title: Structural Pursuit Over Multiple Undirected Graphs

abstract: Gaussian graphical models are useful to analyze and visualize conditional dependence relationships between interacting units. Motivated from network analysis under di erent experimental conditions, such as gene networks for disparate cancer subtypes, we model structural changes over multiple networks with possible heterogeneities. In particular, we estimate multiple precision matrices describing dependencies among interacting units through maximum penalized likelihood. Of particular interest are homogeneous groups of similar entries across and zero-entries of these matrices, referred to as clustering and sparseness structures, respectively. A non-convex method is proposed to seek a sparse representation for each matrix and identify clusters of the entries across the matrices. Computationally, we develop an e cient method on the basis of di erence convex programming, the augmented Lagrangian method and the block-wise coordinate descent method, which is scalable to hundreds of graphs of thousands nodes through a simple necessary and sufficient partition rule, which divides nodes into smaller disjoint subproblems excluding zero-coe cients nodes for arbitrary graphs with convex relaxation. Theoretically, a finite-sample error bound is derived for the proposed method to reconstruct the clustering and sparseness structures. This leads to consistent reconstruction of these two structures simultaneously, permitting the number of unknown parameters to be exponential in the sample size, and yielding the optimal performance of the oracle estimator as if the true structures were given a priori. Simulation studies suggest that the method enjoys the benefit of pursuing these two disparate kinds of structures, and compares favorably against its convex counterpart in the accuracy of structure pursuit and parameter estimation.


title: Urdu compound Character Recognition using feed forward neural networks

abstract: Urdu compound Character Recognition is a scarcely developed area and requires robust techniques to develop as Urdu being a family of Arabic script is cursive, right to left in nature and characters change their shapes and sizes when they are placed at initial, middle or at the end of a word. The developed system consists of two main modules segmentation and classification. In the segmentation phase pixels strength is measured to detect words in a sentence and joints of characters in a compound/connected word for segmentation. In the next phase these segmented characters are feeded to a trained Neural Network for classification and recognition, where Feed Forward Neural Network is trained on 56 different classes of characters each having 100 samples. The main purpose of the system is to test the algorithm developed for segmentation of compound characters. The prototype of the system has been developed in Matlab, currently achieves 70% accuracy on the average


title: Shotgun assembly of the assassin bug Brontostoma colossus mitochondrial genome (Heteroptera, Reduviidae)

abstract: Abstract The complete mitochondrial genome of the assassin bug Brontostoma colossus (Distant, 1902) (Heteroptera: Reduviidae) has been sequenced using a genome-skimming approach on an Illumina Hiseq 2000 platform. Fifty-four additional heteropteran mitogenomes, including five assassin bug species, were retrieved to allow for comparisons and phylogenetic analyses. The mitochondrial genome of B. colossus was determined to be 16,625 bp long, and consists of 13 protein-coding genes (PCGs), 23 transfer-RNA genes (tRNAs), two ribosomal-RNA genes (rRNAs), and one control region. The nucleotide composition is biased toward adenine and thymine (A + T = 73.4%). Overall, architecture, nucleotide composition and genome asymmetry are similar among all available assassin bug mitogenomes. All PCGs have usual start-codons (Met and Ile). Three T and two TA incomplete termination codons were identified adjacent to tRNAs, which was consistent with the punctuation model for primary transcripts processing followed by 3′ polyadenylation of mature mRNA. All tRNAs exhibit the classic clover-leaf secondary structure except for tRNASer(AGN) in which the DHU arm forms a simple loop. Two notable features are present in the B. colossus mitogenome: (i) a 131 bp duplicated unit including the complete tRNAArg gene, resulting in 23 potentially functional tRNAs in total, and (ii) a 857 bp duplicated region comprising 277 bp of the srRNA gene and 580 bp of the control region. A phylogenetic analysis based on 55 true bug mitogenomes confirmed that B. colossus belongs to Reduviidae, but contradicted a widely accepted hypothesis. This highlights the limits of phylogenetic analyses based on mitochondrial data only.


title: Enhancing data parallelism for Ant Colony Optimization on GPUs

abstract: Graphics Processing Units (GPUs) have evolved into highly parallel and fully programmable architecture over the past five years, and the advent of CUDA has facilitated their application to many real-world applications. In this paper, we deal with a GPU implementation of Ant Colony Optimization (ACO), a population-based optimization method which comprises two major stages: tour construction and pheromone update. Because of its inherently parallel nature, ACO is well-suited to GPU implementation, but it also poses significant challenges due to irregular memory access patterns. Our contribution within this context is threefold: (1) a data parallelism scheme for tour construction tailored to GPUs, (2) novel GPU programming strategies for the pheromone update stage, and (3) a new mechanism called I-Roulette to replicate the classic roulette wheel while improving GPU parallelism. Our implementation leads to factor gains exceeding 20x for any of the two stages of the ACO algorithm as applied to the TSP when compared to its sequential counterpart version running on a similar single-threaded high-end CPU. Moreover, an extensive discussion focused on different implementation paths on GPUs shows the way to deal with parallel graph connected components. This, in turn, suggests a broader area of inquiry, where algorithm designers may learn to adapt similar optimization methods to GPU architecture.


title: Efficient ConvNet-based marker-less motion capture in general scenes with a low number of cameras

abstract: We present a novel method for accurate marker-less capture of articulated skeleton motion of several subjects in general scenes, indoors and outdoors, even from input filmed with as few as two cameras. Our approach unites a discriminative image-based joint detection method with a model-based generative motion tracking algorithm through a combined pose optimization energy. The discriminative part-based pose detection method, implemented using Convolutional Networks (ConvNet), estimates unary potentials for each joint of a kinematic skeleton model. These unary potentials are used to probabilistically extract pose constraints for tracking by using weighted sampling from a pose posterior guided by the model. In the final energy, these constraints are combined with an appearance-based model-to-image similarity term. Poses can be computed very efficiently using iterative local optimization, as ConvNet detection is fast, and our formulation yields a combined pose estimation energy with analytic derivatives. In combination, this enables to track full articulated joint angles at state-of-the-art accuracy and temporal stability with a very low number of cameras.


Multiclass Classification with Filter Trees


title: GHG Emissions and Energy Minimization using Heat Integrated Pressure Swing Distillation Sequence (HiPSDS) for the Separation of Azeotropic Mixture

abstract: present work elaborates the process design methodology for the evaluation of the distillation systems based on the economic, exergetic and environmental point of view, the greenhouse gas (GHG) emissions. The methodology proposes the Heat Integrated Pressure Swing Distillation Sequence (HiPSDS) is economic and reduces the GHG emissions than the conventional Extractive Distillation Sequence (EDS) and the Pressure Swing Distillation Sequence (PSDS) for the case study of isobutyl alcohol and isobutyl acetate with the solvents for EDS and with low pressure variations for PSDS and HiPSDS. The study demonstrates that the exergy analysis can predict the results of the economic and environmental evaluation associated with the process design.


title: Text Detection, Localization and Segmentation in Compressed Videos

abstract: Video text information plays an important role in semantic-based video analysis, indexing and retrieval. Video texts are closely related to the content of a video. Text-based video analysis, browsing and retrieval are usually carried out in the following for steps: video text detection, localization, segmentation and recognition. Videos are commonly stored in compressed formats where MPEG coding techniques are adopted. In this paper, a DCT coefficient based multilingual video text detection and localization scheme for compressed videos is proposed. Candidate text blocks are detected in terms of block texture constraint. An adaptive method for the horizontal and vertical aligned text lines determination is then designed according to the run length of the horizontal and vertical block numbers. The remaining block regions are further verified by local block texture constraints. And the text block region can be localized by virtue of the horizontal and vertical block texture projections. Finally, a foreground and background integrated (FBI) video text segmentation approach is adopted in this paper to eliminate the complex background in text regions. The final experimental results show the effectiveness of our methods.


title: The multiple meanings of open government data: Understanding different stakeholders and their perspectives

abstract: Abstract As a field of practice and research that is fast-growing and a locus for much attention and activity, open government data (OGD) has attracted stakeholders from a variety of origins. They bring with them a variety of meanings for OGD. The purpose of this paper is to show how the different stakeholders and their different perspectives on OGD can be analyzed in a given context. Taking Chile as an OGD exemplar, stakeholder analysis is used to identify and categorize stakeholder groups in terms of their relative power and interest as either primary (in this case, politicians, public officials, public sector practitioners, international organizations) or secondary (civil society activists, funding donors, ICT providers, academics). Stakeholder groups sometimes associated with OGD but absent from significant involvement in Chile – such as private sector- and citizen-users – are also identified. Four different perspectives on open government data – bureaucratic, political, technological, and economic – are identified from a literature review. Template analysis is used to analyze text – OGD-related reports, conference presentations, and interviews in Chile – in terms of those perspectives. This shows bureaucratic and political perspectives to be more dominant than the other two, and also some presence for a politico-economic perspective not identified from the original literature review. The information value chain is used to identify a “missing middle” in current Chilean OGD perspectives: a lack of connection between a reality of data provision and an aspiration of developmental results. This pattern of perspectives can be explained by the capacities and interests of key stakeholders, with those in turn being shaped by Chile's history, politics, and institutions. Overall, stakeholder analysis and perspectives analysis are shown from this case to be workable techniques for OGD that add value by exposing the identity, power, motivations, and worldview of key actors. They provide a necessary foundation of knowledge for both researchers and practitioners who need to understand the different meanings of OGD in any particular context.


title: Nonlinear model predictive control with constraint satisfactions for a quadcopter

abstract: This paper presents a nonlinear model predictive control (NMPC) strategy combined with constraint satisfactions for a quadcopter. The full dynamics of the quadcopter describing the attitude and position are nonlinear, which are quite sensitive to changes of inputs and disturbances. By means of constraint satisfactions, partial nonlinearities and modeling errors of the control-oriented model of full dynamics can be transformed into the inequality constraints. Subsequently, the quadcopter can be controlled by an NMPC controller with the updated constraints generated by constraint satisfactions. Finally, the simulation results applied to a quadcopter simulator are provided to show the e ectiveness of he proposed strategy.


title: Survey of multi-objective optimization methods for engineering

abstract: A survey of current continuous nonlinear multi-objective optimization (MOO) concepts and methods is presented. It consolidates and relates seemingly different terminology and methods. The methods are divided into three major categories: methods with a priori articulation of preferences, methods with a posteriori articulation of preferences, and methods with no articulation of preferences. Genetic algorithms are surveyed as well. Commentary is provided on three fronts, concerning the advantages and pitfalls of individual methods, the different classes of methods, and the field of MOO as a whole. The Characteristics of the most significant methods are summarized. Conclusions are drawn that reflect often-neglected ideas and applicability to engineering problems. It is found that no single approach is superior. Rather, the selection of a specific method depends on the type of information that is provided in the problem, the user’s preferences, the solution requirements, and the availability of software.


title: Distral: Robust Multitask Reinforcement Learning

abstract: Most deep reinforcement learning algorithms are data inefficient in complex and rich environments, limiting their applicability to many scenarios. One direction for improving data efficiency is multitask learning with shared neural network parameters, where efficiency may be improved through transfer across related tasks. In practice, however, this is not usually observed, because gradients from different tasks can interfere negatively, making learning unstable and sometimes even less data efficient. Another issue is the different reward schemes between tasks, which can easily lead to one task dominating the learning of a shared model. We propose a new approach for joint training of multiple tasks, which we refer to as Distral (Distill & transfer learning). Instead of sharing parameters between the different workers, we propose to share a "distilled" policy that captures common behaviour across tasks. Each worker is trained to solve its own task while constrained to stay close to the shared policy, while the shared policy is trained by distillation to be the centroid of all task policies. Both aspects of the learning process are derived by optimizing a joint objective function. We show that our approach supports efficient transfer on complex 3D environments, outperforming several related methods. Moreover, the proposed learning process is more robust and more stable---attributes that are critical in deep reinforcement learning.


title: Fundamentals of Biometric Authentication Technologies

abstract: Biometric authentication technologies are used for the machine identification of individuals. The human-generated patterns used may be primarily physiological or behavioral, but usually contain elements of both components. Examples include voice, handwriting, face, eye and fingerprint identification. In this paper, we look at these technologies and their applications in general, developing a systematic approach to classifying, analyzing and evaluating them. A general system model is shown and test results for a number of technologies are considered.


Issues in Retrospective Conversion for a Small Special Collection: A Case Study.


title: GaN high power amplifier MMIC for 30W Compact Doherty amplifier for LTE active antenna system

abstract: In this paper, an high efficiency and high output power amplifier MMIC based on 0.25 um GaN HEMT proress is presented. The MMIC has been designed with an output impedance moving technique to help a Doherty amplifier to be implemented in a small area. A compact Doherty amplifier for an active antenna system of a LTE base station has been implemented with the developed GaN high power amplifier (HPA) MMIC. The Doherty amplifier has been measured with power added efficiency (PAE) of 42 % at 7 dB back-off output power of 38 dBm, the maximum PAE of 55 % at the peak saturated output power of 45dBm in the frequency range 2.62 GHz ∼ 2.69 GHz. The Doherty amplifier has the linear gain more than 27 dB in the frequency range and ACLR performance better than −31 dBc with the 20 MHz bandwidth WCDMA signal without helping any linearization technique. The Doherty amplifier has been assembled in PCB with the size of 25 mm × 25 mm including input LTCC divider and output combining networks.


title: Handwritten/Printed Receipt Classification Using Attention-Based Convolutional Neural Network

abstract: This paper presents an approach for the classification of handwritten and printed receipts based on a convolutional neural network (CNN). One of the main challenges related to such classification is the diversity of the background interference in the receipt images. To overcome this problem, we propose a new technique named "attention-based CNN" (ABCNN), inspired by the concept of "attention" in visual neuroscience. This approach helps us to focus on the receipt in an image without bounding box annotation. Our experimental results showed that the proposed ABCNN (i) significantly improves the classification accuracy compared to normal CNN (from 95% to 98.25%), and (ii) enables the network to process images directly without object detection, and (iii) it is faster to train and test the network.


title: An Empirical Evaluation of Visual Question Answering for Novel Objects

abstract: We study the problem of answering questions about images in the harder setting, where the test questions and corresponding images contain novel objects, which were not queried about in the training data. Such setting is inevitable in real world–owing to the heavy tailed distribution of the visual categories, there would be some objects which would not be annotated in the train set. We show that the performance of two popular existing methods drop significantly (21–28%) when evaluated on novel objects cf. known objects. We propose methods which use large existing external corpora of (i) unlabeled text, i.e. books, and (ii) images tagged with classes, to achieve novel object based visual question answering. We systematically study both, an oracle case where the novel objects are known textually, as well as a fully automatic case without any explicit knowledge of the novel objects, but with the minimal assumption that the novel objects are semantically related to the existing objects in training. The proposed methods for novel object based visual question answering are modular and can potentially be used with many visual question answering architectures. We show consistent improvements with the two popular architectures and give qualitative analysis of the cases where the model does well and of those where it fails to bring improvements.


title: Determining critical success factors of mobile banking adoption in Malaysia

abstract: This study is to examine critical success factors that influence the adoption of mobile banking in Malaysia using extended Technology Acceptance Model (TAM). The proposed model was empirically evaluated by using survey data collected from 300 banking users concerning their perceptions of mobile banking. The findings indicate that this model can predict consumer intention to use mobile banking. Specifically, perceived usefulness, perceived credibility and awareness about mobile banking have significant effect on user's attitude thus influence the intention toward mobile banking. The results may provide further insights into mobile banking strategies.


title: A Semi-Automatic Approach for Detecting Dataset References in Social Science Texts

abstract: Today, full-texts of scientific articles are often stored in different locations than the used datasets. Dataset registries aim at a closer integration by making datasets citable but authors typically refer to datasets using inconsistent abbreviations and heterogeneous metadata (e.g. title, publication year). It is thus hard to reproduce research results, to access datasets for further analysis, and to determine the impact of a dataset. Manually detecting references to datasets in scientific articles is time-consuming and requires expert knowledge in the underlying research domain.We propose and evaluate a semi-automatic three-step approach for finding explicit references to datasets in social sciences articles.We first extract pre-defined special features from dataset titles in the da|ra registry, then detect references to datasets using the extracted features, and finally match the references found with corresponding dataset titles. The approach does not require a corpus of articles (avoiding the cold start problem) and performs well on a test corpus. We achieved an F-measure of 0.84 for detecting references in full-texts and an F-measure of 0.83 for finding correct matches of detected references in the da|ra dataset registry.


title: The placebo response in medicine: minimize, maximize or personalize?

abstract: The therapeutic outcome of a drug or procedure is influenced by the placebo response in both drug development and clinical practice. Enck and colleagues examine how the placebo response can be utilized in these settings to ensure that the most desirable outcome is attained.


title: Understanding MOSFET mismatch for analog design

abstract: This paper addresses misconceptions about MOSFET mismatch for analog design. V/sub t/ mismatch does not follow a simplistic 1/(/spl radic/area) law, especially for wide/short and narrow/long devices, which are common geometries in analog circuits. Further, Vt and gain factor are not appropriate parameters for modeling mismatch. A physically based mismatch model can be used to obtain dramatic improvements in the prediction of mismatch. This model is applied to MOSFET current mirrors to show some non-obvious effects over bias, geometry, and multiple unit devices.


title: Automatic grammar correction for second-language learners

abstract: A computer conversational system can potentially help a foreignlanguage student improve his/her fluency through practice dialogues. One of its potential roles could be to correct ungrammatical sentences. This paper 1 describes our research on a sentencelevel, generation-based approach to grammar correction: first, a word lattice of candidate corrections is generated from an illformed input. A traditional n-gram language model is used to produce a small set of N-best candidates, which are then reranked by parsing using a stochastic context-free grammar. We evaluate this approach in a flight domain with simulated ill-formed sentences. We discuss its potential applications in a few related tasks. Index Terms: computer-assisted language learning, dialogue systems, natural language generation.


title: Plant Growth Promoting Characterization of Indigenous Azotobacteria Isolated from Soils in Iran

abstract: It has been well known that the bacteria of the genus Azotobacter, in addition to the beneficial N(2)-fixing activity, are able to improve plant growth by a number of direct and indirect mechanisms. To identify this potential in indigenous azotobacteria, the efficiency of 17 isolates of Azotobacter from the rhizosphere of wheat and barley plants cultivated in salt- and/or drought-affected soils in Iran were evaluated for their ability to dissolve inorganic and organic phosphates, siderophore secretion, indole acetic acid (IAA) production; and protease, chitinase, and ACC deaminase (ACCD) activities. First, they were biochemically characterized and one isolate (strain) was identified by 16S rDNA sequencing. Eight isolates were designated as Azotobacter vinelandii and the remaining isolates were identified as A. chroococcum. All isolates hydrolyzed the organic and inorganic phosphate compounds and effectively produced IAA. Fifteen isolates produced siderophore, but only one isolate showed protease activity which is being reported for the first time in relation to Azotobacter. None of the 17 isolates was capable of producing ACCD or chitinase. However, polymerase chain reaction amplification of the ACCD coding genes, by the use of the gene-specific primers, indicated that not all contain the ACCD gene. The standard screening methods with slight modifications, especially in the case of ACCD assay, were applied. The results showed that the use of specific screening methods, modified according to bacterial nutritional requirements, are the efficient methods for precise evaluation of the plant growth promoting rhizobacteria activity.


title: Algorithms implementing distributed shared memory

abstract: Four basic algorithms for implementing distributed shared memory are compared. Conceptually, these algorithms extend local virtual address spaces to span multiple hosts connected by a local area network, and some of them can easily be integrated with the hosts' virtual memory systems. The merits of distributed shared memory and the assumptions made with respect to the environment in which the shared memory algorithms are executed are described. The algorithms are then described, and a comparative analysis of their performance in relation to application-level access behavior is presented. It is shown that the correct choice of algorithm is determined largely by the memory access behavior of the applications. Two particularly interesting extensions of the basic algorithms are described, and some limitations of distributed shared memory are noted. >


title: URBE: Web Service Retrieval Based on Similarity Evaluation

abstract: In this work, we present UDDI registry by example (Urbe), a novel approach for Web service retrieval based on the evaluation of similarity between Web service interfaces. Our approach assumes that the Web service interfaces are defined with Web service description language (WSDL) and the algorithm combines the analysis of their structures and the analysis of the terms used inside them. The higher the similarity, the less are the differences among their interfaces. As a consequence, Urbe is useful when we need to find a Web service suitable to replace an existing one that fails. Especially in autonomic systems, this situation is very common since we need to ensure the self-management, the self-configuration, the self-optimization, the self-healing, and the self-protection of the application that is based on the failed Web service. A semantic-oriented variant of the approach is also proposed, where we take advantage of annotations semantically enriching WSDL specifications. Semantic Annotation for WSDL (SAWSDL) is adopted as a language to annotate a WSDL description. The Urbe approach has been implemented in a prototype that extends a universal description, discovery and integration (UDDI) compliant Web service registry.


Creative teaching, effective learning in higher education


title: Design Considerations for Multilevel CMOS/Nano Memristive Memory

abstract: With technology migration into nano and molecular scales several hybrid CMOS/nano logic and memory architectures have been proposed that aim to achieve high device density with low power consumption. The discovery of the memristor has further enabled the realization of denser nanoscale logic and memory systems by facilitating the implementation of multilevel logic. This work describes the design of such a multilevel nonvolatile memristor memory system, and the design constraints imposed in the realization of such a memory. In particular, the limitations on load, bank size, number of bits achievable per device, placed by the required noise margin for accurately reading and writing the data stored in a device are analyzed. Also analyzed are the nondisruptive read and write methodologies for the hybrid multilevel memristor memory to program and read the memristive information without corrupting it. This work showcases two write methodologies that leverage the best traits of memristors when used in either linear (low power) or nonlinear drift (fast speeds) modes. The system can therefore be tailored depending on the required performance parameters of a given application for a fast memory or a slower but very energy-efficient system. We propose for the first time, a hybrid memory that aims to incorporate the area advantage provided by the utilization of multilevel logic and nanoscale memristive devices in conjunction with CMOS for the realization of a high density nonvolatile multilevel memory.


title: A Historical Survey of Ethnic Conflict in Nigeria.

abstract: This article focused on the historical survey of ethnic conflict in the Nigerian society and its scope is vast in a way that it stresses the country’s ethnic conflicts beginning from the Pre-colonialism state down the post-colonialism. The main objective of this article is to explore deeper analyses on politics, ethnic conflict and its causes in the multi-cultural society like that of the giant of Africa and the consequences therefore. The approach used in this study is consistent with the general research question on the educational meaning of politics in comparison to the modern politics as experiencing among the political leaders and parties; in responding to the question, a data from a published author is quoted as sample report in answer to the situation in the country. Finally, the article also elaborates on how to manage ethnic conflict amidst of differences and plural society like in the case of the said nation.


title: Automatic 3D Face Reconstruction based on Single 2D Image

abstract: Face recognition combines a number of advantages as it offers speed and reliability of detection at distance from the subject, which makes its candidacy as a biometric feature a fertile area of pattern recognition and image processing research. In general, changes to head pose and facial expression cause a 2D image of a face to become an unreliable form of identification, while a 3D representation suggests all vertices of the 3D facial model as candidate and useful features for face classification and recognition. Established 3D face reconstruction, however, requires multiple 2D images of a subject at different angles but such images are not always available, and proposals to construct a 3D facial model from a single 2D image of the face are hampered by severe restrictions on that image (frontal pose, neutral expression). This paper offers a new proposal to overcome deficiencies of 3D facial model reconstruction from one image, by improvements using an affine transformation and a 3D statistical face model. The experimental results that are presented for these new algorithms indicate that they are efficient and that this method is promising.


LEARNING TO COLLABORATE FOR QUESTION ANSWERING AND ASKING


What Can Machines Learn, and What Does It Mean for Occupations and the Economy?


title: A simple system for table extraction irrespective of boundary thickness and removal of detected spurious lines

abstract: Several types of table layout structures are ubiquitous in digitalized document images and are characterized by their row and column separators. Document image may consist of several undesirable lines introduced due to improper scanning, crease formation, accidental remarks etc., in addition to the desired lines in tables. Since tables being an effective component of document images for representing an information, one needs to extract the table from the document images. The proposed method aims at removing unwanted straight lines in binary document images, without affecting the essential details of the table by a two-step process. In the first step, the extraction of necessary details of the tables containing lines as row and column separators along with their respective frames is performed using Mask Processing. The second step involves the detection and removal of all straight lines using a Pseudo Diagonal Image (PDI) and its rotation. The proposed method exploits the novelty in utilizing a single mask for the detection of tables instead of multiple masks, hence the computational complexity for processing is lesser. Independency in the thickness of table boundary while extraction is also an effective characterization of the proposed algorithm. The Obtained result shows 93.35% precision and 92.33% recall.


title: A Novel ZCS-PWM Flyback Converter With a Simple ZCS-PWM Commutation Cell

abstract: This paper proposes a novel zero-current-switching pulsewidth-modulation (ZCS-PWM) flyback dc/dc converter using a simple ZCS-PWM commutation cell. The main switch and auxiliary switch operate at ZCS turn-on and turn-off conditions, and all uncontrolled devices in the proposed converter operate at zero-voltage-switching (ZVS) turn-on and turn-off. In addition, given constant frequency and decreasing commutation losses, the proposed converter has no additional current stress and conduction loss in the main switch compared to the conventional hard switching flyback converter. The averaging approach is used to estimate and examine the steady-state of the proposed converter. The principle of operation, theoretical analysis, and experimental results of the new ZCS-PWM flyback converter, rated 150 W and operating at 80 kHz, are provided in this paper to verify the performance of the proposed converter.


title: Microfabricated Surface-Electrode Ion Trap for Scalable Quantum Information Processing

abstract: Individual laser-cooled {sup 24}Mg{sup +} ions are confined in a linear Paul trap with a novel geometry where gold electrodes are located in a single plane and the ions are trapped 40 {mu}m above this plane. The relatively simple trap design and fabrication procedure are important for large-scale quantum information processing (QIP) using ions. Measured ion motional frequencies are compared to simulations. Measurements of ion recooling after cooling is temporarily suspended yield a heating rate of approximately 5 motional quanta per millisecond for a trap frequency of 2.83 MHz, sufficiently low to be useful for QIP.


title: LidarBoost: Depth superresolution for ToF 3D shape scanning

abstract: Depth maps captured with time-of-flight cameras have very low data quality: the image resolution is rather limited and the level of random noise contained in the depth maps is very high. Therefore, such flash lidars cannot be used out of the box for high-quality 3D object scanning. To solve this problem, we present LidarBoost, a 3D depth superresolution method that combines several low resolution noisy depth images of a static scene from slightly displaced viewpoints, and merges them into a high-resolution depth image. We have developed an optimization framework that uses a data fidelity term and a geometry prior term that is tailored to the specific characteristics of flash lidars. We demonstrate both visually and quantitatively that LidarBoost produces better results than previous methods from the literature.


title: Relational Generative Topographic Map

abstract: The generative topographic mapping (GTM) has been proposed as a statistical model to represent high-dimensional data by a distribution induced by a sparse lattice of points in a low-dimensional latent space, such that visualization, compression, and data inspection become possible. The formulation in terms of a generative statistical model has the benefit that relevant parameters of the model can be determined automatically based on an expectation maximization scheme. Further, the model offers a large flexibility such as a direct out-of-sample extension and the possibility to obtain different degrees of granularity of the visualization without the need of additional training. Original GTM is restricted to Euclidean data points in a given Euclidean vector space. Often, data are not explicitly embedded in a Euclidean vector space, rather pairwise dissimilarities of data can be computed, i.e. the relations between data points are given rather than the data vectors themselves. We propose a method which extends the GTM to relational data and which allows us to achieve a sparse representation of data characterized by pairwise dissimilarities, in latent space. The method, relational GTM, is demonstrated on several benchmarks.


title: Cannabinoid receptor 1 binding activity and quantitative analysis of Cannabis sativa L. smoke and vapor.

abstract: Cannabis sativa L. (cannabis) extracts, vapor produced by the Volcano vaporizer and smoke made from burning cannabis joints were analyzed by GC-flame ionization detecter (FID), GC-MS and HPLC. Three different medicinal cannabis varieties were investigated Bedrocan, Bedrobinol and Bediol. Cannabinoids plus other components such as terpenoids and pyrolytic by-products were identified and quantified in all samples. Cannabis vapor and smoke was tested for cannabinoid receptor 1 (CB1) binding activity and compared to pure Delta(9)-tetrahydrocannabinol (Delta(9)-THC). The top five major compounds in Bedrocan extracts were Delta(9)-THC, cannabigerol (CBG), terpinolene, myrcene, and cis-ocimene in Bedrobinol Delta(9)-THC, myrcene, CBG, cannabichromene (CBC), and camphene in Bediol cannabidiol (CBD), Delta(9)-THC, myrcene, CBC, and CBG. The major components in Bedrocan vapor (>1.0 mg/g) were Delta(9)-THC, terpinolene, myrcene, CBG, cis-ocimene and CBD in Bedrobinol Delta(9)-THC, myrcene and CBD in Bediol CBD, Delta(9)-THC, myrcene, CBC and terpinolene. The major components in Bedrocan smoke (>1.0 mg/g) were Delta(9)-THC, cannabinol (CBN), terpinolene, CBG, myrcene and cis-ocimene in Bedrobinol Delta(9)-THC, CBN and myrcene in Bediol CBD, Delta(9)-THC, CBN, myrcene, CBC and terpinolene. There was no statistically significant difference between CB1 binding of pure Delta(9)-THC compared to cannabis smoke and vapor at an equivalent concentration of Delta(9)-THC.


title: A Survey and Future Vision of Data Mining in Educational Field

abstract: Educational data mining is an emerging trend, concerned with developing techniques for exploring, and analyzing the huge data that come from the educational context. EDM is poised to leverage an enormous amount of research from data mining community and apply that research to educational problems in learning, cognition and assessment. In recent years, Educational data mining has proven to be more successful at many of these educational statistics problems due to enormous computing power and data mining algorithms. This paper surveys the history and applications of data mining techniques in the educational field. The objective is to introduce data mining to traditional educational system, web-based educational system, intelligent tutoring system, and e-learning. This paper describes how to apply the main data mining techniques such as prediction, classification, relationship mining, clustering, and social area networking to educational data.


title: The semantics of space: Integrating linguistic typology and cognitive neuroscience

abstract: Abstract In the cognitive neuroscience literature on the distinction between categorical and coordinate spatial relations, it has often been observed that categorical spatial relations are referred to linguistically by words like English prepositions, many of which specify binary oppositions—e.g., above/below, left/right, on/off, in/out. However, the actual semantic content of English prepositions, and of comparable word classes in other languages, has not been carefully considered. This paper has three aims. The first and most important aim is to inform cognitive neuroscientists interested in spatial representation about relevant research on the kinds of categorical spatial relations that are encoded in the 6000+ languages of the world. Emphasis is placed on cross-linguistic similarities and differences involving deictic relations, topological relations, and projective relations, the last of which are organized around three distinct frames of reference—intrinsic, relative, and absolute. The second aim is to review what is currently known about the neuroanatomical correlates of linguistically encoded categorical spatial relations, with special focus on the left supramarginal and angular gyri, and to suggest ways in which cross-linguistic data can help guide future research in this area of inquiry. The third aim is to explore the interface between language and other mental systems, specifically by summarizing studies which suggest that although linguistic and perceptual/cognitive representations of space are at least partially distinct, language nevertheless has the power to bring about not only modifications of perceptual sensitivities but also adjustments of cognitive styles.


title: Using WordNet to Measure Semantic Orientations of Adjectives

abstract: Current WordNet-based measures of distance or similarity focus almost exclusively on WordNet’s taxonomic relations. This effectively restricts their applicability to the syntactic categories of noun and verb. We investigate a graph-theoretic model of WordNet’s most important relation—synonymy—and propose measures that determine the semantic orientation of adjectives for three factors of subjective meaning. Evaluation against human judgments shows the effectiveness of the resulting measures.


title: PID control of two-stage inverted pendulum

abstract: A Proportional-integral-derivative (PID) control of highly nonlinear and multivariable two stage pendulum is presented. The aim is to settle cart at desired position within 4 seconds while both the pendulums are inclined vertically upwards. Three PID controllers were separately designed for control of each subsystem. Tuning of controller gains were achieved using trial and error approach. A mathematical model of the system was derived which was utilised to construct a simulink. Simulations were performed which elaborated feasibility of proposed approach.


title: MIMO Radar with Widely Separated Antennas

abstract: MIMO (multiple-input multiple-output) radar refers to an architecture that employs multiple, spatially distributed transmitters and receivers. While, in a general sense, MIMO radar can be viewed as a type of multistatic radar, the separate nomenclature suggests unique features that set MIMO radar apart from the multistatic radar literature and that have a close relation to MIMO communications. This article reviews some recent work on MIMO radar with widely separated antennas. Widely separated transmit/receive antennas capture the spatial diversity of the target's radar cross section (RCS). Unique features of MIMO radar are explained and illustrated by examples. It is shown that with noncoherent processing, a target's RCS spatial variations can be exploited to obtain a diversity gain for target detection and for estimation of various parameters, such as angle of arrival and Doppler. For target location, it is shown that coherent processing can provide a resolution far exceeding that supported by the radar's waveform.


title: Multibody Structure and Motion : 3-D Reconstruction of Independently Moving Objects

abstract: This paper extends the recovery of structure and motion to image sequences with several independently moving objects. The motion, structure, and camera calibration are all a-priori unknown. The fundamental constraint that we introduce is that multiple motions must share the same camera parameters. Existing work on independent motions has not employed this constraint, and therefore has not gained over independent static-scene reconstructions. ::: ::: We show how this constraint leads to several new results in structure and motion recovery, where Euclidean reconstruction becomes possible in the multibody case, when it was underconstrained for a static scene. We show how to combine motions of high-relief, low-relief and planar objects. Additionally we show that structure and motion can be recovered from just 4 points in the uncalibrated, fixed camera, case. ::: ::: Experiments on real and synthetic imagery demonstrate the validity of the theory and the improvement in accuracy obtained using multibody analysis.


title: Compressive light field photography using overcomplete dictionaries and optimized projections

abstract: Light field photography has gained a significant research interest in the last two decades; today, commercial light field cameras are widely available. Nevertheless, most existing acquisition approaches either multiplex a low-resolution light field into a single 2D sensor image or require multiple photographs to be taken for acquiring a high-resolution light field. We propose a compressive light field camera architecture that allows for higher-resolution light fields to be recovered than previously possible from a single image. The proposed architecture comprises three key components: light field atoms as a sparse representation of natural light fields, an optical design that allows for capturing optimized 2D light field projections, and robust sparse reconstruction methods to recover a 4D light field from a single coded 2D projection. In addition, we demonstrate a variety of other applications for light field atoms and sparse coding, including 4D light field compression and denoising.


title: Speech Emotion Recognition Using Deep Neural Network and Extreme Learning Machine

abstract: Speech emotion recognition is a challenging problem partly because it is unclear what features are effective for the task. In this paper we propose to utilize deep neural networks (DNNs) to extract high level features from raw data and show that they are effective for speech emotion recognition. We first produce an emotion state probability distribution for each speech segment using DNNs. We then construct utterance-level features from segment-level probability distributions. These utterancelevel features are then fed into an extreme learning machine (ELM), a special simple and efficient single-hidden-layer neural network, to identify utterance-level emotions. The experimental results demonstrate that the proposed approach effectively learns emotional information from low-level features and leads to 20% relative accuracy improvement compared to the stateof-the-art approaches. Index Terms: Emotion recognition, Deep neural networks, Extreme learning machine


title: Hardware software partitioning using genetic algorithm

abstract: Hardware software co-design is gaining importance with the advent of CAD for embedded systems. A key phase in such designs is partitioning the specification into hardware and software implementation sets. The problem being combinatorically explosive, several greedy search algorithms have been proposed for hardware software partitioning. In this paper, we model the hardware software partitioning problem as a Constraint Satisfaction Problem (CSP), and present a genetic algorithm based approach to solve the CSP in order to obtain the partitioning solution.


title: Efficient interactive fuzzy keyword search

abstract: Traditional information systems return answers after a user submits a complete query. Users often feel "left in the dark" when they have limited knowledge about the underlying data, and have to use a try-and-see approach for finding information. A recent trend of supporting autocomplete in these systems is a first step towards solving this problem. In this paper, we study a new information-access paradigm, called "interactive, fuzzy search," in which the system searches the underlying data "on the fly" as the user types in query keywords. It extends autocomplete interfaces by (1) allowing keywords to appear in multiple attributes (in an arbitrary order) of the underlying data; and (2) finding relevant records that have keywords matching query keywords approximately. This framework allows users to explore data as they type, even in the presence of minor errors. We study research challenges in this framework for large amounts of data. Since each keystroke of the user could invoke a query on the backend, we need efficient algorithms to process each query within milliseconds. We develop various incremental-search algorithms using previously computed and cached results in order to achieve an interactive speed. We have deployed several real prototypes using these techniques. One of them has been deployed to support interactive search on the UC Irvine people directory, which has been used regularly and well received by users due to its friendly interface and high efficiency.


title: A novel approach to collaborative testing in a crowdsourcing environment

abstract: Software testing processes are generally labor-intensive and often involve substantial collaboration among testers, developers, and even users. However, considerable human resource capacity exists on the Internet in social networks, expert communities, or internet forums-referred to as crowds. Effectively using crowd resources to support collaborative testing is an interesting and challenging topic. This paper defines the collaborative testing problem in a crowd environment as an NP-Complete job assignment problem and formulates it as an integer linear programming (ILP) problem. Although package tools can be used to obtain the optimal solution to an ILP problem, computational complexity makes these tools unsuitable for solving large-scale problems. This study uses a greedy approach with four heuristic strategies to solve the problem. This is called the crowdsourcing-based collaborative testing approach. This approach includes two phases, training phase and testing phase. The training phase transforms the original problem into an ILP problem. The testing phase solves the ILP using heuristic strategies. A prototype system, called the Collaborative Testing System (COTS), is also implemented. The experiment results show that the proposed heuristic algorithms produce good quality approximate solutions in an acceptable timeframe.


title: Dysfunctional Attitudes and Perfectionism and Their Relationship to Anxious and Depressive Symptoms in Boys with Autism Spectrum Disorders.

abstract: In spite of increasing interest in cognitive behaviour therapy for emotional disorders in children with autism spectrum disorders (ASD), little research has explored the relevance of the cognitive model in this population. This study explores dysfunctional attitudes and perfectionism in boys with ASD and the relationship with anxious and depressive symptoms. Compared to a typically developing group (n = 42), boys with ASD (n = 41) endorsed more dysfunctional attitudes and reported higher emotional symptoms. The relationship between emotional and cognitive variables was weak in both groups, although in the ASD group dysfunctional attitudes were significantly associated with reported obsessive–compulsive symptoms. Reasons for elevated dysfunctional attitudes in the ASD group are discussed and the roles of cognitive inflexibility and social impairments are explored.


title: Surge capability of 1.2kV SiC diodes with high-temperature implantation

abstract: This paper presents a high-temperature implanted 4H-SiC JBS diode with improved surge capability. The fabrication of the P+ region is implemented with 500 °C implantation. It was found that Ti can form ohmic contact on high temperature implanted P+ region without any additional annealing. This could simplify the ohmic contact process for MPS fabrication. In this work, a wide transition P+ region between cell and termination is designed, which can alleviate snapback phenomenon and improve the surge capability.


title: High-impedance electromagnetic surfaces with a forbidden frequency band

abstract: A new type of metallic electromagnetic structure has been developed that is characterized by having high surface impedance. Although it is made of continuous metal, and conducts dc currents, it does not conduct ac currents within a forbidden frequency band. Unlike normal conductors, this new surface does not support propagating surface waves, and its image currents are not phase reversed. The geometry is analogous to a corrugated metal surface in which the corrugations have been folded up into lumped-circuit elements, and distributed in a two-dimensional lattice. The surface can be described using solid-state band theory concepts, even though the periodicity is much less than the free-space wavelength. This unique material is applicable to a variety of electromagnetic problems, including new kinds of low-profile antennas.


title: Enhancing citation recommendation with various evidences

abstract: With the tremendous amount of citations available in digital library, how to suggest citations automatically, to meet the information needs of researchers has become an important problem. In this paper, we propose a model which treats citation recommendation as a special retrieval task to address this challenge. First, users provide a target paper with some metadata to our system. Second, the system retrieves a relevant candidate citation set. Then the candidate citations are reranked by well-chosen citation evidence, such as publication time preference, self-citation preference, co-citation preference and publication reputation preference. Especially, various measures are introduced to integrate the evidence. We experimented with the proposed model on an established bibliographic corpus-ACL Anthology Network, the results show that the model is valuable in practice, and citation recommendation can be significantly improved using proposed evidences.


title: Family caregivers’ attributions about care-recipient behaviour: Does caregiver relationship satisfaction mediate the attribution-distress relationship?

abstract: Objectives. The relations of caregiver attributions about care-recipient's problem behaviour to caregiving relationship satisfaction and caregiver distress were examined. ::: ::: ::: ::: Design. This is a cross sectional study. Seventy-five family caregivers of individuals diagnosed with various disabling health conditions were recruited and interviewed. Caregiver attributions (internality, intentionality, responsibility, and controllability), caregiving relationship satisfaction, and caregiver distress variables were measured. ::: ::: ::: ::: Results. Structural equation techniques tested an a priori model of the latent constructs of caregiver attributions and caregiver relationship satisfaction to caregiver distress. Maladaptive caregiver attributions (i.e., more trait, higher intentionality, higher responsibility, and higher controllability) about care-recipients’ problem behaviours predicted lower caregiving relationship satisfaction, which in turn was predictive of higher caregiver distress. Unexpectedly, caregiver attributions were not directly related to caregiver distress. However, attributions had an indirect effect on distress through relationship satisfaction. Younger caregivers experienced higher caregiver distress. ::: ::: ::: ::: Conclusions. Caregivers’ explanations about care-recipient's problem behaviour are indicative of their satisfaction in the relationship with the care recipient, and poor caregiving relationship satisfaction is predictive of caregiver distress. Caregiver attributions and relationship quality may be considered in interventions with family caregivers.


title: Ab-Initio Study on the Magnetic Structures in the Ordered Mn $_{3}$Pt Alloy

abstract: We study the electronic states of the magnetically ordered Mn3Pt alloy within the density functional theory. Mn3Pt has been believed that one third of Mn atoms have no magnetic moment in an antiferromagnetic phase (so-called the F-phase) realized in the temperature range of 400 K <T<475 K. We show that this experimentally suggested spin configuration is energetically so much unfavorable that it would be irrelevant to the F-phase. We discuss the possibility that the spin moments on the one third of Mn atoms are not paramagnetic but thermally fluctuating in the F-phase. The present results have an immediate connection with the recent neutron scattering study [T. Ikeda and Y. Tsunoda, J. Phys. Soc. Jpn., vol. 72, pp. 2614-2621, October 2003].


title: Automatic glaucoma detection using adaptive threshold based technique in fundus image

abstract: Glaucoma is a kind of ocular disorder that results in a damaged optic nerve which is responsible for transmitting images to the brain. The conventional methods to detect glaucoma like Optical Coherence Tomography (OCT) and Heidelberg Retinal Tomography (HRT) are expensive and need specialized manpower. A digital fundus image can be used to identify glaucoma. This paper describes an efficient method to analyze a computer-aided fundus image which can act as a diagnostic tool for detection of glaucoma. A technique based on histogram of the image is used to study some statistical features of the image such as mean and standard deviation. A relationship between them is established to find a threshold value for segmenting optic disc and optic cup. An adaptive threshold based method which is independent of image quality and invariant to noise is used to segment the optic disc, optic cup and the cup-to-disc ratio CDR which is used to screen glaucoma. The experimental results obtained are compared with those of the ophthalmologist and are found to have a high accuracy. Also in addition the proposed method is efficient having a low computational cost.


Data Warehouse Life-Cycle and Design


title: Unwanted Transgenes Re-Discovered in Oaxacan Maize

abstract: An apparatus (10) for tensioning belt webbing (14) around an occupant of a vehicle to restrain movement of the occupant includes a rotary trochoidal motor (40). The motor (40) includes a housing (44) having a wall with an inner surface (46) defining a chamber (48). A rotor (42) is rotatable in the chamber and has fluid engagement surfaces (50, 52, 54). An output shaft (66) is supported for rotation relative to the housing (44). A fluid source (80) is actuatable to supply fluid under pressure into the chamber (48) and against the fluid engagement surfaces (50, 52, 54) of the rotor (42) to rotate the rotor in a first direction of rotation in the chamber to thereby rotate the output shaft (66). A belt webbing engagement member (22) is engageable with the belt webbing (14) to tension the belt webbing. The output shaft (66) is interconnected with the belt webbing engagement member (22) to transmit force from the rotating output shaft to the belt webbing engagement member to tension the belt webbing (14).


title: Controllability and Posture Control of a Wheeled Pendulum Moving on an Inclined Plane

abstract: The paper considers a specific class of wheeled mobile robots, namely, mobile wheeled pendulums (MWPs). Robots pertaining to this class are composed of two wheels rotating about a central body. The main feature of MWP pertains to the central body, which can rotate about the wheel axis. As such motion is undesirable, the problem of the stabilization of the central body in an MWP is crucial. The novelty of the work reported here resides in the construction of: 1) the system controllability Lie algebra for the purpose of a rigorous controllability analysis and the computation of the largest feedback-linearizable subsystem; 2) a controller by input-output linearization of the system for achieving the desired steering rate of the robot while stabilizing the central body; 3) a controller based on the internal properties of the system to achieve the desired heading velocity of the robot; and 4) a controller based on the sliding-mode approach for controlling both the position and the orientation of the robot. The entire control structure that permits full control of the robot posture comprises three imbricated loops. Simulations showing good performance of the controlled system are provided. Preliminary tests performed on an experimental platform confirm the validity of the controller.


title: SCAN: Schedules fonr Clinical Assessment in Neuropsychiatry

abstract: • After more than 12 years of development, the ninth edition of the Present State Examination (PSE-9) was published, together with associated instruments and computer algorithm, in 1974. The system has now been expanded, in the framework of the World Health Organization/Alcohol, Drug Abuse, and Mental Health Administration Joint Project on Standardization of Diagnosis and Classification, and is being tested with the aim of developing a comprehensive procedure for clinical examination that is also capable of generating many of the categories of theInternational Classification of Diseases, 10th edition, and theDiagnostic and Statistical Manual of Mental Disorders, revised third edition. The new system is known as SCAN (Schedules for Clinical Assessment in Neuropsychiatry). It includes the 10th edition of the PSE as one of its core schedules, preliminary tests of which have suggested that reliability is similar to that of PSE-9. SCAN is being field tested in 20 centers in 11 countries. A final version is expected to be available in January 1990.


title: Optimizing Quantum Circuits for Arithmetic

abstract: Many quantum algorithms make use of oracles which evaluate classical functions on a superposition of inputs. In order to facilitate implementation, testing, and resource estimation of such algorithms, we present quantum circuits for evaluating functions that are often encountered in the quantum algorithm literature. This includes Gaussians, hyperbolic tangent, sine/cosine, inverse square root, arcsine, and exponentials. We use insights from classical high-performance computing in order to optimize our circuits and implement a quantum software stack module which allows to automatically generate circuits for evaluating piecewise smooth functions in the computational basis. Our circuits enable more detailed cost analyses of various quantum algorithms, allowing to identify concrete applications of future quantum computing devices. Furthermore, our resource estimates may guide future research aiming to reduce the costs or even the need for arithmetic in the computational basis altogether.


title: Compositional Vector Space Models for Knowledge Base Inference.

abstract: Traditional approaches to knowledge base completion have been based on symbolic representations. Lowdimensional vector embedding models proposed recently for this task are attractive since they generalize to possibly unlimited sets of relations. A significant drawback of previous embedding models for KB completion is that they merely support reasoning on individual relations (e.g., bornIn(X, Y ) ⇒ nationality(X, Y )). In this work, we develop models for KB completion that support chains of reasoning on paths of any length using compositional vector space models. We construct compositional vector representations for the paths in the KB graph from the semantic vector representations of the binary relations in that path and perform inference directly in the vector space. Unlike previous methods, our approach can generalize to paths that are unseen in training and, in a zero-shot setting, predict target relations without supervised training data for that relation.


title: Knowledge Curation and Knowledge Fusion: Challenges, Models and Applications

abstract: Large-scale knowledge repositories are becoming increasingly important as a foundation for enabling a wide variety of complex applications. In turn, building high-quality knowledge repositories critically depends on the technologies of knowledge curation and knowledge fusion, which share many similar goals with data integration, while facing even more challenges in extracting knowledge from both structured and unstructured data, across a large variety of domains, and in multiple languages. Our tutorial highlights the similarities and differences between knowledge management and data integration, and has two goals. First, we introduce the Database community to the techniques proposed for the problems of entity linkage and relation extraction by the Knowledge Management, Natural Language Processing, and Machine Learning communities. Second, we give a detailed survey of the work done by these communities in knowledge fusion, which is critical to discover and clean errors present in sources and the many mistakes made in the process of knowledge extraction from sources. Our tutorial is example driven and hopes to build bridges between the Database community and other disciplines to advance research in this important area.


title: A course-based usability analysis of Cilk Plus and OpenMP

abstract: Cilk Plus and OpenMP are parallel language extensions for the C and C++ programming languages. The CPLEX Study Group of the ISO/IEC C Standards Committee is developing a proposal for a parallel programming extension to C that combines ideas from Cilk Plus and OpenMP. We conducted a preliminary comparison of Cilk Plus and OpenMP in a master's level course on security to evaluate the design tradeoffs in the usability and security of these two approaches. The eventual goal is to inform decision-making within the committee. We found several usability problems worthy of further investigation based on student performance, including declaring and using reductions, multi-line compiler directives, and the understanda-bility of task assignment to threads.


title: A fuzzy linear regression model for interval type-2 fuzzy sets

abstract: This paper presents a regression model for interval type-2 fuzzy sets based on the least squares estimation technique. Unknown coefficients are assumed to be triangular fuzzy numbers. The basic idea is to determine aggregation intervals for type-1 fuzzy sets, membership functions of whose are low membership function and upper membership function of interval type-2 fuzzy set. These aggregation intervals were called weighted intervals. Low and upper membership functions of input and output interval type-2 fuzzy sets for developed regression models are considered as piecewise linear functions.


title: CROWDSAFE: crowd sourcing of crime incidents and safe routing on mobile devices

abstract: Crowd sourcing is based on a simple but powerful concept: Virtually anyone has the potential to plug in valuable information. The concept revolves around large groups of people or community handling tasks that have traditionally been associated with a specialist or small group of experts. With the advent of the smart devices, many mobile applications are already tapping into crowd sourcing to report community issues and traffic problems, but more can be done. While most of these applications work well for the average user, it neglects the information needs of particular user communities. We present CROWDSAFE, a novel convergence of Internet crowd sourcing and portable smart devices to enable real time, location based crime incident searching and reporting. It is targeted to users who are interested in crime information. The system leverages crowd sourced data to provide novel features such as a Safety Router and value added crime analytics. We demonstrate the system by using crime data in the metropolitan Washington DC area to show the effectiveness of our approach. Also highlighted is its ability to facilitate greater collaboration between citizens and civic authorities. Such collaboration shall foster greater innovation to turn crime data analysis into smarter and safe decisions for the public.


title: Design of Microstrip Patch Antenna Using Slotted Partial Ground And Addition Of Stairs And Stubs For UWB Application

abstract: This paper presents the design with optimum geometry of a novel UWB rectangular patch antenna. A simple narrowband patch antenna is designed before proceeding to the ::: geometry of UWB antenna. A special configuration of patch ::: antenna with slotted partial ground and addition of stairs and stubs was designed and optimized using CST Microwave Studio (CSTMWS). The designed antenna was fabricated, tested and compared with the simulation results. The proposed antenna’s characteristics were investigated with various options and found to operate satisfactorily. A remarkable improvement has been noticed in this design. Moreover, the antennas structure offers great advantages due to its simple designs and small dimensions.


title: Discovery of Meaningful Rules in Time Series

abstract: The ability to make predictions about future events is at the heart of much of science; so, it is not surprising that prediction has been a topic of great interest in the data mining community for the last decade. Most of the previous work has attempted to predict the future based on the current value of a stream. However, for many problems the actual values are irrelevant, whereas the shape of the current time series pattern may foretell the future. The handful of research efforts that consider this variant of the problem have met with limited success. In particular, it is now understood that most of these efforts allow the discovery of spurious rules. We believe the reason why rule discovery in real-valued time series has failed thus far is because most efforts have more or less indiscriminately applied the ideas of symbolic stream rule discovery to real-valued rule discovery. In this work, we show why these ideas are not directly suitable for rule discovery in time series. Beyond our novel definitions/representations, which allow for meaningful and extendable specifications of rules, we further show novel algorithms that allow us to quickly discover high quality rules in very large datasets that accurately predict the occurrence of future events.


title: Text Classification Using Support Vector Machine with Mixture of Kernel

abstract: Recent studies have revealed that emerging modern machine learning techniques are advantageous to statistical models for text classification, such as SVM. In this study, we discuss the applications of the support vector machine with mixture of kernel (SVM-MK) to design a text classification system. Differing from the standard SVM, the SVM-MK uses the 1-norm based object function and adopts the convex combinations of single feature basic kernels. Only a linear programming problem needs to be resolved and it greatly reduces the computational costs. More important, it is a transparent model and the optimal feature subset can be obtained automatically. A real Chinese corpus from FudanUniversityis used to demonstrate the good performance of the SVM- MK.


title: Uncertainty Analysis of Monthly Streamflow Forecasting

abstract: Streamflow forecasting is an important factor in water resources planning and management. In this study Feed Forward Artificial Neural Network (FFANN) was used for monthly streamflow forecasting. Three scenarios were considered for modeling. Principal Component Analysis (PCA) is used for reducing the model architecture complexity and input data reduction. Twelve statistical criteria were used to evaluate the model performance. Also for quantifying the accuracy of forecast, uncertainty analysis was conducted using Monte Carlo simulation. Results indicated that the model in general is capable to forecast monthly streamflow time series satisfactorily. However the model is underestimated in extreme values. Also, uncertainty analysis shows that the model forecasted monthly streamflow time series properly in the first two scenarios while in the third scenario most of the forecasted values lie out of the upper confidence interval.


title: Comparison of Collision Avoidance Systems and Applicability to Rail Transport

abstract: The paper presents an overview of the state of the art in collision avoidance related with transportation systems like the automatic identification system (AIS) for maritime transportation, traffic alert and collision avoidance system/automatic dependent surveillance-broadcast (TCAS/ADS-B) for aircraft, and the car-2-car communication system (C2C) for road transportation. The examined systems rely on position detection and direct communication among vehicles. Alike a collision avoidance system for railway transportation "RCAS" is introduced. Focussing on the communication aspects, possible applicability of the examined state of the art systems to RCAS is studied. The analysis are performed at different communication system layers, namely application (APP) layer, media access control (MAC) layer and physical layer (PHY), which are the most relevant for a single hop network broadcast system as favorized in RCAS. Since multihop and addressed communication are not foreseen in a first RCAS approach, the network layer is not taken into account.


title: Correlations between membrane immersion depth, orientation, and salt-resistance of tryptophan-rich antimicrobial peptides

abstract: Abstract The efficacies of many antimicrobial peptides are greatly reduced in the presence of high salt concentrations therefore limiting their development as pharmaceutical compounds. PEM-2-W5K/A9W, a short Trp-rich antimicrobial peptide developed based on the structural studies of PEM-2, has been shown to be highly active against various bacterial strains with less hemolytic activity. Here, correlations between membrane immersion depth, orientation, and salt-resistance of PEM-2 and PEM-2-W5K/A9W have been investigated via solution structure and paramagnetic resonance enhancement studies. The antimicrobial activities of PEM-2-W5K/A9W and PEM-2 against various bacterial and fungal strains including multidrug-resistant and clinical isolates under high salt conditions were tested. The activities of the salt-sensitive peptide PEM-2 were reduced and diminished at high salt concentrations, whereas the activities of PEM-2-W5K/A9W were less affected. The results indicated that the strong salt-resistance of PEM-2-W5K/A9W may arise from the peptide positioning itself deeply into microbial cell membranes and thus able to disrupt the membranes more efficiently.


title: VUDDY: A Scalable Approach for Vulnerable Code Clone Discovery

abstract: The ecosystem of open source software (OSS) has been growing considerably in size. In addition, code clones - code fragments that are copied and pasted within or between software systems - are also proliferating. Although code cloning may expedite the process of software development, it often critically affects the security of software because vulnerabilities and bugs can easily be propagated through code clones. These vulnerable code clones are increasing in conjunction with the growth of OSS, potentially contaminating many systems. Although researchers have attempted to detect code clones for decades, most of these attempts fail to scale to the size of the ever-growing OSS code base. The lack of scalability prevents software developers from readily managing code clones and associated vulnerabilities. Moreover, most existing clone detection techniques focus overly on merely detecting clones and this impairs their ability to accurately find "vulnerable" clones. In this paper, we propose VUDDY, an approach for the scalable detection of vulnerable code clones, which is capable of detecting security vulnerabilities in large software programs efficiently and accurately. Its extreme scalability is achieved by leveraging function-level granularity and a length-filtering technique that reduces the number of signature comparisons. This efficient design enables VUDDY to preprocess a billion lines of code in 14 hour and 17 minutes, after which it requires a few seconds to identify code clones. In addition, we designed a security-aware abstraction technique that renders VUDDY resilient to common modifications in cloned code, while preserving the vulnerable conditions even after the abstraction is applied. This extends the scope of VUDDY to identifying variants of known vulnerabilities, with high accuracy. In this study, we describe its principles and evaluate its efficacy and effectiveness by comparing it with existing mechanisms and presenting the vulnerabilities it detected. VUDDY outperformed four state-of-the-art code clone detection techniques in terms of both scalability and accuracy, and proved its effectiveness by detecting zero-day vulnerabilities in widely used software systems, such as Apache HTTPD and Ubuntu OS Distribution.


title: Is Adolescence a Sensitive Period for Sociocultural Processing

abstract: Adolescence is a period of formative biological and social transition. Social cognitive processes involved in navigating increasingly complex and intimate relationships continue to develop throughout adolescence. Here, we describe the functional and structural changes occurring in the brain during this period of life and how they relate to navigating the social environment. Areas of the social brain undergo both structural changes and functional reorganization during the second decade of life, possibly reflecting a sensitive period for adapting to one's social environment. The changes in social environment that occur during adolescence might interact with increasing executive functions and heightened social sensitivity to influence a number of adolescent behaviors. We discuss the importance of considering the social environment and social rewards in research on adolescent cognition and behavior. Finally, we speculate about the potential implications of this research for society.


title: Character-Level neural networks for short text classification

abstract: Since short text is characterized of the short length, sparse features and strong context dependency, the traditional models have a limited precision. Motivated by this, this article offers an empirical exploration on a character-level model which implements a combination of convolutional neural network(CNN) and recurrent neural networks(RNN) for short text classification. Including the highway networks framework so that we can address the difficult of training and improve the accuracy of classification. The evaluations on several datasets showed that our proposed model outperforms the standard CNN and traditional models on short text classification mission.


title: A battery/supercapacitor hybrid energy storage system for DC microgrids

abstract: This paper explores the frequency coordinating virtual impedance concept and proposes a control strategy for the co-ordination control of a hybrid energy storage system in DC microgrids. It investigates power decoupling methods to better attenuate the ripple power by filtration. Lead-acid battery and supercapacitor are used to form a hybrid energy storage system and are connected to a common DC grid through bi-directional DC-DC converters. Battery converter can absorb low frequency power variations while the high frequency power variations can be absorbed by supercapacitor converter. Battery supplies the long term power demand and supercapacitor responds to the short term power fluctuations during transient process in this proposed method. For the reduction of ripple power in the battery, this paper discusses the insertion of high order low/high pass filters for battery and supercapacitor converters instead of lower order low/high pass filters. The effectiveness of the proposed concept is shown through simulation and experimental results.


title: Fake news on Twitter during the 2016 U.S. presidential election

abstract: The spread of fake news on social media became a public concern in the United States after the 2016 presidential election. We examined exposure to and sharing of fake news by registered voters on Twitter and found that engagement with fake news sources was extremely concentrated. Only 1% of individuals accounted for 80% of fake news source exposures, and 0.1% accounted for nearly 80% of fake news sources shared. Individuals most likely to engage with fake news sources were conservative leaning, older, and highly engaged with political news. A cluster of fake news sources shared overlapping audiences on the extreme right, but for people across the political spectrum, most political news exposure still came from mainstream media outlets.


title: Adversarial Distillation for Efficient Recommendation with External Knowledge

abstract: Integrating external knowledge into the recommendation system has attracted increasing attention in both industry and academic communities. Recent methods mostly take the power of neural network for effective knowledge representation to improve the recommendation performance. However, the heavy deep architectures in existing models are usually incorporated in an embedded manner, which may greatly increase the model complexity and lower the runtime efficiency. ::: To simultaneously take the power of deep learning for external knowledge modeling as well as maintaining the model efficiency at test time, we reformulate the problem of recommendation with external knowledge into a generalized distillation framework. The general idea is to free the complex deep architecture into a separate model, which is only used in the training phrase, while abandoned at test time. In particular, in the training phrase, the external knowledge is processed by a comprehensive teacher model to produce valuable information to teach a simple and efficient student model. Once the framework is learned, the teacher model is abandoned, and only the succinct yet enhanced student model is used to make fast predictions at test time. In this article, we specify the external knowledge as user review, and to leverage it in an effective manner, we further extend the traditional generalized distillation framework by designing a Selective Distillation Network (SDNet) with adversarial adaption and orthogonality constraint strategies to make it more robust to noise information. ::: Extensive experiments verify that our model can not only improve the performance of rating prediction, but also can significantly reduce time consumption when making predictions as compared with several state-of-the-art methods.


title: A Taxonomy for Artificial Embryogeny

abstract: A major challenge for evolutionary computation is to evolve phenotypes such as neural networks, sensory systems, or motor controllers at the same level of complexity as found in biological organisms. In order to meet this challenge, many researchers are proposing indirect encodings, that is, evolutionary mechanisms where the same genes are used multiple times in the process of building a phenotype. Such gene reuse allows compact representations of very complex phenotypes. Development is a natural choice for implementing indirect encodings, if only because nature itself uses this very process. Motivated by the development of embryos in nature, we define artificial embryogeny (AE) as the subdiscipline of evolutionary computation (EC) in which phenotypes undergo a developmental phase. An increasing number of AE systems are currently being developed, and a need has arisen for a principled approach to comparing and contrasting, and ultimately building, such systems. Thus, in this paper, we develop a principled taxonomy for AE. This taxonomy provides a unified context for long-term research in AE, so that implementation decisions can be compared and contrasted along known dimensions in the design space of embryogenic systems. It also allows predicting how the settings of various AE parameters affect the capacity to efficiently evolve complex phenotypes.


title: Cubature quadrature Kalman filter

abstract: In this correspondence, the authors develop a novel method based on spherical radial cubature and Gauss-Laguerre quadrature rule for non-linear state estimation problems. The proposed filter, referred as cubature quadrature Kalman filter (CQKF) would be able to overcome inherent disadvantages associated with the earlier reported cubature Kalman filter (CKF). The theory and formulation of CQKF has been presented. Using two well-known non-linear examples, the superior performance of CQKF has been demonstrated. Owing to computational efficiency (compared to the particle and grid-based filter) and enhanced accuracy compared to the extended Kalman filter and the CKF, the developed algorithm may find place in on-board real life applications.


title: Review of Agent-Based Systems in the Manufacturing Section

abstract: The manufacturing sector is considered an important field to the industry. Its products help manufacturers to achieve more control with less labor and more output with less cost. Current technology and especially Artificial Intelligence effectively increase productivity and reduce the cost of operations, which is the key to commercial success. The main representatives of information technology in the industrial applications are the agents. They provide an efficient way to design and implement engineering environments and the agents' technology is recognized as a new approach for the CAD/CAPP/CAM systems. This paper provides a view of the current agent technology in forenamed systems and constitutes an attempt to a) categorize multi-agent systems according to the coordination mode among the agents and b) classify the agents that take action on them, depending on their functions.


title: Sex differences in the human olfactory system

abstract: Abstract The olfactory system (accessory) implicated in reproductive physiology and behavior in mammals is sexually dimorphic. These brain sex differences present two main characteristics: they are seen in neural circuits related to sexual behavior and sexual physiology and they take one of two opposite morphological patterns (male > female or female > male). The present work reports sex differences in the olfactory system in a large homogeneous sample of men (40) and women (51) using of voxel-based morphology. Gray matter concentration showed sexual dimorphism in several olfactory regions. Women have a higher concentration in the orbitofrontal cortex involving Brodmann's areas 10, 11 and 25 and temporomedial cortex (bilateral hippocampus and right amygdala), as well as their left basal insular cortex. In contrast, men show a higher gray matter concentration in the left entorhinal cortex (Brodmann's area 28), right ventral pallidum, dorsal left insular cortex and a region of the orbitofrontal cortex (Brodmann's area 25). This study supports the hypothesis that the mammalian olfactory system is a sexually dimorphic network and provides a theoretical framework for the morphofunctional approach to sex differences in the human brain.


title: Sentiment analysis of short informal texts

abstract: We describe a state-of-the-art sentiment analysis system that detects (a) the sentiment of short informal textual messages such as tweets and SMS (message-level task) and (b) the sentiment of a word or a phrase within a message (term-level task). The system is based on a supervised statistical text classification approach leveraging a variety of surface-form, semantic, and sentiment features. The sentiment features are primarily derived from novel high-coverage tweet-specific sentiment lexicons. These lexicons are automatically generated from tweets with sentiment-word hashtags and from tweets with emoticons. To adequately capture the sentiment of words in negated contexts, a separate sentiment lexicon is generated for negated words. ::: ::: The system ranked first in the SemEval-2013 shared task 'Sentiment Analysis in Twitter' (Task 2), obtaining an F-score of 69.02 in the message-level task and 88.93 in the term-level task. Post-competition improvements boost the performance to an F-score of 70.45 (message-level task) and 89.50 (term-level task). The system also obtains state-of-the-art performance on two additional datasets: the SemEval-2013 SMS test set and a corpus of movie review excerpts. The ablation experiments demonstrate that the use of the automatically generated lexicons results in performance gains of up to 6.5 absolute percentage points.


title: The Fetal Cerebellum: Development and Common Malformations

abstract: The cerebellum undergoes a protracted development, making it particularly vulnerable to a broad spectrum of developmental events. Acquired destructive and hemorrhagic insults may also occur. The main steps of cerebellar development are reviewed. The normal imaging patterns of the cerebellum in prenatal ultrasound and magnetic resonance imaging (MRI) are described with emphasis on the limitations of these modalities. Because of confusion in the literature regarding the terminology used for cerebellar malformations, some terms (agenesis, hypoplasia, dysplasia, and atrophy) are clarified. Three main pathologic settings are considered and the main diagnoses that can be suggested are described: retrocerebellar fluid enlargement with normal or abnormal biometry (Dandy-Walker malformation, Blake pouch cyst, vermian agenesis), partially or globally decreased cerebellar biometry (cerebellar hypoplasia, agenesis, rhombencephalosynapsis, ischemic and/or hemorrhagic damage), partially or globally abnormal cerebellar echogenicity (ischemic and/or hemorrhagic damage, cerebellar dysplasia, capillary telangiectasia). The appropriate timing for performing MRI is also discussed.


title: Efficient railway tracks detection and turnouts recognition method using HOG features

abstract: Railway tracks detection and turnouts recognition are the basic tasks in driver assistance systems, which can determine the interesting regions for detecting obstacles and signals. In this paper, a novel railway tracks detection and turnouts recognition method using HOG (Histogram of Oriented Gradients) features was presented. At first, the approach computes HOG features and establishes integral images, and then extracts railway tracks by region-growing algorithm. Then based on recognizing the open direction of the turnout, we find the path where the train will travel through. Experiments demonstrated that our method was able to correctly extract tracks and recognize turnouts even in very bad illumination conditions and run fast enough for practical use. In addition, our approach only needs a computer and a cheap camera installed in the railroad vehicle, not specialized hardwares and equipment.


title: Flip Chipped INGaAs Photodiode Arrays for Gated Imaging with Eye-Safe Lasers

abstract: An InGaAs photodiode based short wave infrared camera with an actively Q-switched Er:glass laser was used for gated imaging of objects at approximately 500 m at night.


title: Image processing based proposed drone for detecting and controlling street crimes

abstract: Drone technology is being used for military, agriculture, aerial photography, surveillance, remote sensing and many more purposes. In this paper, drone plane is proposed for monitoring and targeting the street crime criminals based on real time image processing techniques. Operations of proposed plane controlled with two processing units, 1st processing unit is for implementation of real time image processing techniques and 2nd processing unit will handle the rest of controlling, monitoring and targeting operations. Drone plane will monitor circular area of 5 kilometers and it will automatically perform all operations and can be controlled by operator. Shape detection algorithms have been tested to find accuracy in target detection and analysis the processing time before implementing in such environment and results provide optimal accuracy in matching weapons type with name and shape in predefined database.


Academic-Industrial Perspective on the Development and Deployment of a Moderation System for a Newspaper Website.


title: Accuracy and Consensus in Judgments of Trustworthiness From Faces: Behavioral and Neural Correlates

abstract: Perceivers’ inferences about individuals based on their faces often show high interrater consensus and can even accurately predict behavior in some domains. Here we investigated the consensus and accuracy of judgments of trustworthiness. In Study 1, we showed that the type of photo judged makes a significant difference for whether an individual is judged as trustworthy. In Study 2, we found that inferences of trustworthiness made from the faces of corporate criminals did not differ from inferences made from the faces of noncriminal executives. In Study 3, we found that judgments of trustworthiness did not differ between the faces of military criminals and the faces of military heroes. In Study 4, we tempted undergraduates to cheat on a test. Although we found that judgments of intelligence from the students’ faces were related to students’ scores on the test and that judgments of students’ extraversion were correlated with self-reported extraversion, there was no relationship between judgments of trustworthiness from the students’ faces and students’ cheating behavior. Finally, in Study 5, we examined the neural correlates of the accuracy of judgments of trustworthiness from faces. Replicating previous research, we found that perceptions of trustworthiness from the faces in Study 4 corresponded to participants’ amygdala response. However, we found no relationship between the amygdala response and the targets’ actual cheating behavior. These data suggest that judgments of trustworthiness may not be accurate but, rather, reflect subjective impressions for which people show high agreement.


title: Wrist-Powered Partial Hand Prosthesis Using a Continuum Whiffle Tree Mechanism: A Case Study

abstract: Among the advances in upper extremity prostheses in the past decades, only a small portion of the results were obtained for partial hand prostheses, possibly due to the highly diverse partial hand presentations and limited space for component integration. In an attempt to address these challenges, this paper presents the design, construction, installation, and experimental characterization of a wrist-powered partial hand prosthesis developed in Shanghai Jiao Tong University (hereafter referred to as the JTP hand), customized for a specific amputee. The JTP hand possesses: 1) a continuum whiffle tree mechanism to allow adaptive grasping; 2) a force-magnifying partial gear pair to enhance the power of the grip; and 3) a phalange-embedded disengageable ratchet to enable or disable backdrivability. Various grasps and gestures were formed using the JTP hand. The obtained results suggest that the proposed design might be a viable option for patients with transmetacarpal amputation.


title: Incremental Parsing With The Perceptron Algorithm

abstract: This paper describes an incremental parsing approach where parameters are estimated using a variant of the perceptron algorithm. A beam-search algorithm is used during both training and decoding phases of the method. The perceptron approach was implemented with the same feature set as that of an existing generative model (Roark, 2001a), and experimental results show that it gives competitive performance to the generative model on parsing the Penn treebank. We demonstrate that training a perceptron model to combine with the generative model during search provides a 2.1 percent F-measure improvement over the generative model alone, to 88.8 percent.


title: Deep Convolutional Neural Network Textual Features and Multiple Kernel Learning for Utterance-level Multimodal Sentiment Analysis

abstract: We present a novel way of extracting features from short texts, based on the activation values of an inner layer of a deep convolutional neural network. We use the extracted features in multimodal sentiment analysis of short video clips representing one sentence each. We use the combined feature vectors of textual, visual, and audio modalities to train a classifier based on multiple kernel learning, which is known to be good at heterogeneous data. We obtain 14% performance improvement over the state of the art and present a parallelizable decision-level data fusion method, which is much faster, though slightly less accurate.


title: Replicable Types and Subtypes of Personality : German NEO-PIR versus NEO-FFI

abstract: In two German samples we investigated whether three major personality prototypes (resilient, overcontrolled, and undercontrolled) can be replicated across different Big Five instruments (NEO-FFI and NEO-PI-R), and whether they can be differentiated into replicable subtypes. For prototypes and subtypes, replicability within and consistency across samples was quantitatively measured. Three replicable prototypes were confirmed, but only two replicable resilient subtypes (well adjusted and assertive) were found. It is concluded that the search for a hierarchical taxonomy of people will require more investigation. Copyright # 2002 John Wiley & Sons, Ltd.


title: Trust as a Process A Three-Dimensional Approach

abstract: What is trust? Should trust be used as a variable or as a process? Is trust in people similar in nature to trust in institutions? These are three main questions I address in this article. I argue that trust is a complex and multi-dimensional phenomenon, which consists of a mix of trust in strong ties, weak ties, and institutions. I explain the need for a new approach to trust, using the Soviet Union as an example. I argue that rigid distinctions in social capital theory between high-trust and low-trust societies fail to account for the complexity of trust. In this article, I view trust as a process. I also suggest a new definition of trust based on the notion of agency, which underscores the idea of temporality and incorporates the roles of the past, present, and future.


Hybrid Particle Swarm Optimization-Firefly algorithm (HPSOFF) for combinatorial optimization of non-slicing VLSI floorplanning


title: DCNN and DNN based multi-modal depression recognition

abstract: In this paper, we propose an audio visual multimodal depression recognition framework composed of deep convolutional neural network (DCNN) and deep neural network (DNN) models. For each modality, corresponding feature descriptors are input into a DCNN to learn high-level global features with compact dynamic information, which are then fed into a DNN to predict the PHQ-8 score. For multi-modal depression recognition, the predicted PHQ-8 scores from each modality are integrated in a DNN for the final prediction. In addition, we propose the Histogram of Displacement Range as a novel global visual descriptor to quantify the range and speed of the facial landmarks' displacements. Experiments have been carried out on the Distress Analysis Interview Corpus-Wizard of Oz (DAIC-WOZ) dataset for the Depression Sub-challenge of the Audio-Visual Emotion Challenge (AVEC 2016), results show that the proposed multi-modal depression recognition framework obtains very promising results on both the development set and test set, which outperforms the state-of-the-art results.