title: vTPM: Virtualizing the trusted platform module

abstract: We present the design and implementation of a system that enables trusted computing for an unlimited number of virtual machines on a single hardware platform. To this end, we virtualized the Trusted Platform Module (TPM). As a result, the TPM's secure storage and cryptographic functions are available to operating systems and applications running in virtual machines. Our new facility supports higher-level services for establishing trust in virtualized environments, for example remote attestation of software integrity. ::: ::: We implemented the full TPM specification in software and added functions to create and destroy virtual TPM instances. We integrated our software TPM into a hypervisor environment to make TPM functions available to virtual machines. Our virtual TPM supports suspend and resume operations, as well as migration of a virtual TPM instance with its respective virtual machine across platforms. We present four designs for certificate chains to link the virtual TPM to a hardware TPM, with security vs. efficiency trade-offs based on threat models. Finally, we demonstrate a working system by layering an existing integrity measurement application on top of our virtual TPM facility.


Automated Historical Fact-Checking by Passage Retrieval, Word Statistics, and Virtual Question-Answering.


title: Cloning and characterisation of a cysteine proteinase gene expressed in amastigotes of Leishmania (L.) amazonensis

abstract: The present study describes the cloning and characterisation of a gene encoding a cysteine proteinase isoform, Llacys1, expressed in amastigote forms of Leishmania (L.) amazonensis. Recombinant clones containing the Llacys1 gene were isolated from genomic DNA by PCR amplification and screening of an amastigote cDNA library. Sequence analysis of the Llacys1 gene showed a high identity to sequence of Leishmania (L.) pifanoi Lpcys1, Leishmania (L.) major cpa, Leishmania (L.) mexicana LCPa, and Leishmania (L.) chagasi Ldccys2. The Llacys1 gene is present in a single copy per L. (L.) amazonensis haploid genome and was mapped on a chromosome of approximately 700 kb. Two transcripts of the Llacys1 gene were identified, one of 2.4 kb transcribed in both forms of L. (L.) amazonensis, and another of 1.6 kb weakly expressed in amastigotes. Related forms of Llacys1 gene exist in other species of Leishmania genus, including L. (L.) major, L. (L.) mexicana, L. (L.) chagasi and Leishmania (V.) braziliensis. The Llacys1 expression in Escherichia coli was obtained when the nucleotide sequence corresponding to the signal sequence was deleted, suggesting that this signal sequence was recognised by Escherichia coli and cleaved, generating a truncated protein.


title: Effectiveness and Efficiency of Open Relation Extraction

abstract: A large number of Open Relation Extraction approaches have been proposed recently, covering a wide range of NLP machinery, from “shallow” (e.g., part-of-speech tagging) to “deep” (e.g., semantic role labeling‐SRL). A natural question then is what is the tradeoff between NLP depth (and associated computational cost) versus effectiveness. This paper presents a fair and objective experimental comparison of 8 state-of-the-art approaches over 5 different datasets, and sheds some light on the issue. The paper also describes a novel method, EXEMPLAR, which adapts ideas from SRL to less costly NLP machinery, resulting in substantial gains both in efficiency and effectiveness, over binary and n-ary relation extraction tasks.


title: 2.5d dual contouring: A robust approach to creating building models from aerial lidar point clouds

abstract: We present a robust approach to creating 2.5D building models from aerial LiDAR point clouds. The method is guaranteed to produce crack-free models composed of complex roofs and vertical walls connecting them. By extending classic dual contouring into a 2.5D method, we achieve a simultaneous optimization over the three dimensional surfaces and the two dimensional boundaries of roof layers. Thus, our method can generate building models with arbitrarily shaped roofs while keeping the verticality of connecting walls. An adaptive grid is introduced to simplify model geometry in an accurate manner. Sharp features are detected and preserved by a novel and efficient algorithm.


title: Minimization of Transformed L_1 Penalty: Theory, Difference of Convex Function Algorithm, and Robust Application in Compressed Sensing

abstract: We study the minimization problem of a non-convex sparsity promoting penalty function, the transformed \(l_1\) (TL1), and its application in compressed sensing (CS). The TL1 penalty interpolates \(l_0\) and \(l_1\) norms through a nonnegative parameter \(a \in (0,+\infty )\), similar to \(l_p\) with \(p \in (0,1]\), and is known to satisfy unbiasedness, sparsity and Lipschitz continuity properties. We first consider the constrained minimization problem, and discuss the exact recovery of \(l_0\) norm minimal solution based on the null space property (NSP). We then prove the stable recovery of \(l_0\) norm minimal solution if the sensing matrix A satisfies a restricted isometry property (RIP). We formulated a normalized problem to overcome the lack of scaling property of the TL1 penalty function. For a general sensing matrix A, we show that the support set of a local minimizer corresponds to linearly independent columns of A. Next, we present difference of convex algorithms for TL1 (DCATL1) in computing TL1-regularized constrained and unconstrained problems in CS. The DCATL1 algorithm involves outer and inner loops of iterations, one time matrix inversion, repeated shrinkage operations and matrix-vector multiplications. The inner loop concerns an \(l_1\) minimization problem on which we employ the Alternating Direction Method of Multipliers. For the unconstrained problem, we prove convergence of DCATL1 to a stationary point satisfying the first order optimality condition. In numerical experiments, we identify the optimal value \(a=1\), and compare DCATL1 with other CS algorithms on two classes of sensing matrices: Gaussian random matrices and over-sampled discrete cosine transform matrices (DCT). Among existing algorithms, the iterated reweighted least squares method based on \(l_{1/2}\) norm is the best in sparse recovery for Gaussian matrices, and the DCA algorithm based on \(l_1\) minus \(l_2\) penalty is the best for over-sampled DCT matrices. We find that for both classes of sensing matrices, the performance of DCATL1 algorithm (initiated with \(l_1\) minimization) always ranks near the top (if not the top), and is the most robust choice insensitive to the conditioning of the sensing matrix A. DCATL1 is also competitive in comparison with DCA on other non-convex penalty functions commonly used in statistics with two hyperparameters.


title: The differences between overground and treadmill walking in nonlinear, entropy-based and frequency variables derived from accelerometers in young and older women : preliminary report

abstract: PURPOSE ::: The aim of this study was to compare gait stability and variability between walking conditions and age groups. ::: ::: ::: METHODS ::: Twenty-six healthy younger and older females participated. Trunk acceleration in the vertical (V), medial-lateral (ML) and anteriorposterior (AP) directions during 5 minutes walking overground and 3 minutes walking on the treadmill at self-selected speed were recorded. Root mean square and standard deviations of acceleration, stride time and its variability, Lyapunov exponents (LE), multiscale entropy (MSE) and harmonic ratios (HR) were computed. ::: ::: ::: RESULTS ::: Both age groups showed significantly higher stride time variability and short-term LE in all directions during overground walking. For the older group, overground walking showed higher V and AP standard deviation. Significantly lower values for overground walking were observed for long-term LE (V and ML for the younger group, ML for the older group), HR (ML for the older group) and MSE (V for the older group). Significant age-related differences were found for V long-term LE for overground walking. ::: ::: ::: CONCLUSIONS ::: The present findings suggest that both linear and advanced computational techniques for gait stability and variability assessment in older adults are sensitive to walking conditions.


title: Cognitive Radio Ad-hoc Networks: A Routing Perspective

abstract: Cognitive radio is considered the enabling technology of the dynamic spectrum access paradigm which is envisaged to solve the spectrum scarcity problem, thus facilitating the accommodation of new wireless services as well as providing an effective solution to the ever increasing user demand. It allows the unlicensed users to utilize dynamically and opportunistically the spectrum assigned to the primary users, when it is temporarily not used. To reach this aim, unlicensed users must be able to change their transmission and reception parameters to communicate with each other without causing interference to the primary users. The main objective of this thesis is to address the routing problem under the challenging scenario characterized by the uncertain availability of the spectral resource. The routing problem is furthermore challenging in presence of primary user’s mobility. To deal with this scenario, we proposed to utilize jointly path and spectrum diversity in routing to assure an improved spectral efficiency. We also proposed a mobility-aware channel-availability based channel selection technique, which performs the best channel selection in terms of spectrum availability in presence of primary user’s mobility. Performance evaluation is conducted through simulations, and the simulation results reveal the benefits of introducing the proposed proposals.


title: Sparse Unsupervised Capsules Generalize Better

abstract: We show that unsupervised training of latent capsule layers using only the reconstruction loss, without masking to select the correct output class, causes a loss of equivariances and other desirable capsule qualities. This implies that supervised capsules networks can't be very deep. Unsupervised sparsening of latent capsule layer activity both restores these qualities and appears to generalize better than supervised masking, while potentially enabling deeper capsules networks. We train a sparse, unsupervised capsules network of similar geometry to Sabour et al (2017) on MNIST, and then test classification accuracy on affNIST using an SVM layer. Accuracy is improved from benchmark 79% to 90%.


title: Studying Software Implementations of Elliptic Curve Cryptography

abstract: Elliptic Curve Cryptography (ECC) provides a similar level of security to conventional integer-based public-key algorithms, but with much shorter keys. ECC over binary field is of special interest because the operations in binary field are thought more space and time efficient. However, the software implementations of ECC over binary field are still slow, especially on low-end processors that are used in small computing devices such as sensor nodes. In this paper, we study the software implementations of ECC on processors with different word sizes. With a set of algorithms that we identified, we can perform 163-bit ECC in 13.9 seconds on an 8-bit processor at a clock rate of 8 MHz.


title: Guaranteed cost control and its application to networked control systems

abstract: Network-induced delays in the networked control system are inevitable and have a significant effect on system performance. In this paper, time-varying delays are regarded as the sum of the mean delays and uncertain delays. Furthermore, NCS is modeled as a discrete-time model with structural uncertainty for its time-varying network-induced delays. Based on the model, a guaranteed cost control law is presented which can stabilize the closed loop system and guarantees an adequate level of quality of performance. Theoretical analyses and simulation results compared to standard discrete linear quadratic regulator designed for discrete-time systems show that the control strategy presented in this paper is effective and feasible.


title: An effective algorithm to achieve accurate sinusoidal amplitude control with a low-resolution encoder

abstract: This paper presents a novel control methodology which uses an extremely low-resolution encoder to achieve a desired harmonic trajectory. The particular application of interest is a cellular microinjection technology called the Ros-Drill (Rotationally Oscillating Drill) for ICSI (Intra-Cytoplasmic Sperm Injection). It is an inexpensive set-up, which creates high-frequency (e.g. 500 Hz) and small-stroke (e.g. 0.2 degree) rotational oscillations at the tip of an injection pipette mimicking a harmonic motion profile. Such a motion control procedure presents no particular difficulty when it uses motion sensors with appropriate resolution. However, size, costs and accessibility of technology on hardware components for our charter severely constrain the sensory capabilities. The main objective is to achieve rotational amplitudes with peak-to-peak stroke of 0.4 degree and frequency of 500 Hz, using an encoder with resolution of 0.09 degree. This low-resolution encoder (which provides only 4 encoder steps for the entire stroke) presents two complications, a) large quantization errors at and between two successive measurements; b) stochasticity of actual amplitude for the desired discrete encoder readings. This paper proposes a method which enables accurate peak-to-peak stroke control using the same hardware, by essentially converting a stochastic stroke control problem into a deterministic dwell time balancing operation. The study describes a control strategy consisting of two-stage tuning: 1) coarse tuning which provides a 4-step desired encoder readings in peak-to-peak swings, 2) fine tuning which adapts the control gains to achieve the actual peak-to-peak stroke based on the deterministic relationships between the oscillation amplitudes and the encoder readings. Simulations and experiments are provided to validate the proposed method.


title: One hundred patients treated with osseointegrated transfemoral amputation prostheses--rehabilitation perspective.

abstract: Treatment with osseointegrated transfemoral prostheses has been shown to improve quality of life. The treatment has been performed in Sweden since 1990 and consists of two surgical procedures followed by rehabilitation. During the first years, the rehabilitation process was not standardized. In 1999, a treatment protocol called OPRA (Osseointegrated Prostheses for the Rehabilitation of Amputees) was established. This article describes the current rehabilitation protocol and illustrates the overall results. The OPRA rehabilitation protocol is graded to stimulate the process of osseointegration and prepare the patient for unrestricted prosthetic use. It includes initial training with a short training prosthesis followed by gradually increased prosthetic activity. Between May 1990 and June 2008, we treated 100 patients with 106 implants (6 bilaterally; 61% males, 39% females; mean age 43 years; mean time since amputation 11.5 years.) The majority had amputations due to trauma (67%) or tumor (21%) (other = 12%). Currently, 68 patients are using their prostheses (follow-up: 3 months- 17.5 years) and 32 are not (4 are deceased, 7 are before second surgery, 6 are in initial training, 4 are not using prosthesis, and 11 had the implant removed). The majority of treatment failures occurred in patients before we established the OPRA protocol. The implementation of graded rehabilitation is considered to be of utmost importance for improved results.


title: A 5.8 nW CMOS Wake-Up Timer for Ultra-Low-Power Wireless Applications

abstract: This work presents an ultra-low-power oscillator designed for wake-up timers in compact wireless sensors. In a conventional relaxation oscillator, a capacitor periodically resets to a fixed voltage using a continuous comparator, thereby generating an output clock. The reset is triggered by a continuous comparator and thus the clock period is dependent on the delay of the continuous comparator which therefore needs to be fast compared to the period, making this approach power hungry. To avoid the power penalty of a fast continuous comparator, a constant charge subtraction scheme is proposed in this paper. As a constant amount of charge is subtracted for each cycle, rather than discharging/charging the capacitor to a fixed voltage, the clock period becomes independent of comparator delay. Therefore, the high power continuous comparator can be replaced with a coarse clocked comparator, facilitating low-power time tracking. For precise wake-up signal generation, an accurate continuous comparator is only enabled for one clock period at the end of the specified wakeup time. A wake-up timer using the proposed scheme is fabricated in a 0.18 µm CMOS process. The timer consumes 5.8 nW at room temperature with temperature stability of 45 ppm/°C (-10 °C to 90 °C) and line sensitivity of 1%/V (1.2 V to 2.2 V) .


HSNORT: A Hybrid Intrusion Detection System using Artificial Intelligence with Snort


Dotplot Patterns: A Literal Look at Pattern Languages


title: A Comparative Study of Web Content Management Systems

abstract: Web Content Management Systems (WCMS) play an increasingly important role in the Internet’s evolution. They are software platforms that facilitate the implementation of a web site or an e-commerce and are gaining popularity due to its flexibility and ease of use. In this work, we explain from a tutorial perspective how to manage WCMS and what can be achieved by using them. With this aim, we select the most popular open-source WCMS; namely, Joomla!, WordPress, and Drupal. Then, we implement three websites that are equal in terms of requirements, visual aspect, and functionality, one for each WCMS. Through a qualitative comparative analysis, we show the advantages and drawbacks of each solution, and the complexity associated. On the other hand, security concerns can arise if WCMS are not appropriately used. Due to the key position that they occupy in today’s Internet, we perform a basic security analysis of the three implement websites in the second part of this work. Specifically, we explain vulnerabilities, security enhancements, which errors should not be done, and which WCMS is initially safer.


Macho Nachos 5 The Implicit Effects of Gendered Food Packaging on 6 Preferences for Healthy and Unhealthy Foods 7


title: The roles of segmental and tandem gene duplication in the evolution of large gene families in Arabidopsis thaliana

abstract: BackgroundMost genes in Arabidopsis thaliana are members of gene families. How do the members of gene families arise, and how are gene family copy numbers maintained? Some gene families may evolve primarily through tandem duplication and high rates of birth and death in clusters, and others through infrequent polyploidy or large-scale segmental duplications and subsequent losses.ResultsOur approach to understanding the mechanisms of gene family evolution was to construct phylogenies for 50 large gene families in Arabidopsis thaliana, identify large internal segmental duplications in Arabidopsis, map gene duplications onto the segmental duplications, and use this information to identify which nodes in each phylogeny arose due to segmental or tandem duplication. Examples of six gene families exemplifying characteristic modes are described. Distributions of gene family sizes and patterns of duplication by genomic distance are also described in order to characterize patterns of local duplication and copy number for large gene families. Both gene family size and duplication by distance closely follow power-law distributions.ConclusionsCombining information about genomic segmental duplications, gene family phylogenies, and gene positions provides a method to evaluate contributions of tandem duplication and segmental genome duplication in the generation and maintenance of gene families. These differences appear to correspond meaningfully to differences in functional roles of the members of the gene families.


title: ECG measurement on a chair without conductive contact

abstract: For the purpose of long-term, everyday electrocardiogram (ECG) monitoring, we present a convenient method of ECG measurement without direct conductive contact with the skin while subjects sat on a chair wearing normal clothes. Measurements were made using electrodes attached to the back of a chair, high-input-impedance amplifiers mounted on the electrodes, and a large ground-plane placed on the chair seat. ECGs were obtained by the presented method for several types of clothing and compared to ECGs obtained from conventional measurement using Ag-AgCl electrodes. Motion artifacts caused by usual desk works were investigated. This study shows the feasibility of the method for long-term, convenient, everyday use


title: A 210 nW 29.3 ppm/°C 0.7 V voltage reference with a temperature range of −50 to 130 °C in 0.13 µm CMOS

abstract: A low-voltage, low-power CMOS voltage reference with high temperature stability in a wide temperature range is presented. The temperature dependence of mobility and oxide capacitance is removed by employing transistors in saturation and triode regions and the temperature dependence of threshold voltage is removed by exploiting the transistors in weak inversion region. Implemented in 0.13um CMOS, the proposed voltage reference achieves temperature coefficient of 29.3ppm/°C against temperature variation of −50 – 130°C and line sensitivity of 337ppm/V against supply variation of 0.7–1.8V, while consuming 210nW from 0.7V supply and occupying 0.023mm2.


title: Deriving Concept-Based User Profiles from Search Engine Logs

abstract: User profiling is a fundamental component of any personalization applications. Most existing user profiling strategies are based on objects that users are interested in (i.e., positive preferences), but not the objects that users dislike (i.e., negative preferences). In this paper, we focus on search engine personalization and develop several concept-based user profiling methods that are based on both positive and negative preferences. We evaluate the proposed methods against our previously proposed personalized query clustering method. Experimental results show that profiles which capture and utilize both of the user's positive and negative preferences perform the best. An important result from the experiments is that profiles with negative preferences can increase the separation between similar and dissimilar queries. The separation provides a clear threshold for an agglomerative clustering algorithm to terminate and improve the overall quality of the resulting query clusters.


title: Coplanar Waveguides Loaded with S-Shaped Split-Ring Resonators: Modeling and Application to Compact Microwave Filters

abstract: This letter studies the transmission characteristics of coplanar waveguides (CPWs) loaded with single-layer S-shaped split-ring resonators (S-SRRs) for the first time. Two structures are analyzed: 1) a CPW simply loaded with an S-SRR, and 2) a CPW loaded with an S-SRR and a series gap. The former exhibits a stopband functionality related to the resonance of the S-SRR excited by the contra-directional magnetic fluxes through the two connected resonator loops; the latter is useful for the implementation of compact bandpass filters. In both cases, a lumped-element equivalent circuit model is proposed with an unequivocal physical interpretation of the circuit elements. These circuits are then validated by comparing the circuit response with extracted parameters to full-wave electromagnetic simulations. The last part of the letter illustrates application of the S-SRR/gap-loaded CPW unit cell to the design of a bandpass filter. The resulting filter is very compact and exhibits competitive performance.


title: Comparative Analysis of Spectral Approaches to Feature Extraction for EEG-Based Motor Imagery Classification

abstract: The quantification of the spectral content of electroencephalogram (EEG) recordings has a substantial role in clinical and scientific applications. It is of particular relevance in the analysis of event-related brain oscillatory responses. This work is focused on the identification and quantification of relevant frequency patterns in motor imagery (MI) related EEGs utilized for brain-computer interface (BCI) purposes. The main objective of the paper is to perform comparative analysis of different approaches to spectral signal representation such as power spectral density (PSD) techniques, atomic decompositions, time-frequency (t-f) energy distributions, continuous and discrete wavelet approaches, from which band power features can be extracted and used in the framework of MI classification. The emphasis is on identifying discriminative properties of the feature sets representing EEG trials recorded during imagination of either left- or right-hand movement. Feature separability is quantified in the offline study using the classification accuracy (CA) rate obtained with linear and nonlinear classifiers. PSD approaches demonstrate the most consistent robustness and effectiveness in extracting the distinctive spectral patterns for accurately discriminating between left and right MI induced EEGs. This observation is based on an analysis of data recorded from eleven subjects over two sessions of BCI experiments. In addition, generalization capabilities of the classifiers reflected in their intersession performance are discussed in the paper.


title: Comprehensive Survey on T-SDN: Software-Defined Networking for Transport Networks

abstract: Paradoxically, with an ever-increasing traffic demand, today transport-network operators experience a progressive erosion of their margins. The alarms of change are set, and software define networking (SDN) is coming to the rescue with the promise of reducing capital expenditures and operational expenses. Driven by economic needs and network innovation facilities, today transport SDN (T-SDN) is a reality. It gained big momentum in the last years, however, in the networking industry, the transport network will be perhaps the last segment to embrace SDN, mainly due to the heterogeneous nature and complexity of the optical equipment composing it. This survey guides the reader through a fascinating technological adventure that provides an organic analysis of the T-SDN development and evolution considering contributions from: academic research, standardization bodies, industrial development, open source projects, and alliances among them. After creating a comprehensive picture of T-SDN, we provide an analysis of many open issues that are expected to need significant future work, and give our vision in this path toward a fully programmable and dynamic transport network.


title: Computational Linguistics and Deep Learning

abstract: Deep Learning waves have lapped at the shores of computational linguistics for several years now, but 2015 seems like the year when the full force of the tsunami hit the major Natural Language Processing (NLP) conferences. However, some pundits are predicting that the final damage will be even worse. Accompanying ICML 2015 in Lille, France, there was another, almost as big, event: the 2015 Deep Learning Workshop. The workshop ended with a panel discussion, and at it, Neil Lawrence said, “NLP is kind of like a rabbit in the headlights of the Deep Learning machine, waiting to be flattened.” Now that is a remark that the computational linguistics community has to take seriously! Is it the end of the road for us? Where are these predictions of steamrollering coming from? At the June 2015 opening of the Facebook AI Research Lab in Paris, its director Yann LeCun said: “The next big step for Deep Learning is natural language understanding, which aims to give machines the power to understand not just individual words but entire sentences and paragraphs.”1 In a November 2014 Reddit AMA (Ask Me Anything), Geoff Hinton said, “I think that the most exciting areas over the next five years will be really understanding text and videos. I will be disappointed if in five years’ time we do not have something that can watch a YouTube video and tell a story about what happened. In a few years time we will put [Deep Learning] on a chip that fits into someone’s ear and have an English-decoding chip that’s just like a real Babel fish.”2 And Yoshua Bengio, the third giant of modern Deep Learning, has also increasingly oriented his group’s research toward language, including recent exciting new developments in neural machine translation systems. It’s not just Deep Learning researchers. When leading machine learning researcher Michael Jordan was asked at a September 2014 AMA, “If you got a billion dollars to spend on a huge research project that you get to lead, what would you like to do?”, he answered: “I’d use the billion dollars to build a NASA-size program focusing on natural language processing, in all of its glory (semantics, pragmatics, etc.).” He went on: “Intellectually I think that NLP is fascinating, allowing us to focus on highly structured inference problems, on issues that go to the core of ‘what is thought’ but remain eminently practical, and on a technology


title: A novel ZCS bidirectional buck-boost DC-DC converter for energy storage applications

abstract: The present article proposes and describes a new ZCS non-isolated bidirectional buck-boost DC-DC converter for energy storage applications in electric vehicles. Usually, the conventional converters are adapted with an auxiliary resonant cell to provide the zero current switching turn-on/turn-off condition for the main switching devices. The advantages of proposed converter has reduced switching losses, reduced component count and improved efficiency. The proposed converter operates either in boost or buck mode. This paper mainly deals with the operating principles, analysis and design simulations of the proposed converter in order to prove the better soft-switching capability, reduced switching losses and efficiency improvement than the conventional converter.


Matching Structure and Semantics: A Survey on Graph-Based Pattern Matching


title: EFFICIENT MARKETS HYPOTHESIS

abstract: The efficient markets hypothesis (EMH) maintains that market prices fully reflect all available information. Developed independently by Paul A. Samuelson and Eugene F. Fama in the 1960s, this idea has been applied extensively to theoretical models and empirical studies of financial securities prices, generating considerable controversy as well as fundamental insights into the price-discovery process. The most enduring critique comes from psychologists and behavioural economists who argue that the EMH is based on counterfactual assumptions regarding human behaviour, that is, rationality. Recent advances in evolutionary psychology and the cognitive neurosciences may be able to reconcile the EMH with behavioural anomalies.


title: Enhancing critical thinking through active learning

abstract: Abstract Today the Framework for 21st Century Learning developed by the Partnership for 21st Century Learning (P21) is widely recognized and has been used in the U.S., Canada and New Zealand. P21 defines and illustrates the skills and knowledge students need and states that critical thinking is fundamental for twenty-first century success and essential for success in an academic context. The Organization for Economic Co-operation and Development (OECD) also values the importance of cultivating critical thinking. However, critical thinking is not a part of the EFL curriculum in Japan, and lessons are not focused on the development of meta-cognitive strategies. How do we help students learn foreign languages and twenty-first Century Skills at the same time? Active learning and content and language integrated learning (CLIL) offer such a learning environment where learners enhance their cognitive skills and gain knowledge while they are learning content and language. This paper reports on a study that explores how active learning with CLIL instruction helps Japanese EFL learners to develop critical thinking skills. In the author’s student-centered instruction based class, critical thinking was stimulated with questions based on the revised Bloom’s taxonomy to develop lower and higher order thinking skills while various scaffolding activities were provided. Pretest-posttest results from the Critical Thinking Disposition Scale (CTDS) and the Cornell Critical Thinking Test (CCTT) Level Z were compared to determine to what extent, if any, EFL learners developed critical thinking disposition and skills through active learning in CLIL classes. The results of the CTDS and CCTT suggest that active learning has value for increasing critical thinking.


title: EEG-based drowsiness estimation for safety driving using independent component analysis

abstract: Preventing accidents caused by drowsiness has become a major focus of active safety driving in recent years. It requires an optimal technique to continuously detect drivers' cognitive state related to abilities in perception, recognition, and vehicle control in (near-) real-time. The major challenges in developing such a system include: 1) the lack of significant index for detecting drowsiness and 2) complicated and pervasive noise interferences in a realistic and dynamic driving environment. In this paper, we develop a drowsiness-estimation system based on electroencephalogram (EEG) by combining independent component analysis (ICA), power-spectrum analysis, correlation evaluations, and linear regression model to estimate a driver's cognitive state when he/she drives a car in a virtual reality (VR)-based dynamic simulator. The driving error is defined as deviations between the center of the vehicle and the center of the cruising lane in the lane-keeping driving task. Experimental results demonstrate the feasibility of quantitatively estimating drowsiness level using ICA-based multistream EEG spectra. The proposed ICA-based method applied to power spectrum of ICA components can successfully (1) remove most of EEG artifacts, (2) suggest an optimal montage to place EEG electrodes, and estimate the driver's drowsiness fluctuation indexed by the driving performance measure. Finally, we present a benchmark study in which the accuracy of ICA-component-based alertness estimates compares favorably to scalp-EEG based.


title: Cervical spine curvature during simulated whiplash

abstract: Objective. To develop a new method to describe cervical spine curvature and evaluate the potential for injury in the upper and lower cervical spine during simulated whiplash. Design. A method was developed to integrate the upper and lower cervical spine rotations and describe the spine curvature. Background. In vivo and in vitro whiplash simulations have documented the development of an S-shape curvature with simultaneous upper cervical spine flexion and lower cervical spine extension immediately following rear-impact. Investigators have hypothesized that the injury potential is highest during the S-shape phase. However, little data exist on the spine curvature during whiplash and its relation to spine injury. Methods. A biofidelic model and a bench-top whiplash apparatus were used in an incremental rear-impact protocol (maximum 8 g) to simulate whiplash of increasing severity. To describe the spine curvature, the upper and lower cervical spine rotations were normalized to corresponding physiological limits. Results. Average peak lower cervical spine extension first exceeded the physiological limits (P < 0:05) at a horizontal T1 acceleration of 5 g. Average peak upper cervical spine extension exceeded the physiological limit at 8 g, while peak upper cervical spine flexion never exceeded the physiological limit. In the S-shape phase, lower cervical spine extension reached 84% of peak extension during whiplash. Conclusions. Both the upper and lower cervical spine are at risk for extension injury during rear-impact. Flexion injury is unlikely.


Design Inspiration from Generative Networks


title: Exact surface solutions for semiconductors: The Dember effect and partial currents

abstract: Exact solution of the correct linearized equations for steady-state transport in semiconductors yields two modes that vary exponentially in space. One involves screening (without entropy production) and one involves diffusion and recombination (with entropy production), neither being quasineutral. They are applied to surface photoexcitation (the Dember effect) and to the adjustment of partial electron and hole currents from surface to bulk. Other transport situations are briefly discussed.


title: In-situ vehicular antenna integration and design aspects for vehicle-to-vehicle communications

abstract: Vehicle-to-vehicle (V2V) communications aim to enhance driver safety and traffic efficiency by using the recently designated frequency bands in the 5.9 GHz range in Europe. Due to the time-frequency selective fading behavior of the vehicular communication channel, multi-antenna techniques can provide enhanced link conditions by means of diversity processing. This paper highlights the integration of a four-element (N =4) linear array antenna into the roof-top compartment of a vehicle to conduct Multiple-Input Multiple-Output (MIMO) high-resolution mobile-to-mobile channel measurements.


An Annotated Corpus of Direct Speech


title: Accuracy and variability of right ventricular volumes and mass assessed by dual-source computed tomography: influence of slice orientation in comparison to magnetic resonance imaging

abstract: OBJECTIVE ::: To evaluate the accuracy and variability of right ventricular (RV) volumes and mass using dual-source computed tomography (DSCT) and the influence of slice orientation in comparison to cardiac magnetic resonance imaging (CMR). ::: ::: ::: METHODS ::: In 33 patients undergoing cardiac DSCT and CMR, RV parameters were calculated using the short-axis (DSCT, CMR) and axial orientation (DSCT). Intra- and interobserver variability were assessed by Bland-Altman analysis. ::: ::: ::: RESULTS ::: Short-axis orientation: RV parameters of the two techniques were not statistically different. Axial orientation: RV volumes and mass were significantly overestimated compared with short-axis parameters whereas EF was similar. The short-axis approach resulted in low variability, although the axial orientation had the least amount of intra- and interobserver variability. ::: ::: ::: CONCLUSION ::: RV parameters can be more accurately assessed by DSCT compared with CMR using short-axis slice orientation. RV volumes and mass are significantly higher using axial compared with short-axis slices, whereas EF is unaffected. RV parameters derived from both approaches yield high reproducibility.


title: The Catalytic Site Atlas 2.0: cataloging catalytic sites and residues identified in enzymes

abstract: Understanding which are the catalytic residues in an enzyme and what function they perform is crucial to many biology studies, particularly those leading to new therapeutics and enzyme design. The original version of the Catalytic Site Atlas (CSA) (http://www.ebi.ac.uk/thornton-srv/databases/CSA) published in 2004, which catalogs the residues involved in enzyme catalysis in experimentally determined protein structures, had only 177 curated entries and employed a simplistic approach to expanding these annotations to homologous enzyme structures. Here we present a new version of the CSA (CSA 2.0), which greatly expands the number of both curated (968) and automatically annotated catalytic sites in enzyme structures, utilizing a new method for annotation transfer. The curated entries are used, along with the variation in residue type from the sequence comparison, to generate 3D templates of the catalytic sites, which in turn can be used to find catalytic sites in new structures. To ease the transfer of CSA annotations to other resources a new ontology has been developed: the Enzyme Mechanism Ontology, which has permitted the transfer of annotations to Mechanism, Annotation and Classification in Enzymes (MACiE) and UniProt Knowledge Base (UniProtKB) resources. The CSA database schema has been re-designed and both the CSA data and search capabilities are presented in a new modern web interface.


title: A Collaborative Brain-Computer Interface for Improving Human Performance

abstract: Electroencephalogram (EEG) based brain-computer interfaces (BCI) have been studied since the 1970s. Currently, the main focus of BCI research lies on the clinical use, which aims to provide a new communication channel to patients with motor disabilities to improve their quality of life. However, the BCI technology can also be used to improve human performance for normal healthy users. Although this application has been proposed for a long time, little progress has been made in real-world practices due to technical limits of EEG. To overcome the bottleneck of low single-user BCI performance, this study proposes a collaborative paradigm to improve overall BCI performance by integrating information from multiple users. To test the feasibility of a collaborative BCI, this study quantitatively compares the classification accuracies of collaborative and single-user BCI applied to the EEG data collected from 20 subjects in a movement-planning experiment. This study also explores three different methods for fusing and analyzing EEG data from multiple subjects: (1) Event-related potentials (ERP) averaging, (2) Feature concatenating, and (3) Voting. In a demonstration system using the Voting method, the classification accuracy of predicting movement directions (reaching left vs. reaching right) was enhanced substantially from 66% to 80%, 88%, 93%, and 95% as the numbers of subjects increased from 1 to 5, 10, 15, and 20, respectively. Furthermore, the decision of reaching direction could be made around 100–250 ms earlier than the subject's actual motor response by decoding the ERP activities arising mainly from the posterior parietal cortex (PPC), which are related to the processing of visuomotor transmission. Taken together, these results suggest that a collaborative BCI can effectively fuse brain activities of a group of people to improve the overall performance of natural human behavior.


title: Interpretable machine learning: definitions, methods, and applications

abstract: Machine-learning models have demonstrated great success in learning complex patterns that enable them to make predictions about unobserved data. In addition to using models for prediction, the ability to interpret what a model has learned is receiving an increasing amount of attention. However, this increased focus has led to considerable confusion about the notion of interpretability. In particular, it is unclear how the wide array of proposed interpretation methods are related, and what common concepts can be used to evaluate them. We aim to address these concerns by defining interpretability in the context of machine learning and introducing the Predictive, Descriptive, Relevant (PDR) framework for discussing interpretations. The PDR framework provides three overarching desiderata for evaluation: predictive accuracy, descriptive accuracy and relevancy, with relevancy judged relative to a human audience. Moreover, to help manage the deluge of interpretation methods, we introduce a categorization of existing techniques into model-based and post-hoc categories, with sub-groups including sparsity, modularity and simulatability. To demonstrate how practitioners can use the PDR framework to evaluate and understand interpretations, we provide numerous real-world examples. These examples highlight the often under-appreciated role played by human audiences in discussions of interpretability. Finally, based on our framework, we discuss limitations of existing methods and directions for future work. We hope that this work will provide a common vocabulary that will make it easier for both practitioners and researchers to discuss and choose from the full range of interpretation methods.


title: Learning to Rank Non-Factoid Answers: Comment Selection in Web Forums

abstract: Recent initiatives in IR community have shown the importance of going beyond factoid Question Answering (QA) in order to design useful real-world applications. Questions asking for descriptions or explanations are much more difficult to be solved, e.g., the machine learning models cannot focus on specific answer words or their lexical type. Thus, researchers have started to explore powerful methods for feature engineering. Two of the most promising methods are convolution tree kernels (CTKs) and convolutional neural networks (CNNs) as they have been shown to obtain high performance in the task of answer sentence selection in factoid QA. In this paper, we design state-of-the-art models for non-factoid QA also carried out on noisy data. In particular, we study and compare models for comment selection in a community QA (cQA) scenario, where the majority of questions regard descriptions or explanations. To deal with such complex task, we incorporate relational information holding between questions and comments as well as domain-specific features into both convolutional models above. Our experiments on a cQA corpus show that both CTK and CNN achieve the state of the art, also according to a direct comparison with the results obtained by the best systems of the SemEval cQA challenge.


title: Position-Aligned translation model for citation recommendation

abstract: The goal of a citation recommendation system is to suggest some references for a snippet in an article or a book, and this is very useful for both authors and the readers. The citation recommendation problem can be cast as an information retrieval problem, in which the query is the snippet from an article, and the relevant documents are the cited articles. In reality, the citation snippet and the cited articles may be described in different terms, and this makes the citation recommendation task difficult. Translation model is very useful in bridging the vocabulary gap between queries and documents in information retrieval. It can be trained on a collection of query and document pairs, which are assumed to be parallel. However, such training data contains much noise: a relevant document usually contains some relevant parts along with irrelevant ones. In particular, the citation snippet may only mention only some parts of the cited article's content. To cope with this problem, in this paper, we propose a method to train translation models on such noisy data, called position-aligned translation model. This model tries to align the query to the most relevant parts of the document, so that the estimated translation probabilities could rely more on them. We test this model in a citation recommendation task for scientific papers. Our experiments show that the proposed method can significantly improve the previous retrieval methods based on translation models.


title: Matching Curves to Imprecise Point Sets using Fr\'echet Distance

abstract: Let $P$ be a polygonal curve in $\mathbb{R}^d$ of length $n$, and $S$ be a point-set of size $k$. The Curve/Point Set Matching problem consists of finding a polygonal curve $Q$ on $S$ such that the Frechet distance from $P$ is less than a given $\varepsilon$. We consider eight variations of the problem based on the distance metric used and the omittability or repeatability of the points. We provide closure to a recent series of complexity results for the case where $S$ consists of precise points. More importantly, we formulate a more realistic version of the problem that takes into account measurement errors. This new problem is posed as the matching of a given curve to a set of imprecise points. We prove that all three variations of the problem that are in P when $S$ consists of precise points become NP-complete when $S$ consists of imprecise points. We also discuss approximation results.


title: Mean-Reverting Portfolio Design with Budget Constraint

abstract: This paper considers the mean-reverting portfolio design problem arising from statistical arbitrage in the financial markets. We first propose a general problem formulation aimed at finding a portfolio of underlying component assets by optimizing a mean-reversion criterion characterizing the mean-reversion strength, taking into consideration the variance of the portfolio and an investment budget constraint. Then several specific problems are considered based on the general formulation, and efficient algorithms are proposed. Numerical results on both synthetic and market data show that our proposed mean-reverting portfolio design methods can generate consistent profits and outperform the traditional design methods and the benchmark methods in the literature.


title: Optimal Dropbox Deployment Algorithm for Data Dissemination in Vehicular Networks

abstract: For vehicular networks, dropboxes are very useful for assisting the data dissemination, as they can greatly increase the contact probabilities between vehicles and reduce the data delivery delay. However, due to the costly deployment of dropboxes, it is impractical to deploy dropboxes in a dense manner. In this paper, we investigate how to deploy the dropboxes optimally by considering the tradeoff between the delivery delay and the cost of dropbox deployment. This is a very challenging issue due to the difficulty of accurate delay estimation and the complexity of solving the optimization problem. To address this issue, we first provide a theoretical framework to estimate the delivery delay accurately. Then, based on the idea of dimension enlargement and dynamic programming, we design a novel optimal dropbox deployment algorithm (ODDA) to obtain the optimal deployment strategy. We prove that ODDA has a fast convergence speed, which is less than $\kappa$ ( $\kappa ) iterations for convergence. We also prove that the computational complexity of ODDA is $O(n \kappa m \;\log m)$ , i.e., ODDA has a polynomial computational complexity for a given $m$ , the number of dropboxes for deployment. Performance evaluation by simulation demonstrates the superior performance of the proposed strategies compared with the benchmark methods.


title: Prevalence of peri‐implantitis in patients not participating in well‐designed supportive periodontal treatments: a cross‐sectional study

abstract: Objectives ::: This retrospective cross-sectional study aimed to evaluate the prevalence of biologic complications of implants in patients treated by fixed implant supported prosthesis without regular maintenance program. ::: ::: Materials and methods ::: One hundred thirty-four patients with 478 implants, installed during a 10-year period (2001–2010), were recruited for clinical and radiographic follow-up examinations. The periodontal and implant health status were assessed to determine the prevalence of peri-implant diseases. ::: ::: Results ::: The mean ± SD loading time for implants was 4.43 ± 2.25 years. Fifty-five percentage of the implants were tissue-level implants. Peri-implantitis was diagnosed in 20% of patients and 8.8% of implants. Subject-based and implant-based prevalence of mucositis amounted to 48.5% and 40%, respectively. Mean crestal bone loss in tissue-level and bone-level implants were 0.28 ± 0.53 mm and 1.37 ± 1.5 mm, respectively. Smoking and lack of keratinized mucosa was associated with peri-implantitis at an odds ratio of OR = 2.57 and 3.89, respectively. ::: ::: Conclusions ::: After a 5-year period of loading without any regular maintenance program, one out of five patients would experience peri-implantitis. Tissue-level implants had lower values of peri-implantitis prevalence and crestal bone loss.


title: Theoretical and Empirical Developments of the Mindfulness-Acceptance-Commitment (MAC) Approach to Performance Enhancement

abstract: As long as athletes strive to attain optimal performance states and consistently reach high performance goals, psychological interventions will be used to assist in the development of skill and the maintenance of performance. In the pursuit of these goals, newer evidence-driven models based on mindfulness- and acceptance-based approaches have been designed to achieve these ends. Based upon questionable efficacy data for traditional psychological skills training procedures that emphasize reduction or control of internal processes, mindfulness- and acceptance-based approaches develop skills of nonjudging mindful awareness, mindful attention, and experiential acceptance to aid in the pursuit of valued goals. The most formalized and researched mindfulness- and acceptance-based approach within sport psychology is the manualized Mindfulness-Acceptance-Commitment (MAC) protocol. In the 8 years since the MAC was first developed and presented, and the 5 years since the first publication on the protocol, the MAC program has accumulated a continually growing empirical base for both its underlying theory and intervention efficacy as a performance enhancement intervention. This article reviews the empirical and theoretical foundations of the mindfulness- and acceptance-based approaches in general, and MAC in particular; reviews the accumulated empirical findings in support of the MAC approach for performance enhancement; and presents recent MAC developments and suggested future directions. It has been nearly a decade since the first professional conference presentation introducing the Mindfulness-Acceptance-Commitment (MAC) approach to performance enhancement (Moore & Gardner, 2001), a theoretically and empirically derived acceptance-based behavioral intervention for the purpose of enhancing the performance and psychological well-being of competitive athletes. Since that time, many advances have occurred in the field of study broadly referred to as acceptance-based behavior therapies (ABBT; Roemer & Orsillo, 2009). This term is used to refer to a broad range of interventions that view the alteration of


title: Context-Aware for Smartphone Sensing in Over-the-Counter Market: Ambient Intelligence in Mobile Stock Markets

abstract: An Ambient Intelligence (AmI) environment is primary developed using intelligent agents and wireless sensor networks. The intelligent agents could automatically obtain contextual information in real time using Near Field Communication (NFC) technique and wireless ad-hoc networks. In this research, we propose a stock trading and recommendation system with mobile devices (Android platform) interface in the over-the-counter market (OTC) environments. The proposed system could obtain the real-time financial information of stock price through a multi-agent architecture with plenty of useful features. In addition, NFC is used to achieve a context-aware environment allowing for automatic acquisition and transmission of useful trading recommendations and relevant stock information for investors. Finally, AmI techniques are applied to successfully create smart investment spaces, providing investors with useful monitoring tools and investment recommendation.


title: Deep Learning for NLP (without Magic)

abstract: Machine learning is everywhere in today’s NLP, but by and large machine learning amounts to numerical optimization of weights for human designed representations and features. The goal of deep learning is to explore how computers can take advantage of data to develop features and representations appropriate for complex interpretation tasks. This tutorial aims to cover the basic motivation, ideas, models and learning algorithms in deep learning for natural language processing. Recently, these methods have been shown to perform very well on various NLP tasks such as language modeling, POS tagging, named entity recognition, sentiment analysis and paraphrase detection, among others. The most attractive quality of these techniques is that they can perform well without any external hand-designed resources or time-intensive feature engineering. Despite these advantages, many researchers in NLP are not familiar with these methods. Our focus is on insight and understanding, using graphical illustrations and simple, intuitive derivations. The goal of the tutorial is to make the inner workings of these techniques transparent, intuitive and their results interpretable, rather than black boxes labeled ”magic here”. The first part of the tutorial presents the basics of neural networks, neural word vectors, several simple models based on local windows and the math and algorithms of training via backpropagation. In this section applications include language modeling and POS tagging. In the second section we present recursive neural networks which can learn structured tree outputs as well as vector representations for phrases and sentences. We cover both equations as well as applications. We show how training can be achieved by a modified version of the backpropagation algorithm introduced before. These modifications allow the algorithm to work on tree structures. Applications include sentiment analysis and paraphrase detection. We also draw connections to recent work in semantic compositionality in vector spaces. The principle goal, again, is to make these methods appear intuitive and interpretable rather than mathematically confusing. By this point in the tutorial, the audience members should have a clear understanding of how to build a deep learning system for word-, sentenceand document-level tasks. The last part of the tutorial gives a general overview of the different applications of deep learning in NLP, including bag of words models. We will provide a discussion of NLP-oriented issues in modeling, interpretation, representational power, and optimization.


Autonomous Task Sequencing for Customized Curriculum Design in Reinforcement Learning


title: Scale-invariant learning and convolutional networks

abstract: The conventional classification schemes -- notably multinomial logistic regression -- used in conjunction with convolutional networks (convnets) are classical in statistics, designed without consideration for the usual coupling with convnets, stochastic gradient descent, and backpropagation. In the specific application to supervised learning for convnets, a simple scale-invariant classification stage turns out to be more robust than multinomial logistic regression, appears to result in slightly lower errors on several standard test sets, has similar computational costs, and features precise control over the actual rate of learning. "Scale-invariant" means that multiplying the input values by any nonzero scalar leaves the output unchanged.


A Schmitt-Trigger and Transistor Sizing based Optimization in Dynamic CMOS Circuits


An analysis of various tools, methods and systems to generate fake accounts for social media


title: ParallelTopics: A probabilistic approach to exploring document collections

abstract: Scalable and effective analysis of large text corpora remains a challenging problem as our ability to collect textual data continues to increase at an exponential rate. To help users make sense of large text corpora, we present a novel visual analytics system, Parallel-Topics, which integrates a state-of-the-art probabilistic topic model Latent Dirichlet Allocation (LDA) with interactive visualization. To describe a corpus of documents, ParallelTopics first extracts a set of semantically meaningful topics using LDA. Unlike most traditional clustering techniques in which a document is assigned to a specific cluster, the LDA model accounts for different topical aspects of each individual document. This permits effective full text analysis of larger documents that may contain multiple topics. To highlight this property of the model, ParallelTopics utilizes the parallel coordinate metaphor to present the probabilistic distribution of a document across topics. Such representation allows the users to discover single-topic vs. multi-topic documents and the relative importance of each topic to a document of interest. In addition, since most text corpora are inherently temporal, ParallelTopics also depicts the topic evolution over time. We have applied ParallelTopics to exploring and analyzing several text corpora, including the scientific proposals awarded by the National Science Foundation and the publications in the VAST community over the years. To demonstrate the efficacy of ParallelTopics, we conducted several expert evaluations, the results of which are reported in this paper.


North-Holland COMPUTATION AT THE EDGE OF CHAOS: PHASE TRANSITIONS AND EMERGENT COMPUTATION


An Appraisal of Oral Mucous Extravasation Cyst Case with Mini Review


title: Classification on Soft Labels Is Robust against Label Noise

abstract: In a scenario of supervised classification of data, labeled training data is essential. Unfortunately, the process by which those labels are obtained is not error-free, for example due to human nature. The aim of this work is to find out what impact noise on the labels has, and we do so by artificially adding it. An algorithm for the noising procedure is described. Not only individual classifiers are studied, but also ensembles of classifiers whose answers are combined, increasing the overall performance. Also, we will answer the question if classifiers trained on soft labels are more resilient to label noise than those trained on hard labels.


title: Creating Value Together: The Emerging Design Space of Peer-to-Peer Currency and Exchange

abstract: Paradigms for the collaborative creation of value through trade and exchange have developed over millennia. Thus, coins emerged in response to a set of challenges in barter and gift exchange. The development of paradigms for trade and exchange continues today, and is accelerating due both to crises in the mainstream global economy, and to new possibilities enabled by information technology. In this discussion paper, we consider alternative and complementary currency and exchange innovations, including local/community currencies, timebanks, crypto-currencies, and person-to-person collaborative economy microenterprises, as a technology design space for currency and exchange. We consider the consequences and trajectories of the rapidly evolving currency ecosystem, particularly with respect to research and development opportunities for CSCW.


Strengths-based positive psychology interventions: a randomized placebo-controlled online trial on long-term effects for a signature strengths- vs. a lesser strengths-intervention


title: Multimodal Memory Modelling for Video Captioning

abstract: Video captioning which automatically translates video clips into natural language sentences is a very important task in computer vision. By virtue of recent deep learning technologies, e.g., convolutional neural networks (CNNs) and recurrent neural networks (RNNs), video captioning has made great progress. However, learning an effective mapping from visual sequence space to language space is still a challenging problem. In this paper, we propose a Multimodal Memory Model (M3) to describe videos, which builds a visual and textual shared memory to model the long-term visual-textual dependency and further guide global visual attention on described targets. Specifically, the proposed M3 attaches an external memory to store and retrieve both visual and textual contents by interacting with video and sentence with multiple read and write operations. First, text representation in the Long Short-Term Memory (LSTM) based text decoder is written into the memory, and the memory contents will be read out to guide an attention to select related visual targets. Then, the selected visual information is written into the memory, which will be further read out to the text decoder. To evaluate the proposed model, we perform experiments on two publicly benchmark datasets: MSVD and MSR-VTT. The experimental results demonstrate that our method outperforms the state-of-theart methods in terms of BLEU and METEOR.


title: Telework: Enablers and Moderators when Assessing Organisational Fit

abstract: Home-based work, also referred to as distance work, virtual work or Telework, is enabled by information and communication technologies, and has been in existence for over 35 years. Even with significant technological improvements, telework has not achieved widespread adoption. Of the many aspects of telework investigated, few studies have examined the effect of organisational features which can affect telework success. This research study examined the role of the organisation in telework adoption and explored the organisational factors that may positively or negatively influence telework diffusion. Telework success was approached as a multidimensional concept and the research draws from previous telework-related research to define a model based on a set of organisational factors, which were potentially able to affect telework adoption and diffusion. The model was tested by means of a dual case study of Information Systems (IS) personnel in two organisations. The comparative case study examined telework adoption in the organisations which were chosen as opposite polar types in order to examine the effect of the organisational factors in extreme opposites of telework maturity. An analysis of the case studies revealed that organisational factors are affected by the adoption process and values will change as telework practices mature. Additionally the relative contribution of each organisational factor was not equal and this changes over time so that the contribution of different factors will vary depending upon the maturity of telework practices in the organisation. Organisational factors that were not included in the original model but that should form part of future studies include national culture and the effect of industry type of the organization.


title: Colorization by Example

abstract: We present a new method for colorizing grayscale images by transferring color from a segmented example image. Rather than relying on a series of independent pixel-level decisions, we develop a new strategy that attempts to account for the higher-level context of each pixel. The colorizations generated by our approach exhibit a much higher degree of spatial consistency, compared to previous automatic color transfer methods [WAM02]. We also demonstrate that our method requires considerably less manual effort than previous user-assisted colorization methods [LLW04]. Given a grayscale image to colorize, we first determine for each pixel which example segment it should learn its color from. This is done automatically using a robust supervised classification scheme that analyzes the low-level feature space defined by small neighborhoods of pixels in the example image. Next, each pixel is assigned a color from the appropriate region using a neighborhood matching metric, combined with spatial filtering for improved spatial coherence. Each color assignment is associated with a confidence value, and pixels with a sufficiently high confidence level are provided as "micro-scribbles" to the optimization-based colorization algorithm of Levin et al. [LLW04], which produces the final complete colorization of the image.


title: Tunnel FET RF Rectifier Design for Energy Harvesting Applications

abstract: Radio-frequency (RF)-powered energy harvesting systems have offered new perspectives in various scientific and clinical applications such as health monitoring, bio-signal acquisition, and battery-less data-transceivers. In such applications, an RF rectifier with high sensitivity, high power conversion efficiency (PCE) is critical to enable the utilization of the ambient RF signal power. In this paper, we explore the high PCE advantage of the steep-slope III-V heterojunction tunnel field-effect transistor (HTFET) RF rectifiers over the Si FinFET baseline design for RF-powered battery-less systems. We investigate the device characteristics of HTFETs to improve the sensitivity and PCE of the RF rectifiers. Different topologies including the two-transistor (2-T) and four-transistor (4-T) complementary-HTFET designs, and the n-type HTFET-only designs are evaluated with design parameter optimizations to achieve high PCE and high sensitivity. The performance evaluation of the optimized 4-T cross-coupled HTFET rectifier has shown an over 50% PCE with an RF input power ranging from -40 dBm to -25 dBm, which significantly extends the RF input power range compared to the baseline Si FinFET design. A maximum PCE of 84% and 85% has been achieved in the proposed 4-T N-HTFET-only rectifier at -33.7 dBm input power and the 4-T cross-coupled HTFET rectifier at -34.5 dBm input power, respectively. The capability of obtaining a high PCE at a low RF input power range reveals the superiority of the HTFET RF rectifiers for battery-less energy harvesting applications.


title: DSPM: A Platform for Personal Data Share and Privacy Protect Based on Metadata

abstract: Personal data represents a post-industrial opportunity. In order to release the huge potential of personal data and enhance the rights of possession, use, and disposal, a highly reliable, secure and available infrastructure is required. Regarding the personal data market, current solutions focus on data discovery and passive privacy protection. What's worse is that the data owner loses the right of control. In this paper, we present a prototype of DSPM: an efficient platform we devise to balance the data sharing and privacy protect, enhance the probability of data discovery and the ability of the individual to control the data. It is shown that DSPM is align with the initiative aims of establishing a user-centric framework for identifying the opportunities, risks and collaborative responses in the use of personal data.


title: The Evolution and Future of Diversity at Work

abstract: This article examines the evolution of diversity in the Journal of Applied Psychology. To begin, we explore foundations of the concept of diversity, including its appearance in both applied contexts and the scholarly literature. We then review the literature on diversity, including the development of its conceptualization and operationalizations over time, in the Journal and in the field of applied psychological science at large. We also examine the processes underlying the effects of diversity, and specific outcomes of diversity in organizations. To conclude, we offer a future research agenda that highlights diversity-related topics and issues important for advancing an understanding of diversity and moving the field forward, especially within the Journal. This work makes several contributions to research on diversity in organizations. First, we provide a lens for examining change in the study of diversity over time as well as a critical examination of the benefits and challenges associated with these changes. Second, we review the underlying mechanisms and key contextual influences on diversity effects in organizations. Third, our review examines the explanatory power of current diversity research and then uses this to develop a research agenda. By organizing the broad body of literature that exists on diversity, our article offers a sharp picture of what gaps in knowledge exist and where future research should focus. (PsycINFO Database Record


Facial Expression Recognition with Recurrent Neural Networks


title: Sulfated Hetero-Polysaccharides Protect SH-SY5Y Cells from H2O2-Induced Apoptosis by Affecting the PI3K/Akt Signaling Pathway

abstract: Parkinson’s disease (PD) is one of the most common neurodegenerative diseases. Recent studies suggest that sulfated hetero-polysaccharides (UF) protect against developing PD. However, the detailed mechanisms of how UF suppress neuronal death have not been fully elucidated. We investigated the cytoprotective mechanisms of UF using human dopaminergic neuroblastoma SH-SY5Y cells as a PD model. UF prevented H2O2-induced apoptotic cell death in SH-SY5Y cells in a dose-dependent manner. An examination of the PI3K/Akt upstream pathway revealed that UF-pretreated cells showed a decreased relative density of Akt, PI3K, and TrkA, and increased the phosphorylation of Akt, PI3K, and NGF; the PI3K inhibitor, LY294002, partially prevented this effect. An examination of the PI3K/Akt downstream pathway revealed the increased expression of the apoptosis-associated markers Bax, p53, CytC, and GSK3β, and the decreased expression of Bcl-2 in UF-treated cells. UF-treated cells also exhibited decreased caspase-3, caspase-8, and caspase-9 activities, which induced cell apoptosis. Our results demonstrate that UF affect the PI3K/Akt pathway, as well as downstream signaling. Therefore, the UF-mediated activation of PI3K/Akt could provide a new potential therapeutic strategy for neurodegenerative diseases associated with oxidative injury. These findings contribute to a better understanding of the critical roles of UF in the treatment of PD.


title: Turbulence Effects on Modified State Observer-Based Adaptive Control: Black Kite Micro Aerial Vehicle

abstract: This paper presents the implementation of a modified state observer-based adaptive dynamic inverse controller for the Black Kite micro aerial vehicle. The pitch and velocity adaptations are computed by the modified state observer in the presence of turbulence to simulate atmospheric conditions. This state observer uses the estimation error to generate the adaptations and, hence, is more robust than model reference adaptive controllers which use modeling or tracking error. In prior work, a traditional proportional-integral-derivative control law was tested in simulation for its adaptive capability in the longitudinal dynamics of the Black Kite micro aerial vehicle. This controller tracks the altitude and velocity commands during normal conditions, but fails in the presence of both parameter uncertainties and system failures. The modified state observer-based adaptations, along with the proportional-integral-derivative controller enables tracking despite these conditions. To simulate flight of the micro aerial vehicle with turbulence, a Dryden turbulence model is included. The turbulence levels used are based on the absolute load factor experienced by the aircraft. The length scale was set to 2.0 meters with a turbulence intensity of 5.0 m/s that generates a moderate turbulence. Simulation results for various flight conditions show that the modified state observer-based adaptations were able to adapt to the uncertainties and the controller tracks the commanded altitude and velocity. The summary of results for all of the simulated test cases and the response plots of various states for typical flight cases are presented.


title: Association between Neuroepithelial Tumor and Multiple Intestinal Polyposis (Turcotʼs Syndrome): Report of a Case and Critical Analysis of the Literature

abstract: We report a case of association of a brain tumor with multiple intestinal polyposis (Turcot's syndrome) and offer a critical analysis of the relevant literature with a view to revising the classification of the syndrome in relation to familial multiple polyposis and Gardner's syndrome. For this purpose, we considered only cases of intestinal polyposis associated with a primary neuroepithelial tumor (medulloblastoma, glioma, or glioblastoma) as originally described by Turcot. Differences emerged, depending on the central nervous system tumor type, which suggests that this neoplastic association may be classified as two distinct syndromes.


Battling algorithmic bias: how do we ensure algorithms treat us fairly?


title: Direct laser writing of micro-supercapacitors on hydrated graphite oxide films

abstract: Microscale supercapacitors provide an important complement to batteries in a variety of applications, including portable electronics. Although they can be manufactured using a number of printing and lithography techniques, continued improvements in cost, scalability and form factor are required to realize their full potential. Here, we demonstrate the scalable fabrication of a new type of all-carbon, monolithic supercapacitor by laser reduction and patterning of graphite oxide films. We pattern both in-plane and conventional electrodes consisting of reduced graphite oxide with micrometre resolution, between which graphite oxide serves as a solid electrolyte. The substantial amounts of trapped water in the graphite oxide makes it simultaneously a good ionic conductor and an electrical insulator, allowing it to serve as both an electrolyte and an electrode separator with ion transport characteristics similar to that observed for Nafion membranes. The resulting micro-supercapacitor devices show good cyclic stability, and energy storage capacities comparable to existing thin-film supercapacitors.


Place of treatment for teenagers and young adults diagnosed with cancer 2003 to 2005


title: Multiband and Dual-Polarized Omnidirectional Antenna for 2G/3G/LTE Application

abstract: A novel multiband and vertically/horizontally polarized omnidirectional antenna is proposed for 2G/3G/LTE mobile communication systems. The proposed antenna is a combination of a modified asymmetric biconical for vertical polarization (VP) and six printed dipoles with concentric placement for horizontal polarization (HP). The VP element consists of two modified cones with different diameters, designed to work in the multiband GSM+3G/4G. Printed dipoles of the HP element are fed by a six-way power splitter printed on the substrate. The HP element is nested between two cones of the VP element, forming a compact structure. The bottom cone can be used as the reflector for VP and HP in the overlapping work frequencies band for 3G and LTE communication systems. The VP element achieves a bandwidth of 17.4% and gain of 1.5 dBi in the 806 ~ 960-MHz band. The HP element achieves a bandwidth of 35% (1880 ~ 2700 MHz) directly overlapping with the VP-element higher frequency band. An isolation of 25 dB is achieved in the overlapping band. The cross-polarization levels in the azimuth plane for both VP and HP are lower than -15 dB.


title: A safe generic adaptation mechanism for smart cars

abstract: Today's vehicles are evolving towards smart cars, which will be able to drive autonomously and adapt to changing contexts. Incorporating self-adaptation in these cyber-physical systems (CPS) promises great benefits, like cheaper software-based redundancy or optimised resource utilisation. As promising as these advantages are, a respective proportion of a vehicle's functionality poses as safety hazards when confronted with fault and failure situations. Consequently, a system's safety has to be ensured with respect to the availability of multiple software applications, thus often resulting in redundant hardware resources, such as dedicated backup control units. To benefit from self-adaptation by means of creating efficient and safe systems, this work introduces a safety concept in form of a generic adaptation mechanism (GAM). In detail, this generic adaptation mechanism is introduced and analysed with respect to generally known and newly created safety hazards, in order to determine a minimal set of system properties and architectural limitations required to safely perform adaptation. Moreover, the approach is applied to the ICT architecture of a smart e-car, thereby highlighting the soundness, general applicability, and advantages of this safety concept and forming the foundation for the currently ongoing implementation of the GAM within a real prototype vehicle.


title: Semantic structuring of video collections fromspeech : segmentation and hyperlinking

abstract: New challenges emerged in the past years as the audiovisual landscape significantly transformed with the emergence of Internet-based TV. The decision of what to watch and in what order no longer belongs to the TV station but the user. New solutions must be offered to users in order to facilitate precise and quick access to the desired information contained in the ever increasing amounts of audiovisual content: for example, to identify a specific event, a TV show fragment containing a certain public person or addressing a particular topic, or to browse sequentially across all segments of videos addressing this topic. These new and varied features imply the development of innovative methods for structuring and exploiting the audiovisual content, which represent the focus of this thesis. We offer automatic topic structuring techniques of audiovisual data to reveal the internal organization of each program. We also study the implications of the structure produced on various tasks such as creating hyperlinks between videos (to allow navigation between TV show fragments that are thematically related) or creating automatic summaries. All the work is carried out on automatic transcripts of the speech pronounced in TV shows, to achieve generic solutions, not dedicated to a particular kind of show.


title: Affordances as a Framework for Robot Control

abstract: The concept of affordances, with its emphasis on the interaction between the organism and the environment, is highly relevant for robotics. In this paper, we present a new formalization of affordances that provides a framework for robot control, learning and planning. We argue that affordances, as relations within the robotenvironment system, can be learned by the robot from its interactions with the environment. The interaction is represented as a nested triple of the form (effect, (entity, behavior)) indicating that the behavior applied in an environment perceived as the entity, would produce a perceivable effect. The robot accumulates such triples of raw sensory-motor data from different interactions and learns the affordance relations through the formation of equivalence classes. We present three studies that implement certain aspects of the formalism on a mobile robot moving in an environment that contains different types of objects. These studies respectively explore (1) the perceptual learning of affordances, (2) the development of goal-directed behaviors from a set of primitive ones through the learning of affordances, and (3) the use of learned affordance relations in planning.


Do stock market investors understand the risk sentiment of corporate annual reports


title: Kinematic analysis of sit-to-stand assistive device for the elderly and disabled

abstract: This paper introduces a robotic walking and sit-to-stand support system, Smart Mobile Walker(SMW), and presents kinematic analysis of the system. For supporting person's sit-to-stand movement, the motion of SMW is modeled by the analysis of kinematics. The results of analysis are used for simulations and its feasibility is investigated by simulations. The feasibility shows our analysis of kinematics can be applied as a base of making optimal motion trajectory for helping sit-to-stand.


title: Potential of temperature-response collagen-genipin sols as a novel submucosal injection material for endoscopic resection

abstract: Background and study aims We developed a novel submucosal (SM) injection material that contained pepsin-solubilized collagen (PSC), genipin (Ge) and phosphate buffer (PB). The aim of this study was to validate safety and usability of it for endoscopic resection (ER). Materials and methods In preliminary studies, 1) appropriate warming time and concentration of Ge, and concentration of NaCl in PB, 2) storage modulus of PSC, Ge, and PB mixture (PSC/Ge), and PSC as a mechanical property, 3) histological finding after injection, and histological toxicity of PSC/Ge was evaluated. We injected PSC/Ge, PSC, sodium hyaluronate (SH), dextrose (DW), and normal saline (NS) into SM of resected porcine stomach. We compared mean height of mucosal elevation after immediate injection (MH) and mean retaining rate at 60 minutes (MR) as ex vivo study. Results Optimal condition of PSC/Ge was Ge 5.5 mMol with 24 hours worming time and NaCl 280 mMol. PSC/Ge had better mechanical property than PSC. It was efficiently integrated and confined to the SM with acceptable toxicity. MH of PSC/Ge (5.1 ± 0.74 mm) and PSC (4.8 ± 0.84 mm) were significantly higher than NS (3.2 ± 0.84 mm). MR of PSC/Ge (100 ± 0.0%) was significantly higher than NS (61.7 ± 11.2%), DW (58.3 ± 11.8%) and SH (61.8 ± 8.6%). Conclusion PSC/Ge and PSC has potential to be safe and usable for ER. PSC/Ge was better than PSC because of better mechanical property than PSC.


title: Dimensions of the Dysexecutive Questionnaire (DEX) examined using Rasch analysis

abstract: Executive dysfunction is very broad term used to capture a range of interacting high-level cognitive, emotional and behavioural difficulties, commonly seen after acquired brain injury (ABI). Many argue that the concept of executive functioning should be subdivided into several separate dimensions. In this study we explore the dimensional structure of a tool designed to assess level of executive functioning, the Dysexecutive (DEX) Questionnaire (Burgess, Alderman, Wilson, Evans, & Emslie, 1996), in order to inform theoretical conceptualisations of executive functioning and improve measurement precision in rehabilitation centres. Rasch analysis was undertaken on the responses of 363 people with ABI to the DEX. Overall, the questionnaire did not perform as a unidimensional, interval-level scale of executive functioning, suggesting that it measures more than one psychological construct. Most subscales previously proposed using factor analysis, including those suggested in the DEX manual, did not perform as un...


title: Building a better NetFlow

abstract: Network operators need to determine the composition of the traffic mix on links when looking for dominant applications, users, or estimating traffic matrices. Cisco's NetFlow has evolved into a solution that satisfies this need by reporting flow records that summarize a sample of the traffic traversing the link. But sampled NetFlow has shortcomings that hinder the collection and analysis of traffic data. First, during flooding attacks router memory and network bandwidth consumed by flow records can increase beyond what is available; second, selecting the right static sampling rate is difficult because no single rate gives the right tradeoff of memory use versus accuracy for all traffic mixes; third, the heuristics routers use to decide when a flow is reported are a poor match to most applications that work with time bins; finally, it is impossible to estimate without bias the number of active flows for aggregates with non-TCP traffic.In this paper we propose Adaptive NetFlow, deployable through an update to router software, which addresses many shortcomings of NetFlow by dynamically adapting the sampling rate to achieve robustness without sacrificing accuracy. To enable counting of non-TCP flows, we propose an optional Flow Counting Extension that requires augmenting existing hardware at routers. Both our proposed solutions readily provide descriptions of the traffic of progressively smaller sizes. Transmitting these at progressively higher levels of reliability allows graceful degradation of the accuracy of traffic reports in response to network congestion on the reporting path.


title: An empirical study of bugs in test code

abstract: Testing aims at detecting (regression) bugs in production code. However, testing code is just as likely to contain bugs as the code it tests. Buggy test cases can silently miss bugs in the production code or loudly ring false alarms when the production code is correct. We present the first empirical study of bugs in test code to characterize their prevalence and root cause categories. We mine the bug repositories and version control systems of 211 Apache Software Foundation (ASF) projects and find 5,556 test-related bug reports. We (1) compare properties of test bugs with production bugs, such as active time and fixing effort needed, and (2) qualitatively study 443 randomly sampled test bug reports in detail and categorize them based on their impact and root causes. Our results show that (1) around half of all the projects had bugs in their test code; (2) the majority of test bugs are false alarms, i.e., test fails while the production code is correct, while a minority of these bugs result in silent horrors, i.e., test passes while the production code is incorrect; (3) incorrect and missing assertions are the dominant root cause of silent horror bugs; (4) semantic (25%), flaky (21%), environment-related (18%) bugs are the dominant root cause categories of false alarms; (5) the majority of false alarm bugs happen in the exercise portion of the tests, and (6) developers contribute more actively to fixing test bugs and test bugs are fixed sooner compared to production bugs. In addition, we evaluate whether existing bug detection tools can detect bugs in test code.


title: Evaluation of LoRa receiver performance under co-technology interference

abstract: LoRa networks achieve a remarkably wide coverage as compared to that of the conventional wireless systems by employing the chirp spread spectrum (CSS). However, single-hop LoRa networks have limited penetration in indoor environments and cannot efficiently utilize the multiple-access dimensions arising from different spreading factors (SF) because the star topology is typically used for LoRa. On the other hand, a multi-hop LoRa network has the potential to realize extensive network coverage by utilizing packet relaying and improving the network capacity by utilizing different SFs. We evaluated the LoRa receiver performance under co-technology interference via simulation and experiments to realize these potentials. The results show that LoRa can survive interference from time-synchronized packets transmitted by multiple transmitters with an identical SF. Furthermore, we examined the orthogonality between different SFs by evaluating the required signal-to-interference ratio (SIR). Finally, we demonstrated the possibility of employing different SFs to construct the pipeline in a multi-hop relay network to increase network efficiency.


title: Convolutional Deep Stacking Networks for distributed compressive sensing

abstract: This paper addresses the reconstruction of sparse vectors in the Multiple Measurement Vectors (MMV) problem in compressive sensing, where the sparse vectors are correlated. This problem has so far been studied using model based and Bayesian methods. In this paper, we propose a deep learning approach that relies on a Convolutional Deep Stacking Network (CDSN) to capture the dependency among the different channels. To reconstruct the sparse vectors, we propose a greedy method that exploits the information captured by CDSN. The proposed method encodes the sparse vectors using random measurements (as done usually in compressive sensing). Experiments using a real world image dataset show that the proposed method outperforms the traditional MMV solver, i.e., Simultaneous Orthogonal Matching Pursuit (SOMP), as well as three of the Bayesian methods proposed for solving the MMV compressive sensing problem. We also show that the proposed method is almost as fast as greedy methods. The good performance of the proposed method depends on the availability of training data (as is the case in all deep learning methods). The training data, e.g., different images of the same class or signals with similar sparsity patterns are usually available for many applications.


title: Inferring relations in knowledge graphs with tensor decompositions

abstract: Multi-relational data, like knowledge graphs, are generated from multiple data sources by extracting entities and their relationships. We often want to include inferred, implicit or likely relationships that are not explicitly stated, which can be viewed as link-prediction in a graph. Tensor decomposition models have been shown to produce state-of-the-art results in link-prediction tasks. We describe a simple but novel extension to an existing tensor decomposition model to predict missing links using similarity among tensor slices, as opposed to an existing tensor decomposition models which assumes each slice to contribute equally in predicting links. Our extended model performs better than the original tensor decomposition and the non-negative tensor decomposition variant of it in an evaluation on several datasets.


title: PreparedJS: secure script-templates for javascript

abstract: Content Security Policies (CSP) provide powerful means to mitigate most XSS exploits. However, CSP's protection is incomplete. Insecure server-side JavaScript generation and attacker control over script-sources can lead to XSS conditions which cannot be mitigated by CSP. In this paper we propose PreparedJS, an extension to CSP which takes these weaknesses into account. Through the combination of a safe script templating mechanism with a light-weight script checksumming scheme, PreparedJS is able to fill the identified gaps in CSP's protection capabilities.


title: Graph structure in the Web

abstract: The study of the web as a graph is not only fascinating in its own right, but also yields valuable insight into web algorithms for crawling, searching and community discovery, and the sociological phenomena which characterize its evolution. We report on experiments on local and global properties of the web graph using two Altavista crawls each with over 200 million pages and 1.5 billion links. Our study indicates that the macroscopic structure of the web is considerably more intricate than suggested by earlier experiments on a smaller scale.


A Deep Learning Approach to Universal Skin Disease Classification


title: Improving 3D face reconstruction from a single image using half-frontal face poses

abstract: In this work we evaluate the influence of pose variation on 3D face reconstruction from a single image. To this end, we present a 3D reconstruction method that combines a fitting technique and a sparse 3D deformable model to estimate the 3D information of 2D images with large pose variations. For our experiments, we synthetically created 2D images by rendering 3D models from the BU-3DFE database in different points of view. Thus, we have a precise ground truth that allows performing a quantitative analysis of the reconstruction accuracy. Our experimental results show that the reconstruction achieves the highest accuracy when using half-frontal face images, and is also more robust to noise and incorrect facial landmarks positioning.


title: Being in a Virtual World: Presence, Environment, Salience, Sound

abstract: We present a theoretical model of how presence is achieved in virtual worlds, from which future frameworks prescribing the sensory design of such worlds can be constructed. Drawing on the study of cognitive metaphors and the concept that sound is an emergent perception we offer an account of the environment as a salient and dynamic construct that functions as a metonym for the nonself. Separating environment from world, we discuss the role of sound in the forming of the environment and argue that it is this environment that establishes the means for presence because it is the process behind the construction of the environment that individuates self from nonself and such differentiation is a defining feature of the concept of presence.


title: GPU Asynchronous Stochastic Gradient Descent to Speed Up Neural Network Training

abstract: The ability to train large-scale neural networks has resulted in state-of-the-art performance in many areas of computer vision. These results have largely come from computational break throughs of two forms: model parallelism, e.g. GPU accelerated training, which has seen quick adoption in computer vision circles, and data parallelism, e.g. A-SGD, whose large scale has been used mostly in industry. We report early experiments with a system that makes use of both model parallelism and data parallelism, we call GPU A-SGD. We show using GPU A-SGD it is possible to speed up training of large convolutional neural networks useful for computer vision. We believe GPU A-SGD will make it possible to train larger networks on larger training sets in a reasonable amount of time.


title: Brain tumor segmentation using Cuckoo Search optimization for Magnetic Resonance Images

abstract: Nature enthused algorithms are the most potent for optimization. Cuckoo Search (CS) algorithm is one such algorithm which is efficient in solving optimization problems in varied fields. This paper appraises the basic concepts of cuckoo search algorithm and its application towards the segmentation of brain tumor from the Magnetic Resonance Images (MRI). The human brain is the most complex structure where identifying the tumor like diseases are extremely challenging because differentiating the components of the brain is complex. The tumor may sometimes occur with the same intensity of normal tissues. The tumor, edema, blood clot and some part of brain tissues appear as same and make the work of the radiologist more complex. In general the brain tumor is detected by radiologist through a comprehensive analysis of MR images, which takes substantially a longer time. The key inventiveness is to develop a diagnostic system using the best optimization technique called the cuckoo search, that would assist the radiologist to have a second opinion regarding the presence or absence of tumor. This paper explores the CS algorithm, performing a profound study of its search mechanisms to discover how it is efficient in detecting tumors and compare the results with the other commonly used optimization algorithms.


title: An Improved Design for a 1–18 GHz Double-Ridged Guide Horn Antenna

abstract: It is a well known fact that the traditional 1-18 GHz double ridge guide horn (DRGH) antenna suffers from pattern deterioration above 12 GHz. At these frequencies, instead of maintaining a single main lobe radiation pattern, the pattern splits up into four lobes. It was shown in the literature that higher order modes are causing the pattern breakup. A benchmark study is performed to establish the performance of typical current and historic 1-18 GHz DRGH antennas. The performance of the antennas are evaluated in terms of gain, VSWR and radiation patterns. An improved 1-18 GHz DRGH antenna is presented. The new design has better gain and VSWR performance without any pattern deterioration. It also consists of significantly fewer parts, reducing the possibility of performance deterioration due to gaps between parts. Two prototypes of the new design were manufactured and tested with excellent agreement between measured and simulated results. The aperture dimensions of the new design are identical to that of the traditional DRGH, making it the only 1-18 GHz DRGH without pattern breakup whose aperture dimensions comply with the requirements specified in MIL-STD-461F - 24.2 by 13.6 cm.