title: Distributed Compressed Sensing off the Grid

abstract: This letter investigates the joint recovery of a frequency-sparse signal ensemble sharing a common frequency-sparse component from the collection of their compressed measurements. Unlike conventional arts in compressed sensing, the frequencies follow an off-the-grid formulation and are continuously valued in [0, 1]. As an extension of atomic norm, the concatenated atomic norm minimization approach is proposed to handle the exact recovery of signals, which is reformulated as a computationally tractable semidefinite program. The optimality of the proposed approach is characterized using a dual certificate. Numerical experiments are performed to illustrate the effectiveness of the proposed approach and its advantage over separate recovery.


title: Improving ant colony optimization for brain MRI image segmentation and brain tumor diagnosis

abstract: Today, using medical imaging devices is essential for disease diagnosis and medical researches. Among these devices, Magnetic Resonance Imaging has the main role. Segmentation of these images is more difficult than natural images because their functional sensitivity is higher than other images. Up to now, many different algorithms have been suggested for segmentation of this type of images. In this paper, we propose an approach in order to improve ant colony algorithm efficiency. In this approach, ant's direction and its tendency to go to the next site is regarded for calculating the probability of choosing the next site by the ant. Moreover, in calculating the probability of the ant's next move, we try to make a balance between the effect of the ant direction and the amount of pheromone distributed. Then this algorithm is used for segmentation of brain magnetic resonance images and diagnosing tumors.


title: Programmable Reverse Engineering

abstract: Program understanding can be enhanced using reverse engineering technologies. The understanding process is heavily dependent on both individuals and their specific cognitive abilities, and on the set of facilities provided by the program understanding environment. Unfortunately, most reverse engineering tools provide a fixed palette of extraction, selection, and organization techniques. This paper describes a programmable approach to reverse engineering. The approach uses a scripting language that enables users to write their own routines for common reverse engineering activities, such as graph layout, metrics, and subsystem decomposition, thereby extending the capabilities of the reverse engineering toolset to better suit their needs. A programmable environment supported by this approach subsumes existing reverse engineering systems by being able to simulate facets of each one.


title: Mathematical modeling of the capacity fade of Li-ion cells

abstract: A capacity fade prediction model has been developed for Li-ion cells based on a semi-empirical approach. Correlations for variation of capacity fade parameters with cycling were obtained with two different approaches. The first approach takes into account only the active material loss, while the second approach includes rate capability losses too. Both methods use correlations for variation of the film resistance with cycling. The state of charge (SOC) of the limiting electrode accounts for the active material loss. The diffusion coefficient of the limiting electrode was the parameter to account for the rate capability losses during cycling.


title: Bridging Neural Machine Translation and Bilingual Dictionaries

abstract: Neural Machine Translation (NMT) has become the new state-of-the-art in several language pairs. However, it remains a challenging problem how to integrate NMT with a bilingual dictionary which mainly contains words rarely or never seen in the bilingual training data. In this paper, we propose two methods to bridge NMT and the bilingual dictionaries. The core idea behind is to design novel models that transform the bilingual dictionaries into adequate sentence pairs, so that NMT can distil latent bilingual mappings from the ample and repetitive phenomena. One method leverages a mixed word/character model and the other attempts at synthesizing parallel sentences guaranteeing massive occurrence of the translation lexicon. Extensive experiments demonstrate that the proposed methods can remarkably improve the translation quality, and most of the rare words in the test sentences can obtain correct translations if they are covered by the dictionary.


title: Molecular phylogeny of the genus Triticum L

abstract: The genus Triticum L. includes the major cereal crop, common or bread wheat (hexaploid Triticum aestivum L.), and other important cultivated species. Here, we conducted a phylogenetic analysis of all known wheat species and the closely related Aegilops species. This analysis was based on chloroplast matK gene comparison along with trnL intron sequences of some species. Polyploid wheat species are successfully divided only into two groups – Emmer (sections Dicoccoides and Triticum) and Timopheevii (section Timopheevii). Results reveal strictly maternal plastid inheritance of synthetic wheat amphiploids included in the study. A concordance of chloroplast origin with the definite nuclear genomes of polyploid species that were inherited at the last hybridization events was found. Our analysis suggests that there were two ancestral representatives of Aegilops speltoides Tausch that participated in the speciation of polyploid wheats with B and G genome in their genome composition. However, G genome species are younger in evolution than ones with B genome. B genome-specific PCR primers were developed for amplification of Acc-1 gene.


title: Improving Deep Neural Network Based Speech Enhancement in Low SNR Environments

abstract: We propose a joint framework combining speech enhancement SE and voice activity detection VAD to increase the speech intelligibility in low signal-noise-ratio SNR environments. Deep Neural Networks DNN have recently been successfully adopted as a regression model in SE. Nonetheless, the performance in harsh environments is not always satisfactory because the noise energy is often dominating in certain speech segments causing speech distortion. Based on the analysis of SNR information at the frame level in the training set, our approach consists of two steps, namely: 1 a DNN-based VAD model is trained to generate frame-level speech/non-speech probabilities; and 2 the final enhanced speech features are obtained by a weighted sum of the estimated clean speech features processed by incorporating VAD information. Experimental results demonstrate that the proposed SE approach effectively improves short-time objective intelligibility STOI by 0.161 and perceptual evaluation of speech quality PESQ by 0.333 over the already-good SE baseline systems at $$-$$-5dB SNR of babble noise.


title: From Gameplay to Symbolic Reasoning: Learning SAT Solver Heuristics in the Style of Alpha(Go) Zero

abstract: Despite the recent successes of deep neural networks in various fields such as image and speech recognition, natural language processing, and reinforcement learning, we still face big challenges in bringing the power of numeric optimization to symbolic reasoning. Researchers have proposed different avenues such as neural machine translation for proof synthesis, vectorization of symbols and expressions for representing symbolic patterns, and coupling of neural back-ends for dimensionality reduction with symbolic front-ends for decision making. However, these initial explorations are still only point solutions, and bear other shortcomings such as lack of correctness guarantees. In this paper, we present our approach of casting symbolic reasoning as games, and directly harnessing the power of deep reinforcement learning in the style of Alpha(Go) Zero on symbolic problems. Using the Boolean Satisfiability (SAT) problem as showcase, we demonstrate the feasibility of our method, and the advantages of modularity, efficiency, and correctness guarantees.


title: Learning mixed kronecker product graph models with simulated method of moments

abstract: There has recently been a great deal of work focused on developing statistical models of graph structure---with the goal of modeling probability distributions over graphs from which new, similar graphs can be generated by sampling from the estimated distributions. Although current graph models can capture several important characteristics of social network graphs (e.g., degree, path lengths), many of them do not generate graphs with sufficient variation to reflect the natural variability in real world graph domains. One exception is the mixed Kronecker Product Graph Model (mKPGM), a generalization of the Kronecker Product Graph Model, which uses parameter tying to capture variance in the underlying distribution [10]. The enhanced representation of mKPGMs enables them to match both the mean graph statistics and their spread as observed in real network populations, but unfortunately to date, the only method to estimate mKPGMs involves an exhaustive search over the parameters. In this work, we present the first learning algorithm for mKPGMs. The O(|E|) algorithm searches over the continuous parameter space using constrained line search and is based on simulated method of moments, where the objective function minimizes the distance between the observed moments in the training graph and the empirically estimated moments of the model. We evaluate the mKPGM learning algorithm by comparing it to several different graph models, including KPGMs. We use multi-dimensional KS distance to compare the generated graphs to the observed graphs and the results show mKPGMs are able to produce a closer match to real-world graphs (10-90% reduction in KS distance), while still providing natural variation in the generated graphs.


title: Exploring Models and Data for Image Question Answering

abstract: This work aims to address the problem of image-based question-answering (QA) with new models and datasets. In our work, we propose to use neural networks and visual semantic embeddings, without intermediate stages such as object detection and image segmentation, to predict answers to simple questions about images. Our model performs 1.8 times better than the only published results on an existing image QA dataset. We also present a question generation algorithm that converts image descriptions, which are widely available, into QA form. We used this algorithm to produce an order-of-magnitude larger dataset, with more evenly distributed answers. A suite of baseline results on this new dataset are also presented.


title: A Virtual Machine Introspection Based Architecture for Intrusion Detection

abstract: Today’s architectures for intrusion detection force the IDS designer to make a difficult choice. If the IDS resides on the host, it has an excellent view of what is happening in that host’s software, but is highly susceptible to attack. On the other hand, if the IDS resides in the network, it is more resistant to attack, but has a poor view of what is happening inside the host, making it more susceptible to evasion. In this paper we present an architecture that retains the visibility of a host-based IDS, but pulls the IDS outside of the host for greater attack resistance. We achieve this through the use of a virtual machine monitor. Using this approach allows us to isolate the IDS from the monitored host but still retain excellent visibility into the host’s state. The VMM also offers us the unique ability to completely mediate interactions between the host software and the underlying hardware. We present a detailed study of our architecture, including Livewire, a prototype implementation. We demonstrate Livewire by implementing a suite of simple intrusion detection policies and using them to detect real attacks.


title: Analysis of vasculature for liver surgical planning

abstract: For liver surgical planning, the structure and morphology of the hepatic vessels and their relationship to tumors are of major interest. To achieve a fast and robust assistance with optimal quantitative and visual information, we present methods for a geometrical and structural analysis of vessel systems. Starting from the raw image data a sequence of image processing steps has to be carried out until a three-dimensional representation of the relevant anatomic and pathologic structures is generated. Based on computed tomography (CT) scans, the following steps are performed. 1) The volume data is preprocessed and the vessels are segmented. 2) The skeleton of the vessels is determined and transformed into a graph enabling a geometrical and structural shape analysis. Using this information the different intrahepatic vessel systems are identified automatically. 3) Based on the structural analysis of the branches of the portal vein, their vascular territories are approximated with different methods. These methods are compared and validated anatomically by means of corrosion casts of human livers. 4) Vessels are visualized with graphics primitives fitted to the skeleton to provide smooth visualizations without aliasing artifacts. The image analysis techniques have been evaluated in the clinical environment and have been used in more than 170 cases so far to plan interventions and transplantations.


title: BlogPulse: Automated trend discovery for weblogs

abstract: Over the past few years, weblogs have emerged as a new communication and publication medium on the Internet. In this paper, we describe the application of data mining, information extraction and NLP algorithms for discovering trends across our subset of approximately 100,000 weblogs. We publish daily lists of key persons, key phrases, and key paragraphs to a public web site, BlogPulse.com. In addition, we maintain a searchable index of weblog entries. On top of the search index, we have implemented trend search, which graphs the normalized trend line over time for a search query and provides a way to estimate the relative buzz of word of mouth for given topics over time.


title: Tensile strength testing for resorbable mesh fixation systems in laparoscopic ventral hernia repair

abstract: BACKGROUND ::: In an attempt to improve patient outcome and quality of life after laparoscopic ventral hernia repair, resorbable fixation devices have been developed to allow adequate mesh fixation while minimizing accompanying side-effects as tack erosion and adhesion formation. ::: ::: ::: MATERIALS AND METHODS ::: In experimental set-up, 24 pigs were treated by laparoscopic mesh placement. Two different meshes (PP/ORC and PP/ePTFE) and four fixation devices were evaluated: a 6.4 mm poly(D,L: )-lactide pushpin (tack I), a 6.8 mm poly(D,L: )-lactide with blunt tip (tack II), a 4.1 mm poly(glycolide-co-L-lactide) (tack III) and one titanium tack (control tack). A first group of animals (n = 12) was euthanized after 2 weeks survival and a second group (n = 12) after 6 months. At euthanasia, a relaparoscopy was performed to assess adhesion formation followed by laparotomy with excision of the entire abdominal wall. Tensile strength of the individual fixation systems was tested with the use of a tensiometer by measuring the force to pull the tack out of the mesh. Additionally, the foreign body reaction to the fixation systems was evaluated histologically as was their potential degradation. ::: ::: ::: RESULTS ::: At 2 weeks the tensile strength was significantly higher for the control tack (31.98 N/cm²) compared to the resorbable devices. Except for tack II, the tensile strength was higher when the devices were fixed in a PP/ePTFE mesh compared to the PP/ORC mesh. After 6 months only tack III was completely resorbed, while tack I (9.292 N/cm²) had the lowest tensile strength. At this time-point similar tensile strength was observed for both tack II (29.56 N/cm²) and the control tack (27.77 N/cm²). Adhesions seem to be more depending on the type of mesh, in favor of PP/ePTFE. ::: ::: ::: CONCLUSION ::: At long term, the 4.1 mm poly(glycolide-co-L-lactide) tack was the only tack completely resorbed while the 6.8 mm poly(D,L: )-lactide tack with blunt tip reached equal strengths to the permanent tack.


title: Design and Implementation of the LogicBlox System

abstract: The LogicBlox system aims to reduce the complexity of software development for modern applications which enhance and automate decision-making and enable their users to evolve their capabilities via a ``self-service'' model. Our perspective in this area is informed by over twenty years of experience building dozens of mission-critical enterprise applications that are in use by hundreds of large enterprises across industries such as retail, telecommunications, banking, and government. We designed and built LogicBlox to be the system we wished we had when developing those applications. In this paper, we discuss the design considerations behind the LogicBlox system and give an overview of its implementation, highlighting innovative aspects. These include: LogiQL, a unified and declarative language based on Datalog; the use of purely functional data structures; novel join processing strategies; advanced incremental maintenance and live programming facilities; a novel concurrency control scheme; and built-in support for prescriptive and predictive analytics.


title: Predicting Academic Performance from Behavioural and Learning Data

abstract: The volume and quality of data, but also their relevance, are crucial when performing data analysis. In this paper, a study of the influence of different types of data is presented, particularly in the context of educational data obtained from Learning Management Systems (LMSs). These systems provide a large amount of data from the student activity but they usually do not describe the results of the learning process, i.e., they describe the behaviour but not the learning results. The starting hypothesis states that complementing behavioural data with other more relevant data (regarding learning outcomes) can lead to a better analysis of the learning process, that is, in particular it is possible to early predict the student final performance. A learning platform has been spe cially developed to collect data not just from the usage but also related to the way students learn and progress in training activities. Data of both types are used to build a progressive predictive system for helping in the learning process. This model is based on a classifier that uses the Support Vector Machine technique. As a result, the system obtains a weekly classification of each student as the probability of belonging to one of three classes: high, medium and low performance. The results show that, supplementing behavioural data with learning data allows us to obtain better predictions about the results of the students in a learning system. Moreover, it can be deduced that the use of heterogeneous data enriches the final performance of the prediction algorithms.


title: Empirical assessment of machine learning based software defect prediction techniques

abstract: The wide-variety of real-time software systems, including telecontrol/telepresence systems, robotic systems, and mission planning systems, can entail dynamic code synthesis based on runtime mission-specific requirements and operating conditions. This necessitates the need for dynamic dependability assessment to ensure that these systems perform as specified and not fail in catastrophic ways. One approach in achieving this is to dynamically assess the modules in the synthesized code using software defect prediction techniques. Statistical models; such as stepwise multi-linear regression models and multivariate models, and machine learning approaches, such as artificial neural networks, instance-based reasoning, Bayesian-belief networks, decision trees, and rule inductions, have been investigated for predicting software quality. However, there is still no consensus about the best predictor model for software defects. In this paper; we evaluate different predictor models on four different real-time software defect data sets. The results show that a combination of IR and instance-based learning along with the consistency-based subset evaluation technique provides a relatively better consistency in accuracy prediction compared to other models. The results also show that "size" and "complexity" metrics are not sufficient for accurately predicting real-time software defects.


title: A Comparison of MIMO Techniques in Downlink Millimeter Wave Cellular Networks with Hybrid Beamforming

abstract: Large antenna arrays will be needed in future millimeter wave (mmWave) cellular networks, enabling a large number of different possible antenna architectures and multiple-input multiple-output (MIMO) techniques. It is still unclear which MIMO technique is most desirable as a function of different network parameters. This paper, therefore, compares the coverage and rate performance of hybrid beamforming enabled multiuser (MU) MIMO and single-user spatial multiplexing (SM) with single-user analog beamforming (SU-BF). A stochastic geometry model for coverage and rate analysis is proposed for MU-MIMO mmWave cellular networks, taking into account important mmWave-specific hardware constraints for hybrid analog/digital precoders and combiners, and a blockage-dependent channel model which is sparse in angular domain. The analytical results highlight the coverage, rate, and power consumption tradeoffs in multiuser mmWave networks. With perfect channel state information at the transmitter and round robin scheduling, MU-MIMO is usually a better choice than SM or SU-BF in mmWave cellular networks. This observation, however, neglects any overhead due to channel acquisition or computational complexity. Incorporating the impact of such overheads, our results can be re-interpreted so as to quantify the minimum allowable efficiency of MU-MIMO to provide higher rates than SM or SU-BF.


title: Spatially-Varying Blur Detection Based on Multiscale Fused and Sorted Transform Coefficients of Gradient Magnitudes

abstract: The detection of spatially-varying blur without having any information about the blur type is a challenging task. In this paper, we propose a novel effective approach to address this blur detection problem from a single image without requiring any knowledge about the blur type, level, or camera settings. Our approach computes blur detection maps based on a novel High-frequency multiscale Fusion and Sort Transform (HiFST) of gradient magnitudes. The evaluations of the proposed approach on a diverse set of blurry images with different blur types, levels, and contents demonstrate that the proposed algorithm performs favorably against the state-of-the-art methods qualitatively and quantitatively.


title: Repeat-Pass SAR Interferometry With Partially Coherent Targets

abstract: By means of the permanent scatterer (PS) technique, repeated spaceborne synthetic aperture radar (SAR) images with relatively low resolution (about 25 m × 5 m for the European Remote Sensing (ERS) and Envisat satellites) can be used to estimate the displacement (1-mm precision) and 3-D location (1-m precision) of targets that show an unchanged electromagnetic signature. The main drawback of the PS technique is the limited spatial density of targets that behave coherently during the whole observation span (hundreds of PSs per square kilometer in urban site and up to few points in vegetated areas). In this paper, we describe a new approach for multitemporal analysis of SAR images that also allows extracting information from partially coherent targets. The basic idea is to loosen the restrictive conditions imposed by the PS technique. The results obtained in different test sites allowed to increase significantly the spatial coverage of the estimate of height and deformation trend, particularly in extraurban areas.


title: Theory and Tools for the Conversion of Analog to Spiking Convolutional Neural Networks

abstract: Deep convolutional neural networks (CNNs) have shown great potential for numerous real-world machine learning applications, but performing inference in large CNNs in real-time remains a challenge. We have previously demonstrated that traditional CNNs can be converted into deep spiking neural networks (SNNs), which exhibit similar accuracy while reducing both latency and computational load as a consequence of their data-driven, event-based style of computing. Here we provide a novel theory that explains why this conversion is successful, and derive from it several new tools to convert a larger and more powerful class of deep networks into SNNs. We identify the main sources of approximation errors in previous conversion methods, and propose simple mechanisms to fix these issues. Furthermore, we develop spiking implementations of common CNN operations such as max-pooling, softmax, and batch-normalization, which allow almost loss-less conversion of arbitrary CNN architectures into the spiking domain. Empirical evaluation of different network architectures on the MNIST and CIFAR10 benchmarks leads to the best SNN results reported to date.


title: Deep Adaptive Feature Embedding with Local Sample Distributions for Person Re-identification

abstract: Person re-identification (re-id) aims to match pedestrians observed by disjoint camera views. It attracts increasing attention in computer vision due to its importance to surveillance system. To combat the major challenge of cross-view visual variations, deep embedding approaches are proposed by learning a compact feature space from images such that the Euclidean distances correspond to their cross-view similarity metric. However, the global Euclidean distance cannot faithfully characterize the ideal similarity in a complex visual feature space because features of pedestrian images exhibit unknown distributions due to large variations in poses, illumination and occlusion. Moreover, intra-personal training samples within a local range are robust to guide deep embedding against uncontrolled variations, which however, cannot be captured by a global Euclidean distance. In this paper, we study the problem of person re-id by proposing a novel sampling to mine suitable \textit{positives} (i.e. intra-class) within a local range to improve the deep embedding in the context of large intra-class variations. Our method is capable of learning a deep similarity metric adaptive to local sample structure by minimizing each sample's local distances while propagating through the relationship between samples to attain the whole intra-class minimization. To this end, a novel objective function is proposed to jointly optimize similarity metric learning, local positive mining and robust deep embedding. This yields local discriminations by selecting local-ranged positive samples, and the learned features are robust to dramatic intra-class variations. Experiments on benchmarks show state-of-the-art results achieved by our method.


title: Simultaneous and exact interval estimates for the contrast of two groups based on an extremely high dimensional variable: application to mass spec data

abstract: MOTIVATION ::: Analysis of high-throughput proteomic/genomic data, in particular, surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) data and microarray data, has led to a multitude of techniques aimed at identifying potential biomarkers. Most of the statistical techniques for comparing two groups are based on qualitative measures such as P-value. A quantitative way such as interval estimation for the contrasts of two groups is more appealing. ::: ::: ::: RESULTS ::: We have devised a simultaneous confidence bands method capable of detecting potential biomarkers, while controlling for overall confidence coverage level, in high-dimensional datasets that discriminate two treatment groups using a permutation scheme. For example, for the SELDI-TOF MS data, we deal with the entire spectrum simultaneously and construct (1 - alpha) confidence bands for the mean differences between groups. Furthermore, peaks were identified based on the maximal differences between the groups as determined by the confidence bands. The analysis method herein described gives both qualitative (P-value) and quantitative data (magnitude of difference). The Clinical Proteomics Programs Databank's ovarian cancer dataset and data from in-house samples containing known spiked-in proteins were analyzed. We were able to identify potential biomarkers similar to those described in previous analysis of the ovarian cancer data, however, while these markers are highly significant between cancer and normal groups, our analysis indicated the absolute difference between the two groups was minimal. In addition, we found additional markers than those previously described with greater differences in average intensities. The proposed confidence bands method successfully detected the spiked-in peaks, as well as, secondary peaks generated by adducts and double-charged species. We also illustrate our method utilizing paired gene expression data from a prostate cancer microarray experiment by constructing confidence bands for the fold changes between cancer and normal samples. ::: ::: ::: AVAILABILITY ::: R-package, 'seie.zip' (license: GNU GPL), is publiclly available at http://research2.dfci.harvard.edu/dfci/MS_spike-in_data/


title: On Design and Evaluation of Harmonic Transponders

abstract: Performance and characterization of harmonic transponders is investigated both theoretically and experimentally. The harmonic response of a transponder based on a Schottky diode is analytically derived in the case when the excitation power is low. Theoretical calculations prove that at high frequencies, capacitive nonlinearity contributes more to frequency multiplication than resistive nonlinearity. This means that the feasibility of a particular diode for harmonic transponder applications largely depends on its varactor properties. Figures-of-merit (FOM) are derived both for the mixing diode and the transponder antenna. The validity of the proposed theoretical model is investigated experimentally using a harmonic transponder operating at 1 GHz fundamental frequency.


title: Developmental science and the media. Early brain development.

abstract: Media coverage of early brain development not only has focused public attention on early childhood but also has contributed to misunderstanding of developmental neuroscience research. This article critically summarizes current research in developmental neuroscience that is pertinent to the central claims of media accounts of early brain development, including (a) scientific understanding of formative early experiences, (b) whether critical periods are typical for brain development, (c) brain development as a lifelong process, (d) biological hazards to early brain growth, and (e) strengths and limits of current technology in developmental brain research. Recommendations are offered for strengthening the constructive contributions of research scientists and their professional organizations to the accurate and timely coverage of scientific issues in the media.


title: Efficient visual code localization with neural networks

abstract: The use of computer-readable visual codes became common in our everyday life both in industrial environments and for private use. The reading process of visual codes consists of two steps, namely, localization and data decoding. In this paper we examine the localization step of visual codes using conventional and deep rectifier neural networks. They are also evaluated in the discrete cosine transform domain and shown to be efficient, which makes full decompression unnecessary for setups involving JPEG images. This approach is also efficient from a storage viewpoint and computation cost viewpoint, since camera hardware can provide a JPEG stream as output in many cases. The use of neural networks implemented on graphics processing unit allows real-time automatic code object localization. In our earlier studies, the proposed approach was evaluated on the most popular code type, quick response code, and some other 2D codes as well. Here, we also prove that deep rectifier networks are also suitable for 1D barcode localization and present extensive evaluation and comparison to state-of-the-art approaches.


title: Emotional State of Being Moved Elicited by Films: A Comparison With Several Positive Emotions

abstract: This study investigated the facial and physiological activities that are associated with the emotional state of being moved. We elicited the emotional states of being moved, amusement, attachment, and calmness by presenting participants with film clips; we assessed their electromyographic, electrodermal, and cardiac responses to the films. Further, we used a high- and low-arousal moving film to examine the effect of different levels of arousal on facial and physiological responses to moving films. We compared facial and physiological responses to positive-emotion and emotionally neutral films. Analysis of subjective emotion scale ratings revealed that the films had successfully elicited the target positive emotions and that the high- and low-arousal moving films had elicited the feeling of being moved in accordance with the anticipated level of intensity. In comparison to the other types of positive-emotion films, the two moving films resulted in an increase in corrugator electromyography activity and skin conductance responses, which in turn were modulated by the arousal level of the moving films. However, cardiac measures such as heart rate did not differ across the different film conditions. These results suggest that film clips can elicit different intensities of the emotional state of being moved and that facial muscle and electrodermal activities but not cardiac activity characterize the film-induced emotional state of being moved.


title: Soft Biometrics and Their Application in Person Recognition at a Distance

abstract: Soft biometric information extracted from a human body (e.g., height, gender, skin color, hair color, and so on) is ancillary information easily distinguished at a distance but it is not fully distinctive by itself in recognition tasks. However, this soft information can be explicitly fused with biometric recognition systems to improve the overall recognition when confronting high variability conditions. One significant example is visual surveillance, where face images are usually captured in poor quality conditions with high variability and automatic face recognition systems do not work properly. In this scenario, the soft biometric information can provide very valuable information for person recognition. This paper presents an experimental study of the benefits of soft biometric labels as ancillary information based on the description of human physical features to improve challenging person recognition scenarios at a distance. In addition, we analyze the available soft biometric information in scenarios of varying distance between camera and subject. Experimental results based on the Southampton multibiometric tunnel database show that the use of soft biometric traits is able to improve the performance of face recognition based on sparse representation on real and ideal scenarios by adaptive fusion rules.


A New Hashing Package for UNIX


title: Herbert Simon’s Decision-Making Approach: Investigation of Cognitive Processes in Experts

abstract: Herbert Simon's research endeavor aimed to understand the processes that participate in human decision making. However, despite his effort to investigate this question, his work did not have the im...


title: Confounder Detection in High-Dimensional Linear Models Using First Moments of Spectral Measures

abstract: In this letter, we study the confounder detection problem in the linear model, where the target variable [Formula: see text] is predicted using its [Formula: see text] potential causes [Formula: see text]. Based on an assumption of a rotation-invariant generating process of the model, recent study shows that the spectral measure induced by the regression coefficient vector with respect to the covariance matrix of [Formula: see text] is close to a uniform measure in purely causal cases, but it differs from a uniform measure characteristically in the presence of a scalar confounder. Analyzing spectral measure patterns could help to detect confounding. In this letter, we propose to use the first moment of the spectral measure for confounder detection. We calculate the first moment of the regression vector-induced spectral measure and compare it with the first moment of a uniform spectral measure, both defined with respect to the covariance matrix of [Formula: see text]. The two moments coincide in nonconfounding cases and differ from each other in the presence of confounding. This statistical causal-confounding asymmetry can be used for confounder detection. Without the need to analyze the spectral measure pattern, our method avoids the difficulty of metric choice and multiple parameter optimization. Experiments on synthetic and real data show the performance of this method.


Adoption of Big Data Solutions: A study on its security determinants using Sec-TOE Framework.


title: Poland-Möbius syndrome and cocaine abuse: a relook at vascular etiology.

abstract: This report presents a severe case of Poland-Möbius syndrome with central apnea at birth, ventilator-dependent, and with brainstem calcifications. The newborn had unilateral defect of the right pectoralis muscle, breast, and limb. He manifested bilateral paralysis of the cranial nerves resulting in shallow respiration, apnea, and dysphagia. He finally required tracheostomy and gastrostomy. Maternal history revealed multiple uses of cocaine during the first 3 months of pregnancy. The patient supports the Poland-Möbius combination and the possibility of vascular disruption sequence.


title: Action of tricyclic antidepressants on cyclic AMP in mucosal cells from guinea pig stomach.

abstract: In dispersed mucosal cells from guinea pig stomach, tricyclic antidepressants (amitriptyline and imipramine) inhibited the increases in cyclic AMP caused by histamine or dimaprit. The inhibition by low concentrations of amitriptyline was competitive, with a pA2 value of 5.8 +/- 0.15 (Ki, 1.5 +/- 0.8 microM). At high concentrations, amitriptyline reduced the maximal response to histamine as well as inhibited the increase in cyclic AMP caused by theophylline and prostaglandin E1, and also reduced the basal respiratory rate of gastric cells. This inhibition was non-competitive (IC50, 100-250 microM). These results suggest that at low concentrations tricyclic antidepressants interact with the H2-receptors on gastric cells in a fashion similar to their interaction with the brain histamine H2-receptors.


title: Psychological Status in Childhood Cancer Survivors: A Report From the Childhood Cancer Survivor Study

abstract: Psychological quality of life (QOL), health-related QOL (HRQOL), and life satisfaction outcomes and their associated risk factors are reviewed for the large cohort of survivors and siblings in the Childhood Cancer Survivor Study (CCSS). This review includes previously published manuscripts that used CCSS data focused on psychological outcome measures, including the Brief Symptom Inventory (BSI-18), the Medical Outcomes Survey Short Form-36 (SF-36), the Cantril Ladder of Life, and other self-report questionnaires. Comparisons and contrasts are made between siblings and survivors, and to normative data when available, in light of demographic/health information and abstracted data from the medical record. These studies demonstrate that a significant proportion of survivors report more symptoms of global distress and poorer physical, but not emotional, domains of HRQOL. Other than brain tumor survivors, most survivors report both good present and expected future life satisfaction. Risk factors for psychological distress and poor HRQOL are female sex, lower educational attainment, unmarried status, annual household income less than $20,000, unemployment, lack of health insurance, presence of a major medical condition, and treatment with cranial radiation and/or surgery. Cranial irradiation impacted neurocognitive outcomes, especially in brain tumor survivors. Psychological distress also predicted poor health behaviors, including smoking, alcohol use, fatigue, and altered sleep. Psychological distress and pain predicted use of complementary and alternative medicine. Overall, most survivors are psychologically healthy and report satisfaction with their lives. However, certain groups of childhood cancer survivors are at high risk for psychological distress, neurocognitive dysfunction, and poor HRQOL, especially in physical domains. These findings suggest targeting interventions for groups at highest risk for adverse outcomes and examining the positive growth that remains despite the trauma of childhood cancer.


Automated Vehicle Parking System using RFID


title: The most controversial topics in Wikipedia: A multilingual and geographical analysis

abstract: We present, visualize and analyse the similarities and differences between the controversial topics related to “edit wars” identified in 10 different language versions of Wikipedia. After a brief review of the related work we describe the methods developed to locate, measure, and categorize the controversial topics in the different languages. Visualizations of the degree of overlap between the top 100 list of most controversial articles in different languages and the content related geographical locations will be presented. We discuss what the presented analysis and visualizations can tell us about the multicultural aspects of Wikipedia, and, in general, about cultures of peer-production with focus on universal and specifically, local features. We demonstrate that Wikipedia is more than just an encyclopaedia; it is also a window into divergent social-spatial priorities, interests and preferences.


title: Grassmann multimodal implicit feature selection

abstract: In pattern recognition field, objects are usually represented by multiple features (multimodal features). For example, to characterize a natural scene image, it is essential to extract a set of visual features representing its color, texture, and shape information. However, integrating multimodal features for recognition is challenging because: (1) each feature has its specific statistical property and physical interpretation, (2) huge number of features may result in the curse of dimensionality (When data dimension is high, the distances between pairwise objects in the feature space become increasingly similar due to the central limit theory. This phenomenon influences negatively to the recognition performance), and (3) some features may be unavailable. To solve these problems, a new multimodal feature selection algorithm, termed Grassmann manifold feature selection (GMFS), is proposed. In particular, by defining a clustering criterion, the multimodal features are transformed into a matrix, and further treated as a point on the Grassmann manifold in Hamm and Lee (Grassmann discriminant analysis: a unifying view on subspace-based learning. In: Proceedings of the 25th international conference on machine learning (ICML), pp. 376---383, Helsinki, Finland [2008]). To deal with the unavailable features, L2-Hausdorff distance, a metric between different-sized matrices, is computed and the kernel is obtained accordingly. Based on the kernel, we propose supervised/unsupervised feature selection algorithms to achieve a physically meaningful embedding of the multimodal features. Experimental results on eight data sets validate the effectiveness the proposed approach.


title: A Composite Right/Left-Handed Rectangular Waveguide with Tilted Corrugations for Millimeter-wave Frequency Scanning Antenna

abstract: A novel structure for a composite right/left-handed (CRLH) corrugated waveguide that operates in the millimeter-wave band is proposed. The CRLH waveguide is comprised of a rectangular waveguide which has tilted corrugations on its bottom broad wall. By operating both above and below the cutoff frequency of the dominant mode, the rectangular waveguide provides, respectively, an inherent series inductance and shunt capacitance, and an inherent shunt inductance, and the tilted corrugations provide a series inductance and capacitance, which can support CRLH propagation. A frequency scanning antenna using this CRLH waveguide was also studied numerically and experimentally. The results demonstrate that the antenna can provide backward-to-forward beam scanning, including the broadside direction. A scanning angle from -9.9 to + 2.2deg was achieved within a 1.8-GHz frequency range in the 60 GHz band.


Financial Attention


A.: Truncated importance sampling for reinforcement learning with experience replay


title: Digital photography with flash and no-flash image pairs

abstract: Digital photography has made it possible to quickly and easily take a pair of images of low-light environments: one with flash to capture detail and one without flash to capture ambient illumination. We present a variety of applications that analyze and combine the strengths of such flash/no-flash image pairs. Our applications include denoising and detail transfer (to merge the ambient qualities of the no-flash image with the high-frequency flash detail), white-balancing (to change the color tone of the ambient image), continuous flash (to interactively adjust flash intensity), and red-eye removal (to repair artifacts in the flash image). We demonstrate how these applications can synthesize new images that are of higher quality than either of the originals.


title: Design of Wideband and Dual Polarized Cavity Antenna Planar Array

abstract: A $6 \times 6$ wideband antenna array with ±45° dual polarizations is proposed. To achieve good radiation efficiency, cavity antenna is used in this work as the basic element. Feeding probes and strip line feeding networks are printed on general PCB substrate. The feeding circuits for exciting different polarizations are stacked and multilayer substrate step structure is used. Here, strip line to downward SMA connector transitions is also designed, which aids in integrating the proposed array with T/R modules located below and extending it to larger phased array. The antenna array is fabricated and measured. Simulated and measured impedance and radiation performances are also given and compared. The proposed antenna arrays show wide bandwidth of 30% from 10.9 to 14.8 GHz, and isolation of higher than 33 dB is also exhibited across the bandwidth. Furthermore, symmetrical patterns and cross-polarizations of below −25 dB are also demonstrated.


title: Learning to Hash with Binary Deep Neural Network

abstract: This work proposes deep network models and learning algorithms for unsupervised and supervised binary hashing. Our novel network design constrains one hidden layer to directly output the binary codes. This addresses a challenging issue in some previous works: optimizing non-smooth objective functions due to binarization. Moreover, we incorporate independence and balance properties in the direct and strict forms in the learning. Furthermore, we include similarity preserving property in our objective function. Our resulting optimization with these binary, independence, and balance constraints is difficult to solve. We propose to attack it with alternating optimization and careful relaxation. Experimental results on three benchmark datasets show that our proposed methods compare favorably with the state of the art.


title: Early completion of occluded objects

abstract: Abstract We show that early vision can use monocular cues to rapidly complete partially-occluded objects. Visual search for easily-detected fragments becomes difficult when the completed shape is similar to others in the display; conversely, search for fragments that are difficult to detect becomes easy when the completed shape is distinctive. Results indicate that completion occurs via the occlusion-triggered removal of occlusion edges and linking of associated regions. We fail to find evidence for a visible filling-in of contours or surfaces, but do find evidence for a ‘functional’ filling-in that prevents the constituent fragments from being rapidly accessed. As such, it is only the completed structures—and not the fragments themselves—that serve as the basis for rapid recognition.


title: GAMENet: Graph Augmented MEmory Networks for Recommending Medication Combination

abstract: Recent progress in deep learning is revolutionizing the healthcare domain including providing solutions to medication recommendations, especially recommending medication combination for patients with complex health conditions. Existing approaches either do not customize based on patient health history, or ignore existing knowledge on drug-drug interactions (DDI) that might lead to adverse outcomes. To fill this gap, we propose the Graph Augmented Memory Networks (GAMENet), which integrates the drug-drug interactions knowledge graph by a memory module implemented as a graph convolutional networks, and models longitudinal patient records as the query. It is trained end-to-end to provide safe and personalized recommendation of medication combination. We demonstrate the effectiveness and safety of GAMENet by comparing with several state-of-the-art methods on real EHR data. GAMENet outperformed all baselines in all effectiveness measures, and also achieved 3.60% DDI rate reduction from existing EHR data.


title: Feature Learning for Meta-Paths in Knowledge Graphs

abstract: In this thesis, we study the problem of feature learning on heterogeneous knowledge graphs. These features can be used to perform tasks such as link prediction, classification and clustering on graphs. Knowledge graphs provide rich semantics encoded in the edge and node types. Meta-paths consist of these types and abstract paths in the graph. Until now, meta-paths can only be used as categorical features with high redundancy and are therefore unsuitable for machine learning models. We propose meta-path embeddings to solve this problem by learning semantical and compact vector representations of them. Current graph embedding methods only embed nodes and edge types and therefore miss semantics encoded in the combination of them. Our method embeds meta-paths using the skipgram model with an extension to deal with the redundancy and high amount of meta-paths in big knowledge graphs. We critically evaluate our embedding approach by predicting links on Wikidata. The experiments indicate that we learn a sensible embedding of the meta-paths but can improve it further.


Using Features From Pre-trained TimeNET For Clinical Predictions


title: Evidence-Based Information on the Clinical Use of Neurofeedback for ADHD

abstract: Neurofeedback (NF) is a training to enhance self-regulatory capacity over brain activity patterns and consequently over brain mental states. Recent findings suggest that NF is a promising alternative for the treatment of attention-deficit/hyperactivity disorder (ADHD). We comprehensively reviewed literature searching for studies on the effectiveness and specificity of NF for the treatment of ADHD. In addition, clinically informative evidence-based data are discussed. We found 3 systematic review on the use of NF for ADHD and 6 randomized controlled trials that have not been included in these reviews. Most nonrandomized controlled trials found positive results with medium-to-large effect sizes, but the evidence for effectiveness are less robust when only randomized controlled studies are considered. The direct comparison of NF and sham-NF in 3 published studies have found no group differences, nevertheless methodological caveats, such as the quality of the training protocol used, sample size, and sample selection may have contributed to the negative results. Further data on specificity comes from electrophysiological studies reporting that NF effectively changes brain activity patterns. No safety issues have emerged from clinical trials and NF seems to be well tolerated and accepted. Follow-up studies support long-term effects of NF. Currently there is no available data to guide clinicians on the predictors of response to NF and on optimal treatment protocol. In conclusion, NF is a valid option for the treatment for ADHD, but further evidence is required to guide its use.


title: Designing B-ISDN network topologies using the genetic algorithm

abstract: The topology design of a B-ISDN network is addressed. We model the topological planning as a nonlinear mixed-integer programming problem. The genetic algorithm, an effective optimization method, is applied to this problem. Since the randomness of the generic algorithm cannot guarantee the biconnectivity requirement in the topologies generated by the genetic algorithm, we propose an algorithm to make all topologies at least biconnected while increasing the overall cost of the topologies the least. The result for a 20-node test case is presented and it is shown that the algorithm we propose has a very good convergence property.


title: Enhancing Bitcoin Transactions with Covenants

abstract: Covenants are Bitcoin Script programs that restrict how funds are allowed to be spent. In previous work [9], Moser et al. implemented covenants with a new Script operation that allows one to programmatically query the transaction. In this paper, we show that covenants can be implemented with a new CHECKSIGFROMSTACK operation that verifies a signature for a message passed as an argument. When the same public key and signature is used together with CHECKSIG, one can recover transaction data, which then allows one to enforce a covenant. To illustrate our technique, we reimplement Moser et al.’s vault construction for securing funds against key compromise. We use Elements Alpha, a sidechain whose Script language has the needed operations.


title: MPPT for Photovoltaic Modules via Newton-Like Extremum Seeking Control

abstract: The paper adapts the Newton-like Extremum-Seeking Control technique to extract the maximum power from photovoltaic panels. This technique uses the gradient and Hessian of the panel characteristic in order to approximate the operating point to its optimum. The paper describes in detail the gradient and Hessian estimations carried out by means of sinusoidal dithering signals. Furthermore, we compare the proposed technique with the common Extremum Seeking Control that only uses the gradient. The comparison is done by means of PSIM simulations and it shows the different transient behaviors and the faster response of the Newton-like Extremum-Seeking Control solution.


title: Geometry-Contrastive Generative Adversarial Network for Facial Expression Synthesis

abstract: In this paper, we propose a geometry-contrastive generative adversarial network GC-GAN for generating facial expression images conditioned on geometry information. Specifically, given an input face and a target expression designated by a set of facial landmarks, an identity-preserving face can be generated guided by the target expression. In order to embed facial geometry onto a semantic manifold, we incorporate contrastive learning into conditional GANs. Experiment results demonstrate that the manifold is sensitive to the changes of facial geometry both globally and locally. Benefited from the semantic manifold, dynamic smooth transitions between different facial expressions are exhibited via geometry interpolation. Furthermore, our method can also be applied in facial expression transfer even there exist big differences in face shape between target faces and driving faces.


title: Automated detection and classification techniques of Acute leukemia using image processing: A review

abstract: Hematological disorders of leukocytes (white blood corpuscles) are very frequent in medical practices. Each year, the number of new cases of leukemia increased with a high mortality rate what can cause a delay in diagnosis and treatment of leukemia. The hematologist faces difficulty in classifying white blood cells included cancerous leukemia cells. Currently, the diagnosis of hematological disorder was made by microscopic analysis of peripheral blood smear by hematological experts. However, this kind of diagnosis is complex, costly process, time-consuming and inherently subjective, hence to avoid such problems a classification technique based on computer vision is required. This document provides a survey of many kinds of literature which talk about leukemia detection using image processing. In this year, many improvements were made in this field. With this document, we provide a new perspective from many kinds of literature so future research can be developed effectively for their research.


title: Compulsory Purchase and Compensation: The Law in Scotland

abstract: Revised and updated - a detailed but straightforward explanation of the law relating to compulsory purchase and compensation in Scotland. The author also considers the wider area of compensation for depreciation in the value of property caused by government actions and decisions falling short of expropriation. He sets out not only to state the law, but to explain how it has emerged in its present form and to consider where change is desirable.


title: Measurement Issues in Galvanic Intrabody Communication: Influence of Experimental Setup

abstract: Significance : The need for increasingly energy-efficient and miniaturized bio-devices for ubiquitous health monitoring has paved the way for considerable advances in the investigation of techniques such as intrabody communication (IBC), which uses human tissues as a transmission medium. However, IBC still poses technical challenges regarding the measurement of the actual gain through the human body. The heterogeneity of experimental setups and conditions used together with the inherent uncertainty caused by the human body make the measurement process even more difficult. Goal : The objective of this study, focused on galvanic coupling IBC, is to study the influence of different measurement equipments and conditions on the IBC channel. Methods : Different experimental setups have been proposed in order to analyze key issues such as grounding, load resistance, type of measurement device and effect of cables. In order to avoid the uncertainty caused by the human body, an IBC electric circuit phantom mimicking both human bioimpedance and gain has been designed. Given the low-frequency operation of galvanic coupling, a frequency range between 10 kHz and 1 MHz has been selected. Results : The correspondence between simulated and experimental results obtained with the electric phantom have allowed us to discriminate the effects caused by the measurement equipment. Conclusion : This study has helped us obtain useful considerations about optimal setups for galvanic-type IBC as well as to identify some of the main causes of discrepancy in the literature.


title: Exploring motor system contributions to the perception of social information: Evidence from EEG activity in the mu/alpha frequency range

abstract: Putative contributions of a human mirror neuron system (hMNS) to the perception of social information have been assessed by measuring the suppression of EEG oscillations in the mu/alpha (8–12 Hz), beta (15–25 Hz) and low-gamma (25–25 Hz) ranges while participants processed social information revealed by point-light displays of human motion. Identical dynamic displays were presented and participants were instructed to distinguish the intention, the emotion, or the gender of a moving image of a person, while they performed an adapted odd-ball task. Relative to a baseline presenting a nonbiological but meaningful motion display, all three biological motion conditions reduced the EEG amplitude in the mu/alpha and beta ranges, but not in the low-gamma range. Suppression was larger in the intention than in the emotion and gender conditions, with no difference between the latter two. Moreover, the suppression in the intention condition was negatively correlated with an accepted measure of empathy (EQ), revealing...


title: Evaluation of Digital Forensic Process Models with Respect to Digital Forensics as a Service

abstract: Digital forensic science is very much still in its infancy, but is becoming increasingly invaluable to investigators. A popular area for research is seeking a standard methodology to make the digital forensic process accurate, robust, and efficient. The first digital forensic process model proposed contains four steps: Acquisition, Identification, Evaluation and Admission. Since then, numerous process models have been proposed to explain the steps of identifying, acquiring, analysing, storage, and reporting on the evidence obtained from various digital devices. In recent years, an increasing number of more sophisticated process models have been proposed. These models attempt to speed up the entire investigative process or solve various of problems commonly encountered in the forensic investigation. In the last decade, cloud computing has emerged as a disruptive technological concept, and most leading enterprises such as IBM, Amazon, Google, and Microsoft have set up their own cloud-based services. In the field of digital forensic investigation, moving to a cloud-based evidence processing model would be extremely beneficial and preliminary attempts have been made in its implementation. Moving towards a Digital Forensics as a Service model would not only expedite the investigative process, but can also result in significant cost savings - freeing up digital forensic experts and law enforcement personnel to progress their caseload. This paper aims to evaluate the applicability of existing digital forensic process models and analyse how each of these might apply to a cloud-based evidence processing paradigm.


title: An Actor-based Simulation for Studying UAV Coordination

abstract: The effectiveness of Unmanned Aerial Vehicles (UAVs) is being increased to reduce the cost and risk of a mission [Doherty et al. 2000]. Since the advent of small sized but high performance UAVs, the use of a group of UAVs for performing a joint mission is of major interest. However, the development of a UAV is expensive, and a small error in automatic control results in a crash. Therefore, it is useful to develop and verify the coordination behavior of UAVs through software simulation prior to real testing. We describe how an actor-based simulation platform supports distributed simulators, and present three cooperation strategies: self-interested UAVs, sharing-based cooperation, and team-based coordination. Our experimental results show how communication among UAVs improves the overall performance of a collection of UAVs on a joint mission.


title: Security Application of Failure Mode and Effect Analysis (FMEA)

abstract: Increasingly complex systems lead to an interweaving of security, safety, availability and reliability concerns. Most dependability analysis techniques do not include security aspects. In order to include security, a holistic risk model for systems is needed. In our novel approach, the basic failure cause, failure mode and failure effect model known from FMEA is used as a template for a vulnerability cause-effect chain, and an FMEA analysis technique extended with security is presented. This represents a unified model for safety and security cause-effect analysis. As an example the technique is then applied to a distributed industrial measurement system.


title: Memory-Enhanced Neural Networks and NMF for Robust ASR

abstract: In this article we address the problem of distant speech recognition for reverberant noisy environments. Speech enhancement methods, e. g., using non-negative matrix factorization (NMF), are succesful in improving the robustness of ASR systems. Furthermore, discriminative training and feature transformations are employed to increase the robustness of traditional systems using Gaussian mixture models (GMM). On the other hand, acoustic models based on deep neural networks (DNN) were recently shown to outperform GMMs. In this work, we combine a state-of-the art GMM system with a deep Long Short-Term Memory (LSTM) recurrent neural network in a double-stream architecture. Such networks use memory cells in the hidden units, enabling them to learn long-range temporal context, and thus increasing the robustness against noise and reverberation. The network is trained to predict frame-wise phoneme estimates, which are converted into observation likelihoods to be used as an acoustic model. It is of particular interest whether the LSTM system is capable of improving a robust state-of-the-art GMM system, which is confirmed in the experimental results. In addition, we investigate the efficiency of NMF for speech enhancement on the front-end side. Experiments are conducted on the medium-vocabulary task of the 2nd ‘CHiME’ Speech Separation and Recognition Challenge, which includes reverberation and highly variable noise. Experimental results show that the average word error rate of the challenge baseline is reduced by 64% relative. The best challenge entry, a noise-robust state-of-the-art recognition system, is outperformed by 25% relative.


title: Why Superman Can Wait: Cognitive Self-Transformation in the Delay of Gratification Paradigm

abstract: We examined middle-class Israeli preschoolers’ cognitive self-transformation in the delay of gratification paradigm. In Study 1, 66 un-caped or Superman-caped preschoolers delayed gratification, half with instructions regarding Superman's delay-relevant qualities. Caped children delayed longer, especially when instructed regarding Superman's qualities. In Study 2 with 43 preschoolers, with the respective relevant superhero qualities emphasized (i.e., patient vs. impulsive), Superman-caped children tended to delay longer than Dash-caped children. In Study 3, 48 preschoolers delayed gratification after being instructed to pretend to be Superman or a child with the same patient qualities, or after watching a video of Superman, with or without pretend instructions. Invoking Superman led to longer delays and instructions regarding Superman's qualities tended to lead to longer delays than watching the Superman video. In accounting for the data, we differentiated cognitive transformations of the reward's consumm...


title: Control of wind turbines: Past, present, and future

abstract: We review the objectives and techniques used in the control of horizontal axis wind turbines at the individual turbine level, where controls are applied to the turbine blade pitch and generator. The turbine system is modeled as a flexible structure operating in the presence of turbulent wind disturbances. Some overview of the various stages of turbine operation and control strategies used to maximize energy capture in below rated wind speeds is given, but emphasis is on control to alleviate loads when the turbine is operating at maximum power. After reviewing basic turbine control objectives, we provide an overview of the common basic linear control approaches and then describe more advanced control architectures and why they may provide significant advantages.


AMOEBA: Hierarchical Clustering Based on Spatial Proximity Using Delaunay Diagram


title: Design and Fabrication of a High-Gain 60-GHz Corrugated Slot Antenna Array With Ridge Gap Waveguide Distribution Layer

abstract: A wideband high-gain high-efficiency $16 \times 16$ -element slot antenna array is presented for 60-GHz band applications. The antenna is designed based on gap waveguide technology. The most important advantage of using this technology is its ability to decrease complexity and cost of fabrication because there is no requirement of electrical contact between the three layers of the antenna structure. The three layers are a corrugated metal plate with radiating slots, a subarray cavity layer, and a ridge gap waveguide (RGW) feed network layer. The corporate feed network is realized by a texture of pins and guiding ridges in a metal plate. Also, in order to excite the antenna with a standard V-band rectangular waveguide, a transition from RGW to WR-15 is designed. The radiating elements, corrugations, cavity layer, power dividers, and transition are designed and optimized to suppress the reflection coefficient at the input WR-15 port over the desired frequency range from 57 to 66 GHz. Finally, the $16 \times 16$ -element slot antenna array is fabricated by the standard milling technology. The measured results demonstrate about 16% of reflection coefficient bandwidth ( $\vert S_{11}\vert dB) covering the 56–65.7-GHz frequency range, and the measured gain is larger than 32.5 dBi over the band with more than 70% antenna efficiency.


REVIEW OF ACCESS CONTROL MODELS FOR CLOUD COMPUTING


title: Deep compression and EIE: Efficient inference engine on compressed deep neural network

abstract: This article consists only of a collection of slides from the author's conference presentation.


title: PlateClick: Bootstrapping Food Preferences Through an Adaptive Visual Interface

abstract: Food preference learning is an important component of wellness applications and restaurant recommender systems as it provides personalized information for effective food targeting and suggestions. However, existing systems require some form of food journaling to create a historical record of an individual's meal selections. In addition, current interfaces for food or restaurant preference elicitation rely extensively on text-based descriptions and rating methods, which can impose high cognitive load, thereby hampering wide adoption. In this paper, we propose PlateClick, a novel system that bootstraps food preference using a simple, visual quiz-based user interface. We leverage a pairwise comparison approach with only visual content. Using over 10,028 recipes collected from Yummly, we design a deep convolutional neural network (CNN) to learn the similarity distance metric between food images. Our model is shown to outperform state-of-the-art CNN by 4 times in terms of mean Average Precision. We explore a novel online learning framework that is suitable for learning users' preferences across a large scale dataset based on a small number of interactions (≤ 15). Our online learning approach balances exploitation-exploration and takes advantage of food similarities using preference-propagation in locally connected graphs. We evaluated our system in a field study of 227 anonymous users. The results demonstrate that our method outperforms other baselines by a significant margin, and the learning process can be completed in less than one minute. In summary, PlateClick provides a light-weight, immersive user experience for efficient food preference elicitation.


title: A 1.8µW 1µV-offset capacitively-coupled chopper instrumentation amplifier in 65nm CMOS

abstract: This paper describes a precision capacitively-coupled chopper instrumentation amplifier (CCIA). It achieves 1µV offset, 134dB CMRR, 120dB PSRR, 0.16% gain accuracy and a noise efficiency factor (NEF) of 3.1, which is more than 3x better than state-of-the-art. It has a rail-to-rail DC common-mode (CM) input range. Furthermore, a positive feedback loop (PFL) is used to boost the input impedance, and a ripple reduction loop (RRL) is used to reduce the ripple associated with chopping. The CCIA occupies only 0.1mm2 in a 65nm CMOS technology. It can operate from a 1V supply, from which it draws only 1.8µA.


title: Solving the sequential ordering problem using branch and bound

abstract: The Sequential Ordering Problem (SOP) is an NP-hard problem with a wide range of applications in the domains of scheduling, logistics and compilers. The development of powerful computers and effective algorithmic techniques has made it possible to devise exact algorithms that can solve larger instances of this problem. In this paper, we present an enhanced exact algorithm for this problem using a branch-and-bound (B&B) approach. The proposed algorithm is based on a new lower-bound technique and a local-search domination technique. The new lower-bound technique uses the dynamic Hungarian algorithm to solve a Minimum-Cost Perfect Matching relaxation of the SOP. The local search domination technique prunes the sub-tree below the current node in the B&B tree if a better partial solution is found. The performance of the proposed algorithm is evaluated experimentally using three different benchmark suites: TSPLIB, SOPLIB and COMPILERS. The results of the experimental evaluation show that the proposed algorithm finds exact solutions considerably faster than previously proposed algorithms. The proposed approach significantly reduces the optimality gap to 0.217, 0.122, and 0.004 for the three respective benchmark sets, and closes five instances that were previously open.


title: Non-line-of-sight error mitigation in TDOA mobile location

abstract: We investigate the non-line-of-sight (NLOS) propagation identification and correction for time difference of arrival (TDOA) based mobile user location in wireless communication systems. Based on the defined TDOA residual, an NLOS base station identification algorithm is proposed. Different choices of the reference location for the residual calculation are compared via simulation. To correct the NLOS error with a certain distribution, we propose a maximum likelihood (ML) estimator for TDOA location systems. Simulation results demonstrate that the proposed NLOS recovering algorithm performs better than that using only LOS measurements, especially when the number of available base stations is small and/or the LOS base stations have an undesirable geometric layout.


title: Learning Word Representations for Sentiment Analysis

abstract: Word embedding has been proven to be a useful model for various natural language processing tasks. Traditional word embedding methods merely take into account word distributions independently from any specific tasks. Hence, the resulting representations could be sub-optimal for a given task. In the context of sentiment analysis, there are various types of prior knowledge available, e.g., sentiment labels of documents from available datasets or polarity values of words from sentiment lexicons. We incorporate such prior sentiment information at both word level and document level in order to investigate the influence each word has on the sentiment label of both target word and context words. By evaluating the performance of sentiment analysis in each category, we find the best way of incorporating prior sentiment information. Experimental results on real-world datasets demonstrate that the word representations learnt by DLJT2 can significantly improve the sentiment analysis performance. We prove that incorporating prior sentiment knowledge into the embedding process has the potential to learn better representations for sentiment analysis.


title: Super-Spreader Identification Using Meta-Centrality

abstract: Super-spreaders are the nodes of a network that can maximize their impacts on other nodes, e.g., in the case of information spreading or virus propagation. Many centrality measures have been proposed to identify such nodes from a given network. However, it has been observed that the identification accuracy based on those measures is not always satisfactory among different types of networks. In addition, the nodes identified by using single centrality are not always placed in the top section, where the super-spreaders are supposed to be, of the ranking generated by simulation. In this paper we take a meta-centrality approach by combining different centrality measures using a modified version of Borda count aggregation method. As a result, we are able to improve the performance of super-spreader identification for a broad range of real-world networks. While doing so, we discover a pattern in the centrality measures involved in the aggregation with respect to the topological structures of the networks used in the experiments. Further, we study the eigenvalues of the Laplacian matrix, also known as Laplacian spectrum, and by using the Earth Mover’s distance as a metric for the spectrum, we are able to identify four clusters to explain the aggregation results.


title: High-Speed Pseudo-CMOS Circuits Using Bulk Accumulation a-IGZO TFTs

abstract: We propose a way to achieve high-speed circuits with dual-gate (DG) bulk-accumulation back-channel-etched (BCE) amorphous indium-gallium–zinc-oxide (a-IGZO) thin-film transistors (TFTs) using the pseudo-CMOS structure. The DG BCE a-IGZO TFTs exhibit field-effect mobility ( $\mu _{\mathrm {\mathbf {FE}}}$ ), threshold voltage ( $V_{\mathrm {\mathbf {th}}}$ ), and subthreshold swing of 30 ± 3 $\mathrm{cm}^{\mathrm {\mathbf {2}}}$ /Vs, 2 ± 0.5 V, and 120 ± 30 mV/decade, respectively. For input voltage ( $V_{\mathrm {\mathbf {DD}}}$ ) of 20 V, seven-stage pseudo-CMOS ring oscillators implemented with the BCE bulk-accumulation a-IGZO TFTs show oscillation frequency of 6.51 MHz, which corresponds to a propagation delay time of 11 ns/stage and is faster than the 17 ns/stage delay of the fastest single-gate-driven ratioed coplanar a-IGZO TFT circuits.


title: Transformation Optics Inspired Multibeam Lens Antennas for Broadband Directive Radiation

abstract: Recent advancements in transformation optics (TO) and metamaterials have inspired tremendous interest in the electromagnetic community, creating a variety of novel antennas with enhanced performance, such as broad bandwidth, large gain, and high polarization efficiency. Although there could be infinitely many transformations for designing a given device, most of them result in rather complicated material compositions. This paper compares two recently introduced TO techniques, both of which lead to much simpler material requirements. In particular, a linear geometrical transformation or a quasi-conformal mapping was employed to design multi-beam collimating lenses, which possess either homogeneous or isotropic constituent materials. A systematic comparison is made for the first time between these two TO design approaches for a specific example of a quad-beam focusing lens, where the advantages and disadvantages of each method are clearly identified. Full-wave numerical simulations were performed to demonstrate the well-collimated beams produced by the TO lenses designed by either transformation. The characteristics of the two lens antennas, such as radiation pattern and bandwidth, were contrasted, providing valuable guidance on design tradeoffs for a specific application.


title: Snooze: Toward a stateful network protocol fuzzer

abstract: Fuzzing is a well-known black-box approach to the security testing of applications. Fuzzing has many advantages in terms of simplicity and effectiveness over more complex, expensive testing approaches. Unfortunately, current fuzzing tools suffer from a number of limitations, and, in particular, they provide little support for the fuzzing of stateful protocols. In this paper, we present SNOOZE, a tool for building flexible, security-oriented, network protocol fuzzers. SNOOZE implements a stateful fuzzing approach that can be used to effectively identify security flaws in network protocol implementations. SNOOZE allows a tester to describe the stateful operation of a protocol and the messages that need to be generated in each state. In addition, SNOOZE provides attack-specific fuzzing primitives that allow a tester to focus on specific vulnerability classes. We used an initial prototype of the SNOOZE tool to test programs that implement the SIP protocol, with promising results. SNOOZE supported the creation of sophisticated fuzzing scenarios that were able to expose real-world bugs in the programs analyzed.


Healthy Youth Development as a Model for Youth Health Promotion: A Review


title: A Capacitively Coupling Multifeed Slot Antenna for Metallic RFID Tag Design

abstract: This letter describes a capacitively coupling multifeed slot antenna, which is comprises a slot radiator and a small dipole coupling source for metallic plate radio frequency identification (RFID) tag design. The features of the design is that the tag can function effectively with three different RFID chips, whose impedances vary widely, and the design of the conjugate impedance match of the RFID tag can be easily achieved. The important advantages of the design are that the tag does not have to be redesigned to work with different RFID chips, and additionally, the indirect coupling mechanism helps solve the mounting issue encountered in the process of mass production of the RFID tag. The experimental tests carried out with tags in which the TI, Alien-H2, and Alien-H3 chips were incorporated show that the maximum read ranges we can achieve are 1.2, 2.8, and 4.2 m, respectively, which has been tested for an RFID reader with only 0.5 W of EIRP. The letter shows that the simulated and measured results are shown to be in good agreement with each other.


title: A new approach for unsupervised segmentation

abstract: Texture is an important spatial feature useful for identifying objects or regions of interest in an image. This paper presents two fuzzy logic-based approaches for colour textures. They are fuzzy texture unit and colour fuzzy texture spectrum. A method is proposed for segmentation using colour fuzzy texture descriptor for colour texture images. This paper discusses two steps. Firstly, the presence of colour texture is locally as well as globally represented. Secondly, with the proposed global descriptor, segmentation operation is performed by SOMs algorithm. Compared with moment features, our method achieves good segmentation results on colour texture images, demonstrating the performance of our proposal.


title: Stretchable Materials for Robust Soft Actuators towards Assistive Wearable Devices

abstract: Soft actuators made from elastomeric active materials can find widespread potential implementation in a variety of applications ranging from assistive wearable technologies targeted at biomedical rehabilitation or assistance with activities of daily living, bioinspired and biomimetic systems, to gripping and manipulating fragile objects, and adaptable locomotion. In this manuscript, we propose a novel two-component soft actuator design and design tool that produces actuators targeted towards these applications with enhanced mechanical performance and manufacturability. Our numerical models developed using the finite element method can predict the actuator behavior at large mechanical strains to allow efficient design iterations for system optimization. Based on two distinctive actuator prototypes' (linear and bending actuators) experimental results that include free displacement and blocked-forces, we have validated the efficacy of the numerical models. The presented extensive investigation of mechanical performance for soft actuators with varying geometric parameters demonstrates the practical application of the design tool, and the robustness of the actuator hardware design, towards diverse soft robotic systems for a wide set of assistive wearable technologies, including replicating the motion of several parts of the human body.


title: Detection of obstructive sleep apnea through ECG signal features

abstract: Obstructive sleep apnea (OSA) is a common disorder in which individuals stop breathing during their sleep. Most of sleep apnea cases are currently undiagnosed because of expenses and practicality limitations of overnight polysomnography (PSG) at sleep labs, where an expert human observer is needed to work over night. New techniques for sleep apnea classification are being developed by bioengineers for most comfortable and timely detection. In this paper, an automated classification algorithm is presented which processes short duration epochs of the electrocardiogram (ECG) data. The automated classification algorithm is based on support vector machines (SVM) and has been trained and tested on sleep apnea recordings from subjects with and without OSA. The results show that our automated classification system can recognize epochs of sleep disorders with a high degree of accuracy, approximately 96.5%. Moreover, the system we developed can be used as a basis for future development of a tool for OSA screening.


title: Separation of singing voice using nonnegative matrix partial co-factorization for singer identification

abstract: In order to improve the performance of singer identification, we propose a system to separate singing voice from music accompaniment for monaural recordings. Our system consists of two key stages. The first stage exploits the nonnegative matrix partial co-factorization (NMPCF), which is a joint matrix decomposition integrating prior knowledge of singing voice and pure accompaniment to separate the mixture signal into singing voice portion and accompaniment portion. In the second stage, based on the separated singing voice obtained by the first stage, the pitches of singing voice are first estimated and then the harmonic components of singing voice can be distinguished. For a frame, the distinguished harmonic components are regarded as reliable while other frequency components unreliable, thus the spectrum is incomplete. With those harmonic components, the complete spectrums of singing voice can be reconstructed by a missing feature method, spectrum reconstruction, obtaining a refined signal with more clean singing voice. Experimental results demonstrate that, from the point view of source separation, the singing voice refinement can further improve $\Delta SNR$ in contrast with the singing voice separation using NMPCF, while for the point view of singer identification, the singing voice separated by NMPCF is more appropriate than the refined singing voice.


title: Big data meets process mining: implementing the alpha algorithm with map-reduce

abstract: Process mining is an approach to extract process models from event logs. Given the distributed nature of modern information systems, event logs are likely to be distributed across different physical machines. Map-Reduce is a scalable approach for efficient computations on distributed data. In this paper we present the design of a Map-Reduce implementation of the Alpha process mining algorithm, to take advantage of the scalability of the Map-Reduce approach. We provide a experimental results that show the performance and scalability of our implementation.


title: BranchScope: A New Side-Channel Attack on Directional Branch Predictor

abstract: We present BranchScope - a new side-channel attack where the attacker infers the direction of an arbitrary conditional branch instruction in a victim program by manipulating the shared directional branch predictor. The directional component of the branch predictor stores the prediction on a given branch (taken or not-taken) and is a different component from the branch target buffer (BTB) attacked by previous work. BranchScope is the first fine-grained attack on the directional branch predictor, expanding our understanding of the side channel vulnerability of the branch prediction unit. Our attack targets complex hybrid branch predictors with unknown organization. We demonstrate how an attacker can force these predictors to switch to a simple 1-level mode to simplify the direction recovery. We carry out BranchScope on several recent Intel CPUs and also demonstrate the attack against an SGX enclave.


CloudRobotics – Distributed Robotics using Cloud Computing


title: Evaluating wide-field-of-view augmented reality with mixed reality simulation

abstract: Full-surround augmented reality, with augmentations spanning the entire human field of view and beyond, is an under-explored topic since there is currently no hardware that can support it. As current AR displays only support relatively small fields of view, most AR applications to-date employ relatively small point-based annotations of the physical world. Anticipating a change in AR capabilities, we experiment with wide-field-of-view annotations that link elements far apart in the visual field. We have built a system that uses full-surround virtual reality to simulate augmented reality with different field of views, with and without tracking artifacts. We conducted a study comparing user performance on five different task groups within an information-seeking scenario, comparing two different fields of view and presence and absence of tracking artifacts. A constrained field of view significantly increased task completion time. We found indications for task time effects of tracking artifacts to vary depending on age.


title: Elimination of ISI Using Improved LMS Based Decision Feedback Equalizer

abstract: This paper deals with the implementation of Least Mean Square (LMS) algorithm in Decision Feedback Equalizer (DFE) for removal of Inter Symbol Interference (ISI) at the receiver. The channel disrupts the transmitted signal by spreading it in time. Although, the LMS algorithm is robust and reliable, it is slow in convergence. In order to increase the speed of convergence, modifications have been made in the algorithm where the weights get updated depending on the severity of disturbance.


title: Acute effects of contract–relax (CR) stretch versus a modified CR technique

abstract: PURPOSE ::: Contract-relax (CR) stretching increases range of motion (ROM) substantively, however its use in athletic environments is limited as the contractions performed in a highly stretched position require partner assistance, are often painful, and may induce muscle damage. Therefore, the acute effects of performing the contractions 'off stretch' in the anatomical position [stretch-return-contract (SRC)] were compared with traditional CR stretching in 14 healthy human volunteers. ::: ::: ::: METHODS ::: Passive ankle joint moment and dorsiflexion ROM were recorded on an isokinetic dynamometer with electromyographic monitoring of the triceps surae, whilst simultaneous real-time motion analysis and ultrasound imaging recorded gastrocnemius medialis muscle and Achilles tendon elongation. The subjects then performed CR or SRC stretches (4 × 10-s stretches and 5-s contractions) randomly on separate days before reassessment. ::: ::: ::: RESULTS ::: Significant increases in dorsiflexion ROM (4.1°-4.0°; P < 0.01) and peak passive moment (10.9-15.1%; P < 0.05) and decreases in the slope of the passive moment curve (19.1-13.3%; P < 0.05), muscle stiffness (21.7-21.3%; P < 0.01) and tendon stiffness (20.4-15.7%; P < 0.01) were observed in CR and SRC, respectively. No between-condition differences were found in any measure (P > 0.05). ::: ::: ::: CONCLUSIONS ::: Similar mechanical and neurological changes were observed between conditions, indicating that identical mechanisms underpin the ROM improvements. These data have important practical implications for the use of this stretching mode in athletic environments as performing the contractions 'off stretch' eliminates the pain response, reduces the risk of inducing muscle damage, and removes the need for partner assistance. Thus, it represents an equally effective, simpler, and yet potentially safer, stretching paradigm.


title: Software transactional memory

abstract: Summary. As we learn from the literature, flexibility in choosing synchronization operations greatly simplifies the task of designing highly concurrent programs. Unfortunately, existing hardware is inflexible and is at best on the level of a Load–Linked/Store–Conditional operation on a single word. Building on the hardware based transactional synchronization methodology of Herlihy and Moss, we offer software transactional memory (STM), a novel software method for supporting flexible transactional programming of synchronization operations. STM is non-blocking, and can be implemented on existing machines using only a Load–Linked/Store–Conditional operation. We use STM to provide a general highly concurrent method for translating sequential object implementations to non-blocking ones based on implementing a k-word compare&swap STM-transaction. Empirical evidence collected on simulated multiprocessor architectures shows that our method always outperforms the non-blocking translation methods in the style of Barnes, and outperforms Herlihy’s translation method for sufficiently large numbers of processors. The key to the efficiency of our software-transactional approach is that unlike Barnes style methods, it is not based on a costly “recursive helping” policy.


title: A wireless low-power pulse oximetry system for patient telemonitoring

abstract: In this paper we present the realization of a wireless low power pulse oximetry telemonitoring system capable to measure and transmit patient's arterial blood-oxygen saturation (SpO2) level and heart rate (HR). The use of the proposed system is suitable for continuous long-time patient monitoring, as a part of a diagnostic procedure. The patient can achieve medical assistance of a chronic condition, or can be supervised during recovery from an acute event or surgical procedure. We use commercially available devices, low power microcontrollers and RF transceivers that perform the measurements (SpO2 and HR) and transmit them to the patient monitoring device. The monitoring device, in form of a PDA that running a personal SpO2 monitor application, receives the SpO2 level and HR, activates the alarms when the monitored parameters exceed the preset limits, and communicates periodically to the telemonitoring server by using WiFi or GSM/GPRS connection.


title: Modified Hysteretic Current Control (MHCC) for Improving Transient Response of Boost Converter

abstract: This paper proposes a modulated hysteretic current control (MHCC) technique to improve the transient response of a DC-DC boost converter, which suffers from low bandwidth due to the existence of the right-half-plane (RHP) zero. The MHCC technique can automatically adjust the on-time value to rapidly increase the inductor current, as well as to shorten the transient response time. In addition, based on the characteristic of the RHP zero, the compensation poles and zero are deliberately adjusted to achieve fast transient response in case of load transient condition and adequate phase margin in steady state. Experimental results show the improvement of transient recovery time over 7.2 times in the load transient response compared with the conventional boost converter design when the load current changes from light to heavy or vice versa. The power consumption overhead is merely 1%.


title: The five W's of "bullying" on Twitter: Who, What, Why, Where, and When

abstract: Machine learning is used to describe how bullying is represented on social media.More than 9million posts referred to bullying episodes on Twitter over two years.Using social media data allows for an examination of all bullying role-players.Investigating real-time posts provides timely, dynamic data about bullying. This paper explores the utility of machine learning methods for understanding bullying, a significant social-psychological issue in the United States, through social media data. Machine learning methods were applied to all public mentions of bullying on Twitter between September 1, 2011 and August 31, 2013 to extract the posts that referred to discrete bullying episodes (N=9,764,583) to address five key questions. Most posts were authored by victims and reporters and referred to general forms of bullying. Posts frequently reflected self-disclosure about personal involvement in bullying. The number of posts that originated from a state was positively associated with the state population size; the timing of the posts reveal that more posts were made on weekdays than on Saturdays and more posts were made during the evening compared to daytime hours. Potential benefits of merging social science and computer science methods to enhance the study of bullying are discussed.


title: Learning Deep Spatial Lung Features by 3D Convolutional Neural Network for Early Cancer Detection

abstract: Accurate early lung cancer detection is essential towards precision oncology and would effectively improve the patients' survival rate. In this work, we explore the lung cancer early detection capacity by learning from deep spatial lung features. A 3D CNN network architecture is constructed with segmented CT lung volumes as training and testing samples. The new model extracts and projects 3D features to the following hidden layers, which preserves the temporal relations between neighboring CT slices. The well-built 3D CNN model consists of 11 layers which generates 12,544 neurons and 16 million parameters classifying whether the patient is diagnosed as cancer or not. ReLU nonlinearity and Sigmoid function are used as activation and classification methods. The model achieves a prediction accuracy of 87.5% where only the biomedical images themselves are used as the input dataset. The model's lowest error rate reaches 12.5% that improves the traditional AlexNet architecture by 2.8%.


title: Snoopertrack: Text detection and tracking for outdoor videos

abstract: In this work we introduced SnooperTrack, an algorithm for the automatic detection and tracking of text objects — such as store names, traffic signs, license plates, and advertisements — in videos of outdoor scenes. The purpose is to improve the performances of text detection process in still images by taking advantage of the temporal coherence in videos. We first propose an efficient tracking algorithm using particle filtering framework with original region descriptors. The second contribution is our strategy to merge tracked regions and new detections. We also propose an improved version of our previously published text detection algorithm in still images. Tests indicate that SnooperTrack is fast, robust, enable false positive suppression, and achieved great performances in complex videos of outdoor scenes.


title: Compositional Human Pose Regression

abstract: Regression based methods are not performing as well as detection based methods for human pose estimation. A central problem is that the structural information in the pose is not well exploited in the previous regression methods. In this work, we propose a structure-aware regression approach. It adopts a reparameterized pose representation using bones instead of joints. It exploits the joint connection structure to define a compositional loss function that encodes the long range interactions in the pose. It is simple, effective, and general for both 2D and 3D pose estimation in a unified setting. Comprehensive evaluation validates the effectiveness of our approach. It significantly advances the state-of-the-art on Human3.6M [20] and is competitive with state-of-the-art results on MPII [3].