Query: Lire: lucene image retrieval: an extensible java CBIR library

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CEDD: Color and Edge Directivity Descriptor: A Compact Descriptor for Image Indexing and Retrieval <=SEP=> This paper deals with a new low level feature that is extracted from the images and can be used for indexing and retrieval. This feature is called “Color and Edge Directivity Descriptor” and incorporates color and texture information in a histogram. CEDD size is limited to 54 bytes per image, rendering this descriptor suitable for use in large image databases. One of the most important attribute of the CEDD is the low computational power needed for its extraction, in comparison with the needs of the most MPEG-7 descriptors. The objective measure called ANMRR is used to evaluate the performance of the proposed feature. An online demo that implements the proposed feature in an image retrieval system is available at: http://orpheus.ee.duth.gr/image_retrieval.

Textural Features Corresponding to Visual Perception <=SEP=> Textural features corresponding to human visual perception are very useful for optimum feature selection and texture analyzer design. We approximated in computational form six basic textural features, namely, coarseness, contrast, directionality, line-likeness, regularity, and roughness. In comparison with psychological measurements for human subjects, the computational measures gave good correspondences in rank correlation of 16 typical texture patterns. Similarity measurements using these features were attempted. The discrepancies between human vision and computerized techniques that we encountered in this study indicate fundamental problems in digital analysis of textures. Some of them could be overcome by analyzing their causes and using more sophisticated techniques.

FCTH: Fuzzy Color and Texture Histogram - A Low Level Feature for Accurate Image Retrieval <=SEP=> This paper deals with the extraction of a new low level feature that combines, in one histogram, color and texture information. This feature is named FCTH - Fuzzy Color and Texture Histogram - and results from the combination of 3 fuzzy systems. FCTH size is limited to 72 bytes per image, rendering this descriptor suitable for use in large image databases. The proposed feature is appropriate for accurately retrieving images even in distortion cases such as deformations, noise and smoothing. It is tested on a large number of images selected from proprietary image databases or randomly retrieved from popular search engines. To evaluate the performance of the proposed feature, the averaged normalized modified retrieval rank was used. An online demo that implements the proposed feature in an image retrieval system is available at: http://orpheus.ee.duth.gr/image_retrieval.

M-tree: An Efficient Access Method for Similarity Search in Metric Spaces <=SEP=> A new access method called M tree is pro posed to organize and search large data sets from a generic metric space i e where ob ject proximity is only de ned by a distance function satisfying the positivity symmetry and triangle inequality postulates We detail algorithms for insertion of objects and split management which keep the M tree always balanced several heuristic split alternatives are considered and experimentally evaluated Algorithms for similarity range and k nearest neighbors queries are also described Re sults from extensive experimentation with a prototype system are reported considering as the performance criteria the number of page I O s and the number of distance computa tions The results demonstrate that the M tree indeed extends the domain of applica bility beyond the traditional vector spaces performs reasonably well in high dimensional data spaces and scales well in case of growing les

Normalized cuts in 3-D for spinal MRI segmentation <=SEP=> Segmentation of medical images has become an indispensable process to perform quantitative analysis of images of human organs and their functions. Normalized Cuts (NCut) is a spectral graph theoretic method that readily admits combinations of different features for image segmentation. The computational demand imposed by NCut has been successfully alleviated with the Nystro/spl uml/m approximation method for applications different than medical imaging. In this paper we discuss the application of NCut with the Nystro/spl uml/m approximation method to segment vertebral bodies from sagittal T1-weighted magnetic resonance images of the spine. The magnetic resonance images were preprocessed by the anisotropic diffusion algorithm, and three-dimensional local histograms of brightness was chosen as the segmentation feature. Results of the segmentation as well as limitations and challenges in this area are presented.

Mining Efficient Taxi Operation Strategies From Large Scale Geo-Location Data <=SEP=> Taxi drivers always look for strategies to locate passengers quickly and therefore increase their profit margin. In reality, the passenger seeking strategies are mostly empirical and substantially vary among taxi drivers. From the history taxi data, the top performing taxi drivers can earn 25% more than the ones with mediocre seeking strategy in the same period of time. A better strategy not only helps taxi drivers earn more with less effort, but also reduce fuel consumption and carbon emissions. It is interesting to examine the influential factors in passenger seeking strategies and find algorithms to guide taxi drivers to passenger hotspots with the right timing. With the abundant availability of history taxicab traces, the existing methods of doing taxi business have been radically changed. This paper focuses on the problem of mining efficient operation strategies from a large-scale history taxi traces collected over one year. Our approach presents generic insights into the dynamics of taxicab services with the objective of maximizing the profit margins for the concerned parties. We propose important metrics, such as trip frequency, hot spots, and taxi mileage, and provide valuable insights toward more efficient operation strategies. We analyze these metrics using techniques, such as Newton’s polynomial interpolation and Gamma distribution, to understand their dynamics. Our strategies use the real taxicab traces from the city of Changsha (P.R.China), may predict the taxi rides at different times by 90.68% per day, and increase the taxi drivers income levels up to 19.38% by controlling appropriate mileage per trip and following the route across more urban hot spots.

Soft-Gated Warping-GAN for Pose-Guided Person Image Synthesis <=SEP=> Despite remarkable advances in image synthesis research, existing works often fail in manipulating images under the context of large geometric transformations. Synthesizing person images conditioned on arbitrary poses is one of the most representative examples where the generation quality largely relies on the capability of identifying and modeling arbitrary transformations on different body parts. Current generative models are often built on local convolutions and overlook the key challenges (e.g. heavy occlusions, different views or dramatic appearance changes) when distinct geometric changes happen for each part, caused by arbitrary pose manipulations. This paper aims to resolve these challenges induced by geometric variability and spatial displacements via a new Soft-Gated Warping Generative Adversarial Network (Warping-GAN), which is composed of two stages: 1) it first synthesizes a target part segmentation map given a target pose, which depicts the region-level spatial layouts for guiding image synthesis with higher-level structure constraints; 2) the Warping-GAN equipped with a soft-gated warping-block learns feature-level mapping to render textures from the original image into the generated segmentation map. Warping-GAN is capable of controlling different transformation degrees given distinct target poses. Moreover, the proposed warping-block is lightweight and flexible enough to be injected into any networks. Human perceptual studies and quantitative evaluations demonstrate the superiority of our WarpingGAN that significantly outperforms all existing methods on two large datasets.

Tracking Depression Dynamics in College Students Using Mobile Phone and Wearable Sensing <=SEP=> There are rising rates of depression on college campuses. Mental health services on our campuses are working at full stretch. In response researchers have proposed using mobile sensing for continuous mental health assessment. Existing work on understanding the relationship between mobile sensing and depression, however, focuses on generic behavioral features that do not map to major depressive disorder symptoms defined in the standard mental disorders diagnostic manual (DSM-5). We propose a new approach to predicting depression using passive sensing data from students' smartphones and wearables. We propose a set of symptom features that proxy the DSM-5 defined depression symptoms specifically designed for college students. We present results from a study of 83 undergraduate students at Dartmouth College across two 9-week terms during the winter and spring terms in 2016. We identify a number of important new associations between symptom features and student self reported PHQ-8 and PHQ-4 depression scores. The study captures depression dynamics of the students at the beginning and end of term using a pre-post PHQ-8 and week by week changes using a weekly administered PHQ-4. Importantly, we show that symptom features derived from phone and wearable sensors can predict whether or not a student is depressed on a week by week basis with 81.5% recall and 69.1% precision.

Real-time super-resolution Sound Source Localization for robots <=SEP=> Sound Source Localization (SSL) is an essential function for robot audition and yields the location and number of sound sources, which are utilized for post-processes such as sound source separation. SSL for a robot in a real environment mainly requires noise-robustness, high resolution and real-time processing. A technique using microphone array processing, that is, Multiple Signal Classification based on Standard EigenValue Decomposition (SEVD-MUSIC) is commonly used for localization. We improved its robustness against noise with high power by incorporating Generalized EigenValue Decomposition (GEVD). However, GEVD-based MUSIC (GEVD-MUSIC) has mainly two issues: 1) the resolution of pre-measured Transfer Functions (TFs) determines the resolution of SSL, 2) its computational cost is expensive for real-time processing. For the first issue, we propose a TF interpolation method integrating time-domain-based and frequency-domain-based interpolation. The interpolation achieves super-resolution SSL, whose resolution is higher than that of the pre-measured TFs. For the second issue, we propose two methods, MUSIC based on Generalized Singular Value Decomposition (GSVD-MUSIC), and Hierarchical SSL (H-SSL). GSVD-MUSIC drastically reduces the computational cost while maintaining noise-robustness in localization. H-SSL also reduces the computational cost by introducing a hierarchical search algorithm instead of using greedy search in localization. These techniques are integrated into an SSL system using a robot embedded microphone array. The experimental result showed: the proposed interpolation achieved approximately 1 degree resolution although we have only TFs at 30 degree intervals, GSVD-MUSIC attained 46.4% and 40.6% of the computational cost compared to SEVD-MUSIC and GEVD-MUSIC, respectively, H-SSL reduces 59.2% computational cost in localization of a single sound source.

Medical image segmentation on GPUs - A comprehensive review <=SEP=> Segmentation of anatomical structures, from modalities like computed tomography (CT), magnetic resonance imaging (MRI) and ultrasound, is a key enabling technology for medical applications such as diagnostics, planning and guidance. More efficient implementations are necessary, as most segmentation methods are computationally expensive, and the amount of medical imaging data is growing. The increased programmability of graphic processing units (GPUs) in recent years have enabled their use in several areas. GPUs can solve large data parallel problems at a higher speed than the traditional CPU, while being more affordable and energy efficient than distributed systems. Furthermore, using a GPU enables concurrent visualization and interactive segmentation, where the user can help the algorithm to achieve a satisfactory result. This review investigates the use of GPUs to accelerate medical image segmentation methods. A set of criteria for efficient use of GPUs are defined and each segmentation method is rated accordingly. In addition, references to relevant GPU implementations and insight into GPU optimization are provided and discussed. The review concludes that most segmentation methods may benefit from GPU processing due to the methods' data parallel structure and high thread count. However, factors such as synchronization, branch divergence and memory usage can limit the speedup.

A compact printed monopole antenna with symmetrical i and rectangular shaped slots for bluetooth/WLAN/WIMAX applications <=SEP=> A compact dual band microstrip fed printed monopole antenna is designed and presented in this paper for Bluetooth/WLAN/WiMAX applications. Two I with rectangular shape slot have been etched on a Roger RT/duroid substrate to form desired monopole antenna. The radiator of the antenna is very compact with an area of only 9.1 × 10.2 mm2. The size of the I & rectangular slots of dual radiating elements were adjusted so as to achieve two different resonant modes for dual band operation. The simulation result shows that the proposed antenna has 10-dB impedance bandwidth of 219.6 MHz and 3.20 GHz which cover the Bluetooth, WLAN 2.4/5.8 GHz and the WiMAX 2.5/3.5 GHz bands. The overall proposed antenna size is 15 × 17 × 0.762 mm 3 with the obtained VSWR less than 1.5.

Phase statistics of interferograms with applications to synthetic aperture radar. <=SEP=> Interferometric methods are well established in optics and radio astronomy. In recent years, interferometric concepts have been applied successfully to synthetic aperture radar (SAR) and have opened up new possibilities in the area of earth remote sensing. However interferometric SAR applications require thorough phase control through the imaging process. The phase accuracy of SAR images is affected by decorrelation effects between the individual surveys. We analyze quantitatively the influence of decorrelation on the phase statistics of SAR interferograms. In particular, phase aberrations as they occur in typical SAR processors are studied in detail. The dependence of the resulting phase bias and variance on processor parameters is presented in several diagrams.

Structured Indoor Modeling <=SEP=> This paper presents a novel 3D modeling framework that reconstructs an indoor scene as a structured model from panorama RGBD images. A scene geometry is represented as a graph, where nodes correspond to structural elements such as rooms, walls, and objects. The approach devises a structure grammar that defines how a scene graph can be manipulated. The grammar then drives a principled new reconstruction algorithm, where the grammar rules are sequentially applied to recover a structured model. The paper also proposes a new room segmentation algorithm and an offset-map reconstruction algorithm that are used in the framework and can enforce architectural shape priors far beyond existing state-of-the-art. The structured scene representation enables a variety of novel applications, ranging from indoor scene visualization, automated floorplan generation, Inverse-CAD, and more. We have tested our framework and algorithms on six synthetic and five real datasets with qualitative and quantitative evaluations. The source code and the data are available at the project website [15].

A piezoelectric vibration based generator for wireless electronics <=SEP=> Enabling technologies for wireless sensor networks have gained considerable attention in research communities over the past few years. It is highly desirable, even necessary in certain situations, for wireless sensor nodes to be self-powered. With this goal in mind, a vibration based piezoelectric generator has been developed as an enabling technology for wireless sensor networks. The focus of this paper is to discuss the modeling, design, and optimization of a piezoelectric generator based on a two-layer bending element. An analytical model of the generator has been developed and validated. In addition to providing intuitive design insight, the model has been used as the basis for design optimization. Designs of 1 cm3 in size generated using the model have demonstrated a power output of 375 μW from a vibration source of 2.5 m s−2 at 120 Hz. Furthermore, a 1 cm3 generator has been used to power a custom designed 1.9 GHz radio transmitter from the same vibration source. (Some figures in this article are in colour only in the electronic version)

Weakly supervised user intent detection for multi-domain dialogues <=SEP=> Users interact with mobile apps with certain intents such as finding a restaurant. Some intents and their corresponding activities are complex and may involve multiple apps; for example, a restaurant app, a messenger app and a calendar app may be needed to plan a dinner with friends. However, activities may be quite personal and third-party developers would not be building apps to specifically handle complex intents (e.g., a DinnerPlanner). Instead we want our intelligent agent to actively learn to understand these intents and provide assistance when needed. This paper proposes a framework to enable the agent to learn an inventory of intents from a small set of task-oriented user utterances. The experiments show that on previously unseen user activities, the agent is able to reliably recognize user intents using graph-based semi-supervised learning methods. The dataset, models, and the system outputs are available to research community.

Does telecommuting really increase productivity? <=SEP=> As many companies have learned in the last decade, the reality of telecommuting does not reflect the hype, the expected potential, or the existing literature.

Conspicuous Consumption and Household Indebtedness <=SEP=> Using a novel, large dataset of consumer transactions in Singapore, we study how conspicuous consumption affects household indebtedness. The coexistence of private housing (condominiums) and subsidized public housing (HDB) allows us to identify conspicuous consumers. Conditional on the same income and other socioeconomic characteristics, those who choose to reside in condominiums—considered a status good—are likely to be more conspicuous than their counterparts living in HDB units. We consistently find that condominium residents spend considerably more (by up to 44%) on conspicuous goods but not differently on inconspicuous goods. Compared with their matched HDB counterparts, more conspicuous consumers have 13% more credit card debt and 151% more delinquent credit card debt. Furthermore, the association between conspicuous consumption and credit card debt is concentrated among younger, male, single individuals. These results suggest that status-seeking-induced conspicuous consumption is an important determinant of household indebtedness.

Why social preferences matter-the impact of non-selfish motives on competition , cooperation and incentives <=SEP=> A substantial number of people exhibit social preferences, which means they are not solely motivated by material self-interest but also care positively or negatively for the material payoffs of relevant reference agents. We show empirically that economists fail to understand fundamental economic questions when they disregard social preferences, in particular, that without taking social preferences into account, it is not possible to understand adequately (i) the effects of competition on market outcomes, (ii) laws governing cooperation and collective action, (iii) effects and the determinants of material incentives, (iv) which contracts and property rights arrangements are optimal, and (v) important forces shaping social norms and market failures. a) Ernst Fehr, Institute for Empirical Research in Economics, University of Zurich, Bluemlisalpstrasse 10, CH-8006 Zurich, Switzerland, email: efehr@iew.unizh.ch. b) Urs Fischbacher, Institute for Empirical Research in Economics, University of Zurich, Bluemlisalpstrasse 10, CH-8006 Zurich, Switzerland, email: fiba@iew.unizh.ch.

CoType: Joint Extraction of Typed Entities and Relations with Knowledge Bases <=SEP=> Extracting entities and relations for types of interest from text is important for understanding massive text corpora. Traditionally, systems of entity relation extraction have relied on human-annotated corpora for training and adopted an incremental pipeline. Such systems require additional human expertise to be ported to a new domain, and are vulnerable to errors cascading down the pipeline. In this paper, we investigate joint extraction of typed entities and relations with labeled data heuristically obtained from knowledge bases (i.e., distant supervision). As our algorithm for type labeling via distant supervision is context-agnostic, noisy training data poses unique challenges for the task. We propose a novel domainindependent framework, called COTYPE, that runs a data-driven text segmentation algorithm to extract entity mentions, and jointly embeds entity mentions, relation mentions, text features and type labels into two low-dimensional spaces (for entity and relation mentions respectively), where, in each space, objects whose types are close will also have similar representations. COTYPE, then using these learned embeddings, estimates the types of test (unlinkable) mentions. We formulate a joint optimization problem to learn embeddings from text corpora and knowledge bases, adopting a novel partial-label loss function for noisy labeled data and introducing an object "translation" function to capture the cross-constraints of entities and relations on each other. Experiments on three public datasets demonstrate the effectiveness of COTYPE across different domains (e.g., news, biomedical), with an average of 25% improvement in F1 score compared to the next best method.

Pruning ConvNets Online for Efficient Specialist Models <=SEP=> Convolutional neural networks (CNNs) excel in various computer vision related tasks but are extremely computationally intensive and power hungry to run on mobile and embedded devices. Recent pruning techniques can reduce the computation and memory requirements of CNNs, but a costly retraining step is needed to restore the classification accuracy of the pruned model. In this paper, we present evidence that when only a subset of the classes need to be classified, we could prune a model and achieve reasonable classification accuracy without retraining. The resulting specialist model will require less energy and time to run than the original full model. To compensate for the pruning, we take advantage of the redundancy among filters and class-specific features. We show that even simple methods such as replacing channels with mean or with the most correlated channel can boost the accuracy of the pruned model to reasonable levels.

First- and second-order optimality conditions for piecewise smooth objective functions <=SEP=> Any piecewise smooth function that is specified by an evaluation procedure involving smooth elemental functions and piecewise linear functions like min and max can be represented in the so-called abs-normal form. By an extension of algorithmic, or automatic, differentiation, one can then compute certain first and second order derivative vectors and matrices that represent a local piecewise linearization and provide additional curvature information. On the basis of these quantities we characterize local optimality by first and second order necessary and sufficient conditions, which generalize the corresponding KKT theory for smooth problems. The key assumption is the Linear Independence Kink Qualification (LIKQ), a generalization of LICQ familiar from nonlinear optimization. It implies that the objective has locally a so-called VU decomposition and renders everything tractable in terms of matrix factorizations and other simple linear algebra operations. By yielding descent directions whenever they are violated the new optimality conditions point the way to a superlinearly convergent generalized QP solver, which is currently under development. We exemplify the theory on two nonsmooth examples of Nesterov.

Precision SAR processing using chirp scaling <=SEP=> Abstmct-A space-variant interpolation is required to compensate for the migration of signal energy through range resolution cells when processing synthetic aperture radar (SAR) data, using either the classical rangelDoppler (RID) algorithm or related frequency domain techniques. In general, interpolation requires significant computation time, and leads to loss of image quality, especially in the complex image. The new chirp scaling algorithm avoids interpolation, yet performs range cell migration correction accurately. The algorithm requires only complex multiplies and Fourier transforms to implement, is inherently phase preserving, and is suitable for wide-swath, largebeamwidth, and large-squint applications. This paper describes the chirp scaling algorithm, summarizes simulation results, presents imagery processed with the algorithm, and reviews quantitative measures of its performance. Based on quantitative comparison, the chirp scaling algorithm provides image quality equal to or better than the precision rangel Doppler processor. Over the range of parameters tested, image quality results approach the theoretical limit, as defined by the system bandwidth.

Multi-Type Itemset Embedding for Learning Behavior Success <=SEP=> Contextual behavior modeling uses data from multiple contexts to discover patterns for predictive analysis. However, existing behavior prediction models often face difficulties when scaling for massive datasets. In this work, we formulate a behavior as a set of context items of different types (such as decision makers, operators, goals and resources), consider an observable itemset as a behavior success, and propose a novel scalable method, "multi-type itemset embedding", to learn the context items' representations preserving the success structures. Unlike most of existing embedding methods that learn pair-wise proximity from connection between a behavior and one of its items, our method learns item embeddings collectively from interaction among all multi-type items of a behavior, based on which we develop a novel framework, LearnSuc, for (1) predicting the success rate of any set of items and (2) finding complementary items which maximize the probability of success when incorporated into an itemset. Extensive experiments demonstrate both effectiveness and efficency of the proposed framework.

The differences in motivations of online game players and offline game players: A combined analysis of three studies at higher education level <=SEP=> Computer games have become a highly popular form of entertainment and have had a large impact on how University students spend their leisure time. Due to their highly motivating properties computer games have come to the attention of educationalists who wish to exploit these highly desirable properties for educational purposes. Several studies have been performed looking at motivations for playing computer games in a general context and in a Higher Education (HE) context. These studies did not focus on the differences in motivations between online and offline game players. Equally the studies did not look at the differences in motivations of people who prefer single player games and people who prefer multiplayer games. If games-based learning is to become a recognised teaching approach then such motivations for playing computer games must be better understood. This paper presents the combined analysis of three studies at HE level, performed over a four year period from 2005 to 2009. The paper focuses on differences of motivations in relation to single player/multiplayer preference and online/ offline game participation. The study found that challenge is the top ranking motivation and recognition is the lowest ranking motivation for playing computer games in general. Challenge is also the top ranking motivation for playing games in HE while fantasy and recognition are the lowest ranking motivations for playing games in HE. Multiplayer gamers derive more competition, cooperation, recognition, fantasy and curiosity from playing games and online gamers derive more challenge, cooperation, recognition and control from playing games. Multiplayer gamers and online gamers ranked competition, cooperation and recognition significantly more important for playing games in HE than single players and offline participants. 2011 Elsevier Ltd. All rights reserved.

An image encryption scheme based on elliptic curve pseudo random and Advanced Encryption System <=SEP=> Elliptic curve cryptography (ECC) has proven to be an effective cryptography. ECC has its own advantages such as efficient key size compared to other public key infrastructures. This paper exploits the Elliptic curve random generator defined by National Institute of Standards and Technology (NIST) to generate a sequence of arbitrary numbers based on curves. The random generation phase is based on public shared key and a changing point G, which is a generator of a curve to obtain random sequences. Then, Advanced Encryption System is applied to these sequences acquiring arbitrary keys for encrypting image. Using AES alongside well distributed randoms provides a prominent encryption technique. Our experiments show that the proposed method fulfills the basics of cryptography including simpleness and correctness. Moreover, the results of the evaluation prove the effectiveness and security of the proposed method.

Homography-based 2D Visual Tracking and Servoing <=SEP=> The objective of this paper is to propose a new homography-based approach to image-based visual tracking and servoing. The visual tracking algorithm proposed in the paper is based on a new efficient second-order minimization method. Theoretical analysis and comparative experiments with other tracking approaches show that the proposed method has a higher convergence rate than standard first-order minimization techniques. Therefore, it is well adapted to real-time robotic applications. The output of the visual tracking is a homography linking the current and the reference image of a planar target. Using the homography, a task function isomorphic to the camera pose has been designed. A new image-based control law is proposed which does not need any measure of the 3D structure of the observed target (e.g. the normal to the plane). The theoretical proof of the existence of the isomorphism between the task function and the camera pose and the theoretical proof of the stability of the control law are provided. The experimental results, obtained with a 6 d.o.f. robot, show the advantages of the proposed method with respect to the existing approaches. KEY WORDS—visual tracking, visual servoing, efficient second-order minimization, homography-based control law

Supervised tensor learning <=SEP=> Tensor representation is helpful to reduce the small sample size problem in discriminative subspace selection. As pointed by this paper, this is mainly because the structure information of objects in computer vision research is a reasonable constraint to reduce the number of unknown parameters used to represent a learning model. Therefore, we apply this information to the vector-based learning and generalize the vector-based learning to the tensor-based learning as the supervised tensor learning (STL) framework, which accepts tensors as input. To obtain the solution of STL, the alternating projection optimization procedure is developed. The STL framework is a combination of the convex optimization and the operations in multilinear algebra. The tensor representation helps reduce the overfitting problem in vector-based learning. Based on STL and its alternating projection optimization procedure, we generalize support vector machines, minimax probability machine, Fisher discriminant analysis, and distance metric learning, to support tensor machines, tensor minimax probability machine, tensor Fisher discriminant analysis, and the multiple distance metrics learning, respectively. We also study the iterative procedure for feature extraction within STL. To examine the effectiveness of STL, we implement the tensor minimax probability machine for image classification. By comparing with minimax probability machine, the tensor version reduces the overfitting problem.

Two-Stream 3-D convNet Fusion for Action Recognition in Videos With Arbitrary Size and Length <=SEP=> 3-D convolutional neural networks (3-D-convNets) have been very recently proposed for action recognition in videos, and promising results are achieved. However, existing 3-D-convNets has two “artificial” requirements that may reduce the quality of video analysis: 1) It requires a fixed-sized (e.g., 112  $\times$ 112) input video; and 2) most of the 3-D-convNets require a fixed-length input (i.e., video shots with fixed number of frames). To tackle these issues, we propose an end-to-end pipeline named Two-stream 3-D-convNet Fusion, which can recognize human actions in videos of arbitrary size and length using multiple features. Specifically, we decompose a video into spatial and temporal shots. By taking a sequence of shots as input, each stream is implemented using a spatial temporal pyramid pooling (STPP) convNet with a long short-term memory (LSTM) or CNN-E model, softmax scores of which are combined by a late fusion. We devise the STPP convNet to extract equal-dimensional descriptions for each variable-size shot, and we adopt the LSTM/CNN-E model to learn a global description for the input video using these time-varying descriptions. With these advantages, our method should improve all 3-D CNN-based video analysis methods. We empirically evaluate our method for action recognition in videos and the experimental results show that our method outperforms the state-of-the-art methods (both 2-D and 3-D based) on three standard benchmark datasets (UCF101, HMDB51 and ACT datasets).

Fidelius Charm: Isolating Unsafe Rust Code <=SEP=> The Rust programming language has a safe memory model that promises to eliminate critical memory bugs. While the language is strong in doing so, its memory guarantees are lost when any unsafe blocks are used. Unsafe code is often needed to call library functions written in an unsafe language inside a Rust program. We present Fidelius Charm (FC), a system that protects a programmer-specified subset of data in memory from unauthorized access through vulnerable unsafe libraries. FC does this by limiting access to the program's memory while executing unsafe libraries. FC uses standard features of Rust and utilizes the Linux kernel as a trusted base for splitting the address space into a trusted privileged region under the control of functions written in Rust and a region available to unsafe external libraries. This paper presents our design and implementation of FC, presents two case studies for using FC in Rust TLS libraries, and reports on experiments showing its performance overhead is low for typical uses.

Low Power Magnetic Full-Adder Based on Spin Transfer Torque MRAM <=SEP=> Power issues have become a major problem of CMOS logic circuits as technology node shrinks below 90 nm. In order to overcome this limitation, emerging logic-in-memory architecture based on nonvolatile memories (NVMs) are being investigated. Spin transfer torque (STT) magnetic random access memory (MRAM) is considered one of the most promising NVMs thanks to its high speed, low power, good endurance, and 3-D back-end integration. This paper presents a novel magnetic full-adder (MFA) design based on perpendicular magnetic anisotropy (PMA) STT-MRAM. It provides advantageous power efficiency and die area compared with conventional CMOS-only full adder (FA). Transient simulations have been performed to validate this design by using an industrial CMOS 40 nm design kit and an accurate STT-MRAM compact model including physical models and experimental measurements.