Query: Conditional Gradient Sliding for Convex Optimization

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An optimal method for stochastic composite optimization <=SEP=> This paper considers an important class of convex programming (CP) problems, namely, the stochastic composite optimization (SCO), whose objective function is given by the summation of general nonsmooth and smooth stochastic components. Since SCO covers non-smooth, smooth and stochastic CP as certain special cases, a valid lower bound on the rate of convergence for solving these problems is known from the classic complexity theory of convex programming. Note however that the optimization algorithms that can achieve this lower bound had never been developed. In this paper, we show that the simple mirror-descent stochastic approximation method exhibits the best-known rate of convergence for solving these problems. Our major contribution is to introduce the accelerated stochastic approximation (AC-SA) algorithm based on Nesterov’s optimal method for smooth CP [32,34], and show that the AC-SA algorithm can achieve the aforementioned lower bound on the rate of convergence for SCO. To the best of our knowledge, it is also the first universally optimal algorithm in the literature for solving non-smooth, smooth and stochastic CP problems. We illustrate the significant advantages of the AC-SA algorithm over existing methods in the context of solving a special but broad class of stochastic programming problems.

Universal approximation bounds for superpositions of a sigmoidal function <=SEP=> Approximation properties of a class of artificial neural networks are established. It is shown that feedforward networks with one layer of sigmoidal nonlinearities achieve inte­ grated squared error of order O(l/n), where n is the number of nodes. The function appruximated is assumed to have a bound on the first moment of the magnitude distribution of the Fourier transform. The nonlinear parameters associated with the sigmoidal nodes, as well as the parameters of linear combination, are adjusted in the approximation. In contrast, it is shown that for series expansions with n terms, in which only the parameters of linear combination are adjusted, the integrated squared approximation error cannot be made smaller than order 1/n2/d uniformly for functions satisfying the same smoothness assumption, where d is the dimension of the input to the function. For the class of functions examined here, the approximation rate and the parsimony of the parameterization of the networks are surprisingly advantageous in high-dimensional settings.

Greed is good: algorithmic results for sparse approximation <=SEP=> This article presents new results on using a greedy algorithm, orthogonal matching pursuit (OMP), to solve the sparse approximation problem over redundant dictionaries. It provides a sufficient condition under which both OMP and Donoho's basis pursuit (BP) paradigm can recover the optimal representation of an exactly sparse signal. It leverages this theory to show that both OMP and BP succeed for every sparse input signal from a wide class of dictionaries. These quasi-incoherent dictionaries offer a natural generalization of incoherent dictionaries, and the cumulative coherence function is introduced to quantify the level of incoherence. This analysis unifies all the recent results on BP and extends them to OMP. Furthermore, the paper develops a sufficient condition under which OMP can identify atoms from an optimal approximation of a nonsparse signal. From there, it argues that OMP is an approximation algorithm for the sparse problem over a quasi-incoherent dictionary. That is, for every input signal, OMP calculates a sparse approximant whose error is only a small factor worse than the minimal error that can be attained with the same number of terms.

Primal-dual subgradient methods for convex problems <=SEP=> In this paper we present a new approach for constructing subgradient schemes for different types of nonsmooth problems with convex structure. Our methods are primaldual since they are always able to generate a feasible approximation to the optimum of an appropriately formulated dual problem. Besides other advantages, this useful feature provides the methods with a reliable stopping criterion. The proposed schemes differ from the classical approaches (divergent series methods, mirror descent methods) by presence of two control sequences. The first sequence is responsible for aggregating the support functions in the dual space, and the second one establishes a dynamically updated scale between the primal and dual spaces. This additional flexibility allows to guarantee a boundedness of the sequence of primal test points even in the case of unbounded feasible set. We present the variants of subgradient schemes for nonsmooth convex minimization, minimax problems, saddle point problems, variational inequalities, and stochastic optimization. In all situations our methods are proved to be optimal from the view point of worst-case black-box lower complexity bounds.

Modelling Radiological Language with Bidirectional Long Short-Term Memory Networks <=SEP=> Motivated by the need to automate medical information extraction from free-text radiological reports, we present a bi-directional long short-term memory (BiLSTM) neural network architecture for modelling radiological language. The model has been used to address two NLP tasks: medical named-entity recognition (NER) and negation detection. We investigate whether learning several types of word embeddings improves BiLSTM’s performance on those tasks. Using a large dataset of chest x-ray reports, we compare the proposed model to a baseline dictionary-based NER system and a negation detection system that leverages the hand-crafted rules of the NegEx algorithm and the grammatical relations obtained from the Stanford Dependency Parser. Compared to these more traditional rule-based systems, we argue that BiLSTM offers a strong alternative for both our tasks.

Neuronal factors determining high intelligence. <=SEP=> Many attempts have been made to correlate degrees of both animal and human intelligence with brain properties. With respect to mammals, a much-discussed trait concerns absolute and relative brain size, either uncorrected or corrected for body size. However, the correlation of both with degrees of intelligence yields large inconsistencies, because although they are regarded as the most intelligent mammals, monkeys and apes, including humans, have neither the absolutely nor the relatively largest brains. The best fit between brain traits and degrees of intelligence among mammals is reached by a combination of the number of cortical neurons, neuron packing density, interneuronal distance and axonal conduction velocity--factors that determine general information processing capacity (IPC), as reflected by general intelligence. The highest IPC is found in humans, followed by the great apes, Old World and New World monkeys. The IPC of cetaceans and elephants is much lower because of a thin cortex, low neuron packing density and low axonal conduction velocity. By contrast, corvid and psittacid birds have very small and densely packed pallial neurons and relatively many neurons, which, despite very small brain volumes, might explain their high intelligence. The evolution of a syntactical and grammatical language in humans most probably has served as an additional intelligence amplifier, which may have happened in songbirds and psittacids in a convergent manner.

Marine debris in central California: quantifying type and abundance of beach litter in Monterey Bay, CA. <=SEP=> Monitoring beach litter is essential for reducing ecological threats towards humans and wildlife. In Monterey Bay, CA information on seasonal and spatial patterns is understudied. Central California's coastal managers require reliable information on debris abundance, distribution, and type, to support policy aimed at reducing litter. We developed a survey method that allowed for trained citizen scientists to quantify the types and abundance of beach litter. Sampling occurred from July 2009-June 2010. Litter abundance ranged from 0.03 to 17.1 items m(-2). Using a mixed model approach, we found season and location have the greatest effect on litter abundance. Styrofoam, the most numerically abundant item, made up 41% of the total amount of litter. Unexpected items included fertilizer pellets. The results of this study provide a baseline on the types and abundance of litter on the central coast and have directly supported policy banning Styrofoam take out containers from local municipalities.

Issues and Perspectives in Meditation Research: In Search for a Definition <=SEP=> Despite the growing interest in the neurobiological correlates of meditation, most research has omitted to take into account the underlying philosophical aspects of meditation and its wider implications. This, in turn, is reflected in issues surrounding definition, study design, and outcomes. Here, I highlight the often ignored but important aspect of definition in the existing scholarship on neuroscience and meditation practice. For a satisfactory account of a neuroscience of meditation, we must aim to retrieve an operational definition that is inclusive of a traditional ontological description as well as the modern neurocognitive account of the phenomena. Moving beyond examining the effects of meditation practice, to take a potential step forward in the direction to establish how meditation works, it becomes crucial to appraise the philosophical positions that underlie the phenomenology of meditation in the originating traditions. This endeavor may challenge our intuitions and concepts in either directions, but issues pertaining to definition, design, and validity of response measures are extremely important for the evolution of the field and will provide a much-needed context and framework for meditation based interventions.

Patent classifications as indicators of intellectual organization <=SEP=> Using the 138,751 patents filed in 2006 under the Patent Cooperation Treaty, coclassification analysis is pursued on the basis of threeand four-digit codes in the International Patent Classification (IPC, 8 edition). The co-classifications among the patents enable us to analyze and visualize the relations among technologies at different levels of aggregation. The hypothesis that classifications might be considered as the organizers of patents into classes, and that therefore co-classification patterns—more than co-citation patterns—might be useful for mapping, is not corroborated. The classifications hang weakly together, even at the four-digit level; at the country level, more specificity can be made visible. However, countries are not the appropriate units of analysis because patent portfolios are largely similar in many advanced countries in terms of the classes attributed. Instead of classes, one may wish to explore the mapping of title words as a better approach to visualize the intellectual organization of patents.

Genetically Improved PSO Algorithm for Efficient Data Clustering <=SEP=> Clustering is an important research topic in data mining that appears in a wide range of unsupervised classification applications. Partitional clustering algorithms such as the k-means algorithm are the most popular for clustering large datasets. The major problem with the k-means algorithm is that it is sensitive to the selection of the initial partitions and it may converge to local optima. In this paper, we present a hybrid two-phase GAI-PSO+k-means data clustering algorithm that performs fast data clustering and can avoid premature convergence to local optima. In the first phase we utilize the new genetically improved particle swarm optimization algorithm (GAI-PSO) which is a population-based heuristic search technique modeled on the hybrid of cultural and social rules derived from the analysis of the swarm intelligence (PSO) and the concepts of natural selection and evolution (GA). The GAI-PSO combines the standard velocity and position update rules of PSOs with the ideas of selection, mutation and crossover from GAs. The GAI-PSO algorithm searches the solution space to find the optimal initial cluster centroids for the next phase. The second phase is a local refining stage utilizing the k-means algorithm which can efficiently converge to the optimal solution. The proposed algorithm combines the ability of the globalized searching of the evolutionary algorithms and the fast convergence of the k-means algorithm and can avoid the drawback of both. The performance of the proposed algorithm is evaluated through several benchmark datasets. The experimental results show that the proposed algorithm is highly forceful and outperforms the previous approaches such as SA, ACO, PSO and k-means for the partitional clustering problem.

Sexually transmitted diseases treatment guidelines, 2010. <=SEP=> These guidelines for the treatment of persons who have or are at risk for sexually transmitted diseases (STDs) were updated by CDC after consultation with a group of professionals knowledgeable in the field of STDs who met in Atlanta on April 18-30, 2009. The information in this report updates the 2006 Guidelines for Treatment of Sexually Transmitted Diseases (MMWR 2006;55[No. RR-11]). Included in these updated guidelines is new information regarding 1) the expanded diagnostic evaluation for cervicitis and trichomoniasis; 2) new treatment recommendations for bacterial vaginosis and genital warts; 3) the clinical efficacy of azithromycin for chlamydial infections in pregnancy; 4) the role of Mycoplasma genitalium and trichomoniasis in urethritis/cervicitis and treatment-related implications; 5) lymphogranuloma venereum proctocolitis among men who have sex with men; 6) the criteria for spinal fluid examination to evaluate for neurosyphilis; 7) the emergence of azithromycin-resistant Treponema pallidum; 8) the increasing prevalence of antimicrobial-resistant Neisseria gonorrhoeae; 9) the sexual transmission of hepatitis C; 10) diagnostic evaluation after sexual assault; and 11) STD prevention approaches.

Teenager ’ s Addiction to Smart Phones and Its Integrated Therapy Method <=SEP=> The fundamental cause of teenagers’ addiction to smart phone is due to stress arising from family and school. Smartphone can cause many problems including musculoskeletal symptom which can develop due to staying in one position for a long time. Furthermore, starring at a small screen for an extended time can cause decreased eye sight, along with amblyopia. Smart phone addiction causes depression and anxiety which will lead to severe mental problems. This will lead to problems associated to one’s social life and human network. Fundamental treatment of cognitive and emotional elements needed to become harmonized as a whole in order to increase the effectiveness of the treatment results. The therapy has been verified to cure the behavioral and emotional aspect related to depression, sense of control, and assimilation in school. From these integrated treatment approaches, this report compared the treatment’s upsides and downsides and also, assessed the possibility the integrated treatment process. We can presume that this report provided the opportunity and support for the creation of a fundamental treatment method that can help teenagers live a healthy life by overcoming addiction to smart phones.

s t )-r technologies social responses to communication c Defining Virtual Reality : Dimensions Determining Telepresence <=SEP=> Virtual reality (VR) is typically defined in terms of technological hardware. This paper attempts to cast a new, variable-based definition of virtual reality that can be used to classify virtual reality in relation to other media. The defintion of virtual reality is based on concepts of “presence” and “telepresence,” which refer to the sense of being in an environment, generated by natural or mediated means, respectively. Two technological dimensions that contribute to telepresence, vividness and interactivity, are discussed. A variety of media are classified according to these dimensions. Suggestions are made for the application of the new definition of virtual reality within the field of communication research.

Advances in ultrasound biomicroscopy. <=SEP=> The visualisation of living tissues at microscopic resolution is attracting attention in several fields. In medicine, the goals are to image healthy and diseased tissue with the aim of providing information previously only available from biopsy samples. In basic biology, the goal may be to image biological models of human disease or to conduct longitudinal studies of small-animal development. High-frequency ultrasonic imaging (ultrasound biomicroscopy) offers unique advantages for these applications. In this paper, the development of ultrasound biomicroscopy is reviewed. Aspects of transducer development, systems design and tissue properties are presented to provide a foundation for medical and biological applications. The majority of applications appear to be developing in the 40-60-MHz frequency range, where resolution on the order of 50 microm can be achieved. Doppler processing in this frequency range is beginning to emerge and some examples of current achievements will be highlighted. The current state of the art is reviewed for medical applications in ophthalmology, intravascular ultrasound, dermatology, and cartilage imaging. Ultrasound biomicroscopic studies of mouse embryonic development and tumour biology are presented. Speculation on the continuing evolution of ultrasound biomicroscopy will be discussed.

A practical approach for content mining of Tweets. <=SEP=> Use of data generated through social media for health studies is gradually increasing. Twitter is a short-text message system developed 6 years ago, now with more than 100 million users generating over 300 million Tweets every day. Twitter may be used to gain real-world insights to promote healthy behaviors. The purposes of this paper are to describe a practical approach to analyzing Tweet contents and to illustrate an application of the approach to the topic of physical activity. The approach includes five steps: (1) selecting keywords to gather an initial set of Tweets to analyze; (2) importing data; (3) preparing data; (4) analyzing data (topic, sentiment, and ecologic context); and (5) interpreting data. The steps are implemented using tools that are publically available and free of charge and designed for use by researchers with limited programming skills. Content mining of Tweets can contribute to addressing challenges in health behavior research.

60 GHz system-on-package antenna array with parasitic microstrip antenna single element <=SEP=> In this paper microstrip antenna for 60 GHz on LTCC is presented. Special techniques are used to satisfy the antenna specification in wide bandwidth (7 GHz, from 57 GHz to 64 GHz) and high gain (15 dBi). An increase in bandwidth is achieved by using aperture the coupling and the multilayer structure. Gain is increased by using parasitic patches which increase the total antenna aperture. In paper was presented 60 GHz parasitic microstrip antenna with two layers of parasitic patches. High gain is obtained as the result of the optimization. However, presented return loss results can not be treated as wide bandwidth.

Checking Business Process Evolution <=SEP=> Business processes support the modeling and the implementation of software as workflows of local and inter-process activities. Taking over structuring and composition, evolution has become a central concern in software development. We advocate it should be taken into account as soon as the modeling of business processes, which can thereafter be made executable using process engines or model-to-code transformations. We show here that business process evolution needs formal analysis in order to compare different versions of processes, identify precisely the differences between them, and ensure the desired consistency. To reach this objective, we first present a model transformation from the BPMN standard notation to the LNT process algebra. We then propose a set of relations for comparing business processes at the formal model level. With reference to related work, we propose a richer set of comparison primitives supporting renaming, refinement, propertyand context-awareness. Thanks to an implementation of our approach that can be used through a Web application, we put the checking of evolution within the reach of business process designers.

On the number of response regions of deep feed forward networks with piece-wise linear activations <=SEP=> This paper explores the complexity of deep feedforward networks with linear presynaptic couplings and rectified linear activations. This is a contribution to the growing body of work contrasting the representational power of deep and shallow network architectures. In particular, we offer a framework for comparing deep and shallow models that belong to the family of piecewise linear functions based on computational geometry. We look at a deep rectifier multi-layer perceptron (MLP) with linear outputs units and compare it with a single layer version of the model. In the asymptotic regime, when the number of inputs stays constant, if the shallow model has kn hidden units and n0 inputs, then the number of linear regions is O(k0n0). For a k layer model with n hidden units on each layer it is Ω(bn/n0c n0). The number bn/n0c grows faster than k0 when n tends to infinity or when k tends to infinity and n ≥ 2n0. Additionally, even when k is small, if we restrict n to be 2n0, we can show that a deep model has considerably more linear regions that a shallow one. We consider this as a first step towards understanding the complexity of these models and specifically towards providing suitable mathematical tools for future analysis.

Business Process Design by Reusing Business Process Fragments from the Cloud <=SEP=> The constant development of technologies forces companies to be more innovative in order to stay competitive. In fact, designing a process from scratch is time consuming, error prone and costly. In this context, companies are heading to reuse process fragments when designing a new process to ensure a high degree of efficiency, with respect to delivery deadlines. However, reusing these fragments may disclose sensitive business activities, especially if these latter are deployed in an untrusted environment. In addition, companies are concerned about their user's privacy. To address these issues, we investigate how to build a new business process by reusing the safest existing fragments coming from various cloud servers, i.e. The ones that comply at best with company's preferences and policies, and offer an appropriate level of safety.

A Communication Theoretical Analysis of Multiple-Access Channel Capacity in Magneto-Inductive Wireless Networks <=SEP=> Magneto-inductive (MI) wireless communications is an emerging subject with a rich set of applications, including local area networks for the Internet-of-Things, wireless body area networks, in-body and on-chip communications, and underwater and underground sensor networks as a low-cost alternative to radio frequency, acoustic or optical methods. Practical MI networks include multiple access channel (MAC) mechanisms for connecting a random number of coils without any specific topology or coil orientation assumptions covering both short and long ranges. However, there is not any information theoretical modeling of MI MAC (MIMAC) capacity of such universal networks with fully coupled frequency selective channel models and exact 3-D coupling model of circular coils instead of long range dipole approximations. In this paper, K-user MIMAC capacity is information theoretically modeled and analyzed, and two-user MIMACs are modeled with explicitly detailed channel responses, bandwidths and coupled thermal noise. K-user MIMAC capacity is achieved through Lagrangian solution with K-user water-filling optimization. Optimum orientations maximizing capacity and received power are theoretically analyzed, and numerically simulated for two-user MIMACs. Constructive gain and destructive interference mechanisms on MIMACs are introduced in comparison with the classical interference based approaches. The theoretical basis promises the utilization of MIMACs in 5G architectures.

A Space Vector Modulation Scheme of the Quasi-Z-Source Three-Level T-Type Inverter for Common-Mode Voltage Reduction <=SEP=> The conventional three-level inverter suffers the limitation of voltage buck operation. In order to give both voltage buck and boost operation capability, the quasi-Z-source three-level T-type inverter (3LT<inline-formula> <tex-math notation="LaTeX">$^2$</tex-math></inline-formula>I) has been proposed. This paper further proposes a space vector modulation (SVM) scheme for the quasi-Z-source 3LT<inline-formula><tex-math notation="LaTeX">$^2$</tex-math> </inline-formula>I to reduce the magnitude and slew rate of common-mode voltage (CMV). By properly selecting the shoot-through phase, the shoot-through states are inserted within zero vector in order not to affect the active states and output voltage. Doing so, the CMV generated by the quasi-Z-source 3LT<inline-formula><tex-math notation="LaTeX"> $^2$</tex-math></inline-formula>I is restricted within one-sixth of dc-link voltage, and voltage boosting and CMV reduction can be simultaneously realized. In addition, high dc-link voltage utilization can be maintained. The proposed scheme has been verified in both simulations and experiments. Comparisons are conducted with the conventional SVM method and the phase-shifted sinusoidal PWM method.

User experience: challenges and opportunities <=SEP=> User expectations, motivations and feelings when using a product or encountering a system have urged the need to investigate beyond the traditional functionality and usability concerns by assessing and designing for the user experience. Having this realized, we can ensure positive user experience as well as desirable products. This paper aims at providing an overview of the main concepts and principles related to user experience. The topics discussed in this paper cover both theories from the academia as well as empirical studies from the industry. Due to misconception of user experience, it was found that industry depends on and uses the traditional usability methods in the product development as user experience practices. As a result, this paper discusses and elaborates the relationship between conventional usability and user experience in addition to the variety of methods which can be used for user experience evaluation during different time spans. Furthermore, user experience extends over different areas such as human computer interaction, and product design and development. Knowledge and concepts related to user experience are scattered due to the dynamic concepts of user experience. In other words, authors tend to discuss the user experience from their own perspective and interest. Hence, this paper is trying to provide and establish a holistic understanding of the user experience by covering different topics related to user experience. The output of this paper may provide and assist researchers and practitioners to establish a base to aid in proper understanding of user experience, its concepts and principles.

High performance communication architecture for smart distribution power grid in developing nations <=SEP=> In a smart distribution power grid, cost efficient and reliable communication architecture plays a crucial role in achieving complete functionality. There are different sets of Quality of Services (QoS) requirements for different data packets transmitting inside the microgrid (a regionally limited smart distribution grid), making it challenging to derive optimal communication architecture. The objective of this research work is to determine the optimal communication technologies for each data packet based on its QoS requirement. In this paper, we have proposed an architecture for a smart distribution power grid with Cyber Physical System enabled microgrids, which accommodate almost all functional requirements of a smart distribution power grid. For easy transition towards optimal communication architecture, we have presented a six-tier communication topology, which is derived from the architecture for a smart distribution power grid. The optimization formulations for each packet structure presented in this paper minimize the overall cost and consider the QoS requirements for each packet. Based on the simulation results, we have made recommendations for optimal communication technologies for each packet and thereby developed a heterogeneous communication architecture for

Sexual Dimorphism in the Parietal Substrate Associated with Visuospatial Cognition Independent of General Intelligence <=SEP=> Sex differences in visuospatial cognition (VSC) with male advantage are frequently reported in the literature. There is evidence for sexual dimorphisms in the human brain, one of which postulates more gray matter (GM) in females and more white matter (WM) in males relative to total intracranial volume. We investigated the neuroanatomy of VSC independent of general intelligence (g) in sex-separated populations, homogenous in age, education, memory performance, a memory- and brain morphology-related gene, and g. VSC and g were assessed with the Wechsler adult intelligence scale. The influence of g on VSC was removed using a hierarchical factor analysis and the Schmid–Leiman solution. Structural high-resolution magnetic resonance images were acquired and analyzed with voxel-based morphometry. As hypothesized, the clusters of positive correlations between local volumes and VSC performance independent of g were found mainly in parietal areas, but also in pre- and postcentral regions, predominantly in the WM in males, whereas in females these correlations were located in parietal and superior temporal areas, predominantly in the GM. Our results suggest that VSC depends more strongly on parietal WM structures in males and on parietal GM structures in females. This sex difference might have to do with the increased axonal and decreased somatodendritic tissue in males relative to females. Whether such sex-specific implementations of the VSC network can be explained genetically as suggested in investigations into the Turner syndrome or as a result of structural neural plasticity upon different experience and usage remains to be shown.

The effect of flow experience on mobile SNS users' loyalty <=SEP=> Purpose – The purpose of this research is to examine the effect of flow experience on mobile social networking service (SNS) users’ loyalty. Design/methodology/approach – Based on 305 valid responses collected from a survey questionnaire, structural equation modeling (SEM) technology was employed to examine the research model. Findings – The results show that both information quality and system quality significantly affect users’ trust and flow experiences, which further determine their loyalty. The results indicate that flow experience is the strongest determinant of users’ loyalty. Practical implications – Mobile SNS providers need to consider user experience when seeking users’ loyalty. They should enhance information quality and system quality in order to improve user trust and flow experience. Originality/value – Although much research has been conducted to explore the effects of extrinsic motivations, such as perceived usefulness, on mobile commerce user behavior, the effect of intrinsic motivations, such as flow experience, is seldom tested. This research found the effect of flow experience on mobile SNS users’ loyalty to be significant.

Engineering Time-Expanded Graphs for Faster Timetable Information <=SEP=> We present an extension of the well-known time-expanded approach for timetable information. By remodeling unimportant stations, we are able to obtain faster query times with less space consumption than the original model. Moreover, we show that our extensions harmonize well with speed-up techniques whose adaption to timetable networks is more challenging than one might expect.

FingerScanner: Embedding a Fingerprint Scanner in a Raspberry Pi <=SEP=> Nowadays, researchers are paying increasing attention to embedding systems. Cost reduction has lead to an increase in the number of platforms supporting the operating system Linux, jointly with the Raspberry Pi motherboard. Thus, embedding devices on Raspberry-Linux systems is a goal in order to make competitive commercial products. This paper presents a low-cost fingerprint recognition system embedded into a Raspberry Pi with Linux.

Depth driven people counting using deep region proposal network <=SEP=> People counting is a crucial subject in video surveillance application. Factors such as severe occlusions, scene perspective distortions in real application scenario make this task challenging. In this paper, we carefully designed a deep detection framework based on depth information for people counting in crowded environments. Our system performs head detection on depth images collected by an overhead vertical Kinect sensor. To the best of our knowledge, this is the first attempt to use the deep convolutional neural networks on depth images for people counting. We explored the impact of the number and quality of RPN positive anchors on the performance of Faster R-CNN and proposed a solution. Our method is very simple but effective, not only showing promising results but also efficiency as it runs in real-time at a frame rate of about 110 frames per second on a GPU.

A Sparse Matrix Library in C + + for High PerformanceArchitectures <=SEP=> We describe an object oriented sparse matrix library in C++ built upon the Level 3 Sparse BLAS proposal [5] for portability and performance across a wide class of machine architectures. The C++ library includes algorithms for various iterative methods and supports the most common sparse data storage formats used in practice. Besides simplifying the subroutine interface, the object oriented design allows the same driving code to be used for various sparse matrix formats, thus addressing many of the di culties encountered with the typical approach to sparse matrix libraries. We emphasize the fundamental design issues of the C++ sparse matrix classes and illustrate their usage with the preconditioned conjugate gradient (PCG) method as an example. Performance results illustrate that these codes are competitive with optimized Fortran 77. We discuss the impact of our design on elegance, performance, maintainability, portability, and robustness.