Query: yaSpMV: yet another SpMV framework on GPUs

Relevant passages:
A new approach for sparse matrix vector product on NVIDIA GPUs <=SEP=> The sparse matrix vector product (SpMV) is a key operation in engineering and scientific computing and, hence, it has been subjected to intense research for a long time. The irregular computations involved in SpMV make its optimization challenging. Therefore, enormous effort has been devoted to devise data formats to store the sparse matrix with the ultimate aim of maximizing the performance. Graphics Processing Units (GPUs) have recently emerged as platforms that yield outstanding acceleration factors. SpMV implementations for NVIDIA GPUs have already appeared on the scene. This work proposes and evaluates a new implementation of SpMV for NVIDIA GPUs based on a new format, ELLPACK-R, that allows storage of the sparse matrix in a regular manner. A comparative evaluation against a variety of storage formats previously proposed has been carried out based on a representative set of test matrices. The results show that, although the performance strongly depends on the specific pattern of the matrix, the implementation based on ELLPACK-R achieves higher overall performance. Moreover, a comparison with standard state-of-the-art superscalar processors reveals that significant speedup factors are achieved with GPUs. Copyright 2010 John Wiley & Sons, Ltd.

Fast scan algorithms on graphics processors <=SEP=> Scan and segmented scan are important data-parallel primitives for a wide range of applications. We present fast, work-efficient algorithms for these primitives on graphics processing units (GPUs). We use novel data representations that map well to the GPU architecture. Our algorithms exploit shared memory to improve memory performance. We further improve the performance of our algorithms by eliminating shared-memory bank conflicts and reducing the overheads in prior shared-memory GPU algorithms. Furthermore, our algorithms are designed to work well on general data sets, including segmented arrays with arbitrary segment lengths. We also present optimizations to improve the performance of segmented scans based on the segment lengths. We implemented our algorithms on a PC with an NVIDIA GeForce 8800 GPU and compared our results with prior GPU-based algorithms. Our results indicate up to 10x higher performance over prior algorithms on input sequences with millions of elements.

Ecient Sparse Matrix-Vector Multiplication on CUDA <=SEP=> The massive parallelism of graphics processing units (GPUs) offers tremendous performance in many high-performance computing applications. While dense linear algebra readily maps to such platforms, harnessing this potential for sparse matrix computations presents additional challenges. Given its role in iterative methods for solving sparse linear systems and eigenvalue problems, sparse matrix-vector multiplication (SpMV) is of singular importance in sparse linear algebra. In this paper we discuss data structures and algorithms for SpMV that are efficiently implemented on the CUDA platform for the fine-grained parallel architecture of the GPU. Given the memory-bound nature of SpMV, we emphasize memory bandwidth efficiency and compact storage formats. We consider a broad spectrum of sparse matrices, from those that are well-structured and regular to highly irregular matrices with large imbalances in the distribution of nonzeros per matrix row. We develop methods to exploit several common forms of matrix structure while offering alternatives which accommodate greater irregularity. On structured, grid-based matrices we achieve performance of 36 GFLOP/s in single precision and 16 GFLOP/s in double precision on a GeForce GTX 280 GPU. For unstructured finite-element matrices, we observe performance in excess of 15 GFLOP/s and 10 GFLOP/s in single and double precision respectively. These results compare favorably to prior state-of-the-art studies of SpMV methods on conventional multicore processors. Our double precision SpMV performance is generally two and a half times that of a Cell BE with 8 SPEs and more than ten times greater than that of a quad-core Intel Clovertown system.

NVIDIA Tesla: A Unified Graphics and Computing Architecture <=SEP=> To enable flexible, programmable graphics and high-performance computing, NVIDIA has developed the Tesla scalable unified graphics and parallel computing architecture. Its scalable parallel array of processors is massively multithreaded and programmable in C or via graphics APIs.

Iterative methods for sparse linear systems <=SEP=> Sometimes we need to solve the linear equation Ax = b for a very big and very sparse A. For example, the Poisson equation where only 5 entries of each row of the matrix A are non-zero. Standard methods such as inverting the matrix A (numerically unstable) or Guass elimination do not take advantage of the sparsity of A. In this lesson we will discuss two iterative methods suitable for sparse linear systems: Jacobi and Gauss-Seidel.

Ranking , Boosting , and Model Adaptation <=SEP=> We present a new ranking algorithm that combines the strengt hs of two previous methods: boosted tree classification, and LambdaR ank, which has been shown to be empirically optimal for a widely used inform ation retrieval measure. The algorithm is based on boosted regression trees , although the ideas apply to any weak learners, and it is significantly fast er in both train and test phases than the state of the art, for comparable accu racy. We also show how to find the optimal linear combination for any two ran kers, and we use this method to solve the line search problem exactly du ring boosting. In addition, we show that starting with a previously tra ined model, and boosting using its residuals, furnishes an effective techn ique for model adaptation, and we give results for a particularly pressing prob lem in Web Search training rankers for markets for which only small amounts o f labeled data are available, given a ranker trained on much more data from a larger market.

Heart rate variability biofeedback increases baroreflex gain and peak expiratory flow. <=SEP=> OBJECTIVE
We evaluated heart rate variability biofeedback as a method for increasing vagal baroreflex gain and improving pulmonary function among 54 healthy adults.


METHODS
We compared 10 sessions of biofeedback training with an uninstructed control. Cognitive and physiological effects were measured in four of the sessions.


RESULTS
We found acute increases in low-frequency and total spectrum heart rate variability, and in vagal baroreflex gain, correlated with slow breathing during biofeedback periods. Increased baseline baroreflex gain also occurred across sessions in the biofeedback group, independent of respiratory changes, and peak expiratory flow increased in this group, independently of cardiovascular changes. Biofeedback was accompanied by fewer adverse relaxation side effects than the control condition.


CONCLUSIONS
Heart rate variability biofeedback had strong long-term influences on resting baroreflex gain and pulmonary function. It should be examined as a method for treating cardiovascular and pulmonary diseases. Also, this study demonstrates neuroplasticity of the baroreflex.

Moderating variables of music training-induced neuroplasticity: a review and discussion <=SEP=> A large body of literature now exists to substantiate the long-held idea that musicians' brains differ structurally and functionally from non-musicians' brains. These differences include changes in volume, morphology, density, connectivity, and function across many regions of the brain. In addition to the extensive literature that investigates these differences cross-sectionally by comparing musicians and non-musicians, longitudinal studies have demonstrated the causal influence of music training on the brain across the lifespan. However, there is a large degree of inconsistency in the findings, with discordance between studies, laboratories, and techniques. A review of this literature highlights a number of variables that appear to moderate the relationship between music training and brain structure and function. These include age at commencement of training, sex, absolute pitch (AP), type of training, and instrument of training. These moderating variables may account for previously unexplained discrepancies in the existing literature, and we propose that future studies carefully consider research designs and methodologies that control for these variables.

KPB-SIFT: a compact local feature descriptor <=SEP=> Invariant feature descriptors such as SIFT and GLOH have been demonstrated to be very robust for image matching and object recognition. However, such descriptors are typically of high dimensionality, e.g. 128-dimension in the case of SIFT. This limits the performance of feature matching techniques in terms of speed and scalability. A new compact feature descriptor, called Kernel Projection Based SIFT (KPB-SIFT), is presented in this paper. Like SIFT, our descriptor encodes the salient aspects of image information in the feature point's neighborhood. However, instead of using SIFT's smoothed weighted histograms, we apply kernel projection techniques to orientation gradient patches. The produced KPB-SIFT descriptor is more compact as compared to the state-of-the-art, does not require pre-training step needed by PCA based descriptors, and shows superior advantages in terms of distinctiveness, invariance to scale, and tolerance of geometric distortions. We extensively evaluated the effectiveness of KPB-SIFT with datasets acquired under varying circumstances.

NTIRE 2017 Challenge on Single Image Super-Resolution: Dataset and Study <=SEP=> This paper introduces a novel large dataset for example-based single image super-resolution and studies the state-of-the-art as emerged from the NTIRE 2017 challenge. The challenge is the first challenge of its kind, with 6 competitions, hundreds of participants and tens of proposed solutions. Our newly collected DIVerse 2K resolution image dataset (DIV2K) was employed by the challenge. In our study we compare the solutions from the challenge to a set of representative methods from the literature and evaluate them using diverse measures on our proposed DIV2K dataset. Moreover, we conduct a number of experiments and draw conclusions on several topics of interest. We conclude that the NTIRE 2017 challenge pushes the state-of-the-art in single-image super-resolution, reaching the best results to date on the popular Set5, Set14, B100, Urban100 datasets and on our newly proposed DIV2K.

A multiagent reinforcement learning algorithm using extended optimal response <=SEP=> Stochastic games provides a theoretical framework to multiagent reinforcement learning. Based on the framework, a multiagent reinforcement learning algorithm for zero-sum stochastic games was proposed by Littman and it was extended to general-sum games by Hu and Wellman. Given a stochastic game, if all agents learn with their algorithm, we can expect that the policies of the agents converge to a Nash equilibrium. However, agents with their algorithm always try to converge to a Nash equilibrium independent of the policies used by the other agents. In addition, in case there are multiple Nash equilibria, agents must agree on the equilibrium where they want to reach. Thus, their algorithm lacks adaptability in a sense. In this paper, we propose a multiagent reinforcement learning algorithm. The algorithm uses the extended optimal response which we introduce in this paper. It will converge to a Nash equilibrium when other agents are adaptable, otherwise it will make an optimal response. We also provide some empirical results in three simple stochastic games, which show that the algorithm can realize what we intend.

Applying MIPVU Metaphor Identification Procedure on Czech <=SEP=> This paper represents the current state of the research project aimed at modifying the MIPVU protocol for metaphor annotation for usage on Czech-language texts. Three annotators were trained to use metaphor identification procedure MIPVU and annotated 2 short text excerpts of about 600 tokens length, then the reliability of annotation was measured using Fleiss’ kappa. The resultant interannotator agreement of 0.70 was below kappa values reported by annotators of VU Amsterdam Metaphor Corpus (Steen et al., 2010) and very similar to the agreement that researchers (Badryzlova et al., 2013) got in their first reliability test with unmodified MIPVU procedure applied on Russian texts. Some modifications of the annotation procedure are proposed in order for it to be more suitable for Czech language. The modifications are based on the observations made by annotators in error analysis and by authors of similar projects aimed to transfer MIPVU procedure to Slavic/inflected languages. The functionality of the annotation procedure refinements now have to be tested in the second reliability test.

A Novel Phase-Shift Control of Semibridgeless Active Rectifier for Wireless Power Transfer <=SEP=> A novel phase-shift control of a semibridgeless active rectifier (S-BAR) is investigated in order to utilize the S-BAR in wireless energy transfer applications. The standard receiver-side rectifier topology is developed by replacing rectifier lower diodes with synchronous switches controlled by a phase-shifted PWM signal. Theoretical and simulation results show that with the proposed control technique, the output quantities can be regulated without communication between the receiver and transmitter. To confirm the performance of the proposed converter and control, experimental results are provided using 8-, 15-, and 23-cm air gap coreless transformer which has dimension of 76 cm × 76 cm, with 120-V input and the output power range of 0 to 1kW with a maximum efficiency of 94.4%.

Creating Social Contagion Through Viral Product Design: A Randomized Trial of Peer Influence in Networks <=SEP=> We examine how firms can create word-of-mouth peer influence and social contagion by designing viral features into their products and marketing campaigns. Word-of-mouth (WOM) is generally considered to be more effective at promoting product contagion when it is personalized and active. Unfortunately, the relative effectiveness of different viral features has not been quantified, nor has their effectiveness been definitively established, largely because of difficulties surrounding econometric identification of endogenous peer effects. We therefore designed a randomized field experiment on a popular social networking website to test the effectiveness of a range of viral messaging capabilities in creating peer influence and social contagion among the 1.4 million friends of 9,687 experimental users. Overall, we find that viral product design features can indeed generate econometrically identifiable peer influence and social contagion effects. More surprisingly, we find that passive-broadcast viral messaging generates a 246% increase in local peer influence and social contagion effects, while adding active-personalized viral messaging only generates an additional 98% increase in contagion. Although active-personalized messaging is more effective in encouraging adoption per message and is correlated with more user engagement and sustained product use, passivebroadcast messaging is used more often enough to eclipse those benefits, generating more total peer adoption in the network. In addition to estimating the effects of viral product design on social contagion and product diffusion, our work also provides a model for how randomized trials can be used to identify peer influence effects in networks.

Multi-factor Authentication as a Service <=SEP=> An architecture for providing multi-factor authentication as a service is proposed, resting on the principle of a loose coupling and separation of duties between network entities and end user devices. The multi-factor authentication architecture leverages Identity Federation and Single-Sign-On technologies, such as the OpenID framework, in order to provide for a modular integration of various factors of authentication. The architecture is robust and scalable enabling service providers to define risk-based authentication policies by way of assurance level requirements, which map to concrete authentication factor capabilities on user devices.

Trajectory planning for car-like vehicles: A modular approach <=SEP=> We consider the problem of trajectory planning with geometric constraints for a car-like vehicle. The vehicle is described by its dynamic model, considering such effects as lateral slipping and aerodynamic drag. We propose a modular solution, where three different problems are identified and solved by specific modules. The execution of the three modules can be orchestrated in different ways in order to produce efficient solutions to a variety of trajectory planning problems (e.g., obstacle avoidance, or overtake). As a specific example, we show how to generate the optimal lap on a specified racing track. The numeric examples provided in the paper are good witnesses of the effectiveness of our strategy.

Annotating Opinions in the World Press <=SEP=> In this paper we present a detailed scheme for annotating expressions of opinions, beliefs, emotions, sentiment and speculation (private states) in the news and other discourse. We explore inter-annotator agreement for individual private state expressions, and show that these low-level annotations are useful for producing higher-level subjective sentence annotations.

Behavioral and Neural Adaptation in Approach Behavior <=SEP=> People often make approachability decisions based on perceived facial trustworthiness. However, it remains unclear how people learn trustworthiness from a population of faces and whether this learning influences their approachability decisions. Here we investigated the neural underpinning of approach behavior and tested two important hypotheses: whether the amygdala adapts to different trustworthiness ranges and whether the amygdala is modulated by task instructions and evaluative goals. We showed that participants adapted to the stimulus range of perceived trustworthiness when making approach decisions and that these decisions were further modulated by the social context. The right amygdala showed both linear response and quadratic response to trustworthiness level, as observed in prior studies. Notably, the amygdala's response to trustworthiness was not modulated by stimulus range or social context, a possible neural dynamic adaptation. Together, our data have revealed a robust behavioral adaptation to different trustworthiness ranges as well as a neural substrate underlying approach behavior based on perceived facial trustworthiness.

Clearing the Skies: A Deep Network Architecture for Single-Image Rain Removal <=SEP=> We introduce a deep network architecture called DerainNet for removing rain streaks from an image. Based on the deep convolutional neural network (CNN), we directly learn the mapping relationship between rainy and clean image detail layers from data. Because we do not possess the ground truth corresponding to real-world rainy images, we synthesize images with rain for training. In contrast to other common strategies that increase depth or breadth of the network, we use image processing domain knowledge to modify the objective function and improve deraining with a modestly sized CNN. Specifically, we train our DerainNet on the detail (high-pass) layer rather than in the image domain. Though DerainNet is trained on synthetic data, we find that the learned network translates very effectively to real-world images for testing. Moreover, we augment the CNN framework with image enhancement to improve the visual results. Compared with the state-of-the-art single image de-raining methods, our method has improved rain removal and much faster computation time after network training.

Manga109 dataset and creation of metadata <=SEP=> We have created Manga109, a dataset of a variety of 109 Japanese comic books publicly available for use for academic purposes. This dataset provides numerous comic images but lacks the annotations of elements in the comics that are necessary for use by machine learning algorithms or evaluation of methods. In this paper, we present our ongoing project to build metadata for Manga109. We first define the metadata in terms of frames, texts and characters. We then present our web-based software for efficiently creating the ground truth for these images. In addition, we provide a guideline for the annotation with the intent of improving the quality of the metadata.

Efficient 3D LIDAR based loop closing using deep neural network <=SEP=> Loop closure detection in 3D LIDAR data is an essential but challenging problem in SLAM system. It is important to reduce global inconsistency or re-localize the robot that loses the localization, while is difficult for the lack of prior information. We present a semi-handcrafted representation learning method for LIDAR point cloud using siamese convolution neural network, which states the loop closure detection to a similarity modeling problem. With the learned representation, the similarity between two LIDAR scans is transformed as the Euclidean distance between the representations respectively. Based on it, we furthermore establish kd-tree to accelerate the searching of similar scans. To demonstrate the performance and effectiveness of the proposed method, the KITTI dataset is employed for comparison with other LIDAR loop closure detection methods. The result shows that our method can achieve both higher accuracy and efficiency.

Recursive Neural Networks for Learning Logical Semantics <=SEP=> Supervised recursive neural network models (RNNs) for sent ence meaning have been successful in an array of sophisticated language tasks , but it remains an open question whether they can learn compositional semantic gra mm rs that support logical deduction. We address this question directly by for the first time evaluating whether each of two classes of neural model — plain RNNs and re cursive neural tensor networks (RNTNs) — can correctly learn relationship such as entailment and contradiction between pairs of sentences, where we have generated controlled data sets of sentences from a logical grammar. Our first exper iment evaluates whether these models can learn the basic algebra of logical r elations involved. Our second and third experiments extend this evaluation to c omplex recursive structures and sentences involving quantification. We find t hat he plain RNN achieves only mixed results on all three experiments, where as the stronger RNTN model generalizes well in every setting and appears capable of learning suitable representations for natural language logical inference.

Analysis of dynamically scheduled tile algorithms for dense linear algebra on multicore architectures <=SEP=> The objective of this paper is to analyze the dynamic scheduling of dense linear algebra algorithms on shared-memory, multicore architectures. Current numerical libraries, e.g., LAPACK, show clear limitations on such emerging systems mainly due to their coarse granularity tasks. Thus, many numerical algorithms need to be redesigned to better fit the architectural design of the multicore platform. The PLASMA library (Parallel Linear Algebra for Scalable Multi-core Architectures) developed at the University of Tennessee tackles this challenge by using tile algorithms to achieve a finer task granularity. These tile algorithms can then be represented by Directed Acyclic Graphs (DAGs), where nodes are the tasks and edges are the dependencies between the tasks. The paramount key to achieve high performance is to implement a runtime environment to efficiently schedule the DAG across the multicore platform. This paper studies the impact on the overall performance of some parameters, both at the level of the scheduler, e.g., window size and locality, and the algorithms, e.g., Left Looking (LL) and Right Looking (RL) variants. The conclusion of this study claims that some commonly accepted rules for dense linear algebra algorithms may need to be revisited.

Underwater electric field measurement and analysis <=SEP=> The University of Idaho (UI) is developing electromagnetic field sensor systems that are attachable to autonomous underwater vehicles with the intent of taking survey measurements in underwater ocean environments. This paper presents the testing of these sensors and compares measurements to predictions. Testing was conducted off the coast of Florida with a moving artificial electric field source and an electric field sensor equipped AUV. At the closest pass, the peak value of the AUV-acquired electric field was consistent with the predicted field of the artificial source when the accuracy of AUV position was known to within ∼1 m.

Who Do I Look Like? Determining Parent-Offspring Resemblance via Gated Autoencoders <=SEP=> Recent years have seen a major push for face recognition technology due to the large expansion of image sharing on social networks. In this paper, we consider the difficult task of determining parent-offspring resemblance using deep learning to answer the question "Who do I look like?" Although humans can perform this job at a rate higher than chance, it is not clear how they do it [2]. However, recent studies in anthropology [24] have determined which features tend to be the most discriminative. In this study, we aim to not only create an accurate system for resemblance detection, but bridge the gap between studies in anthropology with computer vision techniques. Further, we aim to answer two key questions: 1) Do offspring resemble their parents? and 2) Do offspring resemble one parent more than the other? We propose an algorithm that fuses the features and metrics discovered via gated autoencoders with a discriminative neural network layer that learns the optimal, or what we call genetic, features to delineate parent-offspring relationships. We further analyze the correlation between our automatically detected features and those found in anthropological studies. Meanwhile, our method outperforms the state-of-the-art in kinship verification by 3-10% depending on the relationship using specific (father-son, mother-daughter, etc.) and generic models.

259 Second ring-down time and 4.45 million quality factor in 5.5 kHz fused silica birdbath shell resonator <=SEP=> The fused silica birdbath (BB) resonator is an axisymmetric 3D shell resonator that could be used in high-performance MEMS gyroscopes. We report a record quality factor (0 for a 5-mm-radius resonator, which is expected to reduce gyroscope bias drift. We present measurement results for two sizes of resonators with long vibratory decay time constants (τ), high Qs, and low damping asymmetry (Δτ−1) between their n = 2 wine-glass (WG) modes. We find a reduction in damping for larger resonators and a correlation between higher Q and lower as well as evidence of a lower bound on Q for resonators with low damping asymmetry.

Feeling Love and Doing More for Distant Others : Specific Positive Emotions Differentially Affect Prosocial Consumption <=SEP=> Marketers often employ a variety of positive emotions to encourage consumption or promote a particular behavior (e.g., buying, donating, recycling) to benefit an organization or cause. The authors show that specific positive emotions do not universally increase prosocial behavior but, rather, encourage different types of prosocial behavior. Four studies show that whereas positive emotions (i.e., love, hope, pride, and compassion) all induce prosocial behavior toward close entities (relative to a neutral emotional state), only love induces prosocial behavior toward distant others and international organizations. Love’s effect is driven by a distinct form of broadening, characterized by extending feelings of social connection and the boundary of caring to be more inclusive of others regardless of relatedness. Love—as a trait and a momentary emotion— is unique among positive emotions in fostering connectedness that other positive emotions (hope and pride) do not and broadening behavior in a way that other connected emotions (compassion) do not. This research contributes to the broaden-and-build theory of positive emotion by demonstrating a distinct type of broadening for love and adds an important qualification to the general finding that positive emotions uniformly encourage prosocial behavior.

On Compiling CNF into Decision-DNNF <=SEP=> Decision-DNNF is a strict subset of decomposable negation normal form (DNNF) that plays a key role in analyzing the complexity of model counters (the searches performed by these counters have their traces in Decision-DNNF). This paper presents a number of results on Decision-DNNF. First, we introduce a new notion of CNF width and provide an algorithm that compiles CNFs into Decision-DNNFs in time and space that are exponential only in this width. The new width strictly dominates the treewidth of the CNF primal graph: it is no greater and can be bounded when the treewidth of the primal graph is unbounded. This new result leads to a tighter bound on the complexity of model counting. Second, we show that the output of the algorithm can be converted in linear time to a sentential decision diagram (SDD), which leads to a tighter bound on the complexity of compiling CNFs into SDDs.

A Redundancy Dual-backup Method of the Battery Pack for the Two-wheeled Self-balanced Vehicle <=SEP=> 

Automatic mood detection of indian music using mfccs and k-means algorithm <=SEP=> This paper proposes a method of identifying the mood underlying a piece of music by extracting suitable and robust features from music clip. To recognize the mood, K-means clustering and global thresholding was used. Three features were amalgamated to decide the mood tag of the musical piece. Mel frequency cepstral coefficients, frame energy and peak difference are the features of interest. These features were used for clustering and further achieving silhouette plot which formed the basis of deciding the limits of threshold for classification. Experiments were performed on a database of audio clips of various categories. The accuracy of the mood extracted is around 90% indicating that the proposed technique provides encouraging results.