Our traditional notion of a cell is changing dramatically given the increasing degree of heterogeneity in 4G and emerging 5G systems. Rather than belonging to a specific cell, a device would choose the most suitable connection from the plethora of connections available. In such a setting, given the transmission powers differ significantly between downlink (DL) and uplink (UL), a wireless device that sees multiple Base Stations (BSs) may access the infrastructure in a way that it receives the downlink (DL) traffic from one BS and sends uplink (UL) traffic through another BS. This situation is referred to as Downlink and Uplink Decoupling (DUDe). In this paper, the capacity and throughput gains brought by decoupling are rigorously derived using stochastic geometry. Theoretical findings are then corroborated by means of simulation results. A further constituent of this paper is the verification of the theoretically derived results by means of a real-world system simulation platform. Despite theoretical assumptions differing from the very complete system simulator, the trends in the association probabilities and capacity gains are similar. Based on the promising results, we then outline architectural changes needed to facilitate the decoupling of DL and UL.
The loosening and decoupling of the DL and UL association has been indicated in @cite_2 , and has been studied in a few selected pioneering contributions @cite_15 @cite_21 @cite_3 @cite_9 .
@cite_9 @cite_21 @cite_3 @cite_2 @cite_15
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Over the last decade, the growing amount of uplink (UL) and downlink (DL) mobile data traffic has been characterized by substantial asymmetry and time variations. Dynamic time-division duplex (TDD) has the capability to accommodate to the traffic asymmetry by adapting the UL DL configuration to the current traffic demands. In this work, we study a two-tier heterogeneous cellular network (HCN) where the macro tier and small cell tier operate according to a dynamic TDD scheme on orthogonal frequency bands. To offload the network infrastructure, mobile users in proximity can engage in device-to-device (D2D) communications, whose activity is determined by a carrier sensing multiple access (CSMA) scheme to protect the ongoing infrastructure-based and D2D transmissions. We present an analytical framework for evaluating the network performance in terms of load-aware coverage probability and network throughput. The proposed framework allows quantification of the effect on the coverage probability of the most important TDD system parameters, such as the UL DL configuration, the base station density, and the bias factor. In addition, we evaluate how the bandwidth partition and the D2D network access scheme affect the total network throughput. Through the study of the tradeoff between coverage probability and D2D user activity, we provide guidelines for the optimal design of D2D network access. The computational complexity of internal diffusion-limited aggregation (DLA) is examined from both a theoretical and a practical point of view. We show that for two or more dimensions, the problem of predicting the cluster from a given set of paths is complete for the complexity class CC, the subset of P characterized by circuits composed of comparator gates. CC-completeness is believed to imply that, in the worst case, growing a cluster of size n requires polynomial time in n even on a parallel computer. A parallel relaxation algorithm is presented that uses the fact that clusters are nearly spherical to guess the cluster from a given set of paths, and then corrects defects in the guessed cluster through a nonlocal annihilation process. The parallel running time of the relaxation algorithm for two-dimensional internal DLA is studied by simulating it on a serial computer. The numerical results are compatible with a running time that is either polylogarithmic in n or a small power of n. Thus the computational resources needed to grow large clusters are significantly less on average than the worst-case analysis would suggest. For a parallel machine with k processors, we show that random clusters in d dimensions can be generated in ( O )((n k+logk)n2 d) steps. This is a significant speedup over explicit sequential simulation, which takes ( O )(n1+2 d) time on average. Finally, we show that in one dimension internal DLA can be predicted in ( O )(logn) parallel time, and so is in the complexity class NC. We present a framework for robustly estimating registration between a 3D volume image and a 2D projection image and evaluate its precision and robustness in spine interventions for vertebral localization in the presence of anatomical deformation. The framework employs a normalized gradient information similarity metric and multi-start covariance matrix adaptation evolution strategy optimization with local-restarts, which provided improved robustness against deformation and content mismatch. The parallelized implementation allowed orders-of-magnitude acceleration in computation time and improved the robustness of registration via multi-start global optimization. Experiments involved a cadaver specimen and two CT datasets (supine and prone) and 36 C-arm fluoroscopy images acquired with the specimen in four positions (supine, prone, supine with lordosis, prone with kyphosis), three regions (thoracic, abdominal, and lumbar), and three levels of geometric magnification (1.7, 2.0, 2.4). Registration accuracy was evaluated in terms of projection distance error (PDE) between the estimated and true target points in the projection image, including 14 400 random trials (200 trials on the 72 registration scenarios) with initialization error up to ±200 mm and ±10°. The resulting median PDE was better than 0.1 mm in all cases, depending somewhat on the resolution of input CT and fluoroscopy images. The cadaver experiments illustrated the tradeoff between robustness and computation time, yielding a success rate of 99.993 in vertebral labeling (with 'success' defined as PDE <5 mm) using 1,718 664 ± 96 582 function evaluations computed in 54.0 ± 3.5 s on a mid-range GPU (nVidia, GeForce GTX690). Parameters yielding a faster search (e.g., fewer multi-starts) reduced robustness under conditions of large deformation and poor initialization (99.535 success for the same data registered in 13.1 s), but given good initialization (e.g., ±5 mm, assuming a robust initial run) the same registration could be solved with 99.993 success in 6.3 s. The ability to register CT to fluoroscopy in a manner robust to patient deformation could be valuable in applications such as radiation therapy, interventional radiology, and an assistant to target localization (e.g., vertebral labeling) in image-guided spine surgery. A noninterference formulation of MLS applicable to the Secure Ada® Target (SAT) Abstract Model is developed. An analogous formulation is developed to handle the SAT type enforcement policy. Unwinding theorems are presented for both MLS and Multidomain Security (MDS) and the SAT Abstract Model is shown to satisfy both MLS and MDS. Generalizations and extensions are also considered. Abstract The rate of protein-protein association limits the response time due to protein-protein interactions. The bimolecular association rate may be diffusion-controlled or influenced, and in such cases, Brownian dynamics simulations of protein-protein diffusional association may be used to compute association rates. Here, we report Brownian dynamics simulations of the diffusional association of five different protein-protein pairs: barnase and barstar, acetylcholinesterase and fasciculin-2, cytochrome c peroxidase and cytochrome c, the HyHEL-5 antibody and hen egg lysozyme (HEL), and the HyHEL-10 antibody and HEL. The same protocol was used to compute the diffusional association rates for all the protein pairs in order to assess, by comparison to experimentally measured rates, whether the association of these proteins can be explained solely on the basis of diffusional encounter. The simulation protocol is similar to those previously derived for simulation of the association of barnase and barstar, and of acetylcholinesterase and fasciculin-2; these produced results in excellent agreement with experimental data for these protein pairs, with changes in association rate due to mutations reproduced within the limits of expected computational and modeling errors. Here, we find that for all protein pairs, the effects of mutations can be well reproduced by the simulations, even though the degree of the electrostatic translational and orientational steering varies widely between the cases. However, the absolute values of association rates for the acetylcholinesterase: fasciculin-2 and HyHEL-10 antibody: HEL pairs are overestimated. Comparison of bound and unbound protein structures shows that this may be due to gating resulting from protein flexibility in some of the proteins. This may lower the association rates compared to their bimolecular diffusional encounter rates. The cloud radio access network (C-RAN) concept, in which densely deployed access points (APs) are empowered by cloud computing to cooperatively support mobile users (MUs), to improve mobile data rates, has been recently proposed. However, the high density of active APs results in severe interference and also inefficient energy consumption. Moreover, the growing popularity of highly interactive applications with stringent uplink (UL) requirements, e.g., network gaming and real-time broadcasting by wireless users, means that the UL transmission is becoming more crucial and requires special attention. Therefore in this paper, we propose a joint downlink (DL) and UL MU-AP association and beamforming design to coordinate interference in the C-RAN for energy minimization, a problem which is shown to be NP hard. Due to the new consideration of UL transmission, it is shown that the two state-of-the-art approaches for finding computationally efficient solutions of joint MU-AP association and beamforming considering only the DL, i.e., group-sparse optimization and relaxed-integer programming, cannot be modified in a straightforward way to solve our problem. Leveraging on the celebrated UL-DL duality result, we show that by establishing a virtual DL transmission for the original UL transmission, the joint DL and UL optimization problem can be converted to an equivalent DL problem in C-RAN with two inter-related subproblems for the original and virtual DL transmissions, respectively. Based on this transformation, two efficient algorithms for joint DL and UL MU-AP association and beamforming design are proposed, whose performances are evaluated and compared with other benchmarking schemes through extensive simulations. We investigate the limiting description for a finite-difference approximation of a singularly perturbed Allen--Cahn type energy functional. The key issue is to understand the interaction between two small length-scales: the interfacial thickness @math and the mesh size of spatial discretization @math . Depending on their relative sizes, we obtain results in the framework of @math -convergence for the (i) subcritical ( @math ), (ii) critical ( @math ), and (iii) supercritical ( @math ) cases. The first case leads to the same area functional as the spatially continuous case while the third gives the same result as that coming from a ferromagnetic spin energy. The critical case can be regarded as an interpolation between the two. We study convex relaxations of the image labeling problem on a continuous domain with regularizers based on metric interaction potentials. The generic framework ensures existence of minimizers and covers a wide range of relaxations of the original combinatorial problem. We focus on two specific relaxations that differ in flexibility and simplicity—one can be used to tightly relax any metric interaction potential, while the other covers only Euclidean metrics but requires less computational effort. For solving the nonsmooth discretized problem, we propose a globally convergent Douglas-Rachford scheme and show that a sequence of dual iterates can be recovered in order to provide a posteriori optimality bounds. In a quantitative comparison to two other first-order methods, the approach shows competitive performance on synthetic and real-world images. By combining the method with an improved rounding technique for nonstandard potentials, we were able to routinely recover integral solutions within @math - @math of the global optimum for the combinatorial image labeling problem. Multimodal information processing has received considerable attention in recent years. The focus of existing research in this area has been predominantly on the use of fusion technology. In this paper, we suggest that cross-modal association can provide a new set of powerful solutions in this area. We investigate different cross-modal association methods using the linear correlation model. We also introduce a novel method for cross-modal association called Cross-modal Factor Analysis (CFA). Our earlier work on Latent Semantic Indexing (LSI) is extended for applications that use off-line supervised training. As a promising research direction and practical application of cross-modal association, cross-modal information retrieval where queries from one modality are used to search for content in another modality using low-level features is then discussed in detail. Different association methods are tested and compared using the proposed cross-modal retrieval system. All these methods achieve significant dimensionality reduction. Among them CFA gives the best retrieval performance. Finally, this paper addresses the use of cross-modal association to detect talking heads. The CFA method achieves 91.1 detection accuracy, while LSI and Canonical Correlation Analysis (CCA) achieve 66.1 and 73.9 accuracy, respectively. As shown by experiments, cross-modal association provides many useful benefits, such as robust noise resistance and effective feature selection. Compared to CCA and LSI, the proposed CFA shows several advantages in analysis performance and feature usage. Its capability in feature selection and noise resistance also makes CFA a promising tool for many multimedia analysis applications. Deep learning (DL) has revolutionized the field of computer vision and image processing. In medical imaging, algorithmic solutions based on DL have been shown to achieve high performance on tasks that previously required medical experts. However, DL-based solutions for disease detection have been proposed without methods to quantify and control their uncertainty in a decision. In contrast, a physician knows whether she is uncertain about a case and will consult more experienced colleagues if needed. Here we evaluate drop-out based Bayesian uncertainty measures for DL in diagnosing diabetic retinopathy (DR) from fundus images and show that it captures uncertainty better than straightforward alternatives. Furthermore, we show that uncertainty informed decision referral can improve diagnostic performance. Experiments across different networks, tasks and datasets show robust generalization. Depending on network capacity and task dataset difficulty, we surpass 85 sensitivity and 80 specificity as recommended by the NHS when referring 0−20 of the most uncertain decisions for further inspection. We analyse causes of uncertainty by relating intuitions from 2D visualizations to the high-dimensional image space. While uncertainty is sensitive to clinically relevant cases, sensitivity to unfamiliar data samples is task dependent, but can be rendered more robust. Our objective is to evaluate the efficacy of methods that use deep learning (DL) for the automatic fine-grained segmentation of optical coherence tomography (OCT) images of the retina. OCT images from 10 patients with mild non-proliferative diabetic retinopathy were used from a public (U. of Miami) dataset. For each patient, five images were available: one image of the fovea center, two images of the perifovea, and two images of the parafovea. For each image, two expert graders each manually annotated five retinal surfaces (i.e. boundaries between pairs of retinal layers). The first grader's annotations were used as ground truth and the second grader's annotations to compute inter-operator agreement. The proposed automated approach segments images using fully convolutional networks (FCNs) together with Gaussian process (GP)-based regression as a post-processing step to improve the quality of the estimates. Using 10-fold cross validation, the performance of the algorithms is determined by computing the per-pixel unsigned error (distance) between the automated estimates and the ground truth annotations generated by the first manual grader. We compare the proposed method against five state of the art automatic segmentation techniques. The results show that the proposed methods compare favorably with state of the art techniques, resulting in the smallest mean unsigned error values and associated standard deviations, and performance is comparable with human annotation of retinal layers from OCT when there is only mild retinopathy. The results suggest that semantic segmentation using FCNs, coupled with regression-based post-processing, can effectively solve the OCT segmentation problem on par with human capabilities with mild retinopathy. In order to expand the downlink (DL) coverage areas of picocells in the presence of an umbrella macrocell, the concept of range expansion has been recently proposed, in which a positive range expansion bias (REB) is added to the DL received signal strengths (RSSs) of picocell pilot signals at user equipments (UEs). Although range expansion may increase DL footprints of picocells, it also results in severe DL inter-cell interference in picocell expanded regions (ERs), because ER picocell user equipments (PUEs) are not connected to the cells that provide the strongest DL RSSs. In this paper, we derive closed-form formulas to calculate appropriate REBs for two different range expansion strategies, investigate both DL and uplink (UL) inter-cell interference coordination (ICIC) to enhance picocell performance, and propose a new macrocell-picocell cooperative scheduling scheme to mitigate both DL and UL interference caused by macrocells to ER PUEs. Simulation results provide insights on REB selection approaches at picocells, and demonstrate the benefits of the proposed macrocell-picocell cooperative scheduling scheme over alternative approaches. The goal of this paper is the analytical validation of a model of Cermelli and Gurtin [Arch. Ration. Mech. Anal., 148 (1999), pp. 3--52] for an evolution law for systems of screw dislocations under the assumption of antiplane shear. The motion of the dislocations is restricted to a discrete set of glide directions, which are properties of the material. The evolution law is given by a “maximal dissipation criterion,” leading to a system of differential inclusions. Short time existence, uniqueness, cross-slip, and fine cross-slip of solutions are proved. Elastic network models have been successful in elucidating the largest scale collective motions of proteins. These models are based on a set of highly coupled springs, where only the close neighboring amino acids interact, without any residue specificity. Our objective here is to determine whether the equivalent cooperative motions can be obtained upon further coarse-graining of the protein structure along the backbone. The influenza virus hemagglutinin A (HA), composed of N = 1509 residues, is utilized for this analysis. Elastic network model calculations are performed for coarse-grained HA structures containing only N 2, N 10, N 20, and N 40 residues along the backbone. High correlations (>0.95) between residue fluctuations are obtained for the first dominant (slowest) mode of motion between the original model and the coarse-grained models. In the case of coarse-graining by a factor of 1 40, the slowest mode shape for HA is reconstructed for all residues by successively selecting different subsets of residues, shifting one residue at a time. The correlation for this reconstructed first mode shape with the original all-residue case is 0.73, while the computational time is reduced by about three orders of magnitude. The reduction in computational time will be much more significant for larger targeted structures. Thus, the dominant motions of protein structures are robust enough to be captured at extremely high levels of coarse-graining. And more importantly, the dynamics of extremely large complexes are now accessible with this new methodology. Newman’s measure for (dis)assortativity, the linear degree correlation coefficient ?D, is reformulated in terms of the total number Nk of walks in the graph with k hops. This reformulation allows us to derive a new formula from which a degree-preserving rewiring algorithm is deduced, that, in each rewiring step, either increases or decreases ?D conform our desired objective. Spectral metrics (eigenvalues of graph-related matrices), especially, the largest eigenvalue ?1 of the adjacency matrix and the algebraic connectivity ?N?1 (second-smallest eigenvalue of the Laplacian) are powerful characterizers of dynamic processes on networks such as virus spreading and synchronization processes. We present various lower bounds for the largest eigenvalue ?1 of the adjacency matrix and we show, apart from some classes of graphs such as regular graphs or bipartite graphs, that the lower bounds for ?1 increase with ?D. A new upper bound for the algebraic connectivity ?N?1 decreases with ?D. Applying the degree-preserving rewiring algorithm to various real-world networks illustrates that (a) assortative degree-preserving rewiring increases ?1, but decreases ?N?1, even leading to disconnectivity of the networks in many disjoint clusters and that (b) disassortative degree-preserving rewiring decreases ?1, but increases the algebraic connectivity, at least in the initial rewirings. We study a two-dimensional Fermi liquid with a Fermi surface containing the saddle points @math and @math . Including Cooper and Peierls channel contributions leads to a one-loop renormalization group flow to strong coupling for short range repulsive interactions. In a certain parameter range the characteristics of the fixed point, opening of a spin and charge gap and dominant pairing correlations are similar to those of a 2-leg ladder at half-filling. An increase of the electron density we argue leads to a truncation of the Fermi surface with only 4 disconnected arcs remaining. We study the scaling limits of three different aggregation models on ℤd: internal DLA, in which particles perform random walks until reaching an unoccupied site; the rotor-router model, in which particles perform deterministic analogues of random walks; and the divisible sandpile, in which each site distributes its excess mass equally among its neighbors. As the lattice spacing tends to zero, all three models are found to have the same scaling limit, which we describe as the solution to a certain PDE free boundary problem in ℝd. In particular, internal DLA has a deterministic scaling limit. We find that the scaling limits are quadrature domains, which have arisen independently in many fields such as potential theory and fluid dynamics. Our results apply both to the case of multiple point sources and to the Diaconis-Fulton smash sum of domains. We study the pioneer points of the simple random walk on the uniform infinite planar quadrangulation (UIPQ) using an adaptation of the peeling procedure of Angel (Geom Funct Anal 13:935–974, 2003) to the quadrangulation case. Our main result is that, up to polylogarithmic factors, n 3 pioneer points have been discovered before the walk exits the ball of radius n in the UIPQ. As a result we verify the KPZ relation (Modern Phys Lett A 3:819–826, 1988) in the particular case of the pioneer exponent and prove that the walk is subdiffusive with exponent less than 1 3. Along the way, new geometric controls on the UIPQ are established. It is quite important for Device-to-Device (D2D) communication to make decision in selecting which part of the celluar radio resource, namely uplink (UL) resource or downlink (DL) resource, to be reused for the purpose of higher system capacity. In this paper, we propose an interference control scheme in DL mode to limit the base station's (BS) interference to D2D user equipments (DUEs) by selecting cellular user equipments (CUEs) nearer to BS. By comparing the interference control scheme used in UL situation, we derived the two lower bounds of ergodic capacity of reusing UL and DL resource in D2D communication. Numeric results show that the system capacity can be optimized by utilizing effective reusing mode selection strategies. Large classes of data association problems in multiple target tracking applications involving both multiple and single sensor systems can be formulated as multidimensional assignment problems. These NP-hard problems are large scale and sparse with noisy objective function values, but must be solved in “real-time”. Lagrangian relaxation methods have proven to be particularly effective in solving these problems to the noise level in real-time, especially for dense scenarios and for multiple scans of data from multiple sensors. This work presents a new class of constructive Lagrangian relaxation algorithms that circumvent some of the deficiencies of previous methods. The results of several numerical studies demonstrate the efficiency and effectiveness of the new algorithm class.