We show how to build several data structures of central importance to string processing, taking as input the Burrows-Wheeler transform (BWT) and using small extra working space. Let @math be the text length and @math be the alphabet size. We first provide two algorithms that enumerate all LCP values and suffix tree intervals in @math time using just @math bits of working space on top of the input BWT. Using these algorithms as building blocks, for any parameter @math we show how to build the PLCP bitvector and the balanced parentheses representation of the suffix tree topology in @math time using at most @math bits of working space on top of the input BWT and the output. In particular, this implies that we can build a compressed suffix tree from the BWT using just succinct working space (i.e. @math bits) and any time in @math . This improves the previous most space-efficient algorithms, which worked in @math bits and @math time. We also consider the problem of merging BWTs of string collections, and provide a solution running in @math time and using just @math bits of working space. An efficient implementation of our LCP construction and BWT merge algorithms use (in RAM) as few as @math bits on top of a packed representation of the input output and process data as fast as @math megabases per second.
As far as the CSA is concerned, this component can be easily built from the BWT using small space as it is formed (in its simplest design) by just a BWT with rank select functionality enhanced with a suffix array sampling, see also @cite_21 .
@cite_21
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The field of succinct data structures has flourished over the last 16 years. Starting from the compressed suffix array (CSA) by Grossi and Vitter (STOC 2000) and the FM-index by Ferragina and Manzini (FOCS 2000), a number of generalizations and applications of string indexes based on the Burrows-Wheeler transform (BWT) have been developed, all taking an amount of space that is close to the input size in bits. In many large-scale applications, the construction of the index and its usage need to be considered as one unit of computation. Efficient string indexing and analysis in small space lies also at the core of a number of primitives in the data-intensive field of high-throughput DNA sequencing. We report the following advances in string indexing and analysis. We show that the BWT of a string @math can be built in deterministic @math time using just @math bits of space, where @math . Within the same time and space budget, we can build an index based on the BWT that allows one to enumerate all the internal nodes of the suffix tree of @math . Many fundamental string analysis problems can be mapped to such enumeration, and can thus be solved in deterministic @math time and in @math bits of space from the input string. We also show how to build many of the existing indexes based on the BWT, such as the CSA, the compressed suffix tree (CST), and the bidirectional BWT index, in randomized @math time and in @math bits of space. The previously fastest construction algorithms for BWT, CSA and CST, which used @math bits of space, took @math time for the first two structures, and @math time for the third, where @math is any positive constant. Contrary to the state of the art, our bidirectional BWT index supports every operation in constant time per element in its output. We consider a frame asynchronous coded slotted ALOHA (FA-CSA) system for uncoordinated multiple access, where users join the system on a slot-by-slot basis according to a Poisson random process and, in contrast to standard frame synchronous CSA (FS-CSA), users are not frame-synchronized. We analyze the performance of FA-CSA in terms of packet loss rate and delay. In particular, we derive the (approximate) density evolution that characterizes the asymptotic performance of FA-CSA when the frame length goes to infinity. We show that, if the receiver can monitor the system before anyone starts transmitting, a boundary effect similar to that of spatially-coupled codes occurs, which greatly improves the iterative decoding threshold. Furthermore, we derive tight approximations of the error floor (EF) for the finite frame length regime, based on the probability of occurrence of the most frequent stopping sets. We show that, in general, FA-CSA provides better performance in both the EF and waterfall regions as compared to FS-CSA. Moreover, FA-CSA exhibits better delay properties than FS-CSA. In this paper we present a perception system for agriculture robotics that enables an unmanned ground vehicle (UGV) equipped with a multi spectral camera to automatically perform the crop weed detection and classification tasks in real-time. Our approach exploits a pipeline that includes two different convolutional neural networks (CNNs) applied to the input RGB+near infra-red (NIR) images. A lightweight CNN is used to perform a fast and robust, pixel-wise, binary image segmentation, in order to extract the pixels that represent projections of 3D points that belong to green vegetation. A deeper CNN is then used to classify the extracted pixels between the crop and weed classes. A further important contribution of this work is a novel unsupervised dataset summarization algorithm that automatically selects from a large dataset the most informative subsets that better describe the original one. This enables to streamline and speed-up the manual dataset labeling process, otherwise extremely time consuming, while preserving good classification performance. Experiments performed on different datasets taken from a real farm robot confirm the effectiveness of our approach. We show that the compressed suffix array and the compressed suffix tree for a string of length n over an integer alphabet of size σ ≤ n can both be built in O(n) (randomized) time using only O(n log σ) bits of working space. The previously fastest construction algorithms that used O(n log σ) bits of space took times O(n log log σ) and O(n loge n) respectively (where e is any positive constant smaller than 1). We present a novel approach to proving the absence of timing channels. The idea is to partition the programâ s execution traces in such a way that each partition component is checked for timing attack resilience by a time complexity analysis and that per-component resilience implies the resilience of the whole program. We construct a partition by splitting the program traces at secret-independent branches. This ensures that any pair of traces with the same public input has a component containing both traces. Crucially, the per-component checks can be normal safety properties expressed in terms of a single execution. Our approach is thus in contrast to prior approaches, such as self-composition, that aim to reason about multiple (kâ � 2) executions at once. We formalize the above as an approach called quotient partitioning, generalized to any k-safety property, and prove it to be sound. A key feature of our approach is a demand-driven partitioning strategy that uses a regex-like notion called trails to identify sets of execution traces, particularly those influenced by tainted (or secret) data. We have applied our technique in a prototype implementation tool called Blazer, based on WALA, PPL, and the brics automaton library. We have proved timing-channel freedom of (or synthesized an attack specification for) 24 programs written in Java bytecode, including 6 classic examples from the literature and 6 examples extracted from the DARPA STAC challenge problems. A novel regularizer of the PARAFAC decomposition factors capturing the tensor's rank is proposed in this paper, as the key enabler for completion of three-way data arrays with missing entries. Set in a Bayesian framework, the tensor completion method incorporates prior information to enhance its smoothing and prediction capabilities. This probabilistic approach can naturally accommodate general models for the data distribution, lending itself to various fitting criteria that yield optimum estimates in the maximum-a-posteriori sense. In particular, two algorithms are devised for Gaussian- and Poisson-distributed data, that minimize the rank-regularized least-squares error and Kullback-Leibler divergence, respectively. The proposed technique is able to recover the “ground-truth” tensor rank when tested on synthetic data, and to complete brain imaging and yeast gene expression datasets with 50 and 15 of missing entries respectively, resulting in recovery errors at -11 dB and -15 dB. The main contribution of this paper is a simple semisupervised pipeline that only uses the original training set without collecting extra data. It is challenging in 1) how to obtain more training data only from the training set and 2) how to use the newly generated data. In this work, the generative adversarial network (GAN) is used to generate unlabeled samples. We propose the label smoothing regularization for outliers (LSRO). This method assigns a uniform label distribution to the unlabeled images, which regularizes the supervised model and improves the baseline. We verify the proposed method on a practical problem: person re-identification (re-ID). This task aims to retrieve a query person from other cameras. We adopt the deep convolutional generative adversarial network (DCGAN) for sample generation, and a baseline convolutional neural network (CNN) for representation learning. Experiments show that adding the GAN-generated data effectively improves the discriminative ability of learned CNN embeddings. On three large-scale datasets, Market- 1501, CUHK03 and DukeMTMC-reID, we obtain +4.37 , +1.6 and +2.46 improvement in rank-1 precision over the baseline CNN, respectively. We additionally apply the proposed method to fine-grained bird recognition and achieve a +0.6 improvement over a strong baseline. The code is available at https: github.com layumi Person-reID_GAN. Tasks like code generation and semantic parsing require mapping unstructured (or partially structured) inputs to well-formed, executable outputs. We introduce abstract syntax networks, a modeling framework for these problems. The outputs are represented as abstract syntax trees (ASTs) and constructed by a decoder with a dynamically-determined modular structure paralleling the structure of the output tree. On the benchmark Hearthstone dataset for code generation, our model obtains 79.2 BLEU and 22.7 exact match accuracy, compared to previous state-of-the-art values of 67.1 and 6.1 . Furthermore, we perform competitively on the Atis, Jobs, and Geo semantic parsing datasets with no task-specific engineering. A novel algorithm based on random forests with surrogate splits is proposed to address the classification problem of incomplete data without imputation.The algorithm allows each tree to cast a vote even the voting process is interrupted by missing attributes.Experimental results on various acknowledged datasets show that the proposed method is robust and efficient. Random forests (RF) is known as an efficient algorithm in classification, however it depends on the integrity of datasets. Conventional methods in dealing with missing values usually employ estimation and imputation approaches whose efficiency is tied to the assumptions of data features. Recently, algorithm of surrogate decisions in RF was developed and this paper proposes a random forests algorithm with modified surrogate splits (Adjusted Weight Voting Random Forest, AWVRF) which is able to address the incomplete data without imputation.Differing from the present surrogate method, in AWVRF algorithm, when the primary splitting attribute and the surrogate attributes of an internal node are all missing, the undergoing instance is allowed to exit at the current node with a vote. Then the weight of the vote is adjusted by the strength of the involved attributes and the final decision is made by weighted voting. AWVRF does not comprise imputation step, thus it is independent of data features.AWVRF is compared with the methods of mean imputation, LeoFill, knnimpute, BPCAfill and conventional RF with surrogate decisions (surrRF) using 50 times repeated 5-fold cross validation on 10 acknowledged datasets. In a total of 22 experiment settings, the method of AWVRF harvests the highest accuracy in 14 settings and the largest AUC in 7 settings, exhibiting its superiority over other methods. Compared with surrRF, AWVRF is significantly more efficient and remain good discrimination of prediction. Experimental results show that the present AWVRF algorithm can successfully handle the classification task for incomplete data. With the development of construction techniques, it is possible to build large-span bridges, pipelines, dams, and other essential structures in seismically active regions or in active faults. A new method to find the optimal control forces for active tuned mass damper is presented in this paper. Three algorithms: discrete wavelet transform (DWT), particle swarm optimization (PSO), and linear quadratic regulator (LQR). DWT are used to obtain the local energy distribution of the motivation over the frequency bands. PSO is used to determine the gain matrices through the online update of the weighting matrices used in the LQR controller while eliminating the trial and error. The method is tested on a 10-story structure subject to several historical pulse-like near-fault ground motions. The results presented in this paper indicate that the proposed method is more effective at reducing the displacement response of the structure in real time than conventional LQR controllers. The main contribution of this paper is a simple semi-supervised pipeline that only uses the original training set without collecting extra data. It is challenging in 1) how to obtain more training data only from the training set and 2) how to use the newly generated data. In this work, the generative adversarial network (GAN) is used to generate unlabeled samples. We propose the label smoothing regularization for outliers (LSRO). This method assigns a uniform label distribution to the unlabeled images, which regularizes the supervised model and improves the baseline. We verify the proposed method on a practical problem: person re-identification (re-ID). This task aims to retrieve a query person from other cameras. We adopt the deep convolutional generative adversarial network (DCGAN) for sample generation, and a baseline convolutional neural network (CNN) for representation learning. Experiments show that adding the GAN-generated data effectively improves the discriminative ability of learned CNN embeddings. On three large-scale datasets, Market-1501, CUHK03 and DukeMTMC-reID, we obtain +4.37 , +1.6 and +2.46 improvement in rank-1 precision over the baseline CNN, respectively. We additionally apply the proposed method to fine-grained bird recognition and achieve a +0.6 improvement over a strong baseline. The code is available at this https URL In this work, we propose a linear base station (BS) precoding strategy based on switched relaying (SR) processing for downlink multiuser multiple-input multiple-output (MIMO) relay systems. The BS and the MIMO relay station (RS) are both equipped with a codebook of interleaving matrices. For a given channel state information (CSI) the selection function at the BS chooses the optimum interleaving matrix from the codebook based on the maximum sum received signal-to-interference-plus-noise ratio (SINR) or sum rate to design the linear BS precoder. Prior to the payload transmission the BS sends the index corresponding to the selected interleaving matrix to the RS, where the optimum interleaving matrix is selected as the optimum relay processing matrix. The entries of the codebook are randomly generated unitary matrices. Simulation results show that the performance of the proposed techniques is significantly better than prior art in the case of perfect CSI.1 In this paper, we propose a simple but effective semantic part-based weighting aggregation (PWA) for image retrieval. The proposed PWA utilizes the discriminative filters of deep convolutional layers as part detectors. Moreover, we propose the effective unsupervised strategy to select some part detectors to generate the "probabilistic proposals", which highlight certain discriminative parts of objects and suppress the noise of background. The final global PWA representation could then be acquired by aggregating the regional representations weighted by the selected "probabilistic proposals" corresponding to various semantic content. We conduct comprehensive experiments on four standard datasets and show that our unsupervised PWA outperforms the state-of-the-art unsupervised and supervised aggregation methods. Code is available at this https URL. Several works have developed vector-linear maximum-distance separable (MDS) storage codes that minimize the total communication cost required to repair a single coded symbol after an erasure, referred to as repair bandwidth (BW). Vector codes allow communicating fewer sub-symbols per node, instead of the entire content. This allows non trivial savings in repair BW. In sharp contrast, classic codes, like Reed-Solomon (RS), used in current storage systems, are deemed to suffer from naive repair, i.e. downloading the entire stored message to repair one failed node. This mainly happens because they are scalar-linear. In this work, we present a simple framework that treats scalar codes as vector-linear. In some cases, this allows significant savings in repair BW. We show that vectorized scalar codes exhibit properties that simplify the design of repair schemes. Our framework can be seen as a finite field analogue of real interference alignment. Using our simplified framework, we design a scheme that we call clique-repair which provably identifies the best linear repair strategy for any scalar 2-parity MDS code, under some conditions on the sub-field chosen for vectorization. We specify optimal repair schemes for specific (5,3)- and (6,4)-Reed-Solomon (RS) codes. Further, we present a repair strategy for the RS code currently deployed in the Facebook Analytics Hadoop cluster that leads to 20 of repair BW savings over naive repair which is the repair scheme currently used for this code. We present a scalable Bayesian framework for low-rank decomposition of multiway tensor data with missing observations. The key issue of pre-specifying the rank of the decomposition is sidestepped in a principled manner using a multiplicative gamma process prior. Both continuous and binary data can be analyzed under the framework, in a coherent way using fully conjugate Bayesian analysis. In particular, the analysis in the non-conjugate binary case is facilitated via the use of the Polya-Gamma sampling strategy which elicits closed-form Gibbs sampling updates. The resulting samplers are efficient and enable us to apply our framework to large-scale problems, with time-complexity that is linear in the number of observed entries in the tensor. This is especially attractive in analyzing very large but sparsely observed tensors with very few known entries. Moreover, our method admits easy extension to the supervised setting where entities in one or more tensor modes have labels. Our method outperforms several state-of-the-art tensor decomposition methods on various synthetic and benchmark real-world datasets. From a high-volume stream of weighted items, we want to create a generic sample of a certain limited size that we can later use to estimate the total weight of arbitrary subsets. Applied to Internet traffic analysis, the items could be records summarizing the flows of packets streaming by a router. Subsets could be flow records from different time intervals of a worm attack whose signature is later determined. The samples taken in the past thus allow us to trace the history of the attack even though the worm was unknown at the time of sampling. Estimation from the samples must be accurate even with heavy-tailed distributions where most of the weight is concentrated on a few heavy items. We want the sample to be weight sensitive, giving priority to heavy items. At the same time, we want sampling without replacement in order to avoid selecting heavy items multiple times. To fulfill these requirements we introduce priority sampling, which is the first weight-sensitive sampling scheme without replacement that works in a streaming context and is suitable for estimating subset sums. Testing priority sampling on Internet traffic analysis, we found it to perform an order of magnitude better than previous schemes. Priority sampling is simple to define and implement: we consider a steam of items i e 0,…,n − 1 with weights wi. For each item i, we generate a random number αi ∈ (0,1] and create a priority qi e wi αi. The sample S consists of the k highest priority items. Let τ be the (k p 1)th highest priority. Each sampled item i in S gets a weight estimate wi e max wi, τ , while nonsampled items get weight estimate wi e 0. Magically, it turns out that the weight estimates are unbiased, that is, E[wi] e wi, and by linearity of expectation, we get unbiased estimators over any subset sum simply by adding the sampled weight estimates from the subset. Also, we can estimate the variance of the estimates, and find, surprisingly, that the covariance between estimates wi and wj of different weights is zero. Finally, we conjecture an extremely strong near-optimality; namely that for any weight sequence, there exists no specialized scheme for sampling k items with unbiased weight estimators that gets smaller variance sum than priority sampling with k p 1 items. Szegedy settled this conjecture at STOC'06. Exact recovery from contaminated visual data plays an important role in various tasks. By assuming the observed data matrix as the addition of a low-rank matrix and a sparse matrix, theoretic guarantee exists under mild conditions for exact data recovery. Practically matrix nuclear norm is adopted as a convex surrogate of the non-convex matrix rank function to encourage low-rank property and serves as the major component of recently-proposed Robust Principal Component Analysis (R-PCA). Recent endeavors have focused on enhancing the scalability of R-PCA to large-scale datasets, especially mitigating the computational burden of frequent large-scale Singular Value Decomposition (SVD) inherent with the nuclear norm optimization. In our proposed scheme, the nuclear norm of an auxiliary matrix is minimized instead, which is related to the original low-rank matrix by random projection. By design, the modified optimization entails SVD on matrices of much smaller scale, as compared to the original optimization problem. Theoretic analysis well justifies the proposed scheme, along with greatly reduced optimization complexity. Both qualitative and quantitative studies are provided on various computer vision benchmarks to validate its effectiveness, including facial shadow removal, surveillance background modeling and large-scale image tag transduction. It is also highlighted that the proposed solution can serve as a general principal to accelerate many other nuclear norm oriented problems in numerous tasks. We describe new data structures for full and packed storage of dense symmetric triangular arrays that generalize both. Using the new data structures one is led to several new algorithms that save "half" the storage for symmetric matrices and outperform the current blocked based level 3 algorithms in LAPACK. We concentrate on the simplest forms of the new algorithms and show they are a direct generalization of LINPACK. This means that level 3 BLAS's are not required to obtain level 3 performance. The replacement for Level 3 BLAS are so-called kernel routines, see [1], and on IBM platforms they are producible from simple textbook type codes, by the XLF Fortran compiler. In the sequel I will label these "vanilla" codes. On Power3 with a peak performance of 800 MFlops, the results for Cholesky factorization at order n ? 200 is over 720 MFlops and then reaches 735 MFlops at n = 400. Using conventional full format LAPACK DPOTRF with ESSL BLAS's one first gets to 600 MFlops at n ? 600 and only reaches a peak of 620 MFlops. For this result we used simple square blocked full matrix data formats where the blocks themselves are stored in column major (Fortran) order or row major (C) order. The simple algorithms of LU factorization with partial pivoting for this new data format is a direct generalization of LINPACK algorithm DGEFA. Again, no conventional level 3 BLAS's are required; the replacements are again so-called kernel routines. Programming for squared blocked full matrix format can be accomplished in standard Fortran through the use of three and four dimensional arrays. Thus, no new compiler support is necessary. Also we mention that other more complicated algorithms are possible; e.g., recursive ones. The recursive algorithms are also easily programmed via the use of tables that address where the blocks are stored in the two dimensional recursive block array. Finally, we describe block hybrid formats. Doing so allows one to use no additional storage over conventional (full and packed) matrix storage. This means the new algorithms are completely portable. Similarity join processing in the streaming environment has many practical applications such as sensor networks, object tracking and monitoring, and so on. Previous works usually assume that stream processing is conducted over precise data. In this paper, we study an important problem of similarity join processing on stream data that inherently contain uncertainty (or called uncertain data streams), where the incoming data at each time stamp are uncertain and imprecise. Specifically, we formalize this problem as join on uncertain data streams (USJ), which can guarantee the accuracy of USJ answers over uncertain data. To tackle the challenges with respect to efficiency and effectiveness such as limited memory and small response time, we propose effective pruning methods on both object and sample levels to filter out false alarms. We integrate the proposed pruning methods into an efficient query procedure that can incrementally maintain the USJ answers. Most importantly, we further design a novel strategy, namely, adaptive superset prejoin (ASP), to maintain a superset of USJ candidate pairs. ASP is in light of our proposed formal cost model such that the average USJ processing cost is minimized. We have conducted extensive experiments to demonstrate the efficiency and effectiveness of our proposed approaches. This paper establishes information-theoretic limits for estimating a finite-field low-rank matrix given random linear measurements of it. These linear measurements are obtained by taking inner products of the low-rank matrix with random sensing matrices. Necessary and sufficient conditions on the number of measurements required are provided. It is shown that these conditions are sharp and the minimum-rank decoder is asymptotically optimal. The reliability function of this decoder is also derived by appealing to de Caen's lower bound on the probability of a union. The sufficient condition also holds when the sensing matrices are sparse-a scenario that may be amenable to efficient decoding. More precisely, it is shown that if the n × n-sensing matrices contain, on average, Ω(nlog n) entries, the number of measurements required is the same as that when the sensing matrices are dense and contain entries drawn uniformly at random from the field. Analogies are drawn between the aforementioned results and rank-metric codes in the coding theory literature. In fact, we are also strongly motivated by understanding when minimum rank distance decoding of random rank-metric codes succeeds. To this end, we derive minimum distance properties of equiprobable and sparse rank-metric codes. These distance properties provide a precise geometric interpretation of the fact that the sparse ensemble requires as few measurements as the dense one.