title: An Improved Technique for Small-Scale Radio-Tracking of Crayfish and Benthic Fishes in Upland Streams

abstract: Abstract This study measured the accuracy of two radio-tracking techniques: (1) triangulation using a Yagi antenna and (2) a form of direct location, the extended-reach technique, which uses a small loop antenna attached to a lightweight 9-m telescopic pole. The study was conducted in the Cotter River, an upland stream in the Australian Capital Territory. Radio tags were positioned instream to determine whether depth, substrate, or local bank profile affected radio-tracking accuracy. The extended-reach technique was more accurate (mean error ± SE = 1.00 ± 0.21 m) than triangulation (2.57 ± 0.21 m). Decreased accuracy resulted from the triangulation of radio tags positioned close to boulders or in water more than 1 m deep. These variables had no effect on the accuracy of the extended-reach technique. The presence or absence of a steep rocky bank did not affect the accuracy of either radio-tracking method. The increased accuracy of the extended-reach technique provides an improvement over traditional method...


title: Frequency compensation for two-stage operational amplifiers with improved power supply rejection ratio characteristic

abstract: A frequency compensation technique improving characteristic of power supply rejection ratio (PSRR) for two-stage operational amplifiers is presented. This technique is applicable to most known two-stage amplifier configurations. The detailed small-signal analysis of an exemplary amplifier with the proposed compensation and a comparison to its basic version reveal several benefits of the technique which can be effectively exploited in continuous-time filter designs. This comparison shows the possibility of PSRR bandwidth broadening of more than a decade, significant reduction of chip area, the unity-gain bandwidth and power consumption improvement. These benefits are gained at the cost of a non-monotonic phase characteristic of the open-loop differential voltage gain and limitation of a close-loop voltage gain. A prototype-integrated circuit, fabricated based on 0.35 μm complementary metal-oxide semiconductor technology, was used for the technique verification. Two pairs of amplifiers with the classical Miller compensation and a cascoded input stage were measured and compared to their improved counterparts. The measurement data fully confirm the theoretically predicted advantages of the proposed compensation technique.


title: Improvement of axial flux single-rotor single-stator induction machine performance by applying semi-magnetic wedges

abstract: This research investigates an axial flux singlerotor single-stator asynchronous motor (AFAM) with aluminum and copper cage windings. In order to avoid using die casting of the rotor cage winding an open rotor slot structure was implemented. In future, this technique allows using copper cage winding avoiding critically high temperature treatment as in the die casting processing of copper material. However, an open slot structure leads to a large equivalent air gap length. Therefore, semi-magnetic wedges should be used to reduce the effect of open slots and consequently to improve the machine performance. The paper aims to investigate the feasibility of using open slot rotor structure (for avoiding die casting) and impact of semi-magnetic wedges to eliminate negative effects of open slots. The results were mainly obtained by 2D finite element method (FEM) simulations. Measurement results of mechanical performance of the prototype (with aluminum cage winding) given in the paper prove the simulated results.


title: Fast scan algorithms on graphics processors

abstract: Scan and segmented scan are important data-parallel primitives for a wide range of applications. We present fast, work-efficient algorithms for these primitives on graphics processing units (GPUs). We use novel data representations that map well to the GPU architecture. Our algorithms exploit shared memory to improve memory performance. We further improve the performance of our algorithms by eliminating shared-memory bank conflicts and reducing the overheads in prior shared-memory GPU algorithms. Furthermore, our algorithms are designed to work well on general data sets, including segmented arrays with arbitrary segment lengths. We also present optimizations to improve the performance of segmented scans based on the segment lengths. We implemented our algorithms on a PC with an NVIDIA GeForce 8800 GPU and compared our results with prior GPU-based algorithms. Our results indicate up to 10x higher performance over prior algorithms on input sequences with millions of elements.


title: DOI:10.1068/d394t Feel the presence: technologies of touch and distance

abstract: Haptic devices for computers and video-game consoles aim to reproduce touch and to engage the user with ‘force feedback’. Although physical touch is often associated with proximity and intimacy, technologies of touch can reproduce such sensations over a distance, allowing intricate and detailed operations to be conducted through a network such as the Internet. The ‘virtual handshake’ between Boston and London in 2002 is given as an example. This paper is therefore a critical investigation into some technologies of touch, leading to observations about the sociospatial framework in which this technological touching takes place. Haptic devices have now become routinely included with video-game consoles, and have started to be used in computer-aided design and manufacture, medical simulation, and even the cybersex industry. The implications of these new technologies are enormous, as they remould the human–computer interface from being primarily audiovisual to being more truly multisensory, and thereby enhance...


title: Impact of soft resource allocation on n-tier application scalability

abstract: Good performance and efficiency, in terms of high quality of service and resource utilization for example, are important goals in a cloud environment. Through extensive measurements of an n-tier application benchmark (RUBBoS), we show that overall system performance is surprisingly sensitive to appropriate allocation of soft resources (e.g., server thread pool size). Inappropriate soft resource allocation can quickly degrade overall application performance significantly. Concretely, both under-allocation and over-allocation of thread pool can lead to bottlenecks in other resources because of non-trivial dependencies. We have observed some non-obvious phenomena due to these correlated bottlenecks. For instance, the number of threads in the Apache web server can limit the total useful throughput, causing the CPU utilization of the C-JDBC clustering middleware to decrease as the workload increases. We provide a practical iterative solution approach to this challenge through an algorithmic combination of operational queuing laws and measurement data. Our results show that soft resource allocation plays a central role in the performance scalability of complex systems such as n-tier applications in cloud environments.


title: Seasonal changes of photosynthetic characteristics and pigment composition of Sabina vulgaris ant. under water stress

abstract: To explore the eco-physiological adaptation of Sabina vulgaris to drought stress,a drought-controlled experiment was performed using PEG(Polyethylene glycol) with three levels of osmotic potential(0.02,-0.1 and-0.34 MPa,respectively) in the culture solution in the greenhouse for 6 years(1997-2003).The seasonal changes of photosynthetic characteristics and pigment composition were investigated for the whole year in 2003.The pattern of seasonal change in stomatal conductance was shown in bimodal distribution with peaks in both May and September in 2003,and the stomatal conductance in the control was much higher than that of the other two water stress treatments,while the photosynthetic rate of low water stress was the highest among the three treatments.The ratio of chlorophyll a to chlorophyll b(Chl a/b) increased for all three treatments through November to March next year,particularly at the high water stress condition.The total leaf Chl content(Chl a+b) increased for all the three treatments through March to September,and the value was the lowest with the high water stress treatment.The total xanthophyll content(V+A+Z) and(A+Z)/(V+A+Z) were significantly higher in winter(November to March) than in summer(May to July) compared to that in other seasons,which was particularly obvious with high drought treatment.


title: GPU Accelerated Sub-Sampled Newton's Method

abstract: First order methods, which solely rely on gradient information, are commonly used in diverse machine learning (ML) and data analysis (DA) applications. This is attributed to the simplicity of their implementations, as well as low per-iteration computational/storage costs. However, they suffer from significant disadvantages; most notably, their performance degrades with increasing problem ill-conditioning. Furthermore, they often involve a large number of hyper-parameters, and are notoriously sensitive to parameters such as the step-size. By incorporating additional information from the Hessian, second-order methods, have been shown to be resilient to many such adversarial effects. However, these advantages of using curvature information come at the cost of higher per-iteration costs, which in \enquote{big data} regimes, can be computationally prohibitive. ::: In this paper, we show that, contrary to conventional belief, second-order methods, when implemented appropriately, can be more efficient than first-order alternatives in many large-scale ML/ DA applications. In particular, in convex settings, we consider variants of classical Newton\textsf{'}s method in which the Hessian and/or the gradient are randomly sub-sampled. We show that by effectively leveraging the power of GPUs, such randomized Newton-type algorithms can be significantly accelerated, and can easily outperform state of the art implementations of existing techniques in popular ML/ DA software packages such as TensorFlow. Additionally these randomized methods incur a small memory overhead compared to first-order methods. In particular, we show that for million-dimensional problems, our GPU accelerated sub-sampled Newton\textsf{'}s method achieves a higher test accuracy in milliseconds as compared with tens of seconds for first order alternatives.


title: Rigor in Qualitative Research: The Assessment of Trustworthiness

abstract: Despite a growing interest in qualitative research in occupational therapy, little attention has been placed on establishing its rigor. This article presents one model that can be used for the assessment of trustworthiness or merit of qualitative inquiry. Guba's (1981) model describes four general criteria for evaluation of research and then defines each from both a quantitative and a qualitative perspective. Several strategies for the achievement of rigor in qualitative research useful for both researchers and consumers of research are described.


title: Copy-Move Image Forgery Detection Using Local Binary Pattern and Neighborhood Clustering

abstract: This paper introduces a copy-move image forgery detection method based on local binary patterns (LBP) and neighborhood clustering. In the proposed method, an image is first decomposed into three color components. LBP histograms are then calculated from overlapping blocks from each component. The histogram distance between the blocks is calculated and the block-pairs having the minimal distance are retained. If the retained block-pairs are present in all the three color components, they are selected as primary candidates. 8-connected neighborhood clustering is then applied to refine the candidates. The proposed method shows significant improvement in reducing the false positive rates over some recent related methods.


title: Mental health professionals’ attitudes towards mental illness: professional and cultural factors in the INTER NOS study

abstract: Background ::: Research shows that personnel working in mental health facilities may share some of the societal prejudices towards mental illness. This might result in stigmatizing behaviours towards people suffering from mental disorders, undermining the quality of their care.


title: A single-chip tri-band (2100, 1900, 850/800 MHz) WCDMA/HSDPA cellular transceiver

abstract: This paper describes the design and performance of the first tri-band (2100, 1900, 800/850 MHz) single-chip 3G cellular transceiver IC for worldwide use. The transceiver has been designed to meet all narrowband blocker, newly proposed Adjacent Channel II, and Category 10 HSDPA (High Speed Downlink Packet Access) requirements. The design is part of a reconfigurable reference platform for multi-band, multi-mode (GSM/EDGE + WCDMA) radios. The zero-IF receiver is comprised of a novel multi-band quadrature mixer, seventh-order baseband filtering, and a novel DC offset correction scheme, which exhibits no settling time or peak switching transients after gain steps. The receiver lineup is designed to optimize HSDPA throughput and minimize sensitivity to analog baseband filter bandwidth variations. The direct-launch transmitter is made up of a third-order baseband filter, an I/Q modulator with variable gain, an integrated transformer, an RF variable gain amplifier, and a power amplifier driver. At +9.5-dBm output power, the transmitter achieves an error vector magnitude (EVM) of 4%. Fractional-N synthesizers achieve fast lock times of 50 /spl mu/s (150 /spl mu/s) within 20 ppm (0.1 ppm). Automatically calibrated, integrated VCOs achieve a 1.6-GHz tuning range to facilitate coverage over all six 3GPP frequency bands. The IC draws 34 mA in receive (18-mA receiver plus 16-mA fractional-N PLL/VCO) and 50 to 62 mA in transmit (-76 dBm to +9.5 dBm), including PLL/VCO, using a 2.775-V supply voltage. The RF transceiver is integrated with the baseband signal processing and associated passives in a 165-pad package, resulting in the first tri-band 3G radio transceiver with a digital interface which requires no external components.


title: Pose estimation of anime/manga characters: a case for synthetic data

abstract: 2D articulated pose estimation is the task of locating the body joint positions of a human figure in an image. A pose estimator that works on anime/manga images could be an important component of an automatic system to create 3D animation from existing manga or anime, which could significantly the lower cost of media production. To create an accurate pose estimator, however, a sizable and high-quality dataset is needed, and such a dataset can be expensive to create. To alleviate data scarcity, we propose using a database of 3D character models and poses to generate synthetic training data for 2D pose estimators of anime/manga characters based on convolutional neural networks (CNN). We demonstrate that a high-performing estimator can be obtained by pretraining a network on a large synthetic dataset and then fine-tuning it on a small dataset of drawings. We also show that the approach yields a pose estimator competitive with many previous works when applied to a photograph-based dataset, establishing our synthetic data's usefulness beyond the intended domain.


title: Smartphone App Use Among Medical Providers in ACGME Training Programs

abstract: The past decade has witnessed the advent of the smartphone, a device armed with computing power, mobility and downloadable "apps," that has become commonplace within the medical field as both a personal and professional tool. The popularity of medically-related apps suggests that physicians use mobile technology to assist with clinical decision making, yet usage patterns have never been quantified. A digital survey examining smartphone and associated app usage was administered via email to all ACGME training programs. Data regarding respondent specialty, level of training, use of smartphones, use of smartphone apps, desired apps, and commonly used apps were collected and analyzed. Greater than 85% of respondents used a smartphone, of which the iPhone was the most popular (56%). Over half of the respondents reported using apps in their clinical practice; the most commonly used app types were drug guides (79%), medical calculators (18%), coding and billing apps (4%) and pregnancy wheels (4%). The most frequently requested app types were textbook/reference materials (average response: 55%), classification/treatment algorithms (46%) and general medical knowledge (43%). The clinical use of smartphones and apps will likely continue to increase, and we have demonstrated an absence of high-quality and popular apps despite a strong desire among physicians and trainees. This information should be used to guide the development of future healthcare delivery systems; expanded app functionality is almost certain but reliability and ease of use will likely remain major factors in determining the successful integration of apps into clinical practice.


title: A novel method for digital image steganography based on a new three-dimensional chaotic map

abstract: This paper, presents a novel chaos-based image steganography algorithm. Because of efficient property of chaos based security systems besides steganography applicability in providing secure communication, chaos based steganography algorithms served as a hot topic in recent researches. The proposed scheme possess novelties and advantageous such as: 1) Introducing a novel 3-dimensional chaotic map (LCA map) with strong chaotic characteristics and maximum Lyapunov exponent 20.58, which is used for generating three chaotic sequences, each of them represents the number of row, column, and colour component, respectively. 2) Utilizing random selection procedure for selecting subsequences with length of 2L, which L is the length of secret message 3) Specifying L pairs of triples host positions for embedding LSBs and MSBs of secret message by using three high level chaotic maps. 4) Entering some parameters dependent on elementary initial values, host image, and secret message features as a key point for adding additional layer of security alongside providing high sensitivity. 5) Providing high capacity for embedding secret message, which is equal to 50 % of whole image capacity (Mź×źNź×ź12). The proposed method could be applied in different criterion such as, confidential communication and data storing, protection of data alteration, and etc. Our experimental results guarantees that our scheme is not only robust against differential attacks, but also has promising results such as highly sensitive keys, Quality index, PSNR, MSE, and hiding capacity as shown in statistical security analysis.


title: An Electrically Small Spherical UHF RFID Tag Antenna With Quasi-Isotropic Patterns for Wireless Sensor Networks

abstract: An electrically small spherical UHF (911.25 MHz) radio frequency identification (RFID) tag antenna is proposed. A dipole-typed RFID tag antenna is wound into a spherical shape to achieve small size and quasi-isotropic pattern. The overall diameter of the antenna is 25 mm (0.076?), and the resulting electrical size is a kr of 0.24. A short stub is employed for good conjugate impedance matching with a RFID chip (Zchip = 14 - j145 ?). The designed antenna has a -10-dB bandwidth of 7 MHz (0.77%) and the maximum readable distance of 132 cm at 5.2 W EIRP.


title: Action Recognition With Spatio–Temporal Visual Attention on Skeleton Image Sequences

abstract: Action recognition with 3D skeleton sequences became popular due to its speed and robustness. The recently proposed convolutional neural networks (CNNs)-based methods show a good performance in learning spatio–temporal representations for skeleton sequences. Despite the good recognition accuracy achieved by previous CNN-based methods, there existed two problems that potentially limit the performance. First, previous skeleton representations were generated by chaining joints with a fixed order. The corresponding semantic meaning was unclear and the structural information among the joints was lost. Second, previous models did not have an ability to focus on informative joints. The attention mechanism was important for skeleton-based action recognition because different joints contributed unequally toward the correct recognition. To solve these two problems, we proposed a novel CNN-based method for skeleton-based action recognition. We first redesigned the skeleton representations with a depth-first tree traversal order, which enhanced the semantic meaning of skeleton images and better preserved the associated structural information. We then proposed the general two-branch attention architecture that automatically focused on spatio–temporal key stages and filtered out unreliable joint predictions. Based on the proposed general architecture, we designed a global long-sequence attention network with refined branch structures. Furthermore, in order to adjust the kernel’s spatio–temporal aspect ratios and better capture long-term dependencies, we proposed a sub-sequence attention network (SSAN) that took sub-image sequences as inputs. We showed that the two-branch attention architecture could be combined with the SSAN to further improve the performance. Our experiment results on the NTU RGB+D data set and the SBU kinetic interaction data set outperformed the state of the art. The model was further validated on noisy estimated poses from the subsets of the UCF101 data set and the kinetics data set.


title: LogSed: Anomaly Diagnosis through Mining Time-Weighted Control Flow Graph in Logs

abstract: Detecting execution anomalies is very important to monitoring and maintenance of cloud systems. People often use execution logs for troubleshooting and problem diagnosis, which is time consuming and error-prone. There is great demand for automatic anomaly detection based on logs. In this paper, we mine a time-weighted control flow graph (TCFG) that captures healthy execution flows of each component in cloud, and automatically raise anomaly alerts on observing deviations from TCFG. We outlined three challenges that are solved in this paper, including how to deal with the interleaving of multiple threads in logs, how to identify operational logs that do not contain any transactional information, and how to split the border of each transaction flow in the TCFG. We evaluate the effectiveness of our approach by leveraging logs from an IBM public cloud production platform and two simulated systems in the lab environment. The evaluation results show that our TCFG mining and anomaly diagnosis both perform over 80% precision and recall on average.


Menisci around plates and pins dipped in liquid: Interpretation of Wilhelmy plate and solderability measurements


title: A Static Analysis Framework For Detecting SQL Injection Vulnerabilities

abstract: Recently SQL injection attack (SIA) has become a major threat to Web applications. Via carefully crafted user input, attackers can expose or manipulate the back-end database of a Web application. This paper proposes the construction and outlines the design of a static analysis framework (called SAFELI) for identifying SIA vulnerabilities at compile time. SAFELI statically inspects MSIL bytecode of an ASP.NET Web application, using symbolic execution. At each hotspot that submits SQL query, a hybrid constraint solver is used to find out the corresponding user input that could lead to breach of information security. Once completed, SAFELI has the future potential to discover more delicate SQL injection attacks than black-box Web security inspection tools.


title: Predicting exchange rates with sentiment indicators: An empirical evaluation using text mining and multilayer perceptrons

abstract: Recent innovations in text mining facilitate the use of novel data for sentiment analysis related to financial markets, and promise new approaches to the field of behavioural finance. Traditionally, text mining has allowed a near-real time analysis of available news feeds. The recent dissemination of web 2.0 has seen a drastic increase of user participation, providing comments on websites, social networks and blogs, creating a novel source of rich and personal sentiment data potentially of value to behavioural finance. This study explores the efficacy of using novel sentiment indicators from MarketPsych, which analyses social media in addition to newsfeeds to quantify various levels of individual's emotions, as a predictor for financial time series returns of the Australian Dollar (AUD) — US Dollar (USD) exchange rate. As one of the first studies evaluating both news and social media sentiment indicators as explanatory variables for linear and nonlinear regression algorithms, our study aims to make an original contribution to behavioural finance, combining technical and behavioural aspects of model building. An empirical out-of-sample evaluation with multiple input structures compares multivariate linear regression models (MLR) with multilayer perceptron (MLP) neural networks for descriptive modelling. The results indicate that sentiment indicators are explanatory for market movements of exchange rate returns, with nonlinear MLPs showing superior accuracy over linear regression models with a directional out-of-sample accuracy of 60.26% using cross validation.


title: Retinal Vessel Segmentation Using Matched Filter with Joint Relative Entropy

abstract: The matched filter is an effective method for the detection of retinal vessels when combined with other processing techniques. This paper presents a segmentation method to improve the extraction of retinal vessels based on the matched filter. The method combines a morphological approach to enhance retinal vessels before applying the matched filter and a modified joint relative entropy (MJRE) thresholding method to segment the matched filter response. The morphological approach is designed to suppress irregular bright regions and noise while preserving the information of vessel edges, and to improve the contrast of vessels, especially thin ones. The joint relative entropy thresholding is modified to provide an optimal threshold value for segmenting the retinal vessel tree properly. The proposed method is tested on the DRIVE dataset, yielding an average accuracy, specificity and sensitivity of 0.9546, 0.9742 and 0.7527 respectively. Experimental results demonstrate that the proposed method achieved better performance than the state-of-the-art methods.


title: Broadcasting Convolutional Network for Visual Relational Reasoning

abstract: In this paper, we propose the Broadcasting Convolutional Network (BCN) that extracts key object features from the global field of an entire input image and recognizes their relationship with local features. BCN is a simple network module that collects effective spatial features, embeds location information and broadcasts them to the entire feature maps. We further introduce the Multi-Relational Network (multiRN) that improves the existing Relation Network (RN) by utilizing the BCN module. In pixel-based relation reasoning problems, with the help of BCN, multiRN extends the concept of ‘pairwise relations’ in conventional RNs to ‘multiwise relations’ by relating each object with multiple objects at once. This yields in \(\mathcal {O}(n)\) complexity for n objects, which is a vast computational gain from RNs that take \(\mathcal {O}(n^2)\). Through experiments, multiRN has achieved a state-of-the-art performance on CLEVR dataset, which proves the usability of BCN on relation reasoning problems.


title: Formal Language Theory For Natural Language Processing

abstract: This paper reports on a course whose aim is to introduce Formal Language Theory to students with little formal background (mostly linguistics students). The course was first taught at the European Summer School for Logic, Language and Information to a mixed audience of students, undergraduate, graduate and postgraduate, with various backgrounds. The challenges of teaching such a course include preparation of highly formal, mathematical material for students with relatively little formal background; attracting the attention of students of different backgrounds; preparing examples that will emphasize the practical importance of material which is basically theoretical; and evaluation of students' achievements.


title: Differential effects of exposure to ambient vanilla and citrus aromas on mood, arousal and food choice

abstract: Background ::: Aromas have been associated with physiological, psychological affective and behavioral effects. We tested whether effects of low-level exposure to two ambient food-related aromas (citrus and vanilla) could be measured with small numbers of subjects, low-cost physiological sensors and semi-real life settings. Tests included physiological (heart rate, physical activity and response times), psychological (emotions and mood) and behavioral (food choice) measures in a semi-real life environment for 22 participants.


title: Calculating stack distances efficiently

abstract: This paper1 describes our experience using the stack processing algorithm [6] for estimating the number of cache misses in scientific programs. By using a new data structure and various optimization techniques we obtain instrumented run-times within 50 to 100 times the original optimized run-times of our benchmarks.


title: Reinforcement Learning for Autonomous UAV Navigation Using Function Approximation

abstract: Ahstract- Unmanned aerial vehicles (UAV) are commonly used for search and rescue missions in unknown environments, where an exact mathematical model of the environment may not be available. This paper proposes a framework for the UAV to locate a missing human after a natural disaster in such environment, using a reinforcement learning (RL) algorithm. A function approximation based RL algorithm is proposed to deal with a large number of states representation and to obtain a faster convergence time. We conducted both simulated and real implementations to show how the UAVs can successfully learn to carry out the task without colliding with obstacles. Technical aspects for applying RL algorithm to a UAV system and UAV flight control were also addressed.


title: PATUS: A Code Generation and Autotuning Framework for Parallel Iterative Stencil Computations on Modern Microarchitectures

abstract: Stencil calculations comprise an important class of kernels in many scientific computing applications ranging from simple PDE solvers to constituent kernels in multigrid methods as well as image processing applications. In such types of solvers, stencil kernels are often the dominant part of the computation, and an efficient parallel implementation of the kernel is therefore crucial in order to reduce the time to solution. However, in the current complex hardware micro architectures, meticulous architecture-specific tuning is required to elicit the machine's full compute power. We present a code generation and auto-tuning framework \textsc{Patus} for stencil computations targeted at multi- and many core processors, such as multicore CPUs and graphics processing units, which makes it possible to generate compute kernels from a specification of the stencil operation and a parallelization and optimization strategy, and leverages the auto tuning methodology to optimize strategy-dependent parameters for the given hardware architecture.


title: Probabilistic diagnosis using a reformulation of the INTERNIST-1/QMR knowledge base. I. The probabilistic model and inference algorithms.

abstract: In Part I of this two-part series, we report the design of a probabilistic reformulation of the Quick Medical Reference (QMR) diagnostic decision-support tool. We describe a two-level multiply connected belief-network representation of the QMR knowledge base of internal medicine. In the belief-network representation of the QMR knowledge base, we use probabilities derived from the QMR disease profiles, from QMR imports of findings, and from National Center for Health Statistics hospital-discharge statistics. We use a stochastic simulation algorithm for inference on the belief network. This algorithm computes estimates of the posterior marginal probabilities of diseases given a set of findings. In Part II of the series, we compare the performance of QMR to that of our probabilistic system on cases abstracted from continuing medical education materials from Scientific American Medicine. In addition, we analyze empirically several components of the probabilistic model and simulation algorithm.


title: Physical Layer Network Security in the Full-Duplex Relay System

abstract: This paper investigates the secrecy performance of full-duplex relay (FDR) networks. The resulting analysis shows that FDR networks have better secrecy performance than half duplex relay networks, if the self-interference can be well suppressed. We also propose a full duplex jamming relay network, in which the relay node transmits jamming signals while receiving the data from the source. While the full duplex jamming scheme has the same data rate as the half duplex scheme, the secrecy performance can be significantly improved, making it an attractive scheme when the network secrecy is a primary concern. A mathematic model is developed to analyze secrecy outage probabilities for the half duplex, the full duplex and full duplex jamming schemes, and the simulation results are also presented to verify the analysis.


A Modified NoC Router Architecture with Fixed Priority Arbiter


title: Low Complexity Fixed-Point Approximation of Inverse Discrete Cosine Transform

abstract: Thispaper presents anefficient algorithm forcomputing the Inverse Discrete Cosine Transform (IDCT)forimageand video coding applications. Thisalgorithm wassubmitted in response toMPEG'scall forproposals forISO/IEC 23002-2 (Fixed-Point 8x8IDCTandDCT)standard, andwas subsequently adopted intheWorkingDraft1 ofthis standard. Ourproposed algorithm isamultiplication-f ree implementation. Itisbased onamodification ofArai, Agui, andNakajima's (AAN)factorization, andrequires only42 addition and16shift operations perscaled IDtransform. Eachregister inourscaled IDtransform requires atmost22 bits. Thisimplementation complies withtheMPEG IDCT precision specification ISO/IEC 23002-1. IndexTerms - DCT,IDCT,factorization, multiplier-less algorithms


title: Application of Wavelet Scalogram and Coscalogram for Analysis of Biomedical Signals

abstract: In this paper, using wavelet scalograms and coscalogram of concurrent biomedical signals we were able to detect their short-lived temporal interactions. The complete analysis should include investigation of signal coherence in amplitude and phase. This is achieved with a continuous complex wavelet transform. Thus, in the algorithms developed, we use the Morlet wavelet transform and some measures based on scalograms and coscalograms. The algorithms have been developed that assist in quick and simple data analysis. Our analysis gives an indication of the cross-correlation of signals in time and frequency domain. The simultaneously recorded biomedical time series signals used, come from MIMIC database. The determined time shift between these signals, that we used to obtain adequate correlation results, may also be useful to determine various delays or information transmission times in human system.


title: Clock-Enhancing Small Molecules and Potential Applications in Chronic Diseases and Aging

abstract: Normal physiological functions require a robust biological timer called the circadian clock. When clocks are dysregulated, misaligned, or dampened, pathological consequences ensue, leading to chronic diseases and accelerated aging. An emerging research area is the development of clock-targeting compounds that may serve as drug candidates to correct dysregulated rhythms and hence mitigate disease symptoms and age-related decline. In this review, we first present a concise view of the circadian oscillator, physiological networks, and regulatory mechanisms of circadian amplitude. Given a close association of circadian amplitude dampening and disease progression, clock-enhancing small molecules (CEMs) are of particular interest as candidate chronotherapeutics. A recent proof-of-principle study illustrated that the natural polymethoxylated flavonoid nobiletin directly targets the circadian oscillator and elicits robust metabolic improvements in mice. We describe mood disorders and aging as potential therapeutic targets of CEMs. Future studies of CEMs will shed important insight into the regulation and disease relevance of circadian clocks.


title: A block-based noise level estimation from X-ray images in SVD domain

abstract: Accurate and fast estimation of noise levels from medical images has numerous applications in medical image processing, including image enhancement, image segmentation and feature extraction. In this paper, a block-based noise level estimation algorithm in SVD domain is proposed. The proposed algorithm employs the non-overlapping block image segmentation to estimate homogenous image regions. Each homogenous block is used to obtain an independent noise level estimates in SVD domain. For any particular image, the overall noise level estimate is ascertained by averaging over the set of noise level estimates associated with the homogenous image blocks. In this paper, the optimal size of image segmentation blocks is evaluated systematically over a large dataset of x-ray images. The experimental results show that the proposed method offers numerous advantages over some alternative SVD domain method.


title: Design and analysis of direct teaching robot for human-robot cooperation

abstract: The interest in the human-robot cooperative robot has been increased, because it can work together cooperating with human being through physical interaction in the same space. The purpose of this paper is to develop direct teaching robot with high speed and high precision, which enables operator with no robotic experience to easily and conveniently teach robot the task trajectory. In the paper, 7-axis manipulator is designed through analysis of workspace and stiffness of each component are analyzed at each joint. Comparing to stiffness value at each joint, we optimize the link shape. Also, maximum displacement error is calculated at the end-effector using the stiffness of each joint and payload. In addition, applying this x-axis and y-axis stiffness of each joint to kinematic calculation, we achieve the precise kinematic calibration in the various postures.


title: 3D face landmark labelling

abstract: Most 3D face processing systems require feature detection and localisation, for example to crop, register, analyse or recognise faces. The three features often used in the literature are the tip of the nose, and the two inner corner of the eyes. Failure to localise these landmarks can cause the system to fail and they become very difficult to detect under large pose variations or when occlusion is present. In this paper, we present a proof-of-concept for a face labelling system capable of overcoming this problem, as a larger number of landmarks are employed. A set of points containing hand-placed landmarks is used as input data. The aim here is to retrieve the landmark's labels when some part of the face is missing. By using graph matching techniques to reduce the number of candidates, and translation and unit-quaternion clustering to determine a final correspondence, we evaluate the accuracy at which landmarks can be retrieved under changes in expression, orientation and in the presence of occlusions.


title: A 150nW, 5ppm/o C, 100kHz On-Chip clock source for ultra low power SoCs

abstract: This paper presents an ultra low power clock source using a 1μW temperature compensated on-chip digitally controlled oscillator (Osc CMP ) and a 100nW uncompensated oscillator (Osc UCMP ) with respective temperature stabilities of 5ppm/o C and 1.67%/o C. A fast locking circuit re-locks Osc UCMP to Osc CMP often enough to achieve a high effective temperature stability. Measurements of a 130nm CMOS chip show that this combination gives a stability of 5ppm/o C from 20o C to 40o C (14ppm/o C from 20o C to 70o C) at 150nW if temperature changes by 1o C or less every second. This result is 7X lower power than typical XTALs and 6X more stable than prior on-chip solutions.


title: Bilingualism: consequences for mind and brain

abstract: Building on earlier evidence showing a beneficial effect of bilingualism on children’s cognitive development, we review recent studies using both behavioral and neuroimaging methods to examine the effects of bilingualism on cognition in adulthood and explore possible mechanisms for these effects. This research shows that bilingualism has a somewhat muted effect in adulthood but a larger role in older age, protecting against cognitive decline, a concept known as ‘cognitive reserve’. We discuss recent evidence that bilingualism is associated with a delay in the onset of symptoms of dementia. Cognitive reserve is a crucial research area in the context of an aging population; the possibility that bilingualism contributes to cognitive reserve is therefore of growing importance as populations become increasingly diverse. Why bilingualism? It is generally believed that more than half of the world’s population is bilingual [1]. In each of the U.S.A. 1 and Canada 2 , approximately 20% of the population speaks a language at home other than English. These figures are higher in urban areas, rising to about 60% in Los Angeles 3 and 50% in Toronto 4 . In Europe, bilingualism is even more prevalent: in a recent survey, 56% of the population across all European Union countries reported being functionally bilingual, with some countries recording particularly high rates, such as Luxembourg at 99% 5 . Bilinguals, therefore, make up


title: Exploratory Gradient Boosting for Reinforcement Learning in Complex Domains

abstract: High-dimensional observations and complex real-world dynamics present major challenges in reinforcement learning for both function approximation and exploration. We address both of these challenges with two complementary techniques: First, we develop a gradient-boosting style, non-parametric function approximator for learning on $Q$-function residuals. And second, we propose an exploration strategy inspired by the principles of state abstraction and information acquisition under uncertainty. We demonstrate the empirical effectiveness of these techniques, first, as a preliminary check, on two standard tasks (Blackjack and $n$-Chain), and then on two much larger and more realistic tasks with high-dimensional observation spaces. Specifically, we introduce two benchmarks built within the game Minecraft where the observations are pixel arrays of the agent's visual field. A combination of our two algorithmic techniques performs competitively on the standard reinforcement-learning tasks while consistently and substantially outperforming baselines on the two tasks with high-dimensional observation spaces. The new function approximator, exploration strategy, and evaluation benchmarks are each of independent interest in the pursuit of reinforcement-learning methods that scale to real-world domains.


title: Exploring Latent Preferences for Context-Aware Personalized Recommendation Systems

abstract: Context-aware recommendations offer the potential of exploiting social contents and utilize related tags and rating information to personalize the search for content considering a given context. Recommendation systems tackle the problem of trying to identify relevant resources from the vast number of choices available online. In this study, we propose a new recommendation model that personalizes recommendations and improves the user experience by analyzing the context when a user wishes to access multimedia content. We conducted empirical analysis on a dataset from last.fm to demonstrate the use of latent preferences for ranking items under a given context. Additionally, we use an optimization function to maximize the mean average precision measure of the resulted recommendation. Experimental results show a potential improvement to the quality of the recommendation in terms of accuracy when compared with state-of-the-art algorithms.


title: Finding Waldo: Learning about Users from their Interactions

abstract: Visual analytics is inherently a collaboration between human and computer. However, in current visual analytics systems, the computer has limited means of knowing about its users and their analysis processes. While existing research has shown that a user's interactions with a system reflect a large amount of the user's reasoning process, there has been limited advancement in developing automated, real-time techniques that mine interactions to learn about the user. In this paper, we demonstrate that we can accurately predict a user's task performance and infer some user personality traits by using machine learning techniques to analyze interaction data. Specifically, we conduct an experiment in which participants perform a visual search task, and apply well-known machine learning algorithms to three encodings of the users' interaction data. We achieve, depending on algorithm and encoding, between 62% and 83% accuracy at predicting whether each user will be fast or slow at completing the task. Beyond predicting performance, we demonstrate that using the same techniques, we can infer aspects of the user's personality factors, including locus of control, extraversion, and neuroticism. Further analyses show that strong results can be attained with limited observation time: in one case 95% of the final accuracy is gained after a quarter of the average task completion time. Overall, our findings show that interactions can provide information to the computer about its human collaborator, and establish a foundation for realizing mixed-initiative visual analytics systems.


title: Blood Flow and Blood Pressure in Fingers in Generalized Scleroderma

abstract: Sympathetic hyperactivity cannot be implicated in the peripheral vascular disturbances in patients with generalized scleroderma. On the contrary, there seems to be some evidence for a sympathetic hypoactivity in these patients and there seems to be good reason to abandon sympathectomy in the treatment. Vasodilator drugs can be hazardous in late-stage scleroderma due to the possibility of redistribution of blood flow and a decrease in systemic blood pressure. Even a small decrease in systemic blood pressure severely affects blood flow in the low pressure vascular bed found in fingertips of patients with generalized scleroderma.


title: Tolerating malicious device drivers in Linux

abstract: This paper presents SUD, a system for running existing Linux device drivers as untrusted user-space processes. Even if the device driver is controlled by a malicious adversary, it cannot compromise the rest of the system. One significant challenge of fully isolating a driver is to confine the actions of its hardware device. SUD relies on IOMMU hardware, PCI express bridges, and messagesignaled interrupts to confine hardware devices. SUD runs unmodified Linux device drivers, by emulating a Linux kernel environment in user-space. A prototype of SUD runs drivers for Gigabit Ethernet, 802.11 wireless, sound cards, USB host controllers, and USB devices, and it is easy to add a new device class. SUD achieves the same performance as an in-kernel driver on networking benchmarks, and can saturate a Gigabit Ethernet link. SUD incurs a CPU overhead comparable to existing runtime driver isolation techniques, while providing much stronger isolation guarantees for untrusted drivers. Finally, SUD requires minimal changes to the kernel--just two kernel modules comprising 4,000 lines of code--which may at last allow the adoption of these ideas in practice.


title: Targeted Restoration of the Intestinal Microbiota with a Simple, Defined Bacteriotherapy Resolves Relapsing Clostridium difficile Disease in Mice

abstract: Relapsing C. difficile disease in humans is linked to a pathological imbalance within the intestinal microbiota, termed dysbiosis, which remains poorly understood. We show that mice infected with epidemic C. difficile (genotype 027/BI) develop highly contagious, chronic intestinal disease and persistent dysbiosis characterized by a distinct, simplified microbiota containing opportunistic pathogens and altered metabolite production. Chronic C. difficile 027/BI infection was refractory to vancomycin treatment leading to relapsing disease. In contrast, treatment of C. difficile 027/BI infected mice with feces from healthy mice rapidly restored a diverse, healthy microbiota and resolved C. difficile disease and contagiousness. We used this model to identify a simple mixture of six phylogenetically diverse intestinal bacteria, including novel species, which can re-establish a health-associated microbiota and clear C. difficile 027/BI infection from mice. Thus, targeting a dysbiotic microbiota with a defined mixture of phylogenetically diverse bacteria can trigger major shifts in the microbial community structure that displaces C. difficile and, as a result, resolves disease and contagiousness. Further, we demonstrate a rational approach to harness the therapeutic potential of health-associated microbial communities to treat C. difficile disease and potentially other forms of intestinal dysbiosis.


title: Exploring the Space of Black-box Attacks on Deep Neural Networks

abstract: Existing black-box attacks on deep neural networks (DNNs) so far have largely focused on transferability, where an adversarial instance generated for a locally trained model can “transfer” to attack other learning models. In this paper, we propose novel Gradient Estimation black-box attacks for adversaries with query access to the target model’s class probabilities, which do not rely on transferability. We also propose strategies to decouple the number of queries required to generate each adversarial sample from the dimensionality of the input. An iterative variant of our attack achieves close to 100% adversarial success rates for both targeted and untargeted attacks on DNNs. We carry out extensive experiments for a thorough comparative evaluation of black-box attacks and show that the proposed Gradient Estimation attacks outperform all transferability based black-box attacks we tested on both MNIST and CIFAR-10 datasets, achieving adversarial success rates similar to well known, state-of-the-art white-box attacks. We also apply the Gradient Estimation attacks successfully against a real-world content moderation classiﬁer hosted by Clarifai. Furthermore, we evaluate black-box attacks against state-of-the-art defenses. We show that the Gradient Estimation attacks are very effective even against these defenses.


title: PREDICTION OF JUMPING DISTANCE USING A SHORT APPROACH MODEL

abstract: Recent evidence has suggested that relationship between approach speed and distance jumped may not be linear. The aims of this study were (1) to test the hypothesis that using a short approach (6-8-10-12 strides) of increasing length, performance variables will be non-linearly related to distance jumped, (2) to investigate the nature of these relationships for a group of long jumpers and individuals within the group, and (3) to use the regression analysis to determine the optimum number of run-up strides and predict the jumping distance that would be achieved with an optimum length run-up for an individual jumper. Eight male long jumpers with different skill levels (body mass: 75.2±2.2 kg and body height: 188.0±4.2 cm) performed a series of short-approach maximal jumps and the full-length approach in a competition. Kinematic data were collected from video analysis. The relationship between the number of approach strides and velocity, and distance jumped were shown to be best represented by second order polynomial equations. When applied on an individual basis, the predicted jump distances (6.95±.61 m) agreed very well with those found in actual competition (6.96±.58m). As a result, these individual relationships were used to comment on individual optimal approach lengths and to evaluate an individual’s potential for performance in the long jump event. It was concluded that the short approach model of performance is a valuable paradigm for investigating long jump behaviour and the performance potential of individuals. The findings also supported the simplified mathematical model proposed in the literature for the study of long jump performance.


title: Novel power MOSFET-based expander for high frequency ultrasound systems.

abstract: The function of an expander is to obstruct the noise signal transmitted by the pulser so that it does not pass into the transducer or receive electronics, where it can produce undesirable ring-down in an ultrasound imaging application. The most common type is a diode-based expander, which is essentially a simple diode-pair, is widely used in pulse-echo measurements and imaging applications because of its simple architecture. However, diode-based expanders may degrade the performance of ultrasonic transducers and electronic components on the receiving and transmitting sides of the ultrasound systems, respectively. Since they are non-linear devices, they cause excessive signal attenuation and noise at higher frequencies and voltages. In this paper, a new type of expander that utilizes power MOSFET components, which we call a power MOSFET-based expander, is introduced and evaluated for use in high frequency ultrasound imaging systems. The performance of a power MOSFET-based expander was evaluated relative to a diode-based expander by comparing the noise figure (NF), insertion loss (IL), total harmonic distortion (THD), response time (RT), electrical impedance (EI) and dynamic power consumption (DPC). The results showed that the power MOSFET-based expander provided better NF (0.76 dB), IL (-0.3 dB) and THD (-62.9 dB), and faster RT (82 ns) than did the diode-based expander (NF (2.6 dB), IL (-1.4 dB), THD (-56.0 dB) and RT (119 ns)) at 70 MHz. The -6 dB bandwidth and the peak-to-peak voltage of the echo signal received by the transducer using the power MOSFET-based expander improved by 17.4% and 240% compared to the diode-based expander, respectively. The new power MOSFET-based expander was shown to yield lower NF, IL and THD, faster RT and lower ring down than the diode-based expander at the expense of higher dynamic power consumption.


PACES TO T RANS-FER S KILLS WITH R EINFORCEMENT L EARNING


title: Efficient Spatio-Temporal Data Association Using Multidimensional Assignment in Multi-Camera Multi-Target Tracking.

abstract: This paper proposes a novel multi-target tracking method which jointly solves a data association problem using images from multiple cameras. In this work, the spatiotemporal data association problem is formulated as a multidimensional assignment problem (MDA). To achieve a fast, efficient, and easily implementable approximation algorithm, we solve the MDA problem approximately by solving a sequence of bipartite matching problems using random splitting and merging operations. In this formulation, we design a new cost function, considering the accuracy in 3D reconstruction, motion smoothness, visibility from cameras, starting/ending at entrance and exit zone, and false positive. Our approach reconstructs 3D trajectories that represent people’s movement as 3D cylinders whose locations are estimated considering all adjacent frames. The experiments illustrate the proposed method shows the state-of-the-art performance in challenging multi-camera datasets and the computational efficiency with 8 times faster computation than the existing BIP approach.


title: Coordination of Executive Functions in Monolingual and Bilingual Children

abstract: Two groups of 8-year-old children who were monolingual or bilingual completed a complex classification task in which they made semantic judgments on stimuli that were presented either visually or auditorily. The task requires coordinating a variety of executive control components, specifically working memory, inhibition, and shifting. When each of the visual and auditory tasks was presented alone, performance was comparable for children in the two groups. Combining the two modalities into a dual-task paradigm made the task more difficult, and on this combined task bilingual children maintained better accuracy than monolingual children, especially on the visual task. The results are interpreted in terms of the enhanced ability of bilingual children to coordinate the executive control components required in performing this complex task.


title: A framework for facial surgery simulation

abstract: The accurate prediction of the post-surgical facial shape is of paramount importance for surgical planning in facial surgery. In this paper we present a framework for facial surgery simulation which is based on volumetric finite element modeling. We contrast conventional procedures for surgical planning against our system by accompanying a patient during the entire process of planning, medical treatment and simulation. In various preprocessing steps a 3D physically based facial model is reconstructed from CT and laser range scans. All geometric and topological changes are modeled interactively using Alias.™ Applying fully 3D volumetric elasticity allows us to represent important volumetric effects such as incompressibility in a natural and physically accurate way. For computational efficiency, we devised a novel set of prismatic shape functions featuring a globally C1-continuous surface in combination with a C0 interior. Not only is it numerically accurate, but this construction enables us to compute smooth and visually appealing facial shapes.


title: OLINDDA: a cluster-based approach for detecting novelty and concept drift in data streams

abstract: A machine learning approach that is capable of treating data streams presents new challenges and enables the analysis of a variety of real problems in which concepts change over time. In this scenario, the ability to identify novel concepts as well as to deal with concept drift are two important attributes. This paper presents a technique based on the k-means clustering algorithm aimed at considering those two situations in a single learning strategy. Experimental results performed with data from various domains provide insight into how clustering algorithms can be used for the discovery of new concepts in streams of data.


title: Childhood sexual abuse and adult psychiatric and substance use disorders in women: An epidemiological and cotwin control analysis.

abstract: Background Women who report childhood sexual abuse (CSA) are at increased risk for developing psychiatric disorders in adulthood. What is the diagnostic specificity and cause of this association? Methods In a population-based sample of 1411 female adult twins, 3 levels of CSA were assessed by self-report and cotwin report: nongenital, genital, and intercourse. Interviews with twins and parents assessed family background and diagnoses of psychiatric and substance dependence disorders. Odds ratios (ORs) were calculated by logistic regression. Results By self-report, 30.4% reported any CSA and 8.4% reported intercourse. Self-reported CSA was positively associated with all disorders, the highest ORs being seen with bulimia and alcohol and other drug dependence. The ORs were modest and often nonsignificant with nongenital CSA and increased with genital CSA and especially intercourse, where most ORs exceeded 3.0. A similar pattern of findings was seen with CSA as reported by the cotwin, although many ORs were smaller. Controlling for family background factors and parental psychopathology produced a small to modest reduction in ORs. In twin pairs discordant for CSA, the exposed twin was at consistently higher risk of illness. Conclusions Women with CSA have a substantially increased risk for developing a wide range of psychopathology. Most of this association is due to more severe forms of CSA and cannot be explained by background familial factors. Although other biases cannot be ruled out, these results are consistent with the hypothesis that CSA is causally related to an increased risk for psychiatric and substance abuse disorders.


title: A framework for understanding chatbots and their future

abstract: Chatbots have rapidly become a mainstay in software development. A range of chatbots contribute regularly to the creation of actual production software. It is somewhat difficult, however, to precisely delineate hype from reality. Questions arise as to what distinguishes a chatbot from an ordinary software tool, what might be desirable properties of chatbots, and where their future may lie. This position paper introduces a starting framework through which we examine the current state of chatbots and identify directions for future work.


title: Efficient sample reuse in policy search by multiple importance sampling

abstract: Policy search such as reinforcement learning and evolutionary computation is a framework for finding an optimal policy of control problems, but it usually requires a huge number of samples. Importance sampling is a common tool to use samples drawn from a proposal distribution different from the targeted one, and it is widely used by the policy search methods to update the policy from a set of datasets that are collected by previous sampling distributions. However, the proposal distribution is created by a mixture of previous distributions with fixed mixing weights in most of previous studies, and it is often numerically unstable. To overcome this problem, we propose the method of adaptive multiple importance sampling that optimizes the mixing coefficients to minimize the variance of the importance sampling estimator while utilizing as many samples as possible. We apply the proposed method to the five policy search methods such as PGPE, PoWER, CMA-ES, REPS, and NES, and their algorithms are evaluated by some benchmark control tasks. Experimental results show that all the five methods improve sample efficiency. In addition, we show that optimizing the mixing weights achieves stable learning.


title: Ballistocardiogram Signal Processing: A Literature Review

abstract: Time-domain algorithms are focused on detecting local maxima or local minima using a moving window, and therefore finding the interval between the dominant J-peaks of ballistocardiogram (BCG) signal. However, this approach has many limitations due to the nonlinear and nonstationary behavior of the BCG signal. This is because the BCG signal does not display consistent J-peaks, which can usually be the case for overnight, in-home monitoring, particularly with frail elderly. Additionally, its accuracy will be undoubtedly affected by motion artifacts. Second, frequency-domain algorithms do not provide information about interbeat intervals. Nevertheless, they can provide information about heart rate variability. This is usually done by taking the fast Fourier transform or the inverse Fourier transform of the logarithm of the estimated spectrum, i.e., cepstrum of the signal using a sliding window. Thereafter, the dominant frequency is obtained in a particular frequency range. The limit of these algorithms is that the peak in the spectrum may get wider and multiple peaks may appear, which might cause a problem in measuring the vital signs. At last, the objective of wavelet-domain algorithms is to decompose the signal into different components, hence the component which shows an agreement with the vital signs can be selected i.e., the selected component contains only information about the heart cycles or respiratory cycles, respectively. An empirical mode decomposition is an alternative approach to wavelet decomposition, and it is also a very suitable approach to cope with nonlinear and nonstationary signals such as cardiorespiratory signals. Apart from the above-mentioned algorithms, machine learning approaches have been implemented for measuring heartbeats. However, manual labeling of training data is a restricting property.


title: A hierarchical approach to manipulation with diverse actions

abstract: We define the Diverse Action Manipulation (DAMA) problem in which we are given a mobile robot, a set of movable objects, and a set of diverse, possibly non-prehensile manipulation actions, and the goal is to find a sequence of actions that moves each of the objects to a goal configuration. We show that the DAMA problem can be framed as a multi-modal planning problem and describe a hierarchical algorithm that takes advantage of this multi-modal nature. We also extend our earlier forward search sampling algorithm to a bi-directional version. We give results on a complicated manipulation domain and demonstrate that both new algorithms are significantly more efficient than the original, and that the hierarchical algorithm is usually much more efficient than the forward or bi-directional searches.


title: A Comparison Of Input Shaping And Time-Optimal Flexible-Body Control

abstract: Abstract Input shaping is a method of reducing residual vibrations in computer-controlled machines. Shaped command profiles are generated by convolving a sequence of impulses, an input shaper, with the desired command signal. The shaped command profile will move the system without residual vibration. The performance of several types of input shapers is compared to the time-optimal flexible-body control in terms of maneuver speed, amplitude of transient deflection, robustness to modeling errors, excitation of unmodeled high modes, and ease of implementation. To facilitate the comparison, time-optimal flexible-body control is treated as a special case of input shaping.


title: The IAPR TC-12 benchmark -- a new evaluation resource for visual information systems

abstract: In this paper, we describe an image collection created for the CLEF cross-language image retrieval track (ImageCLEF). This image retrieval benchmark (referred to as the IAPR TC-12 Benchmark) has developed from an initiative started by the Technical Committee 12 (TC-12) of the International Association of Pattern Recognition (IAPR). The collection consists of 20,000 images from a private photographic image collection. The construction and composition of the IAPR TC-12 Benchmark is described, including its associated text captions which are expressed in multiple languages, making the collection well-suited for evaluating the effectiveness of both textbased and visual retrieval methods. We also discuss the current and expected uses of the collection, including its use to benchmark and compare different image retrieval systems in ImageCLEF 2006.


title: Adaptive Instructional Systems

abstract: A central and persisting issue in educational technology is the provision of instructional environments and conditions that can comply with individually different educational goals and learning abilities. Instructional approaches and techniques that are geared to meet the needs of the individually different student are called adaptive instruction (Como & Snow, 1986). More specifically, adaptive instruction refers to educational interventions aimed at effectively accommodating individual differences in students while helping each student develop the knowledge and skills required to learn a task. Adaptive instruction is generally characterized as an educational approach that incorporates alternative procedures and strategies for instruction and resource utilization and has the built-in flexibility to permit students to take various routes to, and amounts of time for, learning (Wang & Lindvall, 1984). Glaser (1977) described three essential ingredients of adaptive instruction. First, it provides a variety of alternatives for learning and many goals from which to choose. Second, it attempts to utilize and develop the capabilities that an individual brings to the alternatives for his or her learning and to adjust to the learner’s particular talents, strengths, and weaknesses. Third, it attempts to strengthen an individual’s ability to meet the demands of available educational opportunities and develop the skills necessary for success in the complex world. Adaptive instruction has been used interchangeably with individualized instruction in the literature (Reiser, 1987; Wang & Lindvall, 1984). However, they are different depending on the specific methods and procedures employed during instruction. Any type of instruction presented in a one-on-one setting can


title: GNSS Spoofing Detection and Mitigation Based on Maximum Likelihood Estimation

abstract: Spoofing attacks are threatening the global navigation satellite system (GNSS). The maximum likelihood estimation (MLE)-based positioning technique is a direct positioning method originally developed for multipath rejection and weak signal processing. We find this method also has a potential ability for GNSS anti-spoofing since a spoofing attack that misleads the positioning and timing result will cause distortion to the MLE cost function. Based on the method, an estimation-cancellation approach is presented to detect spoofing attacks and recover the navigation solution. A statistic is derived for spoofing detection with the principle of the generalized likelihood ratio test (GLRT). Then, the MLE cost function is decomposed to further validate whether the navigation solution obtained by MLE-based positioning is formed by consistent signals. Both formulae and simulations are provided to evaluate the anti-spoofing performance. Experiments with recordings in real GNSS spoofing scenarios are also performed to validate the practicability of the approach. Results show that the method works even when the code phase differences between the spoofing and authentic signals are much less than one code chip, which can improve the availability of GNSS service greatly under spoofing attacks.


title: Synchronous reluctance motors with and without permanent magnets for high performance low cost electrical drives

abstract: A synchronous reluctance motor (SynRelM) and a permanent magnet assisted synchronous reluctance motor (PM-assisted SynRelM) at a rated power level between 15 and 40 kW have been designed with the help of finite element analysis (FEA). Their performances levels are compared to an off-the-shelf inverter-fed variable speed squirrel cage induction motor (SCIM) and a surface mounted permanent magnet synchronous motor (SMPMSM), respectively. In the comparison the resulting average torque over the operating speed range and the torque ripple are investigated. Two prototypes have been fabricated and experimental measurements have been carried out to validate simulation data. It was identified that SynRelMs, especially assisted with permanent magnets (PMs), could compete with induction motors — and even PM synchronous motors (PMSMs) under certain operating conditions.


Automatic Medical Image Classification and Abnormality Detection Using K- Nearest Neighbour


title: Minimal tropical basis for Bergman fan of matroid

abstract: The Bergman fan of a matroid is the intersection of tropical hyperplanes defined by the circuits. A tropical basis is a subset of the circuits set that defines the Bergman fan. Yu and Yuster posed a question whether every simple regular matroid has a unique minimal tropical basis of its Bergman fan, and verified it for graphic, cographic matroids and $R_{10}$. We show every simple binary matroid has a unique minimal tropical basis. Since the regular matroid is binary, we positively answered the question.


title: Near-Optimal Adversarial Policy Switching for Decentralized Asynchronous Multi-Agent Systems

abstract: A key challenge in multi-robot and multi-agent systems is generating solutions that are robust to other self-interested or even adversarial parties who actively try to prevent the agents from achieving their goals. The practicality of existing works addressing this challenge is limited to only small-scale synchronous decision-making scenarios or a single agent planning its best response against a single adversary with fixed, procedurally characterized strategies. In contrast this paper considers a more realistic class of problems where a team of asynchronous agents with limited observation and communication capabilities need to compete against multiple strategic adversaries with changing strategies. This problem necessitates agents that can coordinate to detect changes in adversary strategies and plan the best response accordingly. Our approach first optimizes a set of stratagems that represent these best responses. These optimized stratagems are then integrated into a unified policy that can detect and respond when the adversaries change their strategies. The near-optimality of the proposed framework is established theoretically as well as demonstrated empirically in simulation and hardware.


title: Mining Domain-Specific Thesauri from Wikipedia: A Case Study

abstract: Domain-specific thesauri are high-cost, high-maintenance, high-value knowledge structures. We show how the classic thesaurus structure of terms and links can be mined automatically from Wikipedia. In a comparison with a professional thesaurus for agriculture we find that Wikipedia contains a substantial proportion of its concepts and semantic relations; furthermore it has impressive coverage of contemporary documents in the domain. Thesauri derived using our techniques capitalize on existing public efforts and tend to reflect contemporary language usage better than their costly, painstakingly-constructed manual counterparts.


title: Which Internet Policy? Assessing Regional Initiatives in Spain

abstract: We examine the effect of public policy on the growth of Internet use. Using a decentralized country—Spain—as an example, we compare the 17 Spanish regions to test different Internet policy designs, taking into account the quality and number of specific programs promoted by regional governments, as well as the presence or absence of strategic planning in each region. We treat the percentage of Internet users as a dependent variable to compare its diffusion in different territories. Our findings show that educational levels and economic differences explain about half of the variations observed. Furthermore, the regional policies play a significant role in explaining regional variations. We investigate which public policy instruments are more significant for the development of the Internet, and find that focused policy intervention and complex policy initiatives are more significant than other policy instruments in explaining the increase in the percentage of Internet users.


Dialogic Arguments from Social Media


title: BPMN-Q : A Language to Query Business Processes

abstract: Abstract: With the growing role business processes play in today’s business life, they are being seen as an asset for the organization. With hundreds of process models developed by different process designers it would be helpful to look up the repository for models that could handle a similar situation before developing new ones. In this paper we introduce a new visual query language for business processes. The language addresses processes definitions. It extends BPMN for its abstract syntax as BPMN is a standard visual notation for modeling business processes. The overall architecture of the system in which the language can fit, in addition to the details of the query processing are also discussed.


title: Segmenting women fashion magazine readers based on reasons of buying, fashion involvement and age: a study in the Turkish market

abstract: The objective of this study is to understand why women buy fashion magazines and to cluster them accordingly. Hence fashion magazine consumers are clustered into groups based on why they buy fashion magazines, their fashion involvement levels, and their ages. According to the findings of the study, the reasons why women buy fashion magazines may be defined under 13 dimensions. Moreover, three distinct clusters are found and labeled as “Young occasional buyers interested in being informed on different styles and brands”; “Elder deal-prone low-fashion-involved buyers interested in current events and assessments on current events”; and “Settled high-fashion-involved regular magazine buyers interested both in trends and in content, design and visuality of magazines.” All three clusters are distinct and thus should be subject to different marketing strategies. The study is significant because although fashion magazines are one of the most important distribution channels for fashion marketing, research on them ...


title: ANTIRIBOSOMAL P PROTEIN ANTIBODIES IN DIFFERENT POPULATIONS OF PATIENTS WITH SYSTEMIC LUPUS ERYTHEMATOSUS

abstract: We report a significantly increased prevalence of antribosomal P protein antibodies in Malaysian Chinese patients (38 percent) with SLE compared to white caucasian (13 percent) and Afro-Caribbean (20 percent) patients. The increase prevalence was not due to a generalized increase in autoantibody production because anti-dsDNA and anti-SSA antibodies were present in comparable frequencies in the three ethnic groups while anti-Sm and anti-SSB antibodies were rarely found in the Malaysian Chinese patients (AU)


title: A Novel MPPT Algorithm Based on Particle Swarm Optimization for Photovoltaic Systems

abstract: This paper describes a new maximum-power-point-tracking method for a photovoltaic system based on the Lagrange Interpolation Formula and proposes the particle swarm optimization method. The proposed control scheme eliminates the problems of conventional methods by using only a simple numerical calculation to initialize the particles around the global maximum power point. Hence, the suggested control scheme will utilize less iterations to reach the maximum power point. Simulation study is carried out using MATLAB/SIMULINK and compared with the Perturb and Observe method, the Incremental Conductance method, and the conventional Particle Swarm Optimization algorithm. The proposed algorithm is verified with the OPAL-RT real-time simulator. The simulation results confirm that the proposed algorithm can effectively enhance the stability and the fast tracking capability under abnormal insolation conditions.


Resilient overlay networks


title: Benchmark Databases for Video-Based Automatic Sign Language Recognition

abstract: A new, linguistically annotated, video database for automatic sign language recognition is presented. The new RWTH-BOSTON-400 corpus, which consists of 843 sentences, several speakers and separate subsets for training, development, and testing is described in detail. For evaluation and benchmarking of automatic sign language recognition, large corpora are needed. Recent research has focused mainly on isolated sign language recognition methods using video sequences that have been recorded under lab conditions using special hardware like data gloves. Such databases have often consisted generally of only one speaker and thus have been speaker-dependent, and have had only small vocabularies. A new database access interface, which was designed and created to provide fast access to the database statistics and content, makes it possible to easily browse and retrieve particular subsets of the video database. Preliminary baseline results on the new corpora are presented. In contradistinction to other research in this area, all databases presented in this paper will be publicly


Development of a News Recommender System based on Apache Flink


Automated Classification of Circulating Tumor Cells and the Impact of Interobsever Variability on Classifier Training and Performance


title: Compact SPAD-Based Pixel Architectures for Time-Resolved Image Sensors

abstract: This paper reviews the state of the art of single-photon avalanche diode (SPAD) image sensors for time-resolved imaging. The focus of the paper is on pixel architectures featuring small pixel size ( 20%) as a key enabling technology for the successful implementation of high spatial resolution SPAD-based image sensors. A summary of the main CMOS SPAD implementations, their characteristics and integration challenges, is provided from the perspective of targeting large pixel arrays, where one of the key drivers is the spatial uniformity. The main analog techniques aimed at time-gated photon counting and photon timestamping suitable for compact and low-power pixels are critically discussed. The main features of these solutions are the adoption of analog counting techniques and time-to-analog conversion, in NMOS-only pixels. Reliable quantum-limited single-photon counting, self-referenced analog-to-digital conversion, time gating down to 0.75 ns and timestamping with 368 ps jitter are achieved.


title: Timely autonomous identification of UAV safe landing zones

abstract: For many applications such as environmental monitoring in the aftermath of a natural disaster and mountain search-and-rescue, swarms of autonomous Unmanned Aerial Vehicles (UAVs) have the potential to provide a highly versatile and often relatively inexpensive sensing platform. Their ability to operate as an ‘eye-in-the-sky’, processing and relaying real-time colour imagery and other sensor readings facilitate the removal of humans from situations which may be considered dull, dangerous or dirty. However, as with manned aircraft they are likely to encounter errors, the most serious of which may require the UAV to land as quickly and safely as possible. Within this paper we therefore present novel work on autonomously identifying Safe Landing Zones (SLZs) which can be utilised upon occurrence of a safety critical event. Safe Landing Zones are detected and subsequently assigned a safety score either solely using multichannel aerial imagery or, whenever practicable by fusing knowledge in the form of Ordnance Survey (OS) map data with such imagery. Given the real-time nature of the problem we subsequently model two SLZ detection options one of which utilises knowledge enabling the UAV to choose an optimal, viable solution. Results are presented based on colour aerial imagery captured during manned flight demonstrating practical potential in the methods discussed.


title: Efficient and Scalable Metadata Management in EB-Scale File Systems

abstract: Efficient and scalable distributed metadata management is critically important to overall system performance in large-scale distributed file systems, especially in the EB-scale era. Hash-based mapping and subtree partitioning are state-of-the-art distributed metadata management schemes. Hash-based mapping evenly distributes workload among metadata servers, but it eliminates all hierarchical locality of metadata. Subtree partitioning does not uniformly distribute workload among metadata servers, and metadata needs to be migrated to keep the load balanced roughly. Distributed metadata management is relatively difficult since it has to guarantee metadata consistency. Meanwhile, scaling metadata performance is more complicated than scaling raw I/O performance. The complexity further rises with distributed metadata. It results in a primary goal that is to improve metadata management scalability while paying attention to metadata consistency. In this paper, we present a ring-based metadata management mechanism named Dynamic Ring Online Partitioning (DROP). It can preserve metadata locality using locality-preserving hashing, keep metadata consistency, as well as dynamically distribute metadata among metadata server cluster to keep load balancing. By conducting performance evaluation through extensive trace-driven simulations and a prototype implementation, experimental results demonstrate the efficiency and scalability of DROP.


title: RetinaMask: Learning to predict masks improves state-of-the-art single-shot detection for free

abstract: Recently two-stage detectors have surged ahead of single-shot detectors in the accuracy-vs-speed trade-off. Nevertheless single-shot detectors are immensely popular in embedded vision applications. This paper brings single-shot detectors up to the same level as current two-stage techniques. We do this by improving training for the state-of-the-art single-shot detector, RetinaNet, in three ways: integrating instance mask prediction for the first time, making the loss function adaptive and more stable, and including additional hard examples in training. We call the resulting augmented network RetinaMask. The detection component of RetinaMask has the same computational cost as the original RetinaNet, but is more accurate. COCO test-dev results are up to 41.4 mAP for RetinaMask-101 vs 39.1mAP for RetinaNet-101, while the runtime is the same during evaluation. Adding Group Normalization increases the performance of RetinaMask-101 to 41.7 mAP. Code is at:this https URL


title: Near-Optimal Algorithms for the Assortment Planning Problem Under Dynamic Substitution and Stochastic Demand

abstract: Assortment planning of substitutable products is a major operational issue that arises in many industries, such as retailing, airlines and consumer electronics. We consider a single-period joint assortment and inventory planning problem under dynamic substitution with stochastic demands, and provide complexity and algorithmic results as well as insightful structural characterizations of near-optimal solutions for important variants of the problem. First, we show that the assortment planning problem is NP-hard even for a very simple consumer choice model, where each customer is willing to buy only two products. In fact, we show that the problem is hard to approximate within a factor better than 1-1/e. Secondly, we show that for several interesting and practical choice models, one can devise a polynomial-time approximation scheme (PTAS), i.e., the problem can be solved efficiently to within any level of accuracy. To the best of our knowledge, this is the first efficient algorithm with provably near-optimal performance guarantees for assortment planning problems under dynamic substitution. Quite surprisingly, the algorithm we propose stocks only a constant number of different product types; this constant depends only on the desired accuracy level. This provides an important managerial insight that assortments with a relatively small number of product types can obtain almost all of the potential revenue. Furthermore, we show that our algorithm can be easily adapted for more general choice models, and present numerical experiments to show that it performs significantly better than other known approaches.


A Concept for the Fuzzy-adaptive Temperature Control of Mimo Systems


title: Neural Network-Based Equations for Predicting PGA and PGV in Texas, Oklahoma, and Kansas

abstract: Parts of Texas, Oklahoma, and Kansas have experienced increased rates of seismicity in recent years, providing new datasets of earthquake recordings to develop ground motion prediction models for this particular region of the Central and Eastern North America (CENA). This paper outlines a framework for using Artificial Neural Networks (ANNs) to develop attenuation models from the ground motion recordings in this region. While attenuation models exist for the CENA, concerns over the increased rate of seismicity in this region necessitate investigation of ground motions prediction models particular to these states. To do so, an ANN-based framework is proposed to predict peak ground acceleration (PGA) and peak ground velocity (PGV) given magnitude, earthquake source-to-site distance, and shear wave velocity. In this framework, approximately 4,500 ground motions with magnitude greater than 3.0 recorded in these three states (Texas, Oklahoma, and Kansas) since 2005 are considered. Results from this study suggest that existing ground motion prediction models developed for CENA do not accurately predict the ground motion intensity measures for earthquakes in this region, especially for those with low source-to-site distances or on very soft soil conditions. The proposed ANN models provide much more accurate prediction of the ground motion intensity measures at all distances and magnitudes. The proposed ANN models are also converted to relatively simple mathematical equations so that engineers can easily use them to predict the ground motion intensity measures for future events. Finally, through a sensitivity analysis, the contributions of the predictive parameters to the prediction of the considered intensity measures are investigated.


title: City-Scale Map Creation and Updating using GPS Collections

abstract: Applications such as autonomous driving or real-time route recommendations require up-to-date and accurate digital maps. However, manually creating and updating such maps is too costly to meet the rising demands. As large collections of GPS trajectories become widely available, constructing and updating maps using such trajectory collections can greatly reduce the cost of such maps. Unfortunately, due to GPS noise and varying trajectory sampling rates, inferring maps from GPS trajectories can be very challenging. In this paper, we present a framework to create up-to-date maps with rich knowledge from GPS trajectory collections. Starting from an unstructured GPS point cloud, we discover road segments using novel graph-based clustering techniques with prior knowledge on road design. Based on road segments, we develop a scale- and orientation-invariant traj-SIFT feature to localize and recognize junctions using a supervised learning framework. Maps with rich knowledge are created based on discovered road segments and junctions. Compared to state-of-the-art methods, our approach can efficiently construct high-quality maps at city scales from large collections of GPS trajectories.


title: 3D Object Recognition and Pose Estimation for Multiple Objects using Multi-Prioritized RANSAC and Model Updating

abstract: We present a feature-based framework that combines spatial feature clustering, guided sampling for pose generation, and model updating for 3D object recognition and pose estimation. Existing methods fails in case of repeated patterns or multiple instances of the same object, as they rely only on feature discriminability for matching and on the estimator capabilities for outlier rejection. We propose to spatially separate the features before matching to create smaller clusters containing the object. Then, hypothesis generation is guided by exploiting cues collected off- and on-line, such as feature repeatability, 3D geometric constraints, and feature occurrence frequency. Finally, while previous methods overload the model with synthetic features for wide baseline matching, we claim that continuously updating the model representation is a lighter yet reliable strategy. The evaluation of our algorithm on challenging video sequences shows the improvement provided by our contribution.


title: 1 A RESOURCE-BASED APPROACH TO PERFORMANCE AND COMPETITION: An Overview of the Connections between Resources and Competition

abstract: This paper extends the resource-based view of the firm to give an overview of the connections between resources and competition. Specifically, it develops a conceptual framework explaining competitive advantage and performance that incorporate the resource-based view of the firm and Porter?s approach to competitive environment. On the basis of this framework, it shows how firms compete for resources and may use their resources to compete.


title: Combined digital signature and digital watermark scheme for image authentication

abstract: Conventional digital signature schemes for image authentication encode the signature in a file separate from the original image, thus require extra bandwidth to transmit it. Meanwhile, the watermark technique embeds some information in the host image. In this paper, a combined digital watermark and digital signature scheme for image authentication is proposed. The scheme extracts signatures from the original image and embeds them back into the image as watermarks avoiding additional signature files. Since images are always compressed before transmission in the Internet, the proposed scheme is tested for compression tolerance and shows good robustness against JPEG coding. Furthermore, the scheme not only can verify the authenticity and the integrity of images, but also can locate illegal modifications.


title: Decomposition by Partial Linearization: Parallel Optimization of Multi-Agent Systems

abstract: We propose a novel decomposition framework for the distributed optimization of general nonconvex sum-utility functions arising naturally in the system design of wireless multiuser interfering systems. Our main contributions are: i) the development of the first class of (inexact) Jacobi best-response algorithms with provable convergence, where all the users simultaneously and iteratively solve a suitably convexified version of the original sum-utility optimization problem; ii) the derivation of a general dynamic pricing mechanism that provides a unified view of existing pricing schemes that are based, instead, on heuristics; and iii) a framework that can be easily particularized to well-known applications, giving rise to very efficient practical (Jacobi or Gauss-Seidel) algorithms that outperform existing adhoc methods proposed for very specific problems. Interestingly, our framework contains as special cases well-known gradient algorithms for nonconvex sum-utility problems, and many blockcoordinate descent schemes for convex functions.


title: Correcting for missing data in information cascades

abstract: Transmission of infectious diseases, propagation of information, and spread of ideas and influence through social networks are all examples of diffusion. In such cases we say that a contagion spreads through the network, a process that can be modeled by a cascade graph. Studying cascades and network diffusion is challenging due to missing data. Even a single missing observation in a sequence of propagation events can significantly alter our inferences about the diffusion process. We address the problem of missing data in information cascades. Specifically, given only a fraction C' of the complete cascade C, our goal is to estimate the properties of the complete cascade C, such as its size or depth. To estimate the properties of C, we first formulate k-tree model of cascades and analytically study its properties in the face of missing data. We then propose a numerical method that given a cascade model and observed cascade C' can estimate properties of the complete cascade C. We evaluate our methodology using information propagation cascades in the Twitter network (70 million nodes and 2 billion edges), as well as information cascades arising in the blogosphere. Our experiments show that the k-tree model is an effective tool to study the effects of missing data in cascades. Most importantly, we show that our method (and the k-tree model) can accurately estimate properties of the complete cascade C even when 90% of the data is missing.


title: Pluggable Electro-Optical Circuit Board Interconnect Based on Embedded Graded-Index Planar Glass Waveguides

abstract: Widespread adoption of electro-optical circuit boards based on embedded glass waveguide technology would enable seamless optical connectivity from external fiber-optic networks to system embedded optical interconnect architectures. In this paper, we report on the fabrication of planar multimode waveguides within thin glass foils based on a two-step thermal ion exchange process. Novel lamination techniques were developed to allow glass waveguide panels to be reliably integrated into a conventional electronic multilayer printed circuit board. In addition, a complete suite of optical connector technologies were developed to enable both direct fiber-to-board and board-to-board connectivity. We present the design, development, and characterization of a fully integrated connection platform, comprising a $281 \times 233$ ::: mm2 multilayer electro-optical backplane with integrated planar glass waveguides, a pluggable connector system, and five pluggable test cards. Both on-card and externally generated 850 and 1310-nm optical test data were conveyed through the connector and waveguide system and characterised for in-system and system-to-system optical connectivity at data rates up to 32 Gb/s per channel exhibiting bit error rates of less than $10^{-12}$ ::: .


title: Large-Scale Training of Shadow Detectors with Noisily-Annotated Shadow Examples

abstract: This paper introduces training of shadow detectors under the large-scale dataset paradigm. This was previously impossible due to the high cost of precise shadow annotation. Instead, we advocate the use of quickly but imperfectly labeled images. Our novel label recovery method automatically corrects a portion of the erroneous annotations such that the trained classifiers perform at state-of-the-art level. We apply our method to improve the accuracy of the labels of a new dataset that is 20 times larger than existing datasets and contains a large variety of scenes and image types. Naturally, such a large dataset is appropriate for training deep learning methods. Thus, we propose a semantic-aware patch level Convolutional Neural Network architecture that efficiently trains on patch level shadow examples while incorporating image level semantic information. This means that the detected shadow patches are refined based on image semantics. Our proposed pipeline can be a useful baseline for future advances in shadow detection.


title: Approximate Fast Fourier Transforms on graphs via multi-layer sparse approximations

abstract: The fast Fourier transform is an algorithm of paramount importance in signal processing as it allows to apply the Fourier transform in <inline-formula><tex-math notation="LaTeX">$\mathcal {O}(n \log n)$</tex-math></inline-formula> instead of <inline-formula><tex-math notation="LaTeX">$\mathcal {O}(n^2)$</tex-math></inline-formula> arithmetic operations. Graph signal processing is a recent research domain that generalizes classical signal processing tools, such as the Fourier transform, to situations where the signal domain is given by any arbitrary graph instead of a regular grid. Today, there is no method to rapidly apply graph Fourier transforms. In this paper, we propose a method to obtain approximate graph Fourier transforms that can be applied rapidly and stored efficiently. It is based on a greedy approximate diagonalization of the graph Laplacian matrix, carried out using a modified version of the famous Jacobi eigenvalues algorithm. The method is described and analyzed in detail, and then applied to both synthetic and real graphs, showing its potential.