title: Coaxial Line Reflection Methods for Measuring Dielectric Properties of Biological Substances at Radio and Microwave Frequencies-A Review

abstract: Coaxial line reflection methods for measuring dielectric properties of biological materials at radio (RF) and microwave (MF) frequencies are reviewed and compared from the point of view of their relative uncertainties of measurement of lossy substances with high dielectric constant. Advantages and limitations of different methods and some practical recommendations are presented.


title: HIGH POWER VHF FREQUENCY-HOPPING FILTERS WITH HIGH SUPPRESSION OF SECOND HARMONIC

abstract: A compact helix structure implementation and associated design formula of lumped element second-order bandpass fllter circuit for high power frequency-hopping (FH) fllter are proposed in this paper. The fllter schematic provides one, two or three flnite transmission zeros (Tzs), and these Tzs locate in the upper stopband to improve the rejection of stopband above the center frequency, especially the suppression of second harmonic with two Tzs. The fllter is built on a common grounded helix coil of inductive coupled resonator tanks whose suspectance is tunable. Due to the parasitical capacitance of the helix coil, the fllter has a feedback capacitor between input and output. Its working mechanism is revealed both mathematically and graphically. The measured results have a good agreement with the 3D full-wave electromagnetic simulation responses. The experimental fllter has an insertion loss 15dB, a 3-dB bandwidth of 5:8% » 8:3% over entire operating range with the power handling capability greater than 49dBm and the suppression of second harmonic better than 66dB.


title: A Compact, Scanning Tightly Coupled Dipole Array With Parasitic Strips for Next-Generation Wireless Applications

abstract: A wideband one-dimensional tightly coupled dipole array (TCDA) with enhanced impedance matching is presented in this letter, which consists of eight printed dipole units, a coplanar waveguide with ground feeding network, and two rows of parasitic strips. The feed structure is simple and small with good ports isolation. A $1\times 8$ prototype was fabricated and tested. A four-channel phase-amplitude control board was designed to validate the beam-steering function of the proposed TCDA. This array has a height of only $0.176\;\lambda_{\text{low}}$ , and it exhibits a measured 10 dB return-loss operating bandwidth of 92.7% from 2.2 to 6.0 GHz for the scan angle up to $\pm 45^{\circ }$ broadside radiation. The cross-polarization suppression level is better than 15 dB in the whole scanning range. The measured results show high gains $>$ 6.9 dBi, high efficiency $>$ 67%, and low sidelobe level (SLL) $ $-$ 10 dB over the operating band and scanning angles. This antenna array can be applied in the future massive multiple-input–multiple-output beamforming systems for 5G and other applications.


title: OptaSense® Distributed Acoustic and Seismic Sensing Performance for Multi-threat, Multi-environment Border Monitoring

abstract: The OptaSense® Distributed Acoustic Sensing (DAS) technology turns any cable with single-mode optical fiber into a very large and densely sampled acoustic/seismic sensor array, covering up to a 50-kilometer aperture per system with """"virtual"""" sensor separations as small as one eter. The system uses Rayleigh scattering from the mperfections in the fiber to return optical signals measuring local fiber strain from seismic, air and water acoustic disturbances. The scalable system architecture can provide border monitoring over long distances at low cost, high sensitivity, and high location accuracy. This paper presents system performance examples for detection, localization, and classification of multiple threats (e.g. digging and tunneling, personnel footsteps, gunshots, and aircraft) in a variety of environments, including snow cover.


title: A context-free markup language for semi-structured text

abstract: An ad hoc data format is any nonstandard, semi-structured data format for which robust data processing tools are not easily available. In this paper, we present ANNE, a new kind of markup language designed to help users generate documentation and data processing tools for ad hoc text data. More specifically, given a new ad hoc data source, an ANNE programmer edits the document to add a number of simple annotations, which serve to specify its syntactic structure. Annotations include elements that specify constants, optional data, alternatives, enumerations, sequences, tabular data, and recursive patterns. The ANNE system uses a combination of user annotations and the raw data itself to extract a context-free grammar from the document. This context-free grammar can then be used to parse the data and transform it into an XML parse tree, which may be viewed through a browser for analysis or debugging purposes. In addition, the ANNE system generates a PADS/ML description, which may be saved as lasting documentation of the data format or compiled into a host of useful data processing tools. In addition to designing and implementing ANNE, we have devised a semantic theory for the core elements of the language. This semantic theory describes the editing process, which translates a raw, unannotated text document into an annotated document, and the grammar extraction process, which generates a context-free grammar from an annotated document. We also present an alternative characterization of system behavior by drawing upon ideas from the field of relevance logic. This secondary characterization, which we call relevance analysis, specifies a direct relationship between unannotated documents and the context-free grammars that our system can generate from them. Relevance analysis allows us to prove important theorems concerning the expressiveness and utility of our system.


title: Analog computation via neural networks

abstract: Abstract We pursue a particular approach to analog computation, based on dynamical systems of the type used in neural networks research. Our systems have a fixed structure, invariant in time, corresponding to an unchanging number of “neurons”. If allowed exponential time for computation, they turn out to have unbounded power. However, under polynomial-time constraints there are limits on their capabilities, though being more powerful than Turing machines. (A similar but more restricted model was shown to be polynomial-time equivalent to classical digital computation in the previous work (Siegelmann and Sontag, 1992).) Moreover, there is a precise correspondence between nets and standard nonuniform circuits with equivalent resources, and as a consequence one has lower bound constraints on what they can compute. This relationship is perhaps surprising since our analog devices do not change in any manner with input size. We note that these networks are not likely to solve polynomially NP-hard problems, as the equality “ p = np ” in our model implies the almost complete collapse of the standard polynomial hierarchy. In contrast to classical computational models, the models studied here exhibit at least some robustness with respect to noise and implementation errors.


title: Visual Word Recognition in the Left and Right Hemispheres : Anatomical and Functional Correlates of Peripheral

abstract: According to a simple anatomical and functional model of word reading, letters displayed in one hemifield are first analysed through a cascade of contralateral retinotopic areas, which compute increasingly abstract representations. Eventually, an invariant representation of letter identities is created in the visual word form area (VWFA), reproducibly located within the left occipito-temporal sulcus. The VWFA then projects to structures involved in phonological or lexico-semantic processing. This model yields detailed predictions on the reading impairments that may follow left occipitotemporal lesions. Those predictions were confronted to behavioural, anatomical and functional MRI data gathered in normals and in patients suffering from left posterior cerebral artery infarcts. In normal subjects, alphabetic stimuli activated both the VWFA and the right-hemispheric symmetrical region (R-VWFA) relative to fixation, but only the VWFA showed a preference for alphabetic strings over simple chequerboards. The comparison of normalized brain lesions with reading-induced activations showed that the critical lesion site for the classical syndrome of pure alexia can be tightly localized to the VWFA. Reading impairments resulting from deafferentation of an intact VWFA from right- or left-hemispheric input were dissected using the same methods, shedding light on the connectivity of the VWFA. Finally, the putative role of right-hemispheric processing in the letter-by-letter reading strategy was clarified. In a letter-by-letter reader, the R-VWFA assumed some of the functional properties normally specific to the VWFA. These data corroborate our initial model of normal word perception and underline that an alternative right-hemispheric pathway can underlie functional recovery from alexia.


title: The Trypanosoma brucei cAMP phosphodiesterases TbrPDEB1 and TbrPDEB2: flagellar enzymes that are essential for parasite virulence

abstract: Cyclic nucleotide specific phosphodiesterases (PDEs) are pivotal regulators of cellular signaling. They are also important drug targets. Besides catalytic activity and substrate specificity, their subcellular localization and interaction with other cell components are also functionally important. In contrast to the mammalian PDEs, the significance of PDEs in protozoal pathogens remains mostly unknown. The genome of Trypanosoma brucei, the causative agent of human sleeping sickness, codes for five different PDEs. Two of these, TbrPDEB1 and TbrPDEB2, are closely similar, cAMP-specific PDEs containing two GAF-domains in their N-terminal regions. Despite their similarity, these two PDEs exhibit different subcellular localizations. TbrPDEB1 is located in the flagellum, whereas TbrPDEB2 is distributed between flagellum and cytoplasm. RNAi against the two mRNAs revealed that the two enzymes can complement each other but that a simultaneous ablation of both leads to cell death in bloodstream form trypanosomes. RN...


Ensemble of Neural Networks for Multi-label Document Classification


title: Observe locally, infer globally: A space-time MRF for detecting abnormal activities with incremental updates

abstract: We propose a space-time Markov random field (MRF) model to detect abnormal activities in video. The nodes in the MRF graph correspond to a grid of local regions in the video frames, and neighboring nodes in both space and time are associated with links. To learn normal patterns of activity at each local node, we capture the distribution of its typical optical flow with a mixture of probabilistic principal component analyzers. For any new optical flow patterns detected in incoming video clips, we use the learned model and MRF graph to compute a maximum a posteriori estimate of the degree of normality at each local node. Further, we show how to incrementally update the current model parameters as new video observations stream in, so that the model can efficiently adapt to visual context changes over a long period of time. Experimental results on surveillance videos show that our space-time MRF model robustly detects abnormal activities both in a local and global sense: not only does it accurately localize the atomic abnormal activities in a crowded video, but at the same time it captures the global-level abnormalities caused by irregular interactions between local activities.


title: Chapter 16: Text Mining for Translational Bioinformatics

abstract: Text mining for transla- tional bioinformatics is a new field with tremendous research poten- tial. It is a subfield of biomedical natural language processing that concerns itself directly with the problem of relating basic biomed- ical research to clinical practice, and vice versa. Applications of text mining fall both into the category of T1 translational research—trans- lating basic science results into new interventions—and T2 transla- tional research, or translational research for public health. Potential use cases include better phenotyp- ing of research subjects, and phar- macogenomic research. A variety of methods for evaluating text mining applications exist, including corpo- ra, structured test suites, and post hoc judging. Two basic principles of linguistic structure are relevant for building text mining applica- tions. One is that linguistic struc- ture consists of multiple levels. The other is that every level of linguistic structure is characterized by ambi- guity. There are two basic ap- proaches to text mining: rule- based, also known as knowledge- based; and machine-learning- based, also known as statistical. Many systems are hybrids of the two approaches. Shared tasks have had a strong effect on the direction of the field. Like all translational bioinformatics software, text min- ing software for translational bioin- formatics can be considered health-critical and should be sub- ject to the strictest standards of quality assurance and software testing.


title: EDF schedulability test for the E-TDL time-triggered framework

abstract: Time-triggered architectures bring to the applications a considerable degree of determinism and a strong conformity between their execution and their logical model. The Extended Timing Definition Language (E-TDL) is a time-triggered framework for programming real-time applications that can be structured through multiple independent components with fully adaptive behaviors. In this paper, we propose a schedulabil-ity test for E-TDL applications executed under Earliest Deadline First on a single processor. We derive an efficient solution for evaluating the maximal processor demand over the time intervals during which application may change its operational modes. We provide a detailed insight into the schedulability test procedure and discuss its complexity. Simulation results illustrate the performance of the current implementation of the test.


title: A buffer-aware HTTP live streaming approach for SDN-enabled 5G wireless networks

abstract: With increasing interest in the concept of 5G wireless networks and the popularization of mobile devices, users gradually watch videos through mobile devices in streaming mode rather than off-line mode. However, the latency and lags of mobile networks will reduce the quality of service. In HTTP live streaming services, when there are multiple sources for the same content stored in the streaming servers, the suited quality content is able to be selected for playing according to the networking bandwidth conditions between servers and user equipment, and the users can obtain appropriate streaming quality automatically. However, the selection policy is difficult to be approved between 5G wireless communication networks and SDNs. In this study a buffer-aware HTTP live streaming approach is proposed, which evaluates the weights of media segments to decide the transmitting priorities based on the current playing time and adjust the appropriate transmission path through the decision making network controller according to the utilization and stability of the routers and switches in SDN. Finally, the experimental results proved that the approach is able to correct the overall streaming source sequence with the buffer status to effectively upgrade overall streaming service quality, as well as maintain a certain level of streaming quality for SDN-enable 5G wireless networks.


title: A reconfigurable cylindrical dielectric resonator antenna for WiMAX/WLAN applications

abstract: The main objective of the present study is to achieve the reconfigurability of a cylindrical dielectric resonator antenna (CDRA). The CDRA is excited with two different modes; the HEMjjg mode is excited via an aperture etched on the ground under the center of the CDRA, where the TM 01δ mode is excited from an etched slot under the side of the CDRA, the reconflgurabilty is realized using two PIN diodes. Two different cases of PIN diode (D1-ON/D2-OFF and vice versa) are investigated and analyzed. The proposed cylindrical dielectric resonator antenna (CDRA) can provides two bands around 3.77 GHz and 5.5 GHz with a reflection coefficient below than −10dB which is appropriate for WiMAX and WLAN applications, respectively. The antenna gain is stable in the two bands 3.77GHz/5.5GHz with 3.8 dBi and 4.43dBi, respectively. The simulation is performed using two simulators; the CST Microwave Studio and the HFSS Ansoft package.


title: Low power/low voltage high speed CMOS differential track and latch comparator with rail-to-rail input

abstract: A new CMOS differential latched comparator suitable for low voltage, low-power application is presented. The circuit consists of constant-gm rail-to-rail common-mode operational transconductance amplifier followed by a regenerative latch in a track and latch configuration to achieve a relatively constant delay. The use of a track and latch minimizes the total number of gain stages required for a given resolution. Potential offset from the constant-g/sub m/ differential input stage, estimated as the main source of offset, can be minimized by proper choice of transistors sizes. Simulation results show that the circuit requires less than 86 /spl mu/A with a supply voltage of 1.65 V in a standard CMOS 0.18 /spl mu/m digital process. The average delay is less than 1 ns and is approximately independent of the common-mode input voltage.


title: An autostereoscopic projector array optimized for 3D facial display

abstract: Video projectors are rapidly shrinking in size, power consumption, and cost. Such projectors provide unprecedented flexibility to stack, arrange, and aim pixels without the need for moving parts. We present a dense projector display that is optimized in size and resolution to display an autostereoscopic life-sized 3D human face with a wide 110 degree field of view. Applications include 3D teleconferencing and fully synthetic characters for education and interactive entertainment.


title: Part-of-Speech Tagging for Code-Mixed English-Hindi Twitter and Facebook Chat Messages

abstract: The paper reports work on collecting and annotating code-mixed English-Hindi social media text (Twitter and Facebook messages), and experiments on automatic tagging of these corpora, using both a coarse-grained and a fine-grained part-ofspeech tag set. We compare the performance of a combination of language specific taggers to that of applying four machine learning algorithms to the task (Conditional Random Fields, Sequential Minimal Optimization, Naive Bayes and Random Forests), using a range of different features based on word context and wordinternal information.


title: Semi-automatic generation of UML models from natural language requirements

abstract: Going from requirements analysis to design phase is considered as one of the most complex and difficult activities in software development. Errors caused during this activity can be quite expensive to fix in later phases of software development. One main reason for such potential problems is due to the specification of software requirements in Natural Language format. To overcome some of these defects we have proposed a technique, which aims to provide semi- automated assistance for developers to generate UML models from normalized natural language requirements using Natural Language Processing techniques. This technique initially focuses on generating use-case diagram and analysis class model (conceptual model) followed by collaboration model generation for each use-case. Then it generates a consolidated design class model from which code model can also be generated. It also provides requirement traceability both at design and code levels by using Key-Word-In-Context and Concept Location techniques respectively to identify inconsistencies in requirements. Finally, this technique generates XML Metadata Interchange (XMI) files for visualizing generated models in any UML modeling tool having XMI import feature. This paper is an extension to our existing work by enhancing its complete usage with the help of Qualification Verification System as a case study.


title: A Dataset for StarCraft AI & an Example of Armies Clustering

abstract: This paper advocates the exploration of the full state of recorded real-time strategy (RTS) games, by human or robotic players, to discover how to reason about tactics and strategy. We present a dataset of StarCraft games encompassing the most of the games' state (not only player's orders). We explain one of the possible usages of this dataset by clustering armies on their compositions. This reduction of armies compositions to mixtures of Gaussian allow for strategic reasoning at the level of the components. We evaluated this clustering method by predicting the outcomes of battles based on armies compositions' mixtures components


title: Mechanisms of low back pain: a guide for diagnosis and therapy

abstract: Chronic low back pain (CLBP) is a chronic pain syndrome in the lower back region, lasting for at least 3 months. CLBP represents the second leading cause of disability worldwide being a major welfare and economic problem. The prevalence of CLBP in adults has increased more than 100% in the last decade and continues to increase dramatically in the aging population, affecting both men and women in all ethnic groups, with a significant impact on functional capacity and occupational activities. It can also be influenced by psychological factors, such as stress, depression and/or anxiety. Given this complexity, the diagnostic evaluation of patients with CLBP can be very challenging and requires complex clinical decision-making. Answering the question “what is the pain generator” among the several structures potentially involved in CLBP is a key factor in the management of these patients, since a mis-diagnosis can generate therapeutical mistakes. Traditionally, the notion that the etiology of 80% to 90% of LBP cases is unknown has been mistaken perpetuated across decades. In most cases, low back pain can be attributed to specific pain generator, with its own characteristics and with different therapeutical opportunity. Here we discuss about radicular pain, facet Joint pain, sacro-iliac pain, pain related to lumbar stenosis, discogenic pain. Our article aims to offer to the clinicians a simple guidance to identify pain generators in a safer and faster way, relying a correct diagnosis and further therapeutical approach.


title: The Microsoft 2016 Conversational Speech Recognition System

abstract: We describe Microsoft's conversational speech recognition system, in which we combine recent developments in neural-network-based acoustic and language modeling to advance the state of the art on the Switchboard recognition task. Inspired by machine learning ensemble techniques, the system uses a range of convolutional and recurrent neural networks. I-vector modeling and lattice-free MMI training provide significant gains for all acoustic model architectures. Language model rescoring with multiple forward and backward running RNNLMs, and word posterior-based system combination provide a 20% boost. The best single system uses a ResNet architecture acoustic model with RNNLM rescoring, and achieves a word error rate of 6.9% on the NIST 2000 Switchboard task. The combined system has an error rate of 6.2%, representing an improvement over previously reported results on this benchmark task.


title: Learning Independent Object Motion From Unlabelled Stereoscopic Videos

abstract: We present a system for learning motion maps of independently moving objects from stereo videos. The only annotations used in our system are 2D object bounding boxes which introduce the notion of objects in our system. Unlike prior learning based approaches which have focused on predicting dense optical flow fields and/or depth maps for images, we propose to predict instance specific 3D scene flow maps and instance masks from which we derive a factored 3D motion map for each object instance. Our network takes the 3D geometry of the problem into account which allows it to correlate the input images and distinguish moving objects from static ones. We present experiments evaluating the accuracy of our 3D flow vectors, as well as depth maps and projected 2D optical flow where our jointly learned system outperforms earlier approaches trained for each task independently.


title: Stance Detection on Tweets: An SVM-based Approach

abstract: Stance detection is a subproblem of sentiment analysis where the stance of the author of a piece of natural language text for a particular target (either explicitly stated in the text or not) is explored. The stance output is usually given as Favor, Against, or Neither. In this paper, we target at stance detection on sports-related tweets and present the performance results of our SVM-based stance classifiers on such tweets. First, we describe three versions of our proprietary tweet data set annotated with stance information, all of which are made publicly available for research purposes. Next, we evaluate SVM classifiers using different feature sets for stance detection on this data set. The employed features are based on unigrams, bigrams, hashtags, external links, emoticons, and lastly, named entities. The results indicate that joint use of the features based on unigrams, hashtags, and named entities by SVM classifiers is a plausible approach for stance detection problem on sports-related tweets.


title: Fast Parametric Learning with Activation Memorization

abstract: Neural networks trained with backpropagation often struggle to identify classes that have been observed a small number of times. In applications where most class labels are rare, such as language modelling, this can become a performance bottleneck. One potential remedy is to augment the network with a fast-learning non-parametric model which stores recent activations and class labels into an external memory. We explore a simplified architecture where we treat a subset of the model parameters as fast memory stores. This can help retain information over longer time intervals than a traditional memory, and does not require additional space or compute. In the case of image classification, we display faster binding of novel classes on an Omniglot image curriculum task. We also show improved performance for word-based language models on news reports (GigaWord), books (Project Gutenberg) and Wikipedia articles (WikiText-103) --- the latter achieving a state-of-the-art perplexity of 29.2.


title: Nitro: A Fast, Scalable In-Memory Storage Engine for NoSQL Global Secondary Index

abstract: We present Nitro, a high-performance in-memory key-value storage engine used in Couchbase 4.5 Global Secondary Indexes. The Nitro storage engine is well suited for the recent hardware trends like large amounts of memory and many CPU cores. The storage engine leverages latch-free data structures and tries to achieve linear scalability for the index read-write operations. The Nitro storage engine offers concurrent readers and writers, lightweight database snapshots, stable scan, backup and recovery operations. ::: ::: We integrated Nitro into the Couchbase Global Secondary Indexes (GSI) and observed significant improvement in performance compared to our disk oriented storage engine configured with the same amount of memory for buffer cache. On a 32 core machine, we observed an end-to-end GSI server insertion throughput of 1,650,000 entries/sec and index update throughput of 822,000 entries/sec. A single instance of Nitro data structure running on a 40 core machine achieved a peak insertion throughput of 4 million index entries/sec and entry lookup throughput of 10 million lookups/sec.


title: Automated Detection and Classification of Non-Wet Solder Joints

abstract: Non-wet solder joints in processor sockets are causing mother board failures. These board failures can escape to customers resulting in returns and dissatisfaction. The current process to identify these non-wets is to use a 2D or advanced X-ray tool with multidimension capability to image solder joints in processor sockets. The images are then examined by an operator who determines if each individual joint is good or bad. There can be an average of 150 images for an operator to examine for each socket. Each image contains more than 30 joints. These factors make the inspection process time consuming and the output variable depending on the skill and alertness of the operator. This paper presents an automatic defect identification and classification system for the detection of non-wet solder joints. The main components of the proposed system consist of region of interest (ROI) segmentation, feature extraction, reference-free classification, and automatic mapping. The ROI segmentation process is a noise-resilient segmentation method for the joint area. The centroids of the segmented joints (ROIs) are used as feature parameters to detect the suspect joints. The proposed reference-free classification can detect defective joints in the considered images with high accuracy without the need for training data or reference images. An automatic mapping procedure which maps the positions of all joints to a known Master Ball Grid Array file is used to get the precise label and location of the suspect joint for display to the operator and collection of non-wet statistics. The accuracy of the proposed system was determined to be 95.8% based on the examination of 56 sockets (76 496 joints). The false alarm rate is 1.1%. In comparison, the detection rate of a currently available advanced X-ray tool with multidimension capability is in the range of 43% to 75%. The proposed method reduces the operator effort to examine individual images by 89.6% (from looking at 154 images to 16 images) by presenting only images with suspect joints for inspection. When non-wet joints are missed, the presented system has been shown to identify the neighboring joints. This fact provides the operator with the capability to make 100% detection of all non-wets when utilizing a user interface that highlights the suspect joint area. The system works with a 2D X-ray imaging device, which saves cost over more expensive advanced X-ray tools with multidimension capability. The proposed scheme is relatively inexpensive to implement, easy to set up and can work with a variety of 2D X-ray tools.


Localization and Inhibition of Malicious Behaviors through a Model Checking based methodology


title: Designing Optimized MPI Broadcast and Allreduce for Many Integrated Core (MIC) InfiniBand Clusters

abstract: The emergence of co-processors such as Intel Many Integrated Cores (MICs) is changing the landscape of supercomputing. The MIC is a memory constrained environment and its processors also operate at slower clock rates. Furthermore, the communication characteristics between MIC processes are also different compared to communication between host processes. Communication libraries that do not consider these architectural subtleties cannot deliver good communication performance. The performance of MPI collective operations strongly affect the performance of parallel applications. Owing to the challenges introduced by the emerging heterogeneous systems, it is critical to fundamentally re-design collective algorithms to ensure that applications can fully leverage the MIC architecture. In this paper, we propose a generic framework to optimize the performance of important collective operations, such as, MPI Bcast, MPI Reduce and MPI Allreduce, on Intel MIC clusters. We also present a detailed analysis of the compute phases in reduce operations for MIC clusters. To the best of our knowledge, this is the first paper to propose novel designs to improve the performance of collectives on MIC clusters. Our designs improve the latency of the MPI Bcast operation with 4,864 MPI processes by up to 76%. We also observe up to 52.4% improvements in the communication latency of the MPI Allreduce operation with 2K MPI processes on heterogeneous MIC clusters. Our designs also improve the execution time of the WindJammer application by up to 16%.


title: A Semi-Blind Information Hiding Technique Using DWT-SVD and QAM-16 for Medical Images

abstract: In this paper, we propose a new image watermarking scheme for medical applications. Textual data are inserted into the original medical images based singular value decomposition (SVD) after discrete wavelet transform (DWT). Specifically, Quadrature Amplitude Modulation (QAM-16) is used for encoding text characters. Furthermore, Arnold Transform is used to make the watermark more secure before embedding it into the host image. The experimental results show that the proposed watermarking system ensures good imperceptibility and high robustness against various disturbances.


title: Sketching Clothoid Splines Using Shortest Paths

abstract: Clothoid splines are gaining popularity as a curve representation due to their intrinsically pleasing curvature, which varies piecewise linearly over arc length. However, constructing them from hand-drawn strokes remains difficult. Building on recent results, we describe a novel algorithm for approximating a sketched stroke with a fair (i.e., visually pleasing) clothoid spline. Fairness depends on proper segmentation of the stroke into curve primitives — lines, arcs, and clothoids. Our main idea is to cast the segmentation as a shortest path problem on a carefully constructed weighted graph. The nodes in our graph correspond to a vastly overcomplete set of curve primitives that are fit to every subsegment of the sketch, and edges correspond to transitions of a specified degree of continuity between curve primitives. The shortest path in the graph corresponds to a desirable segmentation of the input curve. Once the segmentation is found, the primitives are fit to the curve using non-linear constrained optimization. We demonstrate that the curves produced by our method have good curvature profiles, while staying close to the user sketch.


title: Comparison of Leaf Recognition by Moments and Fourier Descriptors

abstract: We test various features for recognition of leaves of wooden species. We compare Fourier descriptors, Zernike moments, Legendre moments and Chebyshev moments. All the features are computed from the leaf boundary only. Experimental evaluation on real data indicates that Fourier descriptors slightly outperform the other tested features.


title: Adaptive current control for grid-connected converters with LCL-filter

abstract: This paper presents a discrete-time adaptive current controller for grid-connected voltage source PWM converters with LCL-filter. The main attribute of the proposed current controller is that, in steady state, the damping of the LCL resonance does not depend on the grid characteristic since the adaptive feedback gains ensure a predefined behavior for the closed-loop current control. Simulation and experimental results are presented to validate the analysis and to demonstrate the good performance of the proposed controller for grid-connected converters subjected to large grid impedance variation and grid voltage disturbances.


title: Automated classification between age-related macular degeneration and Diabetic macular edema in OCT image using image segmentation

abstract: Age-related macular degeneration (AMD) and Diabetic macular edema (DME) are to lead causes to make a visual loss in people. People are suffered from the use of many time to diagnose and to wait for treatment both of diseases. This paper proposes a step of image segmentation to be divided the optical coherence tomography (OCT) to find the retinal pigment epithelium (RPE) layer and to detect a shape of drusen in RPE layer. Then, the RPE layer is used for finding retinal nerve fiber layer (RNFL) and for detecting a bubble of blood area in RNFL complex. Finally, this method uses a binary classification to classify two diseases characteristic between AMD and DME. We use 16 OCT images of a case study to segmentation and classify two diseases. In the experimental results, 10 images of AMD and 6 images of DME can be detected and classified to accuracy of 87.5%.


Short Range Capacitive Proximity Sensing


title: Hierarchical Singular Value Decomposition of Tensors

abstract: We define the hierarchical singular value decomposition (SVD) for tensors of order $d\geq2$. This hierarchical SVD has properties like the matrix SVD (and collapses to the SVD in $d=2$), and we prove these. In particular, one can find low rank (almost) best approximations in a hierarchical format ($\mathcal{H}$-Tucker) which requires only $\mathcal{O}((d-1)k^3+dnk)$ parameters, where $d$ is the order of the tensor, $n$ the size of the modes, and $k$ the (hierarchical) rank. The $\mathcal{H}$-Tucker format is a specialization of the Tucker format and it contains as a special case all (canonical) rank $k$ tensors. Based on this new concept of a hierarchical SVD we present algorithms for hierarchical tensor calculations allowing for a rigorous error analysis. The complexity of the truncation (finding lower rank approximations to hierarchical rank $k$ tensors) is in $\mathcal{O}((d-1)k^4+dnk^2)$ and the attainable accuracy is just 2-3 digits less than machine precision.


title: Endogeneity and the Dynamics of Internal Corporate Governance

abstract: We use a well-developed dynamic panel generalized method of moments (GMM) estimator to alleviate endogeneity concerns in two aspects of corporate governance research: the effect of board structure on firm performance and the determinants of board structure. The estimator incorporates the dynamic nature of internal governance choices to provide valid and powerful instruments that address unobserved heterogeneity and simultaneity. We re-examine the relation between board structure and performance using the GMM estimator in a panel of 6,000 firms over a period from 1991 to 2003, and find no causal relation between board structure and current firm performance. We illustrate why other commonly used estimators that ignore the dynamic relationship between current governance and past firm performance may be biased. We discuss where it may be appropriate to consider the dynamic panel GMM estimator in corporate governance research, as well as caveats to its use.


title: Comparing SVM and Naive Bayes classifiers for text categorization with Wikitology as knowledge enrichment

abstract: The activity of labeling of documents according to their content is known as text categorization. Many experiments have been carried out to enhance text categorization by adding background knowledge to the document using knowledge repositories like Word Net, Open Project Directory (OPD), Wikipedia and Wikitology. In our previous work, we have carried out intensive experiments by extracting knowledge from Wikitology and evaluating the experiment on Support Vector Machine with 10- fold cross-validations. The results clearly indicate Wikitology is far better than other knowledge bases. In this paper we are comparing Support Vector Machine (SVM) and Naive Bayes (NB) classifiers under text enrichment through Wikitology. We validated results with 10-fold cross validation and shown that NB gives an improvement of +28.78%, on the other hand SVM gives an improvement of +636% when compared with baseline results. Naive Bayes classifier is better choice when external enriching is used through any external knowledge base.


title: TWO-STAGE BIOMETRIC AUTHENTICATION METHOD USING THOUGHT ACTIVITY BRAIN WAVES

abstract: Brain waves are proposed as a biometric for verification of the identities of individuals in a small group. The approach is based on a novel two-stage biometric authentication method that minimizes both false accept error (FAE) and false reject error (FRE). These brain waves (or electroencephalogram (EEG) signals) are recorded while the user performs either one or several thought activities. As different individuals have different thought processes, this idea would be appropriate for individual authentication. In this study, autoregressive coefficients, channel spectral powers, inter-hemispheric channel spectral power differences, inter-hemispheric channel linear complexity and non-linear complexity (approximate entropy) values were used as EEG features by the two-stage authentication method with a modified four fold cross validation procedure. The results indicated that perfect accuracy was obtained, i.e. the FRE and FAE were both zero when the proposed method was tested on five subjects using certain thought activities. This initial study has shown that the combination of the two-stage authentication method with EEG features from thought activities has good potential as a biometric as it is highly resistant to fraud. However, this is only a pilot type of study and further extensive research with more subjects would be necessary to establish the suitability of the proposed method for biometric applications.


title: Poptrie: A Compressed Trie with Population Count for Fast and Scalable Software IP Routing Table Lookup

abstract: Internet of Things leads to routing table explosion. An inexpensive approach for IP routing table lookup is required against ever growing size of the Internet. We contribute by a fast and scalable software routing lookup algorithm based on a multiway trie, called Poptrie. Named after our approach to traversing the tree, it leverages the population count instruction on bit-vector indices for the descendant nodes to compress the data structure within the CPU cache. Poptrie outperforms the state-of-the-art technologies, Tree BitMap, DXR and SAIL, in all of the evaluations using random and real destination queries on 35 routing tables, including the real global tier-1 ISP's full-route routing table. Poptrie peaks between 174 and over 240 Million lookups per second (Mlps) with a single core and tables with 500--800k routes, consistently 4--578% faster than all competing algorithms in all the tests we ran. We provide the comprehensive performance evaluation, remarkably with the CPU cycle analysis. This paper shows the suitability of Poptrie in the future Internet including IPv6, where a larger route table is expected with longer prefixes.


title: A New Data Representation Based on Training Data Characteristics to Extract Drug Name Entity in Medical Text

abstract: One essential task in information extraction from the medical corpus is drug name recognition. Compared with text sources come from other domains, the medical text mining poses more challenges, for example, more unstructured text, the fast growing of new terms addition, a wide range of name variation for the same drug, the lack of labeled dataset sources and external knowledge, and the multiple token representations for a single drug name. Although many approaches have been proposed to overwhelm the task, some problems remained with poor F-score performance less than 0.75. This paper presents a new treatment in data representation techniques to overcome some of those challenges. We propose three data representation techniques based on the characteristics of word distribution and word similarities as a result of word embedding training. The first technique is evaluated with the standard NN model, that is, MLP. The second technique involves two deep network classifiers, that is, DBN and SAE. The third technique represents the sentence as a sequence that is evaluated with a recurrent NN model, that is, LSTM. In extracting the drug name entities, the third technique gives the best F-score performance compared to the state of the art, with its average F-score being 0.8645.


title: Benchmarking cloud serving systems with YCSB

abstract: While the use of MapReduce systems (such as Hadoop) for large scale data analysis has been widely recognized and studied, we have recently seen an explosion in the number of systems developed for cloud data serving. These newer systems address "cloud OLTP" applications, though they typically do not support ACID transactions. Examples of systems proposed for cloud serving use include BigTable, PNUTS, Cassandra, HBase, Azure, CouchDB, SimpleDB, Voldemort, and many others. Further, they are being applied to a diverse range of applications that differ considerably from traditional (e.g., TPC-C like) serving workloads. The number of emerging cloud serving systems and the wide range of proposed applications, coupled with a lack of apples-to-apples performance comparisons, makes it difficult to understand the tradeoffs between systems and the workloads for which they are suited. We present the "Yahoo! Cloud Serving Benchmark" (YCSB) framework, with the goal of facilitating performance comparisons of the new generation of cloud data serving systems. We define a core set of benchmarks and report results for four widely used systems: Cassandra, HBase, Yahoo!'s PNUTS, and a simple sharded MySQL implementation. We also hope to foster the development of additional cloud benchmark suites that represent other classes of applications by making our benchmark tool available via open source. In this regard, a key feature of the YCSB framework/tool is that it is extensible--it supports easy definition of new workloads, in addition to making it easy to benchmark new systems.


title: Integrated Computer-aided Forensic Case Analysis, Presentation, and Documentation based on Multimodal 3D Data

abstract: Three-dimensional (3D) crime scene documentation using 3D scanners and medical imaging modalities like computed tomography (CT) and magnetic resonance imaging (MRI) are increasingly applied in forensic casework. Together with digital photography, these modalities enable comprehensive and non-invasive recording of forensically relevant information regarding injuries/pathologies inside the body and on its surface. Furthermore, it is possible to capture traces and items at crime scenes. Such digitally secured evidence has the potential to similarly increase case understanding by forensic experts and non-experts in court. Unlike photographs and 3D surface models, images from CT and MRI are not self-explanatory. Their interpretation and understanding requires radiological knowledge. Findings in tomography data must not only be revealed, but should also be jointly studied with all the 2D and 3D data available in order to clarify spatial interrelations and to optimally exploit the data at hand. This is technically challenging due to the heterogeneous data representations including volumetric data, polygonal 3D models, and images. This paper presents a novel computer-aided forensic toolbox providing tools to support the analysis, documentation, annotation, and illustration of forensic cases using heterogeneous digital data. Conjoint visualization of data from different modalities in their native form and efficient tools to visually extract and emphasize findings help experts to reveal unrecognized correlations and thereby enhance their case understanding. Moreover, the 3D case illustrations created for case analysis represent an efficient means to convey the insights gained from case analysis to forensic non-experts involved in court proceedings like jurists and laymen. The capability of the presented approach in the context of case analysis, its potential to speed up legal procedures and to ultimately enhance legal certainty is demonstrated by introducing a number of representative forensic cases.


title: Towards Augmented Reality user interfaces in 3D media production

abstract: The idea of using Augmented Reality (AR) user interfaces (UIs) to create 3D media content, such as 3D models for movies and games has been repeatedly suggested over the last decade. Even though the concept is intuitively compelling and recent technological advances have made such an application increasingly feasible, very little progress has been made towards an actual real-world application of AR in professional media production. To this day, no immersive 3D UI has been commonly used by professionals for 3D computer graphics (CG) content creation. In this paper, we are first to publish a requirements analysis for our target application in the professional domain. Based on a survey that we conducted with media professionals, the analysis of professional 3D CG software, and professional training tutorials, we identify these requirements and put them into the context of AR UIs. From these findings, we derive several interaction design principles that aim to address the challenges of real-world application of AR to the production pipeline. We implemented these in our own prototype system while receiving feedback from media professionals. The insights gained in the survey, requirements analysis, and user interface design are relevant for research and development aimed at creating production methods for 3D media production.


title: Multi-crop Convolutional Neural Networks for lung nodule malignancy suspiciousness classification

abstract: Abstract We investigate the problem of lung nodule malignancy suspiciousness (the likelihood of nodule malignancy) classification using thoracic Computed Tomography (CT) images. Unlike traditional studies primarily relying on cautious nodule segmentation and time-consuming feature extraction, we tackle a more challenging task on directly modeling raw nodule patches and building an end-to-end machine-learning architecture for classifying lung nodule malignancy suspiciousness. We present a Multi-crop Convolutional Neural Network (MC-CNN) to automatically extract nodule salient information by employing a novel multi-crop pooling strategy which crops different regions from convolutional feature maps and then applies max-pooling different times. Extensive experimental results show that the proposed method not only achieves state-of-the-art nodule suspiciousness classification performance, but also effectively characterizes nodule semantic attributes (subtlety and margin) and nodule diameter which are potentially helpful in modeling nodule malignancy.


title: Immune checkpoint proteins: a new therapeutic paradigm for cancer--preclinical background: CTLA-4 and PD-1 blockade.

abstract: Much of the recent excitement in the translational field of tumor immunology and immunotherapy has been generated by the recognition that immune checkpoint proteins can be blocked by human antibodies with profound effects in vitro, in animal tumor systems, and in patients. Promising clinical data have already been generated in melanoma and other tumor types with human antibodies directed against cytotoxic T lymphocyte antigen-4 (CTLA-4) and programmed death-1 (PD-1). The preclinical data that supported the clinical development of these two antibodies will be discussed in detail in this review, showing that many of the therapeutic effects of these two agents were predicted by the animal models, as were the immune-related side effects noted with these drugs. In contrast, much of the early work with anti-CTLA-4 antibodies indicated that it had a potent therapeutic effect only when combined with granulocyte-macrophage colony-stimulating factor (GM-CSF)-transduced tumor vaccines, and that the antibody alone was effective only in the most immunogenic tumor models in mice. Intriguingly, in patients, the drug alone clearly has had important therapeutic effects, but the addition of vaccines has not added to its clinical benefit. Murine experiments also suggested that CTLA-4 abrogation might function via important effects on natural T-regulatory cells that were CD4(+), CD25(+high), and FOXp3(+), but this has not been borne out in experiments using peripheral blood mononuclear cells from patients treated with anti-CTLA-4 antibodies, and unlike in animals, in humans the exact mechanism(s) by which CTLA-4 abrogation induced an anti-tumor effect is still unclear. Abrogation of PD-1 functions via different immune signaling pathways than CTLA-4 and is likely to have a different spectrum of effects than blocking CTLA-4. For PD-1 blockade, murine experiments have suggested that the antibody alone and combined with adoptive cell transfer or vaccine approaches would be therapeutically beneficial, and that clear effects on T-cell proliferation and activation, as well as T-regulatory cell function would be observed in patients. The clinical development of anti-PD-1 antibody so far has shown that it has a potent effect when administered alone, and trials of vaccines with anti-PD-1 are just being initiated to test the idea that the predicted effects of that antibody observed in animal systems also would be seen in patients. These observations support the idea that animal preclinical therapeutic experiments are an important guide to the conduct of trials employing abrogation of immune checkpoint proteins in T cells in patients. Nonetheless, clinical investigators must be flexible and prepared to find that the biology of those systems may be very different in humans compared to mice.


title: Self-Adapting Control Parameters in Differential Evolution: A Comparative Study on Numerical Benchmark Problems

abstract: We describe an efficient technique for adapting control parameter settings associated with differential evolution (DE). The DE algorithm has been used in many practical cases and has demonstrated good convergence properties. It has only a few control parameters, which are kept fixed throughout the entire evolutionary process. However, it is not an easy task to properly set control parameters in DE. We present an algorithm-a new version of the DE algorithm-for obtaining self-adaptive control parameter settings that show good performance on numerical benchmark problems. The results show that our algorithm with self-adaptive control parameter settings is better than, or at least comparable to, the standard DE algorithm and evolutionary algorithms from literature when considering the quality of the solutions obtained


title: Efficient Stochastic Inference of Bitwise Deep Neural Networks

abstract: Recently published methods enable training of bitwise neural networks which allow reduced representation of down to a single bit per weight. We present a method that exploits ensemble decisions based on multiple stochastically sampled network models to increase performance figures of bitwise neural networks in terms of classification accuracy at inference. Our experiments with the CIFAR-10 and GTSRB datasets show that the performance of such network ensembles surpasses the performance of the high-precision base model. With this technique we achieve 5.81% best classification error on CIFAR-10 test set using bitwise networks. Concerning inference on embedded systems we evaluate these bitwise networks using a hardware efficient stochastic rounding procedure. Our work contributes to efficient embedded bitwise neural networks.


title: Generalized multilevel current source inverter topology with self-balancing current

abstract: A generalized single-phase multilevel current source inverter (MCSI) topology with self-balancing current is proposed, which uses the duality transformation from the generalized multilevel voltage source inverter (MVSI) topology. The existing single-phase 8- and 6-switch 5-level current source inverters (CSIs) can be derived from this generalized MCSI topology. In the proposed topology, each intermediate DC-link current level can be balanced automatically without adding any external circuits; thus, a true multilevel structure is provided. Moreover, owing to the dual relationship, many research results relating to the operation, modulation, and control strategies of MVSIs can be applied directly to the MCSIs. Some simulation results are presented to verify the proposed MCSI topology.


title: Differentiable parameterization of Catmull-Clark subdivision surfaces

abstract: Subdivision-based representations are recognized as important tools for the generation of high-quality surfaces for Computer Graphics. In this paper we describe two parameterizations of Catmull-Clark subdivision surfaces that allow a variety of algorithms designed for other types of parametric surfaces (i.e., B-splines) to be directly applied to subdivision surfaces. In contrast with the natural parameterization of subdivision surfaces characterized by diverging first order derivatives around extraordinary vertices of valence higher than four, the derivatives associated with our proposed methods are defined everywhere on the surface. This is especially important for Computer-Aided Design (CAD) applications that seek to address the limitations of NURBS-based representations through the more flexible subdivision framework.


title: User behavior prediction in energy consumption in housing using Bayesian networks

abstract: This paper deals with the problem of the user behavior prediction in a home automation system. Anticipating the needed energy for a service is based on the available prediction (like user requests) which contains the uncertainties. When the future users requests are not available in a home automation system thanks to programmatic, it is interesting to predict it to anticipate the energy needed in order to avoid some problems like peak consumption. A general method to predict users requests for services in energy consumption is proposed. The method relies on Bayesian networks to predict and diagnose user's behavior in housing. Some results and perspectives are presented in this paper.


title: Towards Mobile Phone Localization without War-Driving

abstract: This paper identifies the possibility of using electronic compasses and accelerometers in mobile phones, as a simple and scalable method of localization without war-driving. The idea is not fundamentally different from ship or air navigation systems, known for centuries. Nonetheless, directly applying the idea to human-scale environments is non-trivial. Noisy phone sensors and complicated human movements present practical research challenges. We cope with these challenges by recording a person's walking patterns, and matching it against possible path signatures generated from a local electronic map. Electronic maps enable greater coverage, while eliminating the reliance on WiFi infrastructure and expensive war-driving. Measurements on Nokia phones and evaluation with real users confirm the anticipated benefits. Results show a location accuracy of less than 11m in regions where today's localization services are unsatisfactory or unavailable.


title: MillWheel: Fault-Tolerant Stream Processing at Internet Scale

abstract: MillWheel is a framework for building low-latency data-processing applications that is widely used at Google. Users specify a directed computation graph and application code for individual nodes, and the system manages persistent state and the continuous flow of records, all within the envelope of the framework's fault-tolerance guarantees. ::: ::: This paper describes MillWheel's programming model as well as its implementation. The case study of a continuous anomaly detector in use at Google serves to motivate how many of MillWheel's features are used. MillWheel's programming model provides a notion of logical time, making it simple to write time-based aggregations. MillWheel was designed from the outset with fault tolerance and scalability in mind. In practice, we find that MillWheel's unique combination of scalability, fault tolerance, and a versatile programming model lends itself to a wide variety of problems at Google.


title: An overview of Acipenseriformes

abstract: Acipenseriformes occupy a special place in the history of ideas concerning fish evolution, but in many respects, phylogenetic studies of the group remain in their infancy. Even such basic questions as the monophyly of Acipenser (the largest genus) are unanswered. We define relationships based on comparative osteology, which allows us to incorporate well-preserved fossils into analyses. Acipenseriformes has existed at least since the Lower Jurassic (approximately 200 MYBP), and all fossil and recent taxa are from the Holarctic. Phylogenetic relationships among Paleozoic and Early Mesozoic actinopterygians are problematic, but most workers agree that Acipenseriformes is monophyletic and derived from some component of ‘paleonisciform’ fishes. (‘Paleonisciformes’ is a grade of primitive non-neopterygian actinopterygians, sensu Gardiner 1993.) Taxa discussed in comparison here are: †Cheirolepis, Polypterus, †Mimia, †Moythomasia, †Birgeria, †Saurichthys, Lepisosteus and Amia. We review generic diversity within the four nominal families of fossil and recent Acipenseriformes (†Chondrosteidae, †Peipiaosteidae, Polyodontidae, and Acipenseridae), and provide a cladogram summarizing osteological characters for those four groups. Monophyly of the two extant families is well-supported, but there are no comprehensive studies of all of the known species and specimens of †Chondrosteidae and †Peipiaosteidae. As a result, sister-group relationships among †Chondrosteidae, †Peipiaosteidae, and Acipenseroidei (= Polyodontidae + Acipenseridae) are unresolved. We discuss five features fundamental to the biology of acipenseriforms that benefit from the availability of our new phylogenetic hypothesis: (1) specializations of jaws and operculum relevant to jaw protrusion, feeding, and ram ventilation; (2) anadromy or potamodromy and demersal spawning; (3) paedomorphosis and evolution of the group; (4) the bioégeography of Asian and North American polyodontids and scaphirhynchines; and (5) the great abundance of electroreceptive organs in the rostral and opercular regions. Finally, we summarize our nomenclatural recommendations.


title: SolMiner: mining distinct solutions in programs

abstract: Given a programming problem, because of a variety of data structures and algorithms that can be applied and different tradeoffs, such as space-time, to be considered, there may be many distinct solutions. By comparing his/her solution against others' and learning from the distinct solutions, a learner may quickly improve programming skills and gain experience in making trade-offs. Meanwhile, on the Internet many websites provide venues for programming practice and contests. Popular websites receive hundreds of thousands of submissions daily from novices as well as advanced learners. While these websites can automatically judge the correctness of a submission, none extracts distinct solutions from the submissions and provides them to learners. How to automatically identify distinct solutions from a large number of submissions is a challenging and unresolved problem. Due to diverse coding styles and high programming flexibility, submissions implementing the same solution may appear very different from each other; in addition, dealing with submissions at scale imposes extra challenges. We propose S ol M iner , a solution miner, that automatically mines distinct solutions from a large number of submissions. SolMiner leverages static program analysis, data mining, and machine learning to automatically measure the similarity between submissions and identify distinct solutions. We have built a prototype of SolMiner and evaluated it. The evaluation shows that the technique is effective and efficient.


title: Distance Estimation Algorithm for Stereo Pair Images

abstract: Various algorithms have been proposed for estimating camera distance from objects in digital stereo pair videos and images. Existing algorithms assume that digital images were taken with pan and tilt cameras or assume that the angles between the cameras and the object are known. The algorithm that we are proposing has the ability to calculate distance from the camera plane to the object plane, where the cameras do not have the ability to pan and tilt nor the angles between the cameras and the object are known beforehand.


title: Dynamic-Domain RRTs: Efficient Exploration by Controlling the Sampling Domain

abstract: Sampling-based planners have solved difficult problems in many applications of motion planning in recent years. In particular, techniques based on the Rapidly-exploring Random Trees (RRTs) have generated highly successful single-query planners. Even though RRTs work well on many problems, they have weaknesses which cause them to explore slowly when the sampling domain is not well adapted to the problem. In this paper we characterize these issues and propose a general framework for minimizing their effect. We develop and implement a simple new planner which shows significant improvement over existing RRT-based planners. In the worst cases, the performance appears to be only slightly worse in comparison to the original RRT, and for many problems it performs orders of magnitude better.


title: Tuberculosis: Medical management update

abstract: Tuberculosis (TB) is an infectious chronic disease. After decades of steadily declining prevalence, the disease has reemerged in the last 5 years. Symptoms of TB are mild and not specific and can be classified as either systemic or localized to target organs. Microscopic examination of the sputum remains an inexpensive and rapid way to identify highly infectious patients. Four different antimicrobial agents—rifampin, ethambutol, pirazinamide, and isoniazid—form the basis of currently recommended antituberculosis therapy. Tuberculosis could be an occupational risk for health care workers. Dentists must be involved in the health promotion and early detection of TB.


Interactive Natural Language Processing: Building on Success


title: Complexity and Adaptivity in Supply Networks: Building Supply Network Theory Using a Complex Adaptive Systems Perspective*

abstract: Supply networks are composed of large numbers of firms from multiple interrelated industries. Such networks are subject to shifting strategies and objectives within a dynamic environment. In recent years, when faced with a dynamic environment, several disciplines have adopted the Complex Adaptive System (CAS) perspective to gain insights into important issues within their domains of study. Research investigations in the field of supply networks have also begun examining the merits of complexity theory and the CAS perspective. In this article, we bring the applicability of complexity theory and CAS into sharper focus, highlighting its potential for integrating existing supply chain management (SCM) research into a structured body of knowledge while also providing a framework for generating, validating, and refining new theories relevant to real-world supply networks. We suggest several potential research questions to emphasize how a CAS perspective can help in enriching the SCM discipline. We propose that the SCM research community adopt such a dynamic and systems-level orientation that brings to the fore the adaptivity of firms and the complexity of their interrelations that are often inherent in supply networks.


title: Clothes Co-Parsing Via Joint Image Segmentation and Labeling With Application to Clothing Retrieval

abstract: This paper aims at developing an integrated system for clothing co-parsing (CCP), in order to jointly parse a set of clothing images (unsegmented but annotated with tags) into semantic configurations. A novel data-driven system consisting of two phases of inference is proposed. The first phase, referred as “image cosegmentation,” iterates to extract consistent regions on images and jointly refines the regions over all images by employing the exemplar-SVM technique [1] . In the second phase (i.e., “region colabeling”), we construct a multiimage graphical model by taking the segmented regions as vertices, and incorporating several contexts of clothing configuration (e.g., item locations and mutual interactions). The joint label assignment can be solved using the efficient Graph Cuts algorithm. In addition to evaluate our framework on the Fashionista dataset [2] , we construct a dataset called the SYSU-Clothes dataset consisting of 2098 high-resolution street fashion photos to demonstrate the performance of our system. We achieve 90.29%/88.23% segmentation accuracy and 65.52%/63.89% recognition rate on the Fashionista and the SYSU-Clothes datasets, respectively, which are superior compared with the previous methods. Furthermore, we apply our method on a challenging task, i.e., cross-domain clothing retrieval: given user photo depicting a clothing image, retrieving the same clothing items from online shopping stores based on the fine-grained parsing results.


title: A Comparative Study of Wireless Protocols: Bluetooth, UWB, ZigBee, and Wi-Fi

abstract: Bluetooth (over IEEE 802.15.1), ultra-wideband (UWB, over IEEE 802.15.3), ZigBee (over IEEE 802.15.4), and Wi-Fi (over IEEE 802.11) are four protocol standards for short- range wireless communications with low power consumption. From an application point of view, bluetooth is intended for a cordless mouse, keyboard, and hands-free headset, UWB is oriented to high-bandwidth multimedia links, ZigBee is designed for reliable wirelessly networked monitoring and control networks, while Wi-Fi is directed at computer-to-computer connections as an extension or substitution of cabled networks. In this paper, we provide a study of these popular wireless communication standards, evaluating their main features and behaviors in terms of various metrics, including the transmission time, data coding efficiency, complexity, and power consumption. It is believed that the comparison presented in this paper would benefit application engineers in selecting an appropriate protocol.


title: Lenses for Circular Polarization Using Planar Arrays of Rotated Passive Elements

abstract: A planar array of passive lens elements can be phased to approximate the effect of a curved dielectric lens. The rotational orientation of each element can provide the required phase shift for circular polarization. The array elements must be designed so that the hand of circular polarization changes as the electromagnetic wave passes through the lens. An element is presented that is based on an aperture-coupled microstrip patch antenna, and two lenses are designed. Each lens has a diameter of 254 mm and contains 349 elements. The elements have identical dimensions but the rotational orientation of each element is selected to provide a specific lens function. The first lens is designed to collimate radiation from a feed horn into a beam pointing 20° from broadside. At 12.9 GHz the aperture efficiency is 48%. The second lens acts as a Wollaston-type prism. It splits an incident wave according to its circular polarization components.


title: Understanding the influence of dead-time on GaN based synchronous boost converter

abstract: Gallium Nitride (GaN) based power switching devices are known to be superior to conventional Si devices due to properties such as low loss and fast switching speed. However, as a new technology, some special characteristics of GaN-based power devices still remain unknown to the public. This paper tries to investigate the effect of dead-time on a GaN-based synchronous boost converter, as compared to its Si counterpart. It is found out that GaN-based converter is more sensitive to dead-time related loss as a result of fast switching transition and high reverse voltage drop. Improper selection of dead-time value can offset its advantage over Si converter. Analyses also show that GaN HEMTs have different switching characteristics compared to Si MOSFET due to low Cgd to Cds ratio and lack of reverse recovery. These critical findings will help power electronic engineers take better advantage of GaN technology in synchronous rectification and inverter applications.


title: Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction

abstract: We introduce a multi-task setup of identifying and classifying entities, relations, and coreference clusters in scientific articles. We create SciERC, a dataset that includes annotations for all three tasks and develop a unified framework called Scientific Information Extractor (SciIE) for with shared span representations. The multi-task setup reduces cascading errors between tasks and leverages cross-sentence relations through coreference links. Experiments show that our multi-task model outperforms previous models in scientific information extraction without using any domain-specific features. We further show that the framework supports construction of a scientific knowledge graph, which we use to analyze information in scientific literature.


title: Characteristics of technology transfer in business ventures: the case of Daejeon, Korea

abstract: Abstract This article explores the characteristics of venture business and entrepreneurs in Korea to (1) identify technology transfer activities, (2) analyze the differences between technology transfer in linear and nonlinear venture businesses, and (3) guide more effective venture business policy and strategy. This empirical assessment reveals that entrepreneurs have insightful evaluations about their resources and capacities as well as expectations with regard to functions and features of science parks and incubators. Respondents from “linear model”-based start-ups tend to be older and have higher education, employ more basic research and development (R&D) and have more R&D-oriented careers, and have more varied work experience than “nonlinear”-based start-ups. The functions and features of science parks and incubators were generally not considered a critical influence on start-ups nor on the growth of venture businesses. Accordingly, alternative venture-nurturing strategies are discussed as being key to accelerate venture businesses growth.


title: Automatic Real-time Background Cut for Portrait Videos

abstract: We in this paper solve the problem of high-quality automatic real-time background cut for 720p portrait videos. We first handle the background ambiguity issue in semantic segmentation by proposing a global background attenuation model. A spatial-temporal refinement network is developed to further refine the segmentation errors in each frame and ensure temporal coherence in the segmentation map. We form an end-to-end network for training and testing. Each module is designed considering efficiency and accuracy. We build a portrait dataset, which includes 8,000 images with high-quality labeled map for training and testing. To further improve the performance, we build a portrait video dataset with 50 sequences to fine-tune video segmentation. Our framework benefits many video processing applications.


Learning Text Similarity with Siamese Recurrent Networks


title: Perceptions of Sex, Gender, and Puberty Suppression: A Qualitative Analysis of Transgender Youth

abstract: International guidelines recommend the use of Gonadotropin-Releasing Hormone (GnRH) agonists in adolescents with gender dysphoria (GD) to suppress puberty. Little is known about the way gender dysphoric adolescents themselves think about this early medical intervention. The purpose of the present study was (1) to explicate the considerations of gender dysphoric adolescents in the Netherlands concerning the use of puberty suppression; (2) to explore whether the considerations of gender dysphoric adolescents differ from those of professionals working in treatment teams, and if so in what sense. This was a qualitative study designed to identify considerations of gender dysphoric adolescents regarding early treatment. All 13 adolescents, except for one, were treated with puberty suppression; five adolescents were trans girls and eight were trans boys. Their ages ranged between 13 and 18 years, with an average age of 16 years and 11 months, and a median age of 17 years and 4 months. Subsequently, the considerations of the adolescents were compared with views of clinicians treating youth with GD. From the interviews with the gender dysphoric adolescents, three themes emerged: (1) the difficulty of determining what is an appropriate lower age limit for starting puberty suppression. Most adolescents found it difficult to define an appropriate age limit and saw it as a dilemma; (2) the lack of data on the long-term effects of puberty suppression. Most adolescents stated that the lack of long-term data did not and would not stop them from wanting puberty suppression; (3) the role of the social context, for which there were two subthemes: (a) increased media-attention, on television, and on the Internet; (b) an imposed stereotype. Some adolescents were positive about the role of the social context, but others raised doubts about it. Compared to clinicians, adolescents were often more cautious in their treatment views. It is important to give voice to gender dysphoric adolescents when discussing the use of puberty suppression in GD. Otherwise, professionals might act based on assumptions about adolescents’ opinions instead of their actual considerations. We encourage gathering more qualitative research data from gender dysphoric adolescents in other countries.


title: Gene selection for sample classification based on gene expression data: study of sensitivity to choice of parameters of the GA/KNN method

abstract: Motivation: We recently introduced a multivariate approach that selects a subset of predictive genes jointly for sample classification based on expression data. We tested the algorithm on colon and leukemia data sets. As an extension to our earlier work, we systematically examine the sensitivity, reproducibility and stability of gene selection/sample classification to the choice of parameters of the algorithm. Methods: Our approach combines a Genetic Algorithm (GA) and the k-Nearest Neighbor (KNN) method to identify genes that can jointly discriminate between different classes of samples (e.g. normal versus tumor). The GA/KNN method is a stochastic supervised pattern recognition method. The genes identified are subsequently used to classify independent test set samples. Results: The GA/KNN method is capable of selecting a subset of predictive genes from a large noisy data set for sample classification. It is a multivariate approach that can capture the correlated structure in the data. We find that for a given data set gene selection is highly repeatable in independent runs using the GA/KNN method. In general, however, gene selection may be less robust than classification. Availability: The method is available at http://dir.niehs.nih. gov/microarray/datamining


title: Automatic Tracking of a Large Number of Moving Targets in 3D

abstract: This paper addresses the problem of tracking a large number of targets moving in 3D space using multiple calibrated video cameras. Most visual details of the targets are lost in the captured images because of limited image resolution, and the remainder can be easily corrupted due to frequent occlusion, which makes it difficult to determine both across-view and temporal correspondences. We propose a fully automatic tracking system that is capable of detecting and tracking a large number of flying targets in a 3D volume. The system includes a 3D tracking method in the framework of particle filter. Different from previous 2D tracking methods, the proposed method models the 3D attributes of targets and furthest collects weak visual information from multiple views, which makes the tracker robust against occlusion and distraction. The ambiguities in stereo matching when initializing trackers are handled by an effective multiple hypothesis generation and verification mechanism. The whole system is fully automatic in dealing with variable number of targets and robust against detection and matching errors. Our system has successfully been used by biologists to recover the 3D trajectories of hundreds of fruit flies flying freely in a 3D volume.


title: Quantum Algorithms and Circuits for Scientific Computing

abstract: Quantum algorithms for scientific computing require modules implementing fundamental functions, such as the square root, the logarithm, and others. We require algorithms that have a well-controlled numerical error, that are uniformly scalable and reversible (unitary), and that can be implemented efficiently. We present quantum algorithms and circuits for computing the square root, the natural logarithm, and arbitrary fractional powers. We provide performance guarantees in terms of their worst-case accuracy and cost. We further illustrate their performance by providing tests comparing them to the respective floating point implementations found in widely used numerical software.


title: Past, Present, and Future Challenges of the Marine Vessel’s Electrical Power System

abstract: The evolution of the use of electricity in marine vessels is presented and discussed in this paper in an historical perspective. The historical account starts with its first commercial use in the form of light bulbs on the SS Columbia in 1880 for illumination, going forward through use in hybrid propulsion systems with steam turbines and diesel engines and then transitioning to the present with the first fully electric marine vessel based entirely on the use of batteries in 2015. Electricity use is discussed not only in the light of its many benefits but also of the challenges introduced after the emergence of the marine vessel electrical power system. The impact of new conversion technologies like power electronics, battery energy storage, and the dc power system on overall energy efficiency, power quality, and emission level is discussed thoroughly. This paper guides the reader through this development, the present and future challenges by calling attention to the future research needs, and the need to revisit standards that relate to power quality, safety, integrity, and stability of the marine vessel power system, which are strongly impacted by the way electricity is used in the marine vessel.


title: PX-CGRA: Polymorphic approximate coarse-grained reconfigurable architecture

abstract: Coarse-Grained Reconfigurable Architectures (CGRAs) provide tradeoff between the energy-efficiency of Application Specific Integrated Circuits (ASICs) and the flexibility of General Purpose Processors (GPPs). State-of-the-art CGRAs only support exact architectures and precise application executions. However, a majority of the streaming applications such as multimedia and digital signal processing, which are amenable to CGRAs, are inherently error resilient. Therefore, these applications can greatly benefit from the emerging trend of Approximate Computing that leverages this error-resiliency to provide higher energy efficiency proportional to the tolerable accuracy loss (can even be constrained). This paper, for the first time, introduces the novel concept of Polymorphic Approximate CGRA (PX-CGRA) that employs heterogeneous tiles of Polymorphic-Approximated ALU Clusters (PACs) connected in a 2-D mesh style connection. These PACs can implement different approximate modes as well as accurate modes depending upon their selected configuration as per the run-time requirements of executing applications. For designing an efficient PX-CGRA, we propose a bottom-up design flow. In addition, the flow of application mapping on PX-CGRA is discussed including accuracy-level mapping, scheduling, and binding steps. To comprehensively evaluate the efficacy of the proposed CGRA, the complete PX-CGRA architecture in different sizes as well as with different PACs configurations are synthesized using a 15-nm FinFET technology. Our results show up to 15%–45% energy efficiency improvement for 5%–35% output quality degradation, respectively, when compared to the state-of-the-art exact-mode CGRA. Our proposed architecture and design methodology enable a new era of accuracy-configurable CGRAs to provide significant energy gains.


title: Real-Time Cloud Rendering

abstract: This paper presents a method for realistic real-time rendering of clouds suitable for flight simulation and games. It provides a cloud shading algorithm that approximates multiple forward scattering in a preprocess, and first order anisotropic scattering at runtime. Impostors are used to accelerate cloud rendering by exploiting frame-to-frame coherence in an interactive flight simulation. Impostors are shown to be particularly well suited to clouds, even in circumstances under which they cannot be applied to the rendering of polygonal geometry. The method allows hundreds of clouds and hundreds of thousands of particles to be rendered at high frame rates, and improves interaction with clouds by reducing artifacts introduced by direct particle rendering techniques.


title: Learning Spectral Descriptors for Deformable Shape Correspondence

abstract: Informative and discriminative feature descriptors play a fundamental role in deformable shape analysis. For example, they have been successfully employed in correspondence, registration, and retrieval tasks. In recent years, significant attention has been devoted to descriptors obtained from the spectral decomposition of the Laplace-Beltrami operator associated with the shape. Notable examples in this family are the heat kernel signature (HKS) and the recently introduced wave kernel signature (WKS). The Laplacian-based descriptors achieve state-of-the-art performance in numerous shape analysis tasks; they are computationally efficient, isometry-invariant by construction, and can gracefully cope with a variety of transformations. In this paper, we formulate a generic family of parametric spectral descriptors. We argue that to be optimized for a specific task, the descriptor should take into account the statistics of the corpus of shapes to which it is applied (the "signal") and those of the class of transformations to which it is made insensitive (the "noise"). While such statistics are hard to model axiomatically, they can be learned from examples. Following the spirit of the Wiener filter in signal processing, we show a learning scheme for the construction of optimized spectral descriptors and relate it to Mahalanobis metric learning. The superiority of the proposed approach in generating correspondences is demonstrated on synthetic and scanned human figures. We also show that the learned descriptors are robust enough to be learned on synthetic data and transferred successfully to scanned shapes.


title: Dynamics of a Heroin Epidemic Model with Very Population

abstract: Based on the model provided by the Mulone and Straughan [1], we relax the population which are constant and obtain the drug-free equilibrium which is global asymptotically stable under some conditions. The system has only uniqueness positive endemic equilibrium which is globally asymptotically stable by using the second compound matrix.


title: Framing: Toward Clarification of a Fractured Paradigm

abstract: deficient core knowledge, I propose that we turn an osten- sible weakness into a strength. We should identify our mission as bring- ing together insights and theories that would otherwise remain scattered in other disciplines. Because of the lack of interchange among the disci- plines, hypotheses thoroughly discredited in one field may receive wide acceptance in another. Potential research paradigms remain fractured, with pieces here and there but no comprehensive statement to guide re- search. By bringing ideas together in one location, communication can aspire to become a master discipline that synthesizes related theories and concepts and exposes them to the most rigorous, comprehensive state- ment and exploration. Reaching this goal would require a more self-con- scious determination by communication scholars to plumb other fields and feed back their studies to outside researchers. At the same time, such an enterprise would enhance the theoretical rigor of communication scholarship proper. The idea


title: Mean-shift blob tracking through scale space

abstract: The mean-shift algorithm is an efficient technique for tracking 2D blobs through an image. Although the scale of the mean-shift kernel is a crucial parameter, there is presently no clean mechanism for choosing or updating scale while tracking blobs that are changing in size. We adapt Lindeberg's (1998) theory of feature scale selection based on local maxima of differential scale-space filters to the problem of selecting kernel scale for mean-shift blob tracking. We show that a difference of Gaussian (DOG) mean-shift kernel enables efficient tracking of blobs through scale space. Using this kernel requires generalizing the mean-shift algorithm to handle images that contain negative sample weights.


title: An Attack Surface Metric

abstract: Measurement of software security is a long-standing challenge to the research community. At the same time, practical security metrics and measurements are essential for secure software development. Hence, the need for metrics is more pressing now due to a growing demand for secure software. In this paper, we propose using a software system's attack surface measurement as an indicator of the system's security. We formalize the notion of a system's attack surface and introduce an attack surface metric to measure the attack surface in a systematic manner. Our measurement method is agnostic to a software system's implementation language and is applicable to systems of all sizes; we demonstrate our method by measuring the attack surfaces of small desktop applications and large enterprise systems implemented in C and Java. We conducted three exploratory empirical studies to validate our method. Software developers can mitigate their software's security risk by measuring and reducing their software's attack surfaces. Our attack surface reduction approach complements the software industry's traditional code quality improvement approach for security risk mitigation and is useful in multiple phases of the software development lifecycle. Our collaboration with SAP demonstrates the use of our metric in the software development process.


title: Quantification of Man-Machine System Reliability in Process Control

abstract: Application of reliability engineering to process plant design, operation and control has led to a demand for estimates of `human reliability,' or `human error.' However, it is desirable to treat the concept of `human error' with caution and to avoid an approach in which the operator appears to be held solely responsible or even blamed; in reality the error arises out of a quite specific combination of conditions in the man-machine system and it is on the total system that attention should be centered. Problems such as performance of discrete tasks and vigilance tasks or behavior in emergencies have been widely studied. While some of this work appears applicable to process control, there remain significant gaps. A more comprehensive taxonomy of process operator error seems to be needed. Systems exist for reporting incidents in process plants, each with a particular objective such as improvement of personal safety, estimation of insurance rates, or reduction of maintenance costs. Data produced are specialized and often useless for other purposes. In particular, it is difficult to abstract data on malfunction involving the operator. A taxonomy suitable for process control is suggested and a scheme for collection of data on reliability in process plants is outlined. Attention is drawn to the relevance of the process and control system characteristics and to their variability. The importance of the process computer is emphasized, both in its impact on the control system and as a research tool.


title: Factors Influencing Customer Loyalty Toward Online Shopping

abstract: The significant growth of online shopping makes the competition in this industry become more intense. Maintaining customer loyalty has been recognized as one of the essential factor for business survival and growth. The purpose of this study is to examine empirically the influence of satisfaction, trust and commitment on customer loyalty in online shopping. This paper describes a theoretical model for investigating the influence of satisfaction, trust and commitment on customer loyalty toward online shopping. Based on the theoretical model, hypotheses were formulated. The primary data were collected from the respondents which consists of 300 students. Multiple regression and qualitative analysis were used to test the study hypotheses. The empirical study results revealed that satisfaction, trust and commitment have significant impact on student loyalty toward online shopping.


title: Children With Anogenital Symptoms and Signs Referred for Sexual Abuse Evaluations

abstract: Objective To determine whether children referred to a sexual abuse clinic because of anogenital symptoms or signs have examination findings that are suggestive of or probable or definitive for sexual abuse. Design Case series of 157 patients. Setting Child and adolescent ambulatory care sexual abuse clinic. Results A medical records review of 3660 cases was done; 157 cases were identified for study. Most (75%) referrals were from medical clinics. Of 184 complaints, the most common presenting symptom or sign was anogenital bleeding or bruising (29.3%), followed by irritation or redness (21.7%), abnormal anogenital anatomy (20.7%), vaginal discharge (18.4%), lesions (6.5%), and "other" symptoms or signs (3.3%). We used a standardized classification system and determined that 25 patients (15%) had examination findings in the sexual abuse clinic that were suggestive of or probable or definitive for sexual abuse. Although 85 patients had examination findings that corroborated the presenting symptom(s), 70 had nonspecific examination findings or a diagnosis other than sexual abuse. Seventy-two patients had normal examination findings. Only patients with the presenting symptom of lesions had an increased likelihood of a sexual abuse diagnosis. Common examination findings included anogenital erythema, enhanced vascularity of the hymen or vestibule in prepubertal girls, labial adhesions, and culture-negative vaginitis. Conclusions Few children are referred for sexual abuse evaluations based on physical signs or symptoms alone. Children with anogenital symptoms but without a disclosure or suspicion of sexual abuse are unlikely to have examination findings suggestive of abuse. The evaluation of children with anogenital symptoms and signs should include a consideration of alternative conditions and causes not directly related to sexual abuse.


title: Scalable and Sustainable Deep Learning via Randomized Hashing

abstract: Current deep learning architectures are growing larger in order to learn from complex datasets. These architectures require giant matrix multiplication operations to train millions of parameters. Conversely, there is another growing trend to bring deep learning to low-power, embedded devices. The matrix operations, associated with the training and testing of deep networks, are very expensive from a computational and energy standpoint. We present a novel hashing-based technique to drastically reduce the amount of computation needed to train and test neural networks. Our approach combines two recent ideas, Adaptive Dropout and Randomized Hashing for Maximum Inner Product Search (MIPS), to select the nodes with the highest activations efficiently. Our new algorithm for deep learning reduces the overall computational cost of the forward and backward propagation steps by operating on significantly fewer nodes. As a consequence, our algorithm uses only 5% of the total multiplications, while keeping within 1% of the accuracy of the original model on average. A unique property of the proposed hashing-based back-propagation is that the updates are always sparse. Due to the sparse gradient updates, our algorithm is ideally suited for asynchronous, parallel training, leading to near-linear speedup, as the number of cores increases. We demonstrate the scalability and sustainability (energy efficiency) of our proposed algorithm via rigorous experimental evaluations on several datasets.


title: On slide-based contextual cues for presentation reuse

abstract: Reuse of existing presentation materials is prevalent among knowledge workers. However, finding the most appropriate material for reuse is challenging. Existing information management and search tools provide inadequate support for reuse due to their dependence on users' ability to effectively categorize, recall, and recognize existing materials. Based on our findings from an online survey and contextual interviews, we designed and implemented a slide-based contextual recommender, ConReP, for supporting reuse of presentation materials. ConReP utilizes a user-selected slide as a search-key, recommends materials based on similarity to the selected slide, and provides a local-context-based visual representation of the recommendations. Users input provides new insight into presentation reuse and reveals that slide-based search is more effective than keyword-based search, local-context-based visual representation helps in better recall and recognition, and shows the promise of this general approach of exploiting individual slides and local-context for better presentation reuse.


title: Statistical analysis of fNIRS data : A comprehensive review

abstract: Abstract Functional near-infrared spectroscopy (fNIRS) is a non-invasive method to measure brain activities using the changes of optical absorption in the brain through the intact skull. fNIRS has many advantages over other neuroimaging modalities such as positron emission tomography (PET), functional magnetic resonance imaging (fMRI), or magnetoencephalography (MEG), since it can directly measure blood oxygenation level changes related to neural activation with high temporal resolution. However, fNIRS signals are highly corrupted by measurement noises and physiology-based systemic interference. Careful statistical analyses are therefore required to extract neuronal activity-related signals from fNIRS data. In this paper, we provide an extensive review of historical developments of statistical analyses of fNIRS signal, which include motion artifact correction, short source-detector separation correction, principal component analysis (PCA)/independent component analysis (ICA), false discovery rate (FDR), serially-correlated errors, as well as inference techniques such as the standard t-test, F-test, analysis of variance (ANOVA), and statistical parameter mapping (SPM) framework. In addition, to provide a unified view of various existing inference techniques, we explain a linear mixed effect model with restricted maximum likelihood (ReML) variance estimation, and show that most of the existing inference methods for fNIRS analysis can be derived as special cases. Some of the open issues in statistical analysis are also described.