ifhi is a public host then        eIPi,             generate end if        iIPi, gIPi and send to ifi            generate response of else if p is an ARP request for gIPi from hithen        gMACi and send to ifi            generate response of else if p is a Type-A DNS request for hi from hjthen                    iIPi,TTL = next(now)−now and send to ifj            generate response of else if p is a Type-A DNS request for hi from external then        ifE            else if p is a Type-PTR DNS request for hi from hithen        di and send to ifi            generate response of else if p is a TCP-SYN or UDP then        if p is from hi to hj and                             install forward direction flows in switches with actionp.srcMAC ≔ gMACj,p.dstMAC ≔ macj,Forward → ifj                                    install backward direction flows in switches with actionp.srcMAC ≔ gMACi,p.dstMAC ≔ maci,Forward → ifi                    else if p is from private host hi to external then                            install forward direction flows in switches with actionp.srcMAC ≔ gMACi,p.dstMAC ≔ GMAC,Forward → ifE                                    install backward direction flows in switches with actionp.srcMAC ≔ gMACi,p.dstMAC ≔ maci,Forward → ifi                    else if p is from public host si to external or opposite with then                            install outside direction flows in switches with actionp.srcIP ≔ eIPi,p.srcMAC ≔ gMACi,p.dstMAC ≔ GMAC,Forward → ifE                                    install inside direction flows in switches with actionp.dstIP ≔ iIPi,p.srcMAC ≔ gMACi,p.dstMAC ≔ maci,Forward → ifi                    else                            throw pend if            else if p is a ICMP packet then        do action like TCP or UDP packet but do not install flows            else                    throw pend if        endfor    All throw packets, including error packets and connection failure packets, are recorded for subsequent dynamic mutation and attack behavior analysis. Error packets include: 1) packets with incorrect mapping of interfaces, MAC addresses, and IP addresses; and 2) TCP-SYN, UDP, and ICMP packets from an internal host to an external address. Connection failure packets include: 1)DNS packets querying currently unused internal domain names; 2) TCP-SYN, UDP, and ICMP packets connecting to currently unused internal or external addresses; 3) TCP-SYN, UDP, and ICMP packets from a source address that is an internal address and is not in the DNS query list of the destination host; and 4) TCP-SYN, UDP, and ICMP packets that the destination host is a public host, the source address is an external address, and the time is not in the time window of the destination host.x cycles; therefore, the mutation space is SD − Dx. The external addresses of all public hosts are changed randomly according to their mutation rate, and no external address that is assigned to mutated hosts can be the same as the external addresses of all non-mutated hosts; therefore, the mutation space is The domain names of all hosts are periodically changed at fixed times, and no assigned domain name will be repeated in Algorithm 2 Mutation Algorithmfor each time of performing domain name mutation dofor eachhido    di from SD−Dx        random select Dx∪ = {di}        endfor    Dx    remove expired domain name from endforfor each time of performing external address mutation do    for eachsi that do    eIPi from SE−A        random select A ≔ A∪eIPi,         endfor    endforIn the random selection of the domain name and external address in the above algorithm, we consider the following two methods: 1) uniform mutation, that is, the domain names and the external addresses are uniformly selected in the mutation space; and 2) failure-prior mutation, that is, the destination domain names and the addresses of the connection failure packets are preferentially selected.In RDAM, the client host must use the dynamic domain name of the server host to access the services it provides, and the client host can obtain the dynamic domain name of the server host in two ways: 1) for temporary services, the server host obtains its domain name through rDNS (nslookup) and informs the client host; or 2) for long-term services, the client host accesses the security authentication server to obtain the dynamic domain name list according to its authority, and finds the dynamic domain name of the server host in the list.In order to enhance the availability and security of the network, RDAM also used several other techniques to improve the network functionality and increase defense against reconnaissance attacks.When an external host accesses a public host, the destination IP address is different from the actual IP address of the server host. However, in some special application protocols, such as file transfer protocol (FTP), generic routing encapsulation (GRE), etc., the payload contains the server host's IP address to establish connections. Therefore, IP address mutation may disrupt the normal communication of these protocols. RDAM implements application level gateway service (ALG) in the switch connected to the gateway to perform the detection and transformation of the payload when the internal address and the external address of the public host are interchanged, ensuring normal communication through the protocol without requiring the user to implement any special configuration.The attacker can use fingerprinting tools such as Nmap to identify the target host's fingerprint information, such as operating system, set of services, etc., for the purpose of finding a target host’s vulnerabilities. RDAM uses the recognition and replacement method to realize the confusion of some fingerprint information , includiIn order to investigate and validate the feasibility of RDAM, we implemented a RDAM prototype and deployed it in our laboratory network according to We ran several network activities during mutation, including: 1) web browsing, FTP file download, and remote desktop connections of internal hosts and external hosts to public hosts; 2) remote desktop connections of internal hosts to private hosts; and 3) connection of internal hosts to the Internet. All network activities behaved normally, and long-lived connections were maintained during mutations. The network quickly completed the DNS responses and the establishment of connections, while the established data flow packet transmission delay was negligible. Our implementation proved that RDAM is feasible in real networks.In the following, we will present the defense performance of RDAM against scanning attacks and worm propagation, as well as its cost, through theoretical analysis and simulation experiments.Scanning is the primary step in most network attacks. The attacker uses scanning tools, such as Nmap, to discover the active hosts in the target network, and then carries out further attacks. In this paper, we consider the following two typical scanning strategies: 1) non-repeat scans, which never scan the same address twice, usually employed in scanning tools; and 2) repeatable scans, in which the addresses scanned are generated randomly so they may be repeated, usually employed in worms and other malwares. In the following, we will analyze the defense performance of RDAM when using uniform mutation and failure-prior mutation by calculating the expected ratio of hosts missed by the scanner .In repeatable scans, the address or domain name scanned is randomly generated. Therefore, regardless of which mutation method the defender uses, the probability of the scanner hitting a designated host is the same as the probability in the static address environment.Here, we analyze repeatable scanning of domain names and IP addresses from internal and external scanners in RDAM.mD, the probability of the scanner hitting each host in each scan is 1/mD. Then, in k scans the scanner misses each host with probability (1−1/mD)k. Because the probability of each host being hit is independent of each other host, the probability of the scanner missing each host is also the expected ratio of missed hosts. Therefore, the expected ratio of missed hosts in k scans is(1) Domain name scans: First, consider that the internal/external scanner scans domain names. Because the size of the domain name space is mE, RDAM allows Thus, when the number of scans is the same as the external address space size p, so the probability of the scanner hitting each host in each scan is p/mE. Therefore, the expected ratio of missed hosts in k scans is(2) IP address scans by an external scanner: In RDAM, the probability that each public host can be accessed through its external address is mE, RDAM allows Thus, when the number of scans is k scans is(3) IP address scans by an internal scanner: In RDAM, internal hosts can access another host only when they are in the internal DNS query list; therefore, the internal scanner cannot hit any host by scanning IP addresses, and the expected ratio of missed hosts in nI = 12000 internal hosts, of which nE = 6000 hosts were public hosts; the external address space was a Class B subnet (mE = 65536); and the length of the 3LD was 4 (l = 4). mE scans. In existing network address shuffling techniques, such as NASR [p = 1 in To perform simulation experiments, we created a network of SDN switches using Mininet , and dev as NASR , OF-RHM  as NASR , etc., ip = 1, that is, no time window, RDAM allows only 37% of hosts to be missed, while when p = 0.2, RDAM allows 81% of hosts to be missed by adjusting the time window setting.From the figure, we can see that the experimental results are basically the same as those predicted by the theoretical analysis. RDAM allows 96.2% of hosts to be missed by internal and external scanners scanning domain names; an internal scanner cannot hit any host by scanning IP addresses; for external scanners scanning IP addresses, when r denotes the ratio of the attacker scanning rate to the defender mutation rate; that the external address mutation rate of all public hosts is the same; and that they mutate at the same time. When r ≤ 1, the domain name (IP address) will be changed between any two scans; therefore, the scanning process is equivalent to a repeatable scan, and the scan results are consistent with those presented in the previous section.Assume that r ≥ 1, assuming r is an integer.Next, we analyze the case when k′ scan is k′/mD. k ≤ mD scans can be divided into ⌊k/r⌋+1 mutations; therefore, the expected ratio of missed hosts in k scans, which is also the probability of the scanner missing each host, is(1) Domain name scans: First, consider that the internal/external scanners scan domain names. Because the hosts’ domain names are uniformly distributed in the domain name space after each mutation, between two mutations, the probability of the scanner hitting each host in Pmiss decreases as r increases, andThus, we can see that r ≪ k, Pmiss reaches its maximum, and RDAM achieves the best defense performance;1) when r < k ∧ r → k, Pmiss(k) ≈ 1−r/mD; and2) when r ≥ k, Pmiss ≈ 1−r/mD, the domain name mutation offers no additional defense performance compared to the static environment.3) when mE ≪ mD, so mE, RDAM allows 1−mE/mD of the hosts to be missed by the scanner.From the above analysis we can obtain p. Therefore, the expected ratio of missed hosts in k scans is(2) IP address scans by an external scanner: In RDAM, the probability that each public host can be accessed through its external address is p≤Pmiss ≤ ep−. Therefore, when the number of scans is mE, RDAM allows at most ep− and at least 1−p of the hosts to be missed by the scanner.Thus, we can obtain 1−k scans is(3) IP address scans by an internal scanner: In RDAM, the internal scanner cannot hit any host by scanning IP addresses. Therefore, the expected ratio of missed hosts in r increases. The simulation environment is the same as that described in Section 5.1.1. In the simulation, every data point was taken as the average of 10 runs, and the scanner executed a total of mE scans. In particular, the result of the internal/external scanner scanning IP addresses in OF-RHM when the defender adopts uniform mutation is equivalent to the result of the external scanner scanning IP addresses in RDAM when p = 1 in p = 1, that is, no time window, RDAM allows at most 37% of hosts to be missed, and the ratio decreases as r increases, while when p = 0.2, RDAM allows 80% of hosts to be missed by adjusting the time window setting.From the figure, we can see that the experimental results are basically the same as those predicted by the theoretical analysis. RDAM allows 96.2% of hosts to be missed by internal and external scanners scanning domain names; internal scanners cannot hit any host by scanning IP addresses; for external scanners scanning IP addresses, when r denotes the ratio of the attacker scanning rate to the defender mutation rate; that the external address mutation rate of all public hosts is the same; and that they mutate at the same time. Similarly, when r ≤ 1, the scan results are consistent with those presented in Section 5.1.1.Assume that r ≥ 1, assuming r is an integer.Next, we also analyze the case when n . In failure-prior mutation, the domain names missed by the scanner are preferentially selected as hosts’ domain names, and the scanner will never scan those domain names in non-repeat scans, so some hosts will not be hit by the scanner. Denote Xi as the number of inaccessible hosts in the ith mutation interval; then X1 = 0.(1) Domain name scans: First, consider that the internal/external scanner scans domain names, and denote the number of hosts as ith mutation interval, the domain names of the remaining n−Xi hosts are uniformly distributed in the domain name space, the size of which is mD−Xi. In this interval, the probability of the scanner hitting each host in k′ scans is k′(n−Xi)/(mD−Xi). The scanner can execute r scans in this interval, and therefore the number of missed domain names is r−r(n−Xi)/(mD−Xi), which is the number of inaccessible hosts in the i+1th mutation interval. Considering that the number of hosts is n, we can obtain Xi+1 = min/(mD−Xi)).In the k ≤ mD scans could be divided into ⌊k/r⌋ + 1 mutations; therefore, the expected ratio of missed hosts in k scans, which is also the probability of the scanner missing each host, isX1 = 0, Xi+1 = min/(mD−Xi)).Thus we can obtain thatr ≪ k ∧ r ≪ n, Xi is negligible, so 1) when r < k ∧ Xi < n, the larger r is, the larger Xi is, and the better the defense performance is, so Pmiss increases as r increases;2) when r < k ∧ X2 = n, namely, n/(1−n/mD) ≤ r < k, Xi = n,i > 1, we can obtain Pmiss ≈ 1−r/mD, so Pmiss decreases as r increases; and3) when r ≥ k, Pmiss ≈ 1−k/mD.4) when r = n/(1−n/mD), Pmiss reaches its maximum; when r ≥ k, Pmiss reaches its minimum, so mE, RDAM allows at most mE/mD of the hosts to be missed by the scanner.From the above analysis we find that when p. Therefore, the expected ratio of missed hosts in k scans isX1 = 0, Xi+1 = min/(mE − Xi)).(2) IP address scans by an external scanner: In RDAM, the probability that each public host can be accessed through its external address is r < k ∧ X2 = n, namely n/(1−pnE/mE) ≤ r < k, Pmiss ≈ 1−pr/mE can reach its maximum; when r ≥ k, Pmiss ≈ 1−pk/mD reaches its minimum, so mE, RDAM allows at most p of the hosts missed to be by the scanner.Similarly, we find that when k scans is(3) IP address scans by an internal scanner: In RDAM, the internal scanner cannot hit any host by scanning IP addresses. Therefore, the expected ratio of missed hosts in r increases. The simulation environment is the same as that presented in Section 5.1.1. In the simulation, every data point was taken as the average of 10 runs, and the scanner executed a total of mE scans. In particular, the result of the internal/external scanner scanning IP addresses in OF-RHM when the defender adopts failure-prior mutation is equivalent to the result of the external scanner scanning IP addresses in RDAM when p = 1 in r increases, and RDAM allows at most 99.7% (99.3%) and at least 96.2% of hosts to be missed. The internal scanner cannot hit any host by scanning IP addresses. For the external scanner scanning IP addresses, the ratio of missed hosts also first increases then decreases as r increases; when p = 1, that is, no time window, RDAM allows at most 89.9% of hosts to be missed, while when p = 0.2, RDAM allows at most 98.1% and at least 80% of hosts to be missed by adjusting the time window setting.From the figure, we can see that the experimental results are basically the same as those predicted by the theoretical analysis. For the internal scanner  scanning domain names, the ratio of missed hosts first increases then decreases as Through the analysis of the above three cases, we can see that RDAM increases the scanning space of the scanner through the dynamic domain name method, and reduces the probability that the scanner can access hosts by scanning the IP address through the DNS query list and time window. RDAM offers better defense performance than the general network address shuffling method, whether it is for the repeatable scanning attack or the non-repeat scanning attack.Computer worms are programs that self-propagate across a network, exploiting security or policy vulnerabilities in widely used services. “Scan-based worms” propagate by scanning random IP addresses and infecting active vulnerable targets, which need no human activation and thus can propagate much faster than “email worms” . Many weIn this paper, we conducted experiments on worm propagation in the same simulation environment as in Section 5.1.1. Worm propagation behavior was experimented using a worm daemon , which cFrom the experiments and above analyses, we can see that worms based on IP address scanning have the following characteristics in RDAM: 1) worms cannot propagate within internal hosts because internal hosts cannot directly access each other by scanning IP addresses; and 2) worms cannot infect private hosts because external infected hosts cannot access the private host and internal hosts cannot infect each other. Therefore, worms based on IP address scanning in RDAM are equivalent to external repeatable scanning attacks scanning external address spaces. Following the analysis in Section 5.1.1, RDAM can effectively reduce the ratio of infected public hosts through adjustments to the time window, and from With regard to worms based on domain name scanning, an attacker who already knows the domain name space of RDAM can spread a worm based on domain name scanning by setting the domain name scanning space in the worm. However, the space can be easily changed by re-configuration. In addition, according to , the proThe space overhead of RDAM mainly includes the size of the flow table. The time overhead of RDAM mainly includes the delay from the DNS request to establish a connection and the delay resulting from packet modification and forwarding in software-defined switches.w1 sessions with other internal hosts per second, w2 sessions with external hosts per second, and each session on average takes θ seconds to terminate. Then according to Little’s law in queuing theory, the mean length of the flow table is (w1 + 2w2)nIθ. w1 and w2/w1) when nI = 12000, θ = 10s.In RDAM, each TCP/UDP session needs to establish two flow table entries in the software-defined switches in the path. Assume in RDAM, each internal host establishes on average We tested the operational delay caused by RDAM in our laboratory prototype. The software-defined switches and the controller were implemented in the X86 platform operating at 4 GHz using Intel's DPDK; the controller had two cores and 4 GB of memory; the switches had six cores and 8 GB of memory. All internal hosts, the DNS server, the DHCP server, and the security authentication server had two cores operating at 3.7 GHz and 4 GB of memory; the internal hosts ran the Win7 operating system, and the servers ran the Windows Server 2003 operating system. External hosts were generated through a server running Hyper-V with 32 cores operating at 3.9 GHz and 64 GB of memory; each external host had one core and 512 MB of memory running the Windows XP operating system. We tested 3 scenarios: scenarios with no flow table entry and 1 million flow table entries in the switches, and a scenario without RDAM, whereby hosts access each other using IP addresses through common switches. In each scenario, we measured the delay results of connections between internal hosts and connections of external hosts accessing public hosts, ran 100 trials respectively by using 25 internal hosts/external hosts visiting 4 public hosts. RDAM is a network-based defense method, so it can be used simultaneously with host-based defense methods, such as firewall, to strengthen the network security. Also, RDAM does not affect the normal use of VPN , because 1) when a VPN connection is established, the corresponding flow table entries are installed in the SDN switches, so VPN communications will not be interrupted due to domain name and network address mutation, and 2) as we described in Section 4.4.1, RDAM implements ALG in the switch connected to the gateway to perform the detection and address information replacement of the TCP/UDP payloads including those of VPN protocols.The topology of RDAM is that a number of hosts and physical subnets are connected to the SDN network composed of SDN switches and the controller. However, RDAM does not support the topology that hosts and physical subnets are connected to the SDN network behind routers, because communications within routers will not be forwarded to the controller , and thus will not be protected by RDAM.In addition, because internal hosts cannot access each other by scanning IP addresses, so some LAN software based on scanning IP addresses cannot work normally in RDAM. In further work, we will consider establishing a virtual network in RDAM, mapping the IP addresses of the hosts using LAN software to the network to allow these hosts to communicate through a specific port and protocol without leaking the IP addresses.In this paper, we presented a defense technique based on assigning random domain names and network addresses, called RDAM, which defends against adversarial reconnaissance and scanning by dynamically mutating domain names and IP addresses. In RDAM, both the domain name and network address are changed dynamically, and an internal host can access other internal hosts only when it is in the DNS query list, and an external host can access a public host only when it is in the public host's time window. In addition, all internal hosts occupy different subnets and communicate through their virtual gateways.We have presented the basic architecture and communication protocols in this paper, and evaluated the defense performance against scanning attacks and worm propagation, as well as the overhead, through theoretical analysis and experiment. Our evaluation and experimentation show that RDAM can effectively defend against scanning attacks and worm propagation by increasing the scanning space of the scanner through the dynamic domain name method, and reducing the probability that the scanner can access hosts by scanning the IP address through the DNS query list and time window, while satisfying practical operation overhead demand.


Euplotes, a ciliated protozoan, is a useful unicellular model organism. Studies on Euplotes have provided excellent insights into various basic biological principles. We have recently sequenced the macronuclear genome of the common freshwater species Euplotes octocarinatus to provide novel insights into Euplotes genetics and molecular biology.E. octocarinatus Genome Database (EOGD), a functional annotation and analysis platform for the global study of the Euplotes genome. EOGD includes macronuclear genomic and transcriptomic data, predicted gene models, coding sequences, protein sequences, and functional annotations. The GBrowser and BLAST tools are embedded in EOGD to enable the search, visualization and analysis of E. octocarinatus genomic and transcriptomic data.In this study, we present the Euplotes or other ciliates. EOGD will be continuously updated to integrate more datasets and analytical tools. EOGD is freely available at http://ciliates.ihb.ac.cn/database/home/#eo.EOGD is a useful resource for the research community, particularly for researchers who conduct genome-scale analysis and molecular biology studies of  Euplotes is a free-living unicellular eukaryote that belongs to the ciliate phylum. This phylum also includes the model organisms Tetrahymena, Paramecium and Oxytricha. Like most ciliates, Euplotes exhibits nuclear dimorphism, possessing a germline micronucleus (MIC) and a somatic macronucleus (MAC). MIC is diploid, transcriptionally silent, and enables the transmission of genetic information between generations by sexual reproduction. MAC is generated from post-conjugation MIC, which is transcriptionally active during vegetative growth. Similar to other spirotrichous ciliates, Euplotes’s macronuclear chromosomes are tiny (nanochromosome) and mostly encode single genes that are differentially amplified to thousands of copies each [Euplotes from other ciliates: (i) the UGA codon of Euplotes is reassigned as cysteine or selenocysteine [Euplotes [ies each , 2. Two cysteine , 4; and Euplotes , 6.Euplotes is an attractive experimental system for studying basic eukaryotic biological processes. This organism has contributed to the discovery of fundamental eukaryotic mechanisms, such as the key telomerase protein [Euplotes exhibits frequent PRF at stop codons [Euplotes an outstanding model for studying this universal phenomenon.Given its easy collection and laboratory cultivation,  protein , genome  protein , defensi protein , pheromo protein  and stop protein , 4. Morep codons , 6. The E. octocarinatus. We developed the E. octocarinatus Genome Database (EOGD) to provide the scientific research community with easy access to the genomic resources and information of Euplotes. EOGD provides user-friendly functions to access Euplotes genomic and transcriptomic data, as well as introduces the biology, taxonomy and morphology of E. octocarinatus. EOGD will serve as an important platform for researchers to facilitate research on Euplotes or other ciliates.Using high-throughput sequencing approaches, we recently sequenced the macronuclear genome and the transcriptome of the typical species E. octocarinatus: (i) the macronuclear genome sequence data, (ii) the annotations of predicted genes, and (iii) the RNA-seq data of growth stage. Furthermore, EOGD provides a detailed description of the biology, taxonomy, and morphology of E. octocarinatus.EOGD integrates three major data sets of E. octocarinatus macronuclear genome was assembled using a specialized meta-assembly pipeline based on Illumina sequences [de novo prediction software [The equences . A totalequences . Given tequences  were excsoftware , was usesoftware .Tetrahymena thermophile, Paramecium tetraurelia, Ichthyophthirius multifiliis, Oxytricha trifallax, and Stylonychia lemnae, were constructed by OrthoMCL [The functional annotation of genomes, an important part of EOGD, is important in genomic studies. To obtain integrated functional genomic information, multiple annotations were performed. First, all putative proteins were functionally annotated with BLAST  alignments against the NCBI non-redundant (nr) protein database. Second, conserved motifs and functional domains were predicted by InterProScan5 software  against OrthoMCL .E. octocarinatus growth stage. In brief, the E. octocarinatus transcriptome was sequenced with the Illumina deep RNA sequencing strategy. We obtained 39,478,354 short reads with a total length of more than 4.9 Gb [Euplotes macronuclear genome by Tophat [Transcriptomic data are powerful resources for characterizing and validating gene models. EOGD stores the deeply sequenced RNA-seq data of y Tophat . The BioThe schematic structure of EOGD is shown in Fig. EOGD can be accessed through a user-friendly web interface. The EOGD website contains navigation tabs  and a search box on top of each page Fig. .Fig. 2WeEuplotes datasets . Using BLAST tools, the user can obtain the detailed information of the sequence alignment for a region and download the BLAST results through the “DOWNLOAD” button.EOGD provided two methods for users to rapidly search the genes of interest in the database: keywords and sequence search. If the user uses a “Keywords” to search, then any words (case insensitive) can be typed in the search box as search content. Scaffold ID and gene ID were also allowed to search specific genes. All searches will link to the result page Fig. . FurtherEOGD graphically displays RNA-Seq and genome data and implements its search function through Gbrowse. Typically, the user can use “Gene ID”, “Scaffold ID” or a scaffold region to search the database Fig. . Three t− 5). All predicted proteins were then functionally annotated , the microscopic morphology and the detailed taxonomic classification of E. octocarinatus are described according to several previous studies [http://ciliates.ihb.ac.cn/database/download/#eo) for downloading E. octocarinatus genome sequences, protein sequences, coding sequences in FASTA format and annotation information. User can download these data by clicking the “download” button.On the “MORPHOLOGY” page ( studies –21. FurtEuplotes data sets, such as micronuclear genome, mitochondrial genome and proteome data, as well as gene expression profiles, will be uploaded to and integrated in EOGD in the future. Given the high-throughput and relatively low cost of next-generation sequencing, sequencing the genomes and transcriptomes of other Euplotes species in the future is possible.EOGD will be periodically updated. Additional Euplotes biology, and will also enhance our understanding of eukaryotic molecular biology. We will periodically update EOGD by integrating more data. EOGD is an important and useful resource that is freely available to the research community.EOGD allows researchers to access, browse, retrieve and analyze genomic and transcriptomic data and annotations. This hub will promote research on


Netrin-1 plays a key role in axon guidance through binding to its receptor, Deleted in Colorectal Cancer (DCC). The initial step of signaling inside the cell after netrin-1/DCC ligation is the binding of DCC cytoplasmic P3 motif to focal adhesion targeting (FAT) domain of focal adhesion kinase (FAK). Here we report the crystal structure of P3/FAT complex. The helical P3 peptide interacts with a helix-swapped FAT dimer in a 2:2 ratio. Dimeric FAT binding is P3-specific and stabilized by a calcium ion. Biochemical studies showed that DCC-P3 motif and calcium ion could facilitate FAT dimerization in solution. Axon guidance assays confirm that the DCC/FAK complex is essential for netrin-1-induced chemoattraction. We propose that netrin-1/DCC engagement creates a small cluster of P3/FAT for FAK recruitment close to the cell membrane, which exerts a concerted effect with PIP2 for FAK signaling. We also compare P3/FAT binding with paxillin/FAT binding and discuss their distinct recognition specificity on a common FAT domain for axon attraction versus integrin signaling, respectively. Netrin-1 is the prototypical axon guidance cue characterized in the early 1990s4. Interestingly, netrin-1 is bi-functional6. A receptor termed Deleted in Colorectal Cancer (DCC) constitutively expresses on the axonal surface, mediating the chemoattraction by netrin-17. This response is turned into repulsion if another netrin-1 receptor, uncoordinated-5 (UNC5), co-exists with DCC8.During development, the growth of neuronal axons is guided by combined attractive and repulsive cues in the extracellular environment. Different guidance cue receptors on the growth cone of the axon interact with corresponding guidance cues released from target cells to continually explore the environment. The attractive guidance cues direct axons to their targets, whereas repulsive guidance cues generate exclusion zones that axons avoid, which ensures the correct axon navigation along a defined trajectory out of many possible routes9. Recently, antibody-blocking experiments confirmed the UNC5 binding to site 2 (ref. 10). At about the same time in 2014, another structure of the netrin-1 in complex with DCC FN4–FN5 domains was determined11. The two netrin-1/DCC complex structures are in fact complementary, as reviewed in ref.12.The crystal structure of netrin-1 in complex with DCC membrane-proximal fibronectin (FN) type III domains FN5–FN6 reveals that one netrin-1 molecule can simultaneously bind to two DCC receptors through the DCC-specific binding site 1 and a more generic, electrostatic-dominant site 2. Furthermore, we show that UNC5A can functionally replace DCC binding at netrin-1 site 2 to switch the response from attraction to repulsion7 16.The next important question in netrin/receptor signaling concerns the cytoplasmic signaling mechanisms following netrin-1 engagement with DCC on the cell surface. DCC is a single-pass transmembrane receptor composed of 10 extracellular domains and a long cytoplasmic tail of about 350 residues7 Fig. . The cyt17. In order for FAK to carry out its function, it has to be localized to focal adhesion site or the cytoplasmic portion of a receptor. For integrin signaling, FAK recruitment is initiated by binding of the FAT domain to paxillin and/or talin while for growth factor/cytokine receptors, FAT binds to their cytoplasmic tails17. The FERM domain has also been shown to enhance FAK binding19. For netrin-1/DCC signaling it has been shown that the FAT domain of FAK is necessary and sufficient for binding to the DCC-P3 motif16. FAK, together with Src, can assemble a multi-protein complex to bridge receptor DCC with cytoskeleton-associated proteins, and therefore to regulate neurite outgrowth and growth cone turning. As a central node in the signaling network, FAK must be precisely regulated. At FAK’s resting auto-inhibitory state, the FERM domain directly binds the kinase domain in cis, which sequesters auto-phosphorylation site Tyr397 located between the FERM and kinase domains from exposure. The FERM-binding also blocks regulatory phosphorylation sites on the activation loop of the kinase domain. FAK activation requires opening the FERM domain from the kinase domain, allowing auto-phosphorylation of Tyr397, which becomes a docking site for Src kinase SH2 domain. The Src in turn phosphorylates FAK at the activation loop. The activated FAK/Src complex then further phosphorylates downstream substrates20. The central activation issue here is what causes the FERM domain to open up, initiating the process. Very recently, it has been shown that a lipid termed phosphatidylinositol-4,5-bisphosphate, , induces clustering of FAK on the lipid bilayer through binding a conserved basic patch on FERM domain. The neutralization of the basic patch on FERM domain by specific PIP2 binding eventually causes partial domain opening between FERM and kinase, giving rise to FAK activation21.FAK, a non-receptor tyrosine kinase, is composed of an N-terminal 4.1, ezrin, radixin, and moesin homology (FERM) domain, a kinase domain, proline-rich regions, and a C-terminal focal adhesion targeting (FAT) domain Fig. . FAK pla22. Biochemical data revealed that addition of the DCC-P3 motif to FAT results in FAT dimerization in solution. Axon guidance assays confirm that this DCC/FAK complex is physiologically essential for netrin-1-induced chemoattraction. We also show that the P3/FAT interaction is specific for axon attraction signaling and distinct from FAT bound by LD motifs from the paxillin family for integrin focal adhesion signaling. We propose that ligation of netrin-1 with DCC at the cell surface creates a small cluster of P3/FAT for FAK recruitment to proximity of the cell membrane, which exerts a concerted effect with PIP2 for FAK signaling. To our knowledge, this has been the first structural observation of the initial signaling step after ligand/receptor engagement on the cell surface for the axon guidance process.We report here a crystal structure of DCC-P3 motif in complex with FAT domain of FAK. We show that the specific P3-binding facilitates the FAT dimerization through a helix swap mechanism. In addition, FAT dimerization is aided by a calcium ion, which is elevated in cytoplasm via netrin-1 stimulation1424–S1443 within the P3 motif completely abolishes the binding between DCC and FAK, defining this peptide segment as the region required for the FAK binding23. On the FAK side, its C-terminal FAT domain is responsible for the DCC binding16  of the FAT domain swaps between the two protomers. The P3-23m peptide folds into a helix, packing onto the H1/H4 face of the FAT domain with well-defined electron density  and aspartate (D) residues. Although these LD motifs have a consensus sequence, LDxLLxxL, their interactions with multiple proteins seem both overlapping and specific30. Only the LD2 and LD4 motifs of paxillin are able to bind the FAK-FAT domain31. Proline-rich tyrosine kinase 2 (Pyk2), which is a more tissue-specific FAK family member, also has a C-terminal FAT domain that binds leupaxin, a leukocyte-specific isoform of paxillin. Leupaxin has four LD motifs, of which only LD1 and LD4 bind FAT32. Leupaxin is regarded as Pky2’s native ligand, but intriguingly paxillin LD motifs can also bind Pky2-FAT domain33. As a versatile protein-binding module with a shared binding mode for various ligands, it is biologically important that the FAT domain should also possess binding specificity for different ligands.Acting as a protein–protein interacting module, the FAT domain has multiple binding partners to help FAK localize and function in an appropriate cellular context32. By contrast DCC-P3 only binds the H1/H4 face on a dimeric form of FAT domain.So far, several structures of the FAT domain in complex with LD motif have been determined  plays a very important role in electrostatic interactions with the FAT domain. On the H1/H4 face, the conserved aspartate residue forms hydrogen bonds with a lysine residue on the H4 helix of the FAT domain (K1032 in FAK-FAT and K988 in Pyk2-FAT). Interestingly, this lysine residue is also stabilized by another aspartate residue on the same H4 helix (D1036 in FAK-FAT and D992 in Pyk2-FAT)   Fig. . On the AT) Fig. . A systeain Fig. . In otheIn summary, the 4-helix bundle FAT domain with two hydrophobic binding faces serves as a versatile protein–protein interacting module for many ligands. On the other hand, exquisite and differential structural features have defined recognition specificity for different ligands. While the LD-mediated electrostatic LD/FAT interaction specifies integrin-associated focal adhesion signaling, the QQ-mediated specific dimeric P3/FAT interaction appears unique for axon attraction signaling triggered by netrin-1/DCC engagement.35. The P3 motif also assumes a helical conformation. This P3 helix binds to a αβ-groove of the FERM-F3 lobe . The fragment was inserted into the vector pET-Sumo vector with a 6His and sumo tag at the N-terminus. The plasmid was transformed into Escherichia coli BL 21(DE3) (Novagen). The transformed bacteria were cultivated in LB with kanamycin selection. Recombinant protein synthesis was induced when the bacterial culture reached an optical density at 600 nm of 0.8–1.0 by addition of isopropyl β-d-thiogalactopyranoside (IPTG) to a final concentration of 0.2 mM. After 20 h of cultivation at 20 °C, bacteria from 1 l of culture were harvested by centrifugation. Frozen cells were resuspended in 10 vol of lysis buffer containing 50 mM Tris pH 8.0, 300 mM NaCl, 10% (vol/vol) glycerol, and 1 mM phenylmethane sulfonyl fluoride (PMSF), followed by ultrasonication. The lysate was cleared by centrifugation at 16,000 g for 20 min and incubated with Ni-NTA resin . The resin was washed with wash buffer containing 20 mM Tris pH 8.0, 100 mM NaCl, 1 mM PMSF, and 20 mM Imidazole. Target protein was eluted with buffer containing 20 mM Tris pH 8.0, 100 mM NaCl, 1 mM PMSF, and 200 mM Imidazole. Target protein was then mixed up with Ulp1 protease at a ratio of 125:1. The mixture was incubated at 4 °C for overnight while dialysis with buffer containing 20 mM Tris pH 8.0, 100 mM NaCl, and 1 mM PMSF. Then the sumo and the uncleaved protein with sumo tag were removed by a second step of His affinity purification. Target protein without tag was further purified by size exclusion chromatography on a Superdex 200 10/300 column  in 20 mM Tris pH 8.0 and 100 mM NaCl. Fractions containing the monomer FAT domain were pooled and concentrated.FAT encoding NThe synthetic DCC-P3-23m peptide (DDLSEQMASLEGLMKQLNAITGS) was dissolved with dimethyl sulfoxide. Fresh purified FAT domain was then mixed up with the DCC-P3-23m peptide at a molar ratio of 1:4 and diluted to a final concentration of 25 mg/ml. The mixture was then subject to crystallization using hanging drop vapor diffusion method at 16 °C.2 in 0.1 M Tris buffer (pH 8.5). Before diffraction experiments, crystals were soaked in crystallization solution containing additional 10% glycerol for cryoprotection.Crystals of FAT domain in complex with DCC-P3-23m peptide appeared in 3 days and were harvested after 6 days. One microliter of protein solution was mixed with 1 μl of crystallization solution containing 20% PEG8000 and 0.2 M MgCl42). The statistics of the diffraction data are summarized in Table 43 with the structure of apo FAT as the search model (PDB code 1K40). The model building was performed using Coot44, and the structure refinement was carried out using Phenix46. The stereochemical geometry of the structures was analyzed using Molprobity47. The figures were generated using Pymol (http://www.pymol.org). The statistics of the structure refinement are also summarized in Table The diffraction data were collected at Advanced Photon Source (APS) 19 ID. The data were processed, integrated, and scaled together with HKL3000  was used in this experiment. For FAT alone, 100 μg FAT was loaded into the column and analyzed in the buffer containing 20 mM HEPES pH 7.4, 150 mM NaCl, and 10 mM EGTA. To test whether DCC-P3 peptide could facilitate FAT dimerization, 100 μg FAT was mixed with DCC-P3 peptide at a molar ratio of 1:4 and then analyzed in the buffer containing 20 mM HEPES pH 7.4, 150 mM NaCl, and 10 mM EGTA. To further test whether Ca48 following the regulations of the Peking University Animal Care and User Committee. Axon guidance assays were performed as previously described9 with slight modification. At 1 day in vivo, the mouse neurons were microinjected with siRNA to DCC (Qiagen), wild-type DCC/mutant DCC constructs (rat DCC in a pCDNA3.1 vector) with the marker dye dextran Texas Red . The heparin pre-coated beads (Sigma-Aldrich) were then coated with 100 µg/ml netrin-1, dipped with 0.1% agarose (Sigma), and then placed at one corner of the culture dish. After 72 h incubation, neurons were fixed and immune-stained with anti-AnkG antibody (green) (Life Technologies). The direction of axon (green fluorescent) growth of the injected cells (red fluorescent) toward the beads was observed. Axons were considered to be attracted by beads if they pointed toward a plane that lies perpendicular to the axis drawn between the nucleus of the neuron and the center of the bead. A Zesis LSM-710 laser confocal microscope was used for analyzing data. Each image was collected using a ×40 water immersion objective.Primary neurons were cultured from newborn C57BL/6 mouse hippocampusSupplementary Information


Whereas the ferroelectric state of this material hosts nanometer-sized domains which again form micrometer-sized domains of four different configurations, we could figure out the characteristic features of each domain from the SHG mapping with various sizes of the probe beam, i.e., from 0.7 to 3.9 μm in its diameter. In particular, we demonstrate that a single micrometer-sized domain contributes to the SHG as a coherent summation of the constituent nanometer-sized domains, and multi-micrometer-sized domains contribute to the SHG as an incoherent summation of each micro-domain.We investigate the ferroelectric state of a tetragonal BiFeO Among them, BiFeOat 643 K  and can at 643 K . Such a at 643 K –10.3 have been performed by several research groups who could provide detailed symmetry information of tetragonal-like and rhombohedral-like phases, but it should be noted that most of the works have been done with assumptions of the homogeneous and coherent contributions of constituent domains [As an alternative technique to investigate the ferroelectric properties, an optical second harmonic generation (SHG) also has been widely adopted –14. The  domains , 19.3 (T-BFO) can be investigated by using a conventional SHG microscope with proper adjustments of probe beam sizes with respect to the domain size. Since this T-BFO hosts nanometer-sized domains which form specific patterns of micrometer-sized domains, it provides an excellent environment to address how the SHG responses are contributed to by each ferroelectric domain of different sizes and their mixtures. We mapped the sample with a probe beam of several sizes and found large position-dependent variations of the SHG signal which originate from distinct characteristics of the domain distribution. By considering the coherent and incoherent contributions of each domain to the SHG response, we could successfully explain such experimental results. We therefore expect that symmetry information of the individual nanometer-sized domain can be obtained even from the far-field microscopic measurement provided that the proper modeling can be chosen and applied between the incoherent and the coherent approaches.In this paper, we demonstrate that the distribution and characteristics of ferroelectric domains for the tetragonal-like BiFeO3 thin film is grown on a LaAlO3 (001) substrate by using a pulsed laser deposition technique of which details can be found elsewhere [The T-BiFeOlsewhere . The thipp at a frequency of 12 kHz. Figure c-axis [a- or b-axis, and their head-to-tail combination with a domain wall formed along the diagonal directions. Consistently with the PFM result, we hence consider four possible combinations of such domains as depicted in Fig. We first monitored a distribution of the ferroelectric domains by using a scanning probe microscope . PFM measurements were performed at a scan rate of 3 μm/s using Pt-coated Si conductive tips  with applying an ac driving voltage of 3 Ve c-axis . As the 1h point group) with a consideration of each domain configuration [I(2ω) is given in proportion to the induced SH polarization P(2ω) as I(2ω) ∝ |P(2ω)|2 = |χijkEjEk|2, where χijk represents the nonlinear susceptibility of the material, Ei is the electric field component of the fundamental light, and i, j, and k denote the crystallographic axes. The transverse coherence length LT is determined as LT = λR/2D [D is a size of the domain which acts as an SH light source and λ is a wavelength of SH light, i.e., 400 nm. R denotes a distance (0.38 mm) from the sample surface to the objective lens. Note that the coherence length is comparable to the objective lens size (625 μm in its diameter) when D is about 100 nm. Therefore, when we consider the contributions of multi-domains to the SHG, it is reasonable to assume that each nanometer-sized domain having a size of about 50 nm contributes to the SHG responses coherently as I(2ω) ∝ (P1 + P2)2, whereas micro-domains contribute to I(2ω) incoherently as I(2ω) = P12 + P22. Here, P1 and P2 imply the induced SH polarizations of each nano- or micro-domain when only two types of domains are considered.Before showing the experimental result, let us discuss the SHG response simulations of T-BFO  obtained by considering such coherent and incoherent contributions of different domains. For the leftmost case, we consider the coherent summation of two nanometer-sized domains corresponding to the first configuration listed in Fig. x- and y-axes. We take χxxx = 0.35, χxyy = 1.0, and χyxy = 0.4 for the domain with the x-axis polarization and χyyy = −0.35, χyxx = −1.0, and χxyx = −0.4 for the domain with the y-axis polarization. All other susceptibility components are assumed to be zero. For the rightmost case, we consider the fourth configuration of Fig. χxxx, χxyy, and χyxy with changes in the sign of χyyy, χyxx, and χxyx. The azimuth-dependent I(2ω) for the former and latter cases are displayed in the first and fifth plots in Fig. χxxx| < |χxyy| and |χyyy| < |χyxx|. With these two cases as end members, we consider coherent and incoherent summations with different portions f (0.0 < f < 1.0) or 1-f of the contribution from each end member. In the intermediate cases with f = 0.75, 0.5, and 0.25, the incoherent summation leads to the finite value of the minimum I(2ω) and more swollen lobe shape compared to the coherent summation.Figure W, a width defined by two points having the 1/e2 intensity of the maximum value as 0.7, 1.5, 2.1, and 3.9 μm.From now on, we present the experimental SHG results and discuss them based on the coherent and incoherent analyses of the multi-domain contribution. Before each SHG measurement, we characterized the beam size of fundamental light at the sample position using a knife-edge method . Figure I(2ω) obtained with W = 0.7 μm. The displacement for each step is 1 μm, and the mapping area is 25 × 25 μm2. The sample azimuth Ф is fixed as 45° in the XX geometry. I(2ω) exhibits a large variation with no discernible pattern. These results clearly indicate that the domain size in the probed area is comparable to the beam size.Figure I(2ω) at each sample position. Figure P1, P3, and P5 exhibit twofold symmetry, P2 and P4 show a fourfold symmetry. The behaviors in the XY geometry are relatively less distinct. Nevertheless, P1 and P3 have a main lobe at Ф = 135° (315°); P5 has the corresponding maximum at 45° (225°). These results strongly suggest that each measured point has distinct preferred planar orientations of the ferroelectric polarization. Actually, the patterns of the experimental result can be identified by the simulation results shown in Fig. P1 (P3) and P5 correspond to the case f = 1.0 and 0.0, respectively. Also, P2 and P4 can be identified as the case f = 0.5 (incoherent). In other words, the results of P1 and P5 pick up the single configuration of four possible micro-domains shown in Fig. P2 and P4 covers two (or more) micro-domains. Considering the coherence length, it is expected that such micro-domains contribute to the SHG response incoherently. From these results, we therefore confirm that nano-domains compose a single micro-domain and contribute to the SH response coherently, whereas micro-domains contribute to the SHG response incoherently. Furthermore, the part of symmetry information of a single nano-sized domain can be obtained from the analysis of the single micro-domain contribution; the simulation results in Fig. χxxx = 0.35, χxyy = 1.0, and χyxy = 0.4 for the nano-domain with the x-axis polarization.  decreases as expected from the smaller beam fluence. Also, the distribution of I(2ω) becomes to have a less position dependence.We increased the beam size and monitored how the SHG response varies depending on it. Figure W = 1.5 μm, we can still observe the strong position dependence; P1 and P4 reflect two distinct micro-domains, and the other points can be considered as mixtures of such micro-domains. Compared with the results for W = 0.7 μm, it is clear that the incoherent contributions become more discernible for W = 1.5 μm; the minimum value of the SHG intensity becomes non-zero, and it is much larger than for W = 0.7 μm. For W = 2.1 μm, such tendency becomes more pronounced; there is almost no position dependence as shown in Fig. W = 0.7 μm, a single micro-domain can be probed, and hence, the SHG intensity is determined by the coherent contribution from the constituent nano-sized domains. With W = 1.5 μm and 2.1 μm, on the other hand, several micro-domains are probed together, and they contribute to the SHG intensity as an incoherent summation.Azimuth-dependent SHG patterns also show systematic variations as the beam size increases. For W = 3.9 μm of which result is displayed as closed circles in Fig. Ws which are averaged over several points around the beam central position for W = 3.9 μm. Interestingly, such averaged results for W = 0.7, 1.5, and 2.1 μm appear quite similarly in both XX and XY geometry with the single measurement results for W = 3.9 μm. As this average process of the measured SH intensity is identical to the incoherent summation of each micro-domain contribution, this agreement confirms our understanding of the incoherent contribution to the SHG responses of multi-micro-domains.As a final test, we further increased the probe beam size up to 3 film by using a second harmonic generation (SHG) technique. Whereas the normal incidence SHG measurement provides us with clear information about the in-plane component of the ferroelectric polarization, we found large variations of the SHG responses from a point to a point of the sample. This clearly indicates the inhomogeneity of the domain distribution. By reducing the beam size down to 0.7 μm, we demonstrated that the observed SHG results could reveal the symmetry characteristic of individual micrometer-sized domains which is determined by types of constituent nanometer-sized domains. By increasing the beam size up to 3.9 μm, we found the SHG response with a much less position dependence. Actually, we could reproduce this SHG result obtained with a 3.9 μm beam size with averages of several results obtained with smaller beam sizes and confirmed that each micro-domain contributes to the SHG responses incoherently. Therefore, we can have a chance to retrieve symmetry information of the individual nanometer-sized or even micrometer-sized domain if we can apply a proper model between coherent and incoherent analysis of the SHG results.We investigated the ferroelectric domains of a tetragonal BiFeO


We present datasets that can be used for the experimental phasing of crystal structures of two myelin proteins. The structures were recently described in the articles “Periaxin and AHNAK nucleoprotein 2 form intertwined homodimers through domain swapping”  [1] and “The olfactomedin domain from gliomedin is a β-propeller with unique structural properties”  [2]. Crystals of periaxin were derivatized with tungsten and xenon prior to data collection, and diffraction data for these crystals are reported at 3 and 1 wavelengths, respectively. Crystallographic data for two different pressurizing times for xenon are provided. Gliomedin was derivatized with platinum, and data for single-wavelength anomalous dispersion are included. The data can be used to repeat the phasing experiments, to analyze heavy atom binding sites in proteins, as well as to optimize future derivatization experiments of protein crystals with these and other heavy-atom compounds. Specifications TableValue of the data•Structure solution for periaxin and gliomedin can be reproduced.•Heavy atom binding to proteins can be understood.•Derivatization conditions for protein crystals can be optimized.•Different experimental phasing approaches can be employed.•The strength of the anomalous signal and diffraction intensity can be studied.1We recently solved the crystal structures of periaxin and gliomedin 24)2WS4 for 2 days, while Xe-derivatized crystals were prepared at the beamline, by incubating fresh crystals in a Xe chamber at 200 psi for either 2 or 8 min. A gliomedin OLF domain crystal, grown in 0.1 M CHES (pH 9.5) containing 20% PEG 8000, was derivatized with platinum by soaking in 5 mM K2PtCl4 at room temperature.The preparation of recombinant protein and crystallization for the periaxin PDZ -like domain and the gliomedin olfactomedin (OLF) domain have been described All diffraction data were collected on the synchrotron radiation beamline 14.1 at BESSY (Berlin) and processed with XDS The periaxin structure can be solved using a MIRAS  approach with the peak wavelength data for tungsten and the 8-min xenon dataset, in phenix.autosol The gliomedin structure can be solved using SAD  from the attached single dataset of a platinum derivative crystal, for example with programs in the Auto-Rickshaw pipeline Anomalous signals of all the reported datasets are compared in


Cross-domain collaborative filtering (CDCF) solves the sparsity problem by transferring rating knowledge from auxiliary domains. Obviously, different auxiliary domains have different importance to the target domain. However, previous works cannot evaluate effectively the significance of different auxiliary domains. To overcome this drawback, we propose a cross-domain collaborative filtering algorithm based on Feature Construction and Locally Weighted Linear Regression (FCLWLR). We first construct features in different domains and use these features to represent different auxiliary domains. Thus the weight computation across different domains can be converted as the weight computation across different features. Then we combine the features in the target domain and in the auxiliary domains together and convert the cross-domain recommendation problem into a regression problem. Finally, we employ a Locally Weighted Linear Regression (LWLR) model to solve the regression problem. As LWLR is a nonparametric regression method, it can effectively avoid underfitting or overfitting problem occurring in parametric regression methods. We conduct extensive experiments to show that the proposed FCLWLR algorithm is effective in addressing the data sparsity problem by transferring the useful knowledge from the auxiliary domains, as compared to many state-of-the-art single-domain or cross-domain CF methods. The rapid growth of the information on the Internet demands intelligent information agent that can sift through all the available information and find out the most valuable to us. In recent years, recommender systems , 2 are wHowever, in real-world recommender systems, users usually dislike rating items and the items rated are very limited. Thus the rating matrix is very sparse. The sparsity problem has become a major bottleneck for most CF methods. To alleviate this difficulty, recently a number of cross-domain collaborative filtering (CDCF) methods have been proposed . CDCF meCurrently, CDCF methods can be categorized into two classes. One class –11 assumAmong previous works of the first class, Berkovsky et al.  mention  User dimension so as to transfer knowledge through the common user-factor matrix. Hu et al. [Singh and Gordon  propose u et al.  propose Pan et al.  propose Among previous works of the second class, Li et al.  propose In this paper, we assume the auxiliary domains contain dense rating data and share the same aligned users with the target domain. Previous works on this assumption cannot compute proper weights for different auxiliary domains. In order to overcome this drawback, we propose a cross-domain collaborative filtering algorithm based on Feature Construction and Locally Weighted Linear Regression (FCLWLR). We first construct features both in the target domain and in the auxiliary domain. We use different features to represent different domains. Instead of assigning proper weights to different auxiliary domains, we just assign proper weights to different features. Then we combine all the features together and convert the cross-domain recommendation problem into a regression problem. Therefore, the important information in the auxiliary domains can be transferred to the target domain by the constructed features from the auxiliary domains. Finally, a nonparametric regression method, that is, Locally Weighted Linear Regression (LWLR) model , is usedThe remainder of this paper is organized as follows: Some of the earliest work on CDCF was carried out by Berkovsky et al. , who dep (N-CDCF). As neighborhood based CF (N-CF) computes similarity between users or items, which can be subdivided into two types, user-based nearest neighbor (N-CF-U) and item-based nearest neighbor (N-CF-I), the N-CDCF algorithm can also be divided into two types: a user-based neighborhood CDCF model (N-CDCF-U) and an item-based neighborhood CDCF model (N-CDCF-I). For simplicity, we only give a detailed review on N-CDCF-U, and the detailed method of N-CDCF-I is in the same manner.In the first class, Berkovsky et al.  mention D = {D0, D1,…, Dm} denote all the domains for modeling, U = {u1, u2,…, un} denote the users in D, and Ik = {i1k, i2k,…, in(k)k} denote items belonging to the domain Dk  (0 ≤ k ≤ m), where n(k) denotes the item set size of Dk. For a user-based CDCF algorithm, we first calculate the similarity, su,v, between the users u and v who have corated the same set of items. The similarity can be measured by the Pearson correlation:iu,v = iu∩iv   denotes the items over all domains D corated by u and v; ru,i and rv,i are the ratings on item i given by users u and v, respectively; and u and v for all the items rated, respectively. Then the predicted rating of an item p for user u can be calculated by a weighted average strategy [Uu,pk denotes the set of top k users (k neighbors) that are most similar to user u who rated item p.Let strategy :(2)r⌢u,pf.In addition to the above model, the traditional MF model can also be employed to solve the CDCF problems straightforward. The Funk-SVD model is the most commonly used MF model . As showi is associated with a latent vector qi ∈ Rf, and each user u is associated with a latent vector pu ∈ Rf. qi measures the distribution of item i on those latent factors, and pu measures the interest distribution of user u on those latent factors. The resulting dot product, qiTpu, captures the interaction between user u and item i. This approximates user u's rating on item i, which is denoted by In this model, each item pu and qi), the Funk-SVD model minimizes the regularized squared error on the set of known ratingsκ is the set of the  pairs for which rui is known. The constant λ controls the extent of regularization to avoid overfitting and is usually determined by cross-validation [rui and computes the associated prediction errorTo learn the latent vectors  in the opposite direction of the gradient, yieldingThen it modifies the parameters by a magnitude proportional to MD, can be built by horizontally concatenating all matrices as shown in Based on the traditional MF model, we can solve the CDCF problems straightforward. We can pour all the items from different domains together and then an augmented rating matrix, Thus we can use MF model to obtain the latent user factors and latent item factors. These latent factors are used for prediction. In this paper, the MF model on CDCF problems is denoted as MF-CDCF.N-CDCF and MF-CDCF are developed straightforward from single-domain neighborhood and MF based CF methods, respectively. However, single-domain model assumes the homogeneity of items. Obviously, items in different domains may be quite heterogeneous, so N-CDCF and MF-CDCF fail to take this fact into account. Hence, the performance of them is not always satisfactory.R ∈ ℝn×m, Y⊙R ~ UVT, and one item-content matrix V:Singh and Gordon  propose O(km), where k is the number of factors and m is the number of domains. In addition, both CMF and CDTF need to adjust the weights of the auxiliary domains according to the similarities between each auxiliary domain and the target domain. Usually, computing proper weights is a tough problem.Hu et al.  propose R and a binary like/dislike auxiliary data and assumes that both user-specific and item-specific latent feature matrices are the same. Besides the shared latent features, TCF uses two inner matrices to capture the data-dependent information, which is different from the inner matrix used in CBT [Pan et al.  present d in CBT  and RMGMd in CBT . TCF reqUBVT with the following constraint:U ∈ {0,1}n×d and V ∈ {0,1}m×d are membership indicator matrices.In the second class, Li et al.  propose Further, Li et al.  propose Since previous CDCF works cannot assign proper weights to different auxiliary domains, the recommendation performance is not always satisfactory. To overcome this drawback, in this paper, we first construct features in different domains and use the features to represent different domains. Then we combine the constructed features together and convert the original recommendation problem into a regression problem. Instead of assigning proper weights to different auxiliary domains, our aim is to assign proper weights to different features. In order to guarantee the accuracy of the weights for different features, we employ a nonparametric regression method, that is, Locally Weighted Linear Regression (LWLR) model, to solve the regression problem. Below we give the details of our model.D1 is the target domain and U1 and I1 are the sets of users and items in domain D1. We use the trivial location information as the feature vector in the target domain. For example, we represent each user-item interaction  ∈ U1 × I1 × {1,2, 3,4, 5} in the target domain with a feature vector , where Lu and Li denote the location information of user u and item i, respectively. However, such a two-dimensional feature vector is not sufficient to discriminate the user ratings. Hence, we require some other features to reflect the user preferences with the help of rating information from the auxiliary domains.Assume u, i, r) ∈ U1 × I1 × {1,2, 3,4, 5} in the target domain, we can expand the location feature vector  in the target domain with all the row vectors of user u from all the auxiliary domains. Thus the expanded feature vector corresponding to the user-item interaction can be represented as , where rui   represents the complete row vector of user u in the ith auxiliary domain obtained by N-CF-U algorithm.In this paper, we assume the auxiliary domains contain dense rating data and share the same aligned users with the target domain. In this scenario, we employ a user-based nearest neighbor (N-CF-U) algorithm to fill the missing ratings in the auxiliary domains. We expand the trivial location feature vector in the target domain with the corresponding row vectors from all the auxiliary domains. Thus we can effectively add more features to reflect the user preferences. Given a user-item interaction  in the target domain, Fi  (2 ≤ i ≤ m + 1) denotes the feature vector  in the (i − 1)-th auxiliary domain, and R denotes the rating value.Assume u, i, r) ∈ U1 × I1 × {1,2, 3,4, 5} with a feature vector  and a regression function value r. Thus we can represent each user-item interaction as a training sample, and the original recommendation problem can be converted into a regression problem. We use For example, we represent each user-item interaction  can be represented asIn s2 + s3 + ⋯+sm+1 + 2, where si  (2 ≤ i ≤ m + 1) is the item size of the (i − 1)th auxiliary domain, which is always very large in real-world application. As a consequence, the constructed regression problem is very high-dimensional. Below we will propose two methods to improve the learning performance of the constructed regression problem.In the constructed regression problem, the dimension of the feature vector is K rated items from the original rating matrices of the auxiliary domains. All the users on the filtered columns will compose a denser submatrix. Thus we can effectively reduce the dimension of the problem and reserve the user preference as much as possible.Firstly, we can filter out the top Secondly, it always leads to underfitting or overfitting problem for parametric methods on high-dimensional problem. For example, a linear or quadratic regression model may not fit the high-dimensional data well (leading to underfitting problem), while a high-order regression model may fit the high-dimensional data severely (leading to overfitting problem). It is difficult to choose a proper order or a proper form for the parametric regression models. To overcome the drawback of parametric regression models, we employ a nonparametric regression model, that is, Locally Weighted Linear Regression (LWLR), for the constructed regression problem. Details of LWLR model are given in the following.x0 = 1 (this is the intercept term). Then we build LWLR model in the following form:θ denotes the coefficient vector of the linear equation; wk), where r is the rating of user u on item i.We conduct this experiment on Amazon product copurchasing network metadata  which co(1) N-CF-U. A user-based neighborhood CF model: in this experiment, we use k = 10 closest users.(2) UVD. The UV decomposition model: map both users and items to a joint latent factor space of dimensionality f. In our experiment, we try different latent factors {5,10,15,20}. The weight of the regularization terms λ is tried with different values, {0.001, 0.01, 0.1, 1, 10, 100}. The learning rate γ is a constant typically having a value between 0.0 and 1.0. If the learning rate is too small, then learning will occur at a very slow pace. If the learning rate is too large, then oscillation between inadequate solutions may occur. In this paper, for simplicity, we set γ = 0.3.(3) CFONMTF . A coclustering based collaborative filtering model using orthogonal nonnegative matrix trifactorization: following the parameter setting method in [λ and δ alternately, where λ and δ reflect the weights of three different models: ONMTF, user-based, and item-based. In detail, we conducted two experiments on each training set to identify the optimal combination coefficients. Firstly, let λ = 0 and compute the optimal value of δ which corresponds to the best evaluation metric when δ varies from 0 to 1. Secondly, we fix δ to be the optimal value and continue to compute the optimal value of λ. Besides, we choose 20 as the number of user/item clusters, 30% as the percentage of preselected user/item neighbors, and 20 as the size of user/item neighbors.ethod in , we comp(4) N-CDCF-U. A cross-domain version of N-CF-U: in this experiment, we use k = 10 closest users.(5) MF-CDCF. A cross-domain version of UVD model: here the setting is the same as that of UVD.(6) CMF. This is the collective matrix factorization, which couples rating matrices for all domains on the User dimension so as to transfer knowledge through the common user-factor matrix.(7) CDTF. The Cross-Domain Triadic Factorization model: we use the same setting as that in [a ∈  FCLWLR. The proposed method using rating data from all the auxiliary domains: in this experiment, for the rating filling process with N-CF-U algorithm, we also use k = 10 closest users. Different bandwidth parameters τ2 ∈ {0.125,0.25,0.5,1, 2,4, 8} are tried and we use cross-validation method to compute the best parameter value. For simplicity, we also set the learning rate a = 0.3.(9) FCLWLR_CD. This is the proposed method only using rating data from the auxiliary domain of Music CDs.(10) FCLWLR_DVD. This is the proposed method only using rating data from the auxiliary domain of DVDs.(11) FCLWLR_VHS. This is the proposed method only using rating data from the auxiliary domain of VHS video tapes.T denotes the set of test ratings, ri is the ground truth, and We first use mean absolute error (MAE) as an evaluation metric in our experiments. MAE is defined asN recommendations, a recommender is trying to pick the best N items for someone. Hence, a model with a smaller value of MAE does not mean a better recommendation performance. Rather than getting the exact rating right, in TopN recommendations we are interested in predicting whether an item would be among the user's favorites.However, what we often want is not to make a rating prediction for any item but to find the best items. In TopR(u) be a recommended list based on the behavior of the user on the training set, and T(u) is the “liked” list of behaviors that the user has on the test set. Then, the precision of the recommended results is defined asIn our experiments, we also use another two metrics commonly used in information retrieval, that is, precision and recall, to measure the recommendation quality. Let The recall of the recommended results is defined asPrecision gives us an estimate of how many of the items predicted to be “liked” for a user really belong to the “liked” list. Recall estimates how many of all the items in the user's “liked” list were predicted correctly.We construct two data sets to conduct the experiment. In one data set, we selected Books as the target domain and Music CDs, DVDs, and VHS video tapes as the auxiliary domains. In the other data set, we selected Music CDs as the target domain and Books, DVDs, and VHS video tapes as the auxiliary domains.K rated items for each domain, respectively. Thus the submatrices in the auxiliary domains are much denser than the original rating matrices. For the first data set, we filtered out users who have rated at least 30 music CDs, 30 DVDs, and 30 VHS video tapes so as to construct denser rating matrices in the auxiliary domains. Finally, 496 users were selected, and in addition we retrieved all items rated by these users in these four domains and set aside top K rated items for each domain, respectively. For the second data set, we filtered out users who have rated at least 90 Books, 30 DVDs, and 30 VHS video tapes so as to construct denser rating matrices in the auxiliary domains. Finally, 435 users were selected, and in addition we also retrieved all items rated by these users in these four domains and set aside top 20 and tr75, by, respectively, holding out 80% and 25% data from the target domain Book; that is, the remaining data of target domain for training is 20% and 75%. The hold-out data serve as ground truth for testing. Likewise, we also construct two other training sets tr20 and tr75 when choosing Music as the target domain.To simulate the sparse data problem, we constructed two sparse training sets, trh rated items for each domain, respectively. We choose Books as the target domain and Music CDs, DVDs, and VHS video tapes domains as the auxiliary domains. We filtered out users who have rated at least 100 books so that there are enough observations to be split in various proportions of training and testing data for our evaluation. Finally, 586 users were selected, and in addition we retrieved all items rated by these users in the four domains and set aside top 50, TR40, TR30, TR20, and TR10, by, respectively, holding out 50%, 60%, 70%, 80%, and 90% rating data from the target domain Book; that is, the remaining data for training is 50%, 40%, 30%, 20%, and 10%. The testing set is constructed in the following. We first filtered out users who have rated more than 20 books from the set composed by the hold-out data. Then we select 20 books randomly for each filtered user as the testing set. In order to compute the precision and recall, for the testing set, we also map the five classes of original ratings {1,2, 3,4, 5} into 2 classes, “liked” and “disliked.” Usually, an item with a score greater than or equal to 3 is defined as “liked”; otherwise, it is defined as “disliked.”To simulate the sparse data problem, we constructed five sparse training sets, TRN = 3,6, 9 and the set of all the books liked by the user from the 20 books as the “liked” list. We sort the predictive ratings of the 20 books for each user in the testing set and choose the top N books for recommendation. The books in the recommendation list are labeled as “liked.” Hence, we can compute the precision and recall.We define the size of the recommendation list FCLWLR requires that auxiliary domains contain dense rating data. Obviously, a very sparse rating matrix from an auxiliary domain will not improve the recommendation performance in the target domain. In this part, we analyze how the performance of FCLWLR is affected by rating densities in auxiliary domains. We construct the experiment data in the following way.20 as the training data in the target domain. For data in auxiliary domains, we constructed four different data sets, d100, d75, d50, and d25, by, respectively, holding out 0%, 25%, 50%, and 75% rating data. We use MAE to evaluate the performance of FCLWLR on different data sets.For simplicity, we just use trThe comparison results of different algorithms on MAE and on precision and recall are reported in Tables As shown in As shown in 20 to tr75, our method possesses the largest performance improvements, because, with the number of training ratings increasing, the training set size of the converted regression problem also increases. Thus the regression model can effectively avoid overfitting, and the performance can be improved. N-CDCF-U also achieves a not-bad performance when the data is relatively dense, that is, tr75, but the performance decreases very fast when the data becomes sparser, because when the data are sparse, the total similarity used in N-CDCF-U cannot represent the local similarity in the target domain well. However, according to  Precision and recall metrics always depend on the length of the recommended list N < l, where l denotes the length of the “liked” list, the recall metric for any model will not be greater than N/l.(2) If d100, d75), our model FCLWLR performs well. The effect of FCLWLR, however, is unsatisfactory when rating matrices of auxiliary domains are sparse . (2) FCLWLR even performs worse than UVD that is a single-domain CF algorithm, when rating matrices of auxiliary domains are very sparse. The main reason may be that when rating matrices become sparser, noise data from auxiliary domains will have a worse impact on the recommendation performance in the target domain.We have the following observations from In this paper, from the perspective of regression, we propose a cross-domain collaborative filtering algorithm based on Feature Construction and Locally Weighted Linear Regression (FCLWLR). On one side, the FCLWLR model can avoid computing proper weights for different domains, since we construct features in each domain and use the features to represent the domains. On the other side, the FCLWLR model can guarantee the accuracy of the weights for different features, due to the fact that LWLR model is a nonparametric regression method, which can effectively avoid underfitting or overfitting problem. The experimental results have shown that FCLWLR can significantly outperform all other state-of-the-art baseline algorithms at various sparsity levels.In this paper, we have only discussed how to construct features about users with the help of rating information in the auxiliary domains. In our future work, we will explore how to construct features about items and how to combine both user and item features to provide a better recommendation. Besides, FCLWLR requires a relatively rich rating data from the auxiliary domain. The experimental results show that FCLWLR even performs worse than single-domain CF algorithms, when rating matrices of auxiliary domains are very sparse. It would be interesting to compute what sparsity of rating data from auxiliary domains will degrade the effectiveness of FCLWR. It is worth studying in our future work.