title: HHA-based CNN image features for indoor loop closure detection

abstract: Loop closure detection is an important part of visual simultaneous location and mapping (SLAM) system. Most of traditional loop closure detection approaches using hand-crafted features often lack robustness with respect to object occlusions and illumination changes, especially for the complicated indoor environment. Recently, convolutional neural network (CNN) makes a huge impact on many computer vision and pattern recognition applications. Depth images have complementary information to RGB images, which can encode the structural information of the scene. With the availability of inexpensive RGB-D sensors, it is expected that depth information can increase the accuracy in many computer vision applications. In this paper, we focus on indoor loop closure detection using the depth information. For the first time, we introduce a simple CNN model to train the depth images encoded by HHA method for indoor loop closure detection. The experiment demonstrates that HHA based CNN features can fully utilize the structural information of the scene, which shows that it is preferable than using raw depth images for loop closure detection.


title: Design and Calibration of an Optical Micro Motion Sensing System for Micromanipulation Tasks

abstract: An optical sensing system has been developed using a pair of orthogonally placed position sensitive detectors (PSD) to track 3D displacement of a microsurgical instrument tip in real-time. An infrared (IR) diode is used to illuminate the workspace. A ball is attached to the tip of an intraocular shaft to reflect IR rays onto the PSDs. Instrument tip position is then calculated from the centroid positions of reflected IR light on the respective PSDs. The system can be used to assess the accuracy of hand-held microsurgical instruments and operator performance in micromanipulation tasks, such as microsurgeries. In order to eliminate inherent nonlinearity of the PSDs and lenses, calibration is performed using a feedforward neural network. After calibration, percentage RMS error is reduced from about 5.46 % to about 0.16%. The system RMS noise is about 0.7 mum. The sampling rate of the system is 250 Hz.


title: Learning Task-Oriented Grasping for Tool Manipulation from Simulated Self-Supervision

abstract: Tool manipulation is vital for facilitating robots to complete challenging task goals. It requires reasoning about the desired effect of the task and thus properly grasping and manipulating the tool to achieve the task. Task-agnostic grasping optimizes for grasp robustness while ignoring crucial task-specific constraints. In this paper, we propose the Task-Oriented Grasping Network (TOG-Net) to jointly optimize both task-oriented grasping of a tool and the manipulation policy for that tool. The training process of the model is based on large-scale simulated self-supervision with procedurally generated tool objects. We perform both simulated and real-world experiments on two tool-based manipulation tasks: sweeping and hammering. Our model achieves overall 71.1% task success rate for sweeping and 80.0% task success rate for hammering. Supplementary material is available at: this http URL


title: Decoding the neural representation of story meanings across languages

abstract: Drawing from a common lexicon of semantic units, humans fashion narratives whose meaning transcends that of their individual utterances. However, while brain regions that represent lower-level semantic units, such as words and sentences, have been identified, questions remain about the neural representation of narrative comprehension, which involves inferring cumulative meaning. To address these questions, we exposed English, Mandarin, and Farsi native speakers to native language translations of the same stories during fMRI scanning. Using a new technique in natural language processing, we calculated the distributed representations of these stories (capturing the meaning of the stories in high-dimensional semantic space), and demonstrate that using these representations we can identify the specific story a participant was reading from the neural data. Notably, this was possible even when the distributed representations were calculated using stories in a different language than the participant was reading. Our results reveal that identification relied on a collection of brain regions most prominently located in the default mode network. These results demonstrate that neuro-semantic encoding of narratives happens at levels higher than individual semantic units and that this encoding is systematic across both individuals and languages. Hum Brain Mapp, 2017. © 2017 Wiley Periodicals, Inc.


title: Shadow shooter: 360-degree all-around virtual 3d interactive content

abstract: "Shadow Shooter" is a VR shooter game that uses the "e-Yumi 3D" bow interface and real physical interactive content that changes a 360-degree all-around view in a room into virtual game space (Figure 1). This system was constructed by developing our previous interactive "Light Shooter" content based on "The Electric Bow Interface" [Yasumoto and Ohta 2013]. Shadow Shooter expands the virtual game space to all the walls in a room just as in Jones' "Room Alive" [Jones et al. 2014]; however, it does not require large-scale equipment such as multiple projectors. It only requires the e-Yumi 3D device that consists of a real bow's components added to Willis's interface with a mobile projector [Willis et al. 2013]. Thus, we constructed a unique device for Shadow Shooter that easily changes the 360-degree all-around view into a virtual game space.


title: Task-Specific Attentive Pooling of Phrase Alignments Contributes to Sentence Matching

abstract: This work studies comparatively two typical sentence matching tasks: textual entailment (TE) and answer selection (AS), observing that weaker phrase alignments are more critical in TE, while stronger phrase alignments deserve more attention in AS. The key to reach this observation lies in phrase detection, phrase representation, phrase alignment, and more importantly how to connect those aligned phrases of different matching degrees with the final classifier. Prior work (i) has limitations in phrase generation and representation, or (ii) conducts alignment at word and phrase levels by handcrafted features or (iii) utilizes a single framework of alignment without considering the characteristics of specific tasks, which limits the framework's effectiveness across tasks. We propose an architecture based on Gated Recurrent Unit that supports (i) representation learning of phrases of arbitrary granularity and (ii) task-specific attentive pooling of phrase alignments between two sentences. Experimental results on TE and AS match our observation and show the effectiveness of our approach.


Variational Bayesian Inference : Algorithms for Sparse Gaussian Processes and Theoretical Bounds


title: Programming gene and engineered-cell therapies with synthetic biology

abstract: BACKGROUND Gene and engineered-cell therapies promise new treatment modalities for incurable or difficult-to-treat diseases. First-generation gene and engineered-cell therapies are already used in the clinic, including an ex vivo gene-replacement therapy for adenosine deaminase deficiency, chimeric antigen receptor (CAR) T cell therapies for certain types of leukemias and lymphomas, an adeno-associated virus gene therapy for inherited retinal diseases, and investigational therapies for β-thalassemia, sickle cell disease, hemophilia, and spinal muscular atrophy. Despite these early successes, safety concerns may hamper the broader adoption of some of these approaches: For example, overexpression of a therapeutic gene product with a narrow therapeutic window may be toxic, and excessive activation of T cells can be fatal. More-sophisticated control over cellular activity would allow us to reliably “program” cells with therapeutic behaviors, leading to safer and more effective gene and engineered-cell therapies as well as new treatments. ADVANCES Recent advances in synthetic biology are enabling new gene and engineered-cell therapies. These developments include engineered biological sensors that can detect disease biomarkers such as microRNAs and cell-surface proteins; genetic sensors that respond to exogenous small molecules; and new methods for interacting with various components of the cell—editing DNA, modulating RNA, and interfacing with endogenous signaling networks. These new biological modules have therapeutic potential on their own and can also serve as building blocks for sophisticated synthetic gene “circuits” that precisely control the strength, timing, and location of therapeutic function. This advanced control over cellular behavior will facilitate the development of treatments that address the underlying molecular causes of disease as well as provide viable therapeutic strategies in situations where the biomolecular targets have been previously considered “undruggable.” Recent publications have demonstrated several strategies for designing complex therapeutic genetic programs by combining basic sensor, regulatory, and effector modules. These strategies include (i) external small-molecule regulation to control therapeutic activity postdelivery, (ii) sensors of cell-specific biomarkers that activate therapeutic activity only in diseased cells and tissues, and/or (iii) feedback control loops that maintain homeostasis of bodily systems. Example therapeutic systems include a genetic circuit that senses two specific cell-surface markers to activate CAR T cells only in the presence of target cancer cells, a circuit that programmatically differentiates pancreatic progenitor cells into insulin-secreting β-like cells, and a gene network that senses the amount of psoriasis-associated cytokines to release immune-modulatory proteins only during flare-ups. These proof-of-concept systems may lead to new treatments that are dramatically safer and more effective than current therapies. OUTLOOK Rapid progress in synthetic biology and related fields is bringing therapeutic gene circuits ever closer to the clinic. Ongoing efforts in modeling and simulating mammalian genetic circuits will reduce the number of circuit variants that need to be tested to achieve the desired behavior. The platforms used to test genetic circuits are also evolving to more closely resemble the actual human environment in which the circuits will operate. Human organoid, tissue-on-a-chip, and whole-blood models will enable higher-throughput circuit characterization and optimization in a more physiologically relevant setting. Progress in nucleic acid delivery will improve the safety and efficiency with which therapeutic nucleic acids are introduced to target cells, and new methods for immunomodulation will suppress or mitigate unwanted immune responses. Together, these advances will accelerate the development and adoption of synthetic biology-based gene and engineered-cell therapies.


title: An efficient detection of brain tumor using fused feature adaptive firefly backpropagation neural network

abstract: Early diagnosis of tumor will increase the survival probability from a deadly disease called a brain tumor. Classification of the tumor from the tumor affected MRI using the concept of medical image processing assist better treatment and surgical planning. This paper proposes a fused feature adaptive firefly backpropagation neural network for classification which comprises preprocessing, feature extraction, selection, and fusion to achieve high classification accuracy. The preprocessing step uses the average filter for reducing the intensity variation of the images. The Gabor wavelet feature extraction extracts the locality, orientation, and frequency of the tumor image which provides texture information for classification. The kernel principal component analysis (KPCA) feature selection selects the small subset of features to reduce the redundancy and increase the relevancy of the feature. The Gaussian radial basis function (GRBF) for feature fusion provides the distinguished information from the multiple sets of features. Finally, the proposed approach accurately classify the tumor with high accuracy after applying the fusion results. The results are simulated in MATLAB and it proves the improved accuracy, sensitivity, specificity of the classified tumor of the proposed approach.


title: DeepID-Net: Deformable deep convolutional neural networks for object detection

abstract: In this paper, we propose deformable deep convolutional neural networks for generic object detection. This new deep learning object detection framework has innovations in multiple aspects. In the proposed new deep architecture, a new deformation constrained pooling (def-pooling) layer models the deformation of object parts with geometric constraint and penalty. A new pre-training strategy is proposed to learn feature representations more suitable for the object detection task and with good generalization capability. By changing the net structures, training strategies, adding and removing some key components in the detection pipeline, a set of models with large diversity are obtained, which significantly improves the effectiveness of model averaging. The proposed approach improves the mean averaged precision obtained by RCNN [14], which was the state-of-the-art, from 31% to 50.3% on the ILSVRC2014 detection test set. It also outperforms the winner of ILSVRC2014, GoogLeNet, by 6.1%. Detailed component-wise analysis is also provided through extensive experimental evaluation, which provide a global view for people to understand the deep learning object detection pipeline.


title: Hedge Trimmer: A Parse-And-Trim Approach To Headline Generation

abstract: This paper presents Hedge Trimmer, a HEaDline GEneration system that creates a headline for a newspaper story using linguistically-motivated heuristics to guide the choice of a potential headline. We present feasibility tests used to establish the validity of an approach that constructs a headline by selecting words in order from a story. In addition, we describe experimental results that demonstrate the effectiveness of our linguistically-motivated approach over a HMM-based model, using both human evaluation and automatic metrics for comparing the two approaches.


title: Learning from Sensory and Reward Prediction Errors during Motor Adaptation

abstract: Voluntary motor commands produce two kinds of consequences. Initially, a sensory consequence is observed in terms of activity in our primary sensory organs (e.g., vision, proprioception). Subsequently, the brain evaluates the sensory feedback and produces a subjective measure of utility or usefulness of the motor commands (e.g., reward). As a result, comparisons between predicted and observed consequences of motor commands produce two forms of prediction error. How do these errors contribute to changes in motor commands? Here, we considered a reach adaptation protocol and found that when high quality sensory feedback was available, adaptation of motor commands was driven almost exclusively by sensory prediction errors. This form of learning had a distinct signature: as motor commands adapted, the subjects altered their predictions regarding sensory consequences of motor commands, and generalized this learning broadly to neighboring motor commands. In contrast, as the quality of the sensory feedback degraded, adaptation of motor commands became more dependent on reward prediction errors. Reward prediction errors produced comparable changes in the motor commands, but produced no change in the predicted sensory consequences of motor commands, and generalized only locally. Because we found that there was a within subject correlation between generalization patterns and sensory remapping, it is plausible that during adaptation an individual's relative reliance on sensory vs. reward prediction errors could be inferred. We suggest that while motor commands change because of sensory and reward prediction errors, only sensory prediction errors produce a change in the neural system that predicts sensory consequences of motor commands.


title: Experiments with IEEE 1149.4 Klic Test Chip: A Case Study

abstract: The paper presents some results of the experiments with IEEE 1149.4 KLIC Test Chip in the area of active RC filters implemented in thick-film hybrid technology. The influence of analog boundary modules (ABMs) on circuit performance is analysed in the case study of active RC notch filter.


Data, tools and resources for mining social media drug chatter.


title: Limitations Of Co-Training For Natural Language Learning From Large Datasets

abstract: Co-Training is a weakly supervised learning paradigm in which the redundancy of the learning task is captured by training two classifiers using separate views of the same data. This enables bootstrapping from a small set of labeled training data via a large set of unlabeled data. This study examines the learning behavior of co-training on natural language processing tasks that typically require large numbers of training instances to achieve usable performance levels. Using base noun phrase bracketing as a case study, we find that co-training reduces by 36% the difference in error between co-trained classifiers and fully supervised classifiers trained on a labeled version of all available data. However, degradation in the quality of the bootstrapped data arises as an obstacle to further improvement. To address this, we propose a moderately supervised variant of cotraining in which a human corrects the mistakes made during automatic labeling. Our analysis suggests that corrected co-training and similar moderately supervised methods may help cotraining scale to large natural language learning tasks.


title: Playing violent electronic games, hostile attributional style, and aggression-related norms in German adolescents

abstract: The relationship was examined between exposure to and preference for violent electronic games and aggressive norms as well as hostile attributional style. Following a pilot study to sample widely used electronic games varying in violent content, 231 eighth-grade adolescents in Germany reported their use of and attraction to violent electronic games. They also completed measures of hostile attributional style and endorsement of aggressive norms. There were significant gender differences in usage and attraction to violent electronic games, with boys scoring higher than girls. Significant relationships were found between attraction to violent electronic games and the acceptance of norms condoning physical aggression. Violent electronic games were linked indirectly to hostile attributional style through aggressive norms. The findings are discussed with respect to North American research on the aggression-enhancing effect of violent electronic games.


title: Support Vector Feature Selection for Early Detection of Anastomosis Leakage From Bag-of-Words in Electronic Health Records

abstract: The free text in electronic health records (EHRs) conveys a huge amount of clinical information about health state and patient history. Despite a rapidly growing literature on the use of machine learning techniques for extracting this information, little effort has been invested toward feature selection and the features' corresponding medical interpretation. In this study, we focus on the task of early detection of anastomosis leakage (AL), a severe complication after elective surgery for colorectal cancer (CRC) surgery, using free text extracted from EHRs. We use a bag-of-words model to investigate the potential for feature selection strategies. The purpose is earlier detection of AL and prediction of AL with data generated in the EHR before the actual complication occur. Due to the high dimensionality of the data, we derive feature selection strategies using the robust support vector machine linear maximum margin classifier, by investigating: 1) a simple statistical criterion (leave-one-out-based test); 2) an intensive-computation statistical criterion (Bootstrap resampling); and 3) an advanced statistical criterion (kernel entropy). Results reveal a discriminatory power for early detection of complications after CRC (sensitivity 100%; specificity 72%). These results can be used to develop prediction models, based on EHR data, that can support surgeons and patients in the preoperative decision making phase.


Seed Potato Tuber Production and Dissemination Experiences, Challenges and Prospects


title: Extracting News Events from Microblogs

abstract: AbstractTwitter stream has become a large source of information, but the magnitude of tweets posted and the noisy nature of its content makes harvesting of knowledge from Twitter has challenged res...


The anatomy , biological plausibility and efficacy of visceral mobilization in the treatment of pelvic floor dysfunction


title: Combinatorial Testing for Deep Learning Systems

abstract: Deep learning (DL) has achieved remarkable progress over the past decade and been widely applied to many safety-critical applications. However, the robustness of DL systems recently receives great concerns, such as adversarial examples against computer vision systems, which could potentially result in severe consequences. Adopting testing techniques could help to evaluate the robustness of a DL system and therefore detect vulnerabilities at an early stage. The main challenge of testing such systems is that its runtime state space is too large: if we view each neuron as a runtime state for DL, then a DL system often contains massive states, rendering testing each state almost impossible. For traditional software, combinatorial testing (CT) is an effective testing technique to reduce the testing space while obtaining relatively high defect detection abilities. In this paper, we perform an exploratory study of CT on DL systems. We adapt the concept in CT and propose a set of coverage criteria for DL systems, as well as a CT coverage guided test generation technique. Our evaluation demonstrates that CT provides a promising avenue for testing DL systems. We further pose several open questions and interesting directions for combinatorial testing of DL systems.


title: Smart meters for power grid — Challenges, issues, advantages and status

abstract: Smart meter is an advanced energy meter that measures consumption of electrical energy providing additional information compared to a conventional energy meter. Integration of smart meters into electricity grid involves implementation of a variety of techniques and software, depending on the features that the situation demands. Design of a smart meter depends on the requirements of the utility company as well as the customer. This paper discusses various features and technologies that can be integrated with a smart meter. In fact, deployment of smart meters needs proper selection and implementation of a communication network satisfying the security standards of smart grid communication. This paper outlines various issues and challenges involved in design, deployment, utilization, and maintenance of the smart meter infrastructure. In addition, several applications and advantages of smart meter, in the view of future electricity market are discussed in detail. This paper explains the importance of introducing smart meters in developing countries. In addition, the status of smart metering in various countries is also illustrated.


title: Nanorobot Movement: Challenges and Biologically inspired solutions

abstract: Nanorobotics is the technology of creating machines or robots of the size of few hundred nanometres and below consisting of components of nanoscale or molecular size. There is an all around development in nanotechnology towards realization of nanorobots in the last two decades. In the present work, the compilation of advancement in nanotechnology in context to nanorobots is done. The challenges and issues in movement of a nanorobot and innovations present in nature to overcome the difficulties in moving at nano-size regimes are discussed. The efficiency aspect in context to artificial nanorobot is also presented.


title: 3D - Playfair cipher with additional bitwise operation

abstract: 3D-Playfair cipher is multiple letter encryption cipher. In this, trigraphs of plaintext are treated as single units and converted into corresponding cipher text trigraphs. 3D-Playfair Ciphers originates due to the limitations in classical Playfair as it supports only alphabets and may not support two alphabets at the same time. It also eliminates the fact that a diagram and its reverse will encrypt in a similar fashion. 3D-Playfair cipher supports all 26 alphabets, 10 digits and 28 special characters. The theme of research is to furnish security to data which contains alphabets numerals and special characters during transmission that's why additional bitwise operations are introduced and applied on the cipher text of 3D-Playfair cipher with a random key. Linear Feedback Shift Register is a good candidate for generation of random sequences and is selected for generating random keys for bitwise operations to offer better security to cipher text before transmission over the internet. Here X-OR or X-NOR operations are taken into consideration for the bitwise operations based on the key.


title: Transformation and validation of BPMN models to Petri nets models using GROOVE

abstract: BPMN is a language for business process modeling. It is simple and has the ability to represent the behavior of complex systems such as: synchronization, parallelism and choice. However, The semantics of the BPMN language is not formal.


title: Inverse dynamics of the 3-PRR planar parallel robot

abstract: Recursive modelling for the kinematics and dynamics of the known 3-PRR planar parallel robot is established in this paper. Three identical planar legs connecting to the moving platform are located in a vertical plane. Knowing the motion of the platform, we develop first the inverse kinematics and determine the positions, velocities and accelerations of the robot. Further, the principle of virtual work is used in the inverse dynamics problem. Several matrix equations offer iterative expressions and graphs for the power requirement comparison of each of three actuators in two different actuation schemes: prismatic actuators and revolute actuators. For the same evolution of the moving platform in the vertical plane, the power distribution upon the three actuators depends on the actuating configuration, but the total power absorbed by the set of three actuators is the same, at any instant, for both driving systems. The study of the dynamics of the parallel mechanisms is done mainly to solve successfully the control of the motion of such robotic systems.


title: Is aggression in children with behavioural and emotional difficulties associated with television viewing and video game playing ? A systematic review

abstract: Background Possible associations between television viewing and video game playing and children's aggression have become public health concerns. We did a systematic review of studies that examined such associations, focussing on children and young people with behavioural and emotional difficulties, who are thought to be more susceptible. Methods We did computer-assisted searches of health and social science databases, gateways, publications from relevant organizations and for grey literature; scanned bibliographies; hand-searched key journals; and corresponded with authors. We critically appraised all studies. Results A total of 12 studies: three experiments with children with behavioural and emotional difficulties found increased aggression after watching aggressive as opposed to low-aggressive content television programmes, one found the opposite and two no clear effect, one found such children no more likely than controls to imitate aggressive television characters. One case-control study and one survey found that children and young people with behavioural and emotional difficulties watched more television than controls; another did not. Two studies found that children and young people with behavioural and emotional difficulties viewed more hours of aggressive television programmes than controls. One study on video game use found that young people with behavioural and emotional difficulties viewed more minutes of violence and played longer than controls. In a qualitative study children with behavioural and emotional difficulties, but not their parents, did not associate watching television with aggression. All studies had significant methodological flaws. None was based on power calculations. Conclusion This systematic review found insufficient, contradictory and methodologically flawed evidence on the association between television viewing and video game playing and aggression in children and young people with behavioural and emotional difficulties. If public health advice is to be evidence-based, good quality research is needed. Language: en


title: Feeding Hand-Crafted Features for Enhancing the Performance of Convolutional Neural Networks

abstract: Since the convolutional neural network (CNN) is be- lieved to find right features for a given problem, the study of hand-crafted features is somewhat neglected these days. In this paper, we show that finding an appropriate feature for the given problem may be still important as they can en- hance the performance of CNN-based algorithms. Specif- ically, we show that feeding an appropriate feature to the CNN enhances its performance in some face related works such as age/gender estimation, face detection and emotion recognition. We use Gabor filter bank responses for these tasks, feeding them to the CNN along with the input image. The stack of image and Gabor responses can be fed to the CNN as a tensor input, or as a fused image which is a weighted sum of image and Gabor responses. The Gabor filter parameters can also be tuned depending on the given problem, for increasing the performance. From the extensive experiments, it is shown that the proposed methods provide better performance than the conventional CNN-based methods that use only the input images.


title: Anticipatory scheduling: a disk scheduling framework to overcome deceptive idleness in synchronous I/O

abstract: Disk schedulers in current operating systems are generally work-conserving, i.e., they schedule a request as soon as the previous request has finished. Such schedulers often require multiple outstanding requests from each process to meet system-level goals of performance and quality of service. Unfortunately, many common applications issue disk read requests in a synchronous manner, interspersing successive requests with short periods of computation. The scheduler chooses the next request too early; this induces deceptive idleness, a condition where the scheduler incorrectly assumes that the last request issuing process has no further requests, and becomes forced to switch to a request from another process.We propose the anticipatory disk scheduling framework to solve this problem in a simple, general and transparent way, based on the non-work-conserving scheduling discipline. Our FreeBSD implementation is observed to yield large benefits on a range of microbenchmarks and real workloads. The Apache webserver delivers between 29% and 71% more throughput on a disk-intensive workload. The Andrew filesystem benchmark runs faster by 8%, due to a speedup of 54% in its read-intensive phase. Variants of the TPC-B database benchmark exhibit improvements between 2% and 60%. Proportional-share schedulers are seen to achieve their contracts accurately and efficiently.


title: Robust end-of-utterance detection for real-time speech recognition applications

abstract: We propose a sub-band energy based end-of-utterance algorithm that is capable of detecting the time instant when the user has stopped speaking. The proposed algorithm finds the time instant at which many enough sub-band spectral energy trajectories fall and stay for a pre-defined fixed time below adaptive thresholds, i.e. a non-speech period is detected after the end of the utterance. With the proposed algorithm a practical speech recognition system can give timely feedback for the user, thereby making the behaviour of the speech recognition system more predictable and similar across different usage environments and, noise conditions. The proposed algorithm is shown to be more accurate and noise robust than the previously proposed approaches. Experiments with both isolated command word recognition and continuous digit recognition in various noise conditions verify the viability of the proposed approach with an average proper end-of-utterance detection rate of around 94% in both cases, representing 43% error rate reduction over the most competitive previously published method.


title: A Benchmark of Selected Algorithmic Differentiation Tools on Some Problems in Computer Vision and Machine Learning

abstract: Algorithmic differentiation (AD) allows exact computation of derivatives given only an implementation of an objective function. Although many AD tools are available, a proper and efficient implementation of AD methods is not straightforward. The existing tools are often too different to allow for a general test suite. In this paper, we compare 15 ways of computing derivatives including 11 automatic differentiation tools implementing various methods and written in various languages (C++, F#, MATLAB, Julia and Python), 2 symbolic differentiation tools, finite differences and hand-derived computation.We look at three objective functions from computer vision and machine learning. These objectives are for the most part simple, in the sense that no iterative loops are involved, and conditional statements are encapsulated in functions such as abs or logsumexp. However, it is important for the success of AD that such ‘simple’ objective functions are handled efficiently, as so many problems in computer vision and ...


title: Inductive Sensor Performance in Partial Discharges and Noise Separation by Means of Spectral Power Ratios

abstract: Partial discharge (PD) detection is a standardized technique to qualify electrical insulation in machines and power cables. Several techniques that analyze the waveform of the pulses have been proposed to discriminate noise from PD activity. Among them, spectral power ratio representation shows great flexibility in the separation of the sources of PD. Mapping spectral power ratios in two-dimensional plots leads to clusters of points which group pulses with similar characteristics. The position in the map depends on the nature of the partial discharge, the setup and the frequency response of the sensors. If these clusters are clearly separated, the subsequent task of identifying the source of the discharge is straightforward so the distance between clusters can be a figure of merit to suggest the best option for PD recognition. In this paper, two inductive sensors with different frequency responses to pulsed signals, a high frequency current transformer and an inductive loop sensor, are analyzed to test their performance in detecting and separating the sources of partial discharges.


title: Fast Non-Local algorithm for image denoising

abstract: In this paper, improvements to Non-Local Means (NLM) image denoising method is proposed to reduce the computational complexity. In the original NLM algorithm, neighborhood weightages are computed using the window similarity technique. The proposed technique replaces the window similarity by a modified multi-resolution based approach with much fewer comparisons rather than all pixels comparison. This approach also uses the concept of filtering out non-similar neighborhood pixels based on fixed sized window gray mean values. Further, mean values of the variable sized windows in the image are computed efficiently using Summed Image (SI) concept, which requires only 3 additions. The proposed approach is nearly 80 times faster than original Baudes NLM algorithm with close subjective and objective quality measurements.


title: Taking the bite out of automated naming of characters in TV video

abstract: We investigate the problem of automatically labelling appearances of characters in TV or film material with their names. This is tremendously challenging due to the huge variation in imaged appearance of each character and the weakness and ambiguity of available annotation. However, we demonstrate that high precision can be achieved by combining multiple sources of information, both visual and textual. The principal novelties that we introduce are: (i) automatic generation of time stamped character annotation by aligning subtitles and transcripts; (ii) strengthening the supervisory information by identifying when characters are speaking. In addition, we incorporate complementary cues of face matching and clothing matching to propose common annotations for face tracks, and consider choices of classifier which can potentially correct errors made in the automatic extraction of training data from the weak textual annotation. Results are presented on episodes of the TV series ''Buffy the Vampire Slayer''.


title: Self-compassion moderates the relationship between body mass index and both eating disorder pathology and body image flexibility

abstract: Abstract The current study examined whether self-compassion, the tendency to treat oneself kindly during distress and disappointments, would attenuate the positive relationship between body mass index (BMI) and eating disorder pathology, and the negative relationship between BMI and body image flexibility. One-hundred and fifty-three female undergraduate students completed measures of self-compassion, self-esteem, eating disorder pathology, and body image flexibility, which refers to one's acceptance of negative body image experiences. Controlling for self-esteem, hierarchical regressions revealed that self-compassion moderated the relationships between BMI and the criteria. Specifically, the positive relationship between BMI and eating disorder pathology and the negative relationship between BMI and body image flexibility were weaker the higher women's levels of self-compassion. Among young women, self-compassion may help to protect against the greater eating disturbances that coincide with a higher BMI, and may facilitate the positive body image experiences that tend to be lower the higher one's BMI.


title: From Big Data to Big Service

abstract: Big service--the convergence and collaboration of big data systems--presents a solid solution to the challenges brought by big data.


title: The Vertica Analytic Database: C-Store 7 Years Later

abstract: This paper describes the system architecture of the Vertica Analytic Database (Vertica), a commercialization of the design of the C-Store research prototype. Vertica demonstrates a modern commercial RDBMS system that presents a classical relational interface while at the same time achieving the high performance expected from modern "web scale" analytic systems by making appropriate architectural choices. Vertica is also an instructive lesson in how academic systems research can be directly commercialized into a successful product.


title: RecSys Challenge 2015: ensemble learning with categorical features

abstract: In this paper, we describe the winning approach for the RecSys Challenge 2015. Our key points are (1) two-stage classification, (2) massive usage of categorical features, (3) strong classifiers built by gradient boosting and (4) threshold optimization based directly on the competition score. We describe our approach and discuss how it can be used to build scalable personalization systems.


title: Text and Object Detection on Billboards

abstract: To captivate people’s attention in a blink of an eye, a billboard as a commercial advertisement must be attractive and informative. When space means money like on the billboard, much important information must be dropped out. Unfortunately, insufficient details and the difficulty of accessing those details might easily kill the customer’s attention in a flash as well. In this work, we propose a system that automatically connects a billboard with the product website instantly. More specifically, given the billboard image as an input, the system will lead users immediately to the product website for more details, or contact information. The results demonstrate that our system is significantly outperform the Google image search baseline where the product website is always included in the top 10 websites. Finally, We posit that the system can save the customers’ time and complexity to access their product of interest, resulting the increasing of product sale.


title: WebGLStudio: a pipeline for WebGL scene creation

abstract: We present WebGLStudio - a pipeline for the creation and editing of high-quality 3D scenes using WebGL. The pipeline features a 3D Scene-Graph rendering engine; an interactive scene editor allowing detailed setup and configuration of assets in real-time; a web-based asset manager; and a stand-alone rendering module ready to embedded in target applications. We further present a series of implementational details discovered to overcome limitations of the web browser context to enable realistic rendering and performance. The principal contribution of this work to the graphics community is the methodology used to take advantage of several unique aspects of Javascript when used as a 3D programming language; and the demonstration of possibilities involved in real-time editing of the parameters of materials, shaders and post-processing effects, in order for the user/artist to create a 3D scene as desired.


title: A Survey of Emerging M2M Systems: Context, Task, and Objective

abstract: Machine-to-machine (M2M) systems enable machines or devices to collect data, exchange information, and act on the environment without direct human intervention. A device in an M2M system not only collects data for its own usage but also shares the data with other devices automatically to achieve certain goals. Therefore, emerging M2M systems for civil transportation, electric power grid, medical treatment, industrial automation, etc., can be set up based on the networking of devices. In this survey paper, we first introduce the general architecture and communication networks for M2M systems. Then, we categorize emerging M2M systems according to the types of M2M context, M2M task, and M2M objective. We further survey recent solutions for M2M systems from both academia and industry with the insights of the M2M system categorization. Finally, we summarize challenges in developing M2M system solutions.


title: Learning deep compact descriptor with bagging auto-encoders for object retrieval

abstract: Content based object retrieval across large scale surveillance video dataset is a significant and challenging task, in which learning an effective compact object descriptor plays a critical role. In this paper, we propose an efficient deep compact descriptor with bagging auto-encoders. Specifically, we take advantage of discriminative CNN to extract efficient deep features, which not only involve rich semantic information but also can filter background noise. Besides, to boost the retrieval speed, auto-encoders are used to map the high-dimensional real-valued CNN features into short binary codes. Considering the instability of auto-encoder, we adopt a bagging strategy to fuse multiple auto-encoders to reduce the generalization error, thus further improving the retrieval accuracy. In addition, bagging is easy for parallel computing, so retrieval efficiency can be guaranteed. Retrieval experimental results on the dataset of 100k visual objects extracted from multi-camera surveillance videos demonstrate the effectiveness of the proposed deep compact descriptor.


title: A Case Study of the Rustock Rootkit and Spam Bot

abstract: In this paper we present a case study of the steps leading up to the extraction of the spam bot payload found within a backdoor rootkit known as Backdoor.Rustock.B or Spam-Mailbot.c. Following the extraction of the spam module we focus our analysis on the steps necessary to decrypt the communications between the command and control server and infected hosts. Part of the discussion involves a method to extract the encryption key from within the malware binary and use that to decrypt the communications. The result is a better understanding of an advanced botnet communications scheme.


title: Etiology of anterior open bite : a review Ätiologie des frontalen offenen Bisses : Ein Review

abstract: OBJECTIVES ::: Presentation of different causes described in the literature for development of anterior apertognathia. ::: ::: ::: METHODS ::: A review about data referring to patients limited in their anterior tooth function through the positioning of their teeth has been performed. Electronic data bases, two libraries have been searched for information. Of the identified titles 357 articles and chapters were selected. 43 of these were considered. ::: ::: ::: RESULTS ::: Aetiology classifications are inconstant and author dependent, but mostly differentiated into genetic and environmental causes. Specifically named are: habit, tongue, airway obstruction, neuromuscular deficiency, trauma, rheumatoid disease, posture and posterior discrepancy. ::: ::: ::: CONCLUSIONS ::: Many different factors can be relevant for the development of apertognathia and have to be considered. Further research could help differentiating which of the partially contradictory statements are true.


title: Distributed Trade-Based Edge Device Management in Multi-Gateway IoT

abstract: The Internet-of-Things (IoT) envisions an infrastructure of ubiquitous networked smart devices offering advanced monitoring and control services. The current art in IoT architectures utilizes gateways to enable application-specific connectivity to IoT devices. In typical configurations, IoT gateways are shared among several IoT edge devices. Given the limited available bandwidth and processing capabilities of an IoT gateway, the service quality (SQ) of connected IoT edge devices must be adjusted over time not only to fulfill the needs of individual IoT device users but also to tolerate the SQ needs of the other IoT edge devices sharing the same gateway. However, having multiple gateways introduces an interdependent problem, the binding, i.e., which IoT device shall connect to which gateway. ::: ::: In this article, we jointly address the binding and allocation problems of IoT edge devices in a multigateway system under the constraints of available bandwidth, processing power, and battery lifetime. We propose a distributed trade-based mechanism in which after an initial setup, gateways negotiate and trade the IoT edge devices to increase the overall SQ. We evaluate the efficiency of the proposed approach with a case study and through extensive experimentation over different IoT system configurations regarding the number and type of the employed IoT edge devices. Experiments show that our solution improves the overall SQ by up to 56% compared to an unsupervised system. Our solution also achieves up to 24.6% improvement on overall SQ compared to the state-of-the-art SQ management scheme, while they both meet the battery lifetime constraints of the IoT devices.


title: A study of CNN outside of training conditions

abstract: Convolution neural networks (CNN) are the main development in face recognition in recent years. However, their description capacities have been somewhat understudied. In this paper, we show that training CNN only with color images is enough to properly describe depth and near infrared face images by assessing the performance of three publicly available CNN models on these other modalities. Furthermore, we find that, despite displaying results comparable to the human performance on LFW, not all CNN behave like humans recognizing faces in other scenarios.


title: A novel real-time driving fatigue detection system based on wireless dry EEG

abstract: Development of techniques for detection of mental fatigue has varied applications in areas where sustaining attention is of critical importance like security and transportation. The objective of this study is to develop a novel real-time driving fatigue detection methodology based on dry Electroencephalographic (EEG) signals. The study has employed two methods in the online detection of mental fatigue: power spectrum density (PSD) and sample entropy (SE). The wavelet packets transform (WPT) method was utilized to obtain the θ (4-7 Hz), α (8-12 Hz) and β (13-30 Hz) bands frequency components for calculating corresponding PSD of the selected channels. In order to improve the fatigue detection performance, the system was individually calibrated for each subject in terms of fatigue-sensitive channels selection. Two fatigue-related indexes: ( θ+α )/ β and θ / β were computed and then fused into an integrated metric to predict the degree of driving fatigue. In the case of SE extraction, the mean of SE averaged across two EEG channels ('O1h' and 'O2h') was used for fatigue detection. Ten healthy subjects participated in our study and each of them performed two sessions of simulated driving. In each session, subjects were required to drive simulated car for 90 min without any break. The results demonstrate that our proposed methods are effective for fatigue detection. The prediction of fatigue is consistent with the observation of reaction time that was recorded during simulated driving, which is considered as an objective behavioral measure.


title: Teachers' Self-Efficacy Beliefs as Determinants of Job Satisfaction and Students' Academic Achievement: A Study at the School Level.

abstract: Abstract Teachers' self-efficacy beliefs were examined as determinants of their job satisfaction and students' academic achievement. Over 2000 teachers in 75 Italian junior high schools were administered self-report questionnaires to assess self-efficacy beliefs and their job satisfaction Students' average final grades at the end of junior high school were collected in two subsequent scholastic years. Structural equation modeling analyses corroborated a conceptual model in which teachers' personal efficacy beliefs affected their job satisfaction and students' academic achievement, controlling for previous levels of achievement.


title: Rainfall prediction using hybrid neural network approach

abstract: A novel rainfall prediction method has been proposed. In the present work rainfall prediction in Southern part of West Bengal (India) has been conducted. A two-step method has been employed. Greedy forward selection algorithm is used to reduce the feature set and to find the most promising features for rainfall prediction. First, in the training phase the data is clustered by applying k-means algorithm, then for each cluster a separate Neural Network (NN) is trained. The proposed two step prediction model (Hybrid Neural Network or HNN) has been compared with MLP-FFN classifier in terms of several statistical performance measuring metrics. The data for experimental purpose is collected by Dumdum meteorological station (West Bengal, India) over the period from 1989 to 1995. The experimental results have suggested a reasonable improvement over traditional methods in predicting rainfall. The proposed HNN model outperformed the compared models by achieving 84.26% accuracy without feature selection and 89.54% accuracy with feature selection.


title: High performance passive devices for millimeter wave system integration on integrated fan-out (InFO) wafer level packaging technology

abstract: High performance passive devices for millimeter wave (MMW) system, including inductor, ring resonator, power combiner, coupler, balun, transmission line, and antenna, are first realized using integrated fan-out (InFO) wafer level packaging technology. The inductors has quality factor over 40; the power combiner, coupler, and balun show lower transmission loss than on-chip passives; antenna has the efficiency of over 60%. These devices on InFO enable low noise and power MMW system for mobile communication and IoT applications.


title: GTA: Groupware Task Analysis - Modeling Complexity

abstract: The task analysis methods discussed in this presentation stem from Human-Computer Interaction (HCI) and Ethnography (as applied for the design of Computer Supported Cooperative Work CSCW), different disciplines that often are considered conflicting approaches when applied to the same design problems. Both approaches have their strength and weakness, and an integration of them does add value to the early stages of design of cooperation technology. In order to develop an integrated method for groupware task analysis (GTA) a conceptual framework is presented that allows a systematic perspective on complex work phenomena. The framework features a triple focus, considering (a) people, (b) work, and (c) the situation. Integrating various task-modeling approaches requires vehicles for making design information explicit, for which an object oriented formalism will be suggested. GTA consists of a method and framework that have been developed during practical design exercises. Examples from some of these cases will illustrate our approach.


title: Building Cre Knockin Rat Lines Using CRISPR/Cas9.

abstract: Conditional gene inactivation strategy helps researchers to study the gene functions that are critical in embryogenesis or in defined tissues of adulthood. The Cre/loxP system is widely used for conditional gene inactivation/activation in cells or organisms. Cre knockin animal lines are essential for gene expression or inactivation in a spatially and temporally restricted manner. However, to generate a Cre knockin line by traditional approach is laborious. Recently, the clustered regularly interspaced short palindromic repeats and CRISPR-associated protein 9 (CRISPR/Cas9) has been proven as a simple and efficient genome-editing tool. We have used CRISPR/Cas9 system to generate rat strains that carry Cre genes in different targeted gene loci by direct delivery of gRNAs/Cas9/donors into fertilized eggs. Here, we described a stepwise procedure for the generation of Cre knockin rat, including target site selection, RNA preparation, the construction of the template donor, pronuclear injection, and the genotyping of precise Cre insertion in F0 rats. Taken together, the establishment of Cre knockin line can be achieved within 6 weeks.


title: Is Happiness Contagious Online? A Case of Twitter and the 2010 Winter Olympics

abstract: Is happiness contagious online? To answer this question, this paper investigates the posting behavior of users on Twitter.com, a popular online service for sharing short messages. Specifically, we use automated sentiment analysis to study a large sample of over 46,000 Twitter messages that reference the 2010 Winter Olympics. We determined that there are more positive messages than negative, and that positive messages are more likely to be forwarded than negative messages. However, we were not able to confirm with a reliable degree of certainty that the emotional context of messages is directly related to the user's position in the Twitter network. It is likely that there are other factors involved as well. For example, we found that negative users were more prolific posters than positive users, suggesting their more argumentative and passionate nature. This paper concludes with some implications for the Twitter community and a description of our follow-up study.


title: High Self-Control Predicts Good Adjustment, Less Pathology, Better Grades, and Interpersonal Success

abstract: What good is self-control? We incorporated a new measure of individual differences in self-control into two large investigations of a broad spectrum of behaviors. The new scale showed good internal consistency and retest reliability. Higher scores on self-control correlated with a higher grade point average, better adjustment (fewer reports of psychopathology, higher self-esteem), less binge eating and alcohol abuse, better relationships and interpersonal skills, secure attachment, and more optimal emotional responses. Tests for curvilinearity failed to indicate any drawbacks of so-called overcontrol, and the positive effects remained after controlling for social desirability. Low self-control is thus a significant risk factor for a broad range of personal and interpersonal problems.


title: FPGA-Based Low-Power Speech Recognition with Recurrent Neural Networks

abstract: In this paper, a neural network based real-time speech recognition (SR) system is developed using an FPGA for very low-power operation. The implemented system employs two recurrent neural networks (RNNs), one is a speech-tocharacter RNN for acoustic modeling (AM) and the other is for character-level language modeling (LM). The system also employs a statistical word-level LM to improve the recognition accuracy. The results of the AM, the character-level LM, and the word-level LM are combined using a fairly simple N-best search algorithm instead of the hidden Markov model (HMM) based network. The RNNs are implemented using massively parallel processing elements (PEs) for low latency and high throughput. The weights are quantized to 6 bits to store all of them in the on-chip memory of an FPGA. The proposed algorithm is implemented on a Xilinx XC7Z045, and the system can operate much faster than real-time.


title: Dual-Band Dual-Mode Patch Antenna for On-/Off-Body WBAN Communications

abstract: A novel coplanar waveguide (CPW)-fed dual-band dual-mode patch antenna for on-/off-body communications is proposed. The proposed antenna, which comprises a T-shaped patch with parasitic patches, a circular patch, and a CPW feedline, has dimensions of 30×45×3.2 mm 3 The proposed antenna operates in the 2.4-GHz ISM band (2.4-2.485 GHz) for off-body communication, and in the 5.8-GHz ISM band (5.725-5.875 GHz) for on-body communication. The antenna has a patch-like radiation pattern in the 2.4-GHz ISM band and a monopole-like radiation pattern in the 5.8-GHz ISM band.


title: A Simple Space Vector PWM Generation Scheme for Any General $n$-Level Inverter

abstract: This paper proposes a generalized method for the generation of space vector pulsewidth modulation (SVPWM) signals for multilevel inverters. In the proposed method, the actual sector containing the tip of the reference space vector need not be identified. A method is presented to identify the center of a sub-hexagon containing the reference space vector. Using the center of the sub-hexagon, the reference space vector is mapped to the innermost sub-hexagon, and the switching sequence corresponding to a two-level inverter is determined. A new technique is proposed in this paper, by which these two-level vectors are translated to the switching vectors of the multilevel inverter by adding the center of the sub-hexagon to the two-level vectors. The proposed method can be extended to any n-level inverter, and a generalized algorithm is proposed. The scheme is explained for a five-level inverter, and experimental results are presented for a three-level inverter.


title: A frequency tunable origami spherical helical antenna

abstract: This paper presents a novel origami spherical helical antenna. This antenna operates in normal mode and its operating frequency is tunable from 1.77 GHz to 2.32 GHz for applications of TV or Radio stations. By tuning the scaling factor of each step on the origami pattern, the fabricating process can be greatly simplified, and this antenna can be fabricated at a low cost on light-weight planar substrate.


title: Advantages of Radial Basis Function Networks for Dynamic System Design

abstract: Radial basis function (RBF) networks have advantages of easy design, good generalization, strong tolerance to input noise, and online learning ability. The properties of RBF networks make it very suitable to design flexible control systems. This paper presents a review on different approaches of designing and training RBF networks. The recently developed algorithm is introduced for designing compact RBF networks and performing efficient training process. At last, several problems are applied to test the main properties of RBF networks, including their generalization ability, tolerance to input noise, and online learning ability. RBF networks are also compared with traditional neural networks and fuzzy inference systems.


title: A vehicular rooftop, shark-fin, multiband antenna for the GPS/LTE/cellular/DSRC systems

abstract: A shark-fin rooftop multiband antenna for the automotive industry is proposed. The antenna system receives Right Hand Circularly Polarized (RHCP) satellite signals for the Global Positioning System's (GPS) L1-Band and Vertically Linearly Polarized (VLP) signals for the Long Term Evolution (LTE), the cellular frequencies, the Universal Mobile Telecommunications Systems (UMTS) and the Dedicated Short Range Communications (DSRC) system. A ceramic patch antenna element was used for the GPS signals and a printed monopole utilizing printed ground sleeve technique for the cellular and LTE signals. A study of four potential DSRC antenna elements to determine the appropriate antenna element location within the antenna cover to obtain optimum performance is also presented and discussed in detail. The antenna module was measured on a 1-meter diameter ground plane and on a vehicle roof model. The design and measurements results are presented and discussed below.


title: Fuzzy sets as a basis for a theory of possibility

abstract: The theory of possibility described in this paper is related to the theory of fuzzy sets by defining the concept of a possibility distribution as a fuzzy restriction which acts as an elastic constraint on the values that may be assigned to a variable. More specifically, if F is a fuzzy subset of a universe of discourse U = {u} which is characterized by its membership function μF, then a proposition of the form “X is F”, where X is a variable taking values in U, induces a possibility distribution tx which equates the possibility of X taking the value u to μF(u)—the compatibility of u with F. In this way, X becomes a fuzzy variable which is associated with the possibility distribution tx in much the same way as a random variable is associated with a probability distribution. In general, a variable may be associated both with a possibility distribution and a probability distribution, with the weak connection between the two expressed as the possibility/probability consistency principle. ::: ::: A thesis advanced in this paper is that the imprecision that is intrinsic in natural languages is, in the main, possibilistic rather than probabilistic in nature. Thus, by employing the concept of a possibility distribution, a proposition, p, in a natural language may be translated into a procedure which computes the probability distribution of a set of attributes which are implied by p. Several types of conditional translation rules are discussed and, in particular, a translation rule for propositions of the form “X is F is α-possible”, where α is a number in the interval [0,1], is formulated and illustrated by examples.


title: Rapid object detection using a boosted cascade of simple features

abstract: This paper describes a machine learning approach for visual object detection which is capable of processing images extremely rapidly and achieving high detection rates. This work is distinguished by three key contributions. The first is the introduction of a new image representation called the "integral image" which allows the features used by our detector to be computed very quickly. The second is a learning algorithm, based on AdaBoost, which selects a small number of critical visual features from a larger set and yields extremely efficient classifiers. The third contribution is a method for combining increasingly more complex classifiers in a "cascade" which allows background regions of the image to be quickly discarded while spending more computation on promising object-like regions. The cascade can be viewed as an object specific focus-of-attention mechanism which unlike previous approaches provides statistical guarantees that discarded regions are unlikely to contain the object of interest. In the domain of face detection the system yields detection rates comparable to the best previous systems. Used in real-time applications, the detector runs at 15 frames per second without resorting to image differencing or skin color detection.


title: Asymptotically Exact, Embarrassingly Parallel MCMC

abstract: Communication costs, resulting from synchronization requirements during learning, can greatly slow down many parallel machine learning algorithms. In this paper, we present a parallel Markov chain Monte Carlo (MCMC) algorithm in which subsets of data are processed independently, with very little communication. First, we arbitrarily partition data onto multiple machines. Then, on each machine, any classical MCMC method (e.g., Gibbs sampling) may be used to draw samples from a posterior distribution given the data subset. Finally, the samples from each machine are combined to form samples from the full posterior. This embarrassingly parallel algorithm allows each machine to act independently on a subset of the data (without communication) until the final combination stage. We prove that our algorithm generates asymptotically exact samples and empirically demonstrate its ability to parallelize burn-in and sampling in several models.


title: Cardiotoxicity associated with CTLA4 and PD1 blocking immunotherapy

abstract: Immune-checkpoint blocking antibodies have demonstrated objective antitumor responses in multiple tumor types including melanoma, non-small cell lung cancer (NSCLC), and renal cell cancer (RCC). In melanoma, an increase in overall survival has been demonstrated with anti-CTLA-4 and PD-1 inhibition. However, a plethora of immune-mediated adverse events has been reported with these agents. Immune-mediated cardiotoxicity induced by checkpoint inhibitors has been reported in single cases with variable presentation, including myocarditis and pericarditis. Among six clinical cancer centers with substantial experience in the administration of immune-checkpoint blocking antibodies, eight cases of immune-related cardiotoxicity after ipilimumab and/or nivolumab/pembrolizumab were identified. Diagnostic findings, treatment and follow-up are reported. A large variety of cardiotoxic events with manifestations such as heart failure, cardiomyopathy, heart block, myocardial fibrosis and myocarditis was documented. This is the largest case series to date describing cardiotoxicity of immune-checkpoint blocking antibodies. Awareness, monitoring of patients with pre-existing cardiac disorders and prompt evaluation by the treatment team is essential. Treatment including application of steroids is critical for patient safety.


title: Big data analytics for modeling WAT parameter variation induced by process tool in semiconductor manufacturing and empirical study

abstract: With the feature size shrinkage in advanced technology nodes, the modeling of process variations has become more critical for troubleshooting and yield enhancement. Misalignment among equipment tools or chambers in process stages is a major source of process variations. Because a process flow contains hundreds of stages during semiconductor fabrication, tool/chamber misalignment may more significantly affect the variation of transistor parameters in a wafer acceptance test. This study proposes a big data analytic framework that simultaneously considers the mean difference between tools and wafer-to-wafer variation and identifies possible root causes for yield enhancement. An empirical study was conducted to demonstrate the effectiveness of proposed approach and obtained promising results.


title: AN MR BRAIN IMAGES CLASSIFIER VIA PRINCIPAL COMPONENT ANALYSIS AND KERNEL SUPPORT

abstract: Automated and accurate classiflcation of MR brain images is extremely important for medical analysis and interpretation. Over the last decade numerous methods have already been proposed. In this paper, we presented a novel method to classify a given MR brain image as normal or abnormal. The proposed method flrst employed wavelet transform to extract features from images, followed by applying principle component analysis (PCA) to reduce the dimensions of features. The reduced features were submitted to a kernel support vector machine (KSVM). The strategy of K- fold stratifled cross validation was used to enhance generalization of KSVM. We chose seven common brain diseases (glioma, meningioma, Alzheimer's disease, Alzheimer's disease plus visual agnosia, Pick's disease, sarcoma, and Huntington's disease) as abnormal brains, and collected 160 MR brain images (20 normal and 140 abnormal) from Harvard Medical School website. We performed our proposed methods with four difierent kernels, and found that the GRB kernel achieves the highest classiflcation accuracy as 99.38%. The LIN, HPOL, and IPOL kernel achieves 95%, 96.88%, and 98.12%, respectively. We also compared our method to those from literatures in the last decade, and the results showed our DWT+PCA+KSVM with GRB kernel still achieved the best accurate classiflcation results. The averaged processing time for a 256£256 size image on a laptop of P4 IBM with 3GHz processor and 2GB RAM is 0.0448s. From the experimental data, our method was efiective and rapid. It could be applied to the fleld of MR brain image classiflcation and can assist the doctors to diagnose where a patient is normal or abnormal to certain degrees.


title: An alternative sensor Cloud architecture for vital signs monitoring

abstract: Due to recent technological advancements in wireless communications and low-power sensor devices, wireless body area networks (WBANs) has become increasingly popular in pervasive healthcare monitoring. However, continuously collecting patients' physiological signs will result in large amounts of monitored data that require a scalable architecture for storage and analysis. This fact motivates the integration of WBANs with Cloud technology to manage and store humongous data effectively. In this paper, we propose a Cloud-based WBAN framework for real-time health monitoring of patients. The significance of the proposed framework is the use of mobile technology and Cloud computing to provide services for the ill and elderly people in their independent living. This paper describes the general design of the proposed approach as well as a case study and implementation setup for the real-time monitoring and analysis process of Electromyography (EMG) healthcare system.


title: An 8-Week Exercise Intervention Based on Zumba Improves Aerobic Fitness and Psychological Well-Being in Healthy Women

abstract: Background: Although Zumba is practiced by millions of people worldwide, there is a paucity of research about its potential benefits. The objective of this study was to investigate the effects of Zumba on physiological and psychological outcomes in healthy women. Methods: Cardiovascular fitness, body composition, physical self-perception and psychological well-being were assessed before and immediately after 8 weeks of Zumba performed 3 times weekly (Zumba group, n = 22, age: 26.6 ± 5.4 years old; height: 165.8 ± 7.1 cm) or no intervention (control group, n = 22, age: 27.9 ± 6.0 years old; height: 164.7 ± 6.2 cm). All variables were analyzed by a 2-way (Group × Time) analysis of variance with repeated measures, and a Bonferroni post hoc test. Pearson correlation coefficient assessed the relationship between changes in anthropometric, physiological and psychological variables. Results: Zumba provided significant positive changes in maximal aerobic fitness (+3.6%), self-perception of physical strength (+16....


title: Autonomous exploration and mapping of abandoned mines

abstract: This article discusses the software architecture of an autonomous robotic system designed to explore and map abandoned mines. A new set of software tools is presented, enabling robots to acquire maps of unprecedented size and accuracy. On 30 May 2003, the robot "Groundhog" successfully explored and mapped a main corridor of the abandoned Mathies mine near Courtney, Pennsylvania. This article also discusses some of the challenges that arise in the subterranean environments and some the difficulties of building truly autonomous robots.


title: Measures of Reliability in Sports Medicine and Science

abstract: Reliability refers to the reproducibility of values of a test, assay or other measurement in repeated trials on the same individuals. Better reliability implies better precision of single measurements and better tracking of changes in measurements in research or practical settings. The main measures of reliability are within-subject random variation, systematic change in the mean, and retest correlation. A simple, adaptable form of within-subject variation is the typical (standard) error of measurement: the standard deviation of an individual's repeated measurements. For many measurements in sports medicine and science, the typical error is best expressed as a coefficient of variation (percentage of the mean). A biased, more limited form of within-subject variation is the limits of agreement: the 95% likely range of change of an individual's measurements between 2 trials. Systematic changes in the mean of a measure between consecutive trials represent such effects as learning, motivation or fatigue; these changes need to be eliminated from estimates of within-subject variation. Retest correlation is difficult to interpret, mainly because its value is sensitive to the heterogeneity of the sample of participants. Uses of reliability include decision-making when monitoring individuals, comparison of tests or equipment, estimation of sample size in experiments and estimation of the magnitude of individual differences in the response to a treatment. Reasonable precision for estimates of reliability requires approximately 50 study participants and at least 3 trials. Studies aimed at assessing variation in reliability between tests or equipment require complex designs and analyses that researchers seldom perform correctly. A wider understanding of reliability and adoption of the typical error as the standard measure of reliability would improve the assessment of tests and equipment in our disciplines.


title: Computer-assisted bone age assessment: image preprocessing and epiphyseal/metaphyseal ROI extraction

abstract: Clinical assessment of skeletal maturity is based on a visual comparison of a left-hand wrist radiograph with atlas patterns. Using a new digital hand atlas an image analysis methodology is being developed. To assist radiologists in bone age estimation. The analysis starts with a preprocessing function yielding epiphyseal/metaphyseal regions of interest (EMROIs). Then, these regions are subjected to a feature extraction function. Accuracy has been measured independently at three stages of the image analysis: detection of phalangeal tip, extraction of the EMROIs, and location of diameters and lower edge of the EMROIs. Extracted features describe the stage of skeletal development more objectively than visual comparison.


Automated Essay Scoring in the Presence of Biased Ratings


title: Additive manufacture of fashion and jewellery products: a mini review

abstract: Additive Manufacturing (AM) is being used in the applications of dental, biomedical implants, aircraft, and tissue engineering as well as fashion. AM has an edge to provide greater design freedom to create intricate and complex designs that are otherwise difficult to fabricate using traditional methods. As a result, AM is being applied in the development of fashion products like apparels and jewellery and allows mass customisation. This paper presents an overview of the various AM technologies used for the manufacturing of fashion apparels and jewellery. The current state-of-the-art data capturing and modelling tools, as well as additional design procedures necessary for the design and fabrication of three-dimensional (3D) printed fashion products will be discussed. This paper also includes information on useful finishing technology for the application of fashion products as well as prospects of 3D printed fashion products in the near future.


title: Contributions of Concept-Oriented Reading Instruction to Knowledge About Interventions for Motivations in Reading

abstract: We present a theoretical and empirical explication of the intervention of Concept-Oriented Reading Instruction (CORI) that is designed to increase students' reading comprehension and motivation for reading. The framework specifies a set of five motivational constructs that represent goals for the instructional intervention. Necessary cognitive goals in reading are also presented. For this intervention, the five instructional practices of relevance, choice, success, collaboration, and thematic unit that are prominent in CORI are portrayed as components that are aligned with motivational constructs. The impact of CORI on some of the motivational processes, cognitive competencies, and reading comprehension are presented in the form of a meta-analysis of 11 CORI studies with 75 effect sizes on 20 outcome variables. The CORI motivational intervention is compared to laboratory treatments and other field studies.


title: The Effect of User Involvement on System Success: A Contingency

abstract: Despite the fact that commercial computer systems have been in existence for almost three decades, many systems in the process of being implemented may be classed as failures. One of the factors frequently cited as important to successful system development is involving users in the design and implementation process. This paper reports the results of a field study, conducted on data from forty-two systems, that investigates the role of user involvement and factors affecting the employment of user involvement on the success of system development. Path analysis was used to investigate both the direct effects of the contingent variables on system success and the effect of user involvement as a mediating variable between the contingent variables and system success. The results show that high system complexity and constraints on the resources available for system development are associated with less successful systems.


title: Hierarchical Cluster-Based Routing Protocols for Wireless Sensor Networks - A Survey

abstract: Hierarchical cluster-based routing protocols are considered as one of the most efficient routing protocols in wireless sensor networks (WSN) due to its higher energy efficiency, network scalability, and lower data retransmission. In cluster-based routing protocols the network is divided into clusters each with a cluster head (CH) that is used for data aggregation and transmission, and other non CH nodes are used for data sensing. The most important issue with clustering is how to select CH, and manage clusters. For this reason several cluster-based routing protocols have been proposed. In this paper we conduct a comprehensive list of the most recent hierarchical cluster-based routing protocols and discuss how these protocols reduce energy consuming and improve network life time. We classify the protocols according to node capabilities into homogeneous and heterogeneous protocols. While the former achieves uniform energy consumption, the later achieves lower hardware cost. Another level of classification is applied on the listed protocols based on the distance between the CH and the base station. The protocols are classified into single hop and multi hop. We then compare the protocols based on issues face each protocol and how to be overcome and improved by other proposed protocols. Finally we conclude with possible future research.


title: Repeated asenapine treatment does not participate in the mild stress induced FosB/ΔFosB expression in the rat hypothalamic paraventricular nucleus neurons

abstract: Abstract Effect of repeated asenapine (ASE) treatment on FosB/ΔFosB expression was studied in the hypothalamic paraventricular nucleus (PVN) of male rats exposed to chronic mild stress (CMS) for 21 days. Our intention was to find out whether repeated ASE treatment for 14 days may: 1) induce FosB/ΔFosB expression in the PVN; 2) activate selected PVN neuronal phenotypes, synthesizing oxytocin (OXY), vasopressin (AVP), corticoliberin (CRH) or tyrosine hydroxylase (TH); and 3) interfere with the impact of CMS. Control, ASE, CMS, and CMS + ASE treated groups were used. CMS included restraint, social isolation, crowding, swimming, and cold. From the 7th day of CMS, rats received ASE (0.3 mg/kg) or saline (300 μl/rat) subcutaneously, twice a day for 14 days. They were sacrificed on the day 22nd (16–18 h after last treatments). FosB/ΔFosB was visualized with avidin biotin peroxidase complex and OXY, AVP, CRH or TH antibodies by fluorescent dyes. Saline and ASE did not promote FosB/ΔFosB expression in the PVN. CMS and CMS + ASE elicited FosB/ΔFosB-expression in the PVN, whereas, ASE did not augment or attenuate FosB/ΔFosB induction elicited by CMS. FosB/ΔFosB-CRH occurred after CMS and CMS + ASE treatments in the PVN middle sector, while FosB/ΔFosB-AVP and FosB/ΔFosB-OXY after CMS and CMS + ASE treatments in the PVN posterior sector. FosB/ΔFosB-TH colocalization was rare. Larger FosB/ΔFosB profiles, running above the PVN, did not show any colocalizations. The study provides an anatomical/functional knowledge about an unaccented nature of prolonged ASE treatment at the level of PVN and excludes its positive or negative interplay with CMS effect. Data indicate that long-lasting ASE treatment might not act as a stressor acting at the PVN level.


title: A Multi-Threading Architecture to Support Interactive Visual Exploration

abstract: During continuous user interaction, it is hard to provide rich visual feedback at interactive rates for datasets containing millions of entries. The contribution of this paper is a generic architecture that ensures responsiveness of the application even when dealing with large data and that is applicable to most types of information visualizations. Our architecture builds on the separation of the main application thread and the visualization thread, which can be cancelled early due to user interaction. In combination with a layer mechanism, our architecture facilitates generating previews incrementally to provide rich visual feedback quickly. To help avoiding common pitfalls of multi-threading, we discuss synchronization and communication in detail. We explicitly denote design choices to control trade-offs. A quantitative evaluation based on the system VI S P L ORE shows fast visual feedback during continuous interaction even for millions of entries. We describe instantiations of our architecture in additional tools.


title: Simulating quadratic dynamical systems is PSPACE-complete (preliminary version)

abstract: Quadratic Dynamical Systems (QDS), whose definition extends that of Markov chains, are used to model phenomena in a variety of fields like statistical physics and natural evolution. Such systems also play a role in genetic algorithms, a widelyused class of heuristics that are notoriously hard to analyze. Recently Rabinovich et al. took an important step in the study of QDS’s by showing, under some technical assumptions, that such systems converge to a stationary distribution (similar theorems for Markov Chains are well-known). We show, however, that the following sampling problem for QDS’s is PSPACE-hard: Given an initial distribution, produce a random sample from the t’th generation. The hardness result continues to hold for very restricted classes of QDS’s with very simple initial distributions, thus suggesting that QDS’s are intrinsically more complicated than Markov chains. ∗Supported by an IBM Graduate Fellowship and partly under NSF grant CCR-9310214. Email: arora@cs.berkeley.edu. †Work done while at ICSI, Berkeley, and supported in part by a Rothschild postdoctoral fellowship. Email: rabani@theory.lcs.mit.edu. ‡Supported by NSF grant CCR-9310214. Email: vazirani@cs.berkeley.edu.


title: Antihelical shaping of prominent ears using conchal cartilage-grafting adhesion

abstract: OBJECTIVES/HYPOTHESIS ::: No specific otoplasty method has been considered as the absolute standard in patients with prominent ears showing thick and strong auricular cartilage. ::: ::: ::: STUDY DESIGN ::: Retrospective case series study. ::: ::: ::: METHODS ::: The authors reviewed the chart and preoperative and postoperative photos of patients (N = 66) who underwent otoplasty consecutively with a new antihelical shaping technique using conchal cartilage-grafting adhesion between 2003 and 2010. Three mattress sutures between the posterior scapha and concha wall were performed to create an antihelical tubing. A piece of conchal cartilage with an intact perichondrium was inserted through the posterior tubing gap and fixed to the posterior scapha and conchal wall, which induced permanent cartilage adhesion. ::: ::: ::: RESULTS ::: Thirty-nine patients were female and 27 were male. The average age was 23.7 years. Prominent ears were bilateral in 24 patients and unilateral in 42 patients. All patients were observed for a postoperative duration of between 4 months and 24 months. Operative wounds in all patients healed uneventfully. Asymmetric ear projections due to undercorrection were observed in two patients, and mild irregularities or prominences on the postauricular surfaces were observed in three patients. Twenty-two patients (27 corrected ears) were interviewed by telephone to evaluate their long-term satisfaction (an average of 5.2 years after surgery). All except one reported their corrected ears as good. ::: ::: ::: CONCLUSIONS ::: The conchal cartilage-grafting adhesion technique creates a naturally shaped smooth antihelix, allowing the prominent ear to predictably and permanently set back. We believe that the method is indicated in adult patients showing thick and strong auricular cartilage.


title: Specific inulin-type fructan fibers protect against autoimmune diabetes by modulating gut immunity, barrier function, and microbiota homeostasis

abstract: SCOPE ::: Dietary fibers capable of modifying gut barrier and microbiota homeostasis affect the progression of type 1 diabetes (T1D). Here, we aim to compare modulatory effects of inulin-type fructans (ITFs), natural soluble dietary fibers with different degrees of fermentability from chicory root, on T1D development in nonobese diabetic mice. ::: ::: ::: METHODS AND RESULTS ::: Female nonobese diabetic mice were weaned to long- and short-chain ITFs [ITF(l) and ITF(s), 5%] supplemented diet up to 24 weeks. T1D incidence, pancreatic-gut immune responses, gut barrier function, and microbiota composition were analyzed. ITF(l) but not ITF(s) supplementation dampened the incidence of T1D. ITF(l) promoted modulatory T-cell responses, as evidenced by increased CD25+ Foxp3+ CD4+ regulatory T cells, decreased IL17A+ CD4+ Th17 cells, and modulated cytokine production profile in the pancreas, spleen, and colon. Furthermore, ITF(l) suppressed NOD like receptor protein 3 caspase-1-p20-IL-1β inflammasome in the colon. Expression of barrier reinforcing tight junction proteins occludin and claudin-2, antimicrobial peptides β-defensin-1, and cathelicidin-related antimicrobial peptide as well as short-chain fatty acid production were enhanced by ITF(l). Next-generation sequencing analysis revealed that ITF(l) enhanced Firmicutes/Bacteroidetes ratio to an antidiabetogenic balance and enriched modulatory Ruminococcaceae and Lactobacilli. ::: ::: ::: CONCLUSION ::: Our data demonstrate that ITF(l) but not ITF(s) delays the development of T1D via modulation of gut-pancreatic immunity, barrier function, and microbiota homeostasis.


title: Parsing Tweets into Universal Dependencies

abstract: We study the problem of analyzing tweets with Universal Dependencies. We extend the UD guidelines to cover special constructions in tweets that affect tokenization, part-of-speech tagging, and labeled dependencies. Using the extended guidelines, we create a new tweet treebank for English (Tweebank v2) that is four times larger than the (unlabeled) Tweebank v1 introduced by Kong et al. (2014). We characterize the disagreements between our annotators and show that it is challenging to deliver consistent annotation due to ambiguity in understanding and explaining tweets. Nonetheless, using the new treebank, we build a pipeline system to parse raw tweets into UD. To overcome annotation noise without sacrificing computational efficiency, we propose a new method to distill an ensemble of 20 transition-based parsers into a single one. Our parser achieves an improvement of 2.2 in LAS over the un-ensembled baseline and outperforms parsers that are state-of-the-art on other treebanks in both accuracy and speed.


title: An efficient secure communication between set-top box and smart card in DTV broadcasting

abstract: In 2004, Jiang et al. presented a secure communication between set-top-box and smart card in DTV broadcasting. Their proposed protocol has following merits: security, dynamic session key, mutual authentication, lower computation and communication and users could freely chosen password. Moreover, the proposed scheme could prevent smart card clone and MacCormac Hack problems which are serious problems in DTV broadcasting. However, Jiang et al.'s scheme causes much computation and communication overhead of smart cards and STBs. In this paper, we propose an efficient secure communication between set-top box and smart card in DTV broadcasting. Our propose scheme not only has the same merits as that of Jiang et al.'s scheme but also significantly reduces the computation and communication cost. Therefore, our scheme only requires several hashing operations in both smart card side and STB side, and has a very small quantity of transmission data.


title: Designing and Evaluating a Social Gaze-Control System for a Humanoid Robot

abstract: This paper describes a context-dependent social gaze-control system implemented as part of a humanoid social robot. The system enables the robot to direct its gaze at multiple humans who are interacting with each other and with the robot. The attention mechanism of the gaze-control system is based on features that have been proven to guide human attention: nonverbal and verbal cues, proxemics, the visual field of view, and the habituation effect. Our gaze-control system uses Kinect skeleton tracking together with speech recognition and SHORE-based facial expression recognition to implement the same features. As part of a pilot evaluation, we collected the gaze behavior of 11 participants in an eye-tracking study. We showed participants videos of two-person interactions and tracked their gaze behavior. A comparison of the human gaze behavior with the behavior of our gaze-control system running on the same videos shows that it replicated human gaze behavior 89% of the time.


title: Closed-Form BER Expressions of QPSK Constellation for Uplink Non-Orthogonal Multiple Access

abstract: Non-orthogonal multiple access (NOMA) is an attractive multiple access technique to achieve the optimal system capacity region. A great deal of recent attention has been devoted to the study of the NOMA system capacity performance. However, the exact bit error rate (BER) expressions of NOMA systems have not been derived yet. We provide the exact closed-form BER expressions of the QPSK constellation for an uplink NOMA system over an additive white Gaussian noise channel. Finally, the validity of our derived BER expressions is verified through simulations.


title: SVM-Prot: Web-based support vector machine software for functional classification of a protein from its primary sequence.

abstract: Prediction of protein function is of significance in studying biological processes. One approach for function prediction is to classify a protein into functional family. Support vector machine (SVM) is a useful method for such classification, which may involve proteins with diverse sequence distribution. We have developed a web-based software, SVMProt, for SVM classification of a protein into functional family from its primary sequence. SVMProt classification system is trained from representative proteins of a number of functional families and seed proteins of Pfam curated protein families. It currently covers 54 functional families and additional families will be added in the near future. The computed accuracy for protein family classification is found to be in the range of 69.1-99.6%. SVMProt shows a certain degree of capability for the classification of distantly related proteins and homologous proteins of different function and thus may be used as a protein function prediction tool that complements sequence alignment methods. SVMProt can be accessed at http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi.


title: Multi-target Tracking in Multiple Non-overlapping Cameras Using Fast-Constrained Dominant Sets

abstract: In this paper, a unified three-layer hierarchical approach for solving tracking problems in multiple non-overlapping cameras is proposed. Given a video and a set of detections (obtained by any person detector), we first solve within-camera tracking employing the first two layers of our framework and, then, in the third layer, we solve across-camera tracking by merging tracks of the same person in all cameras in a simultaneous fashion. To best serve our purpose, a constrained dominant sets clustering (CDSC) technique, a parametrized version of standard quadratic optimization, is employed to solve both tracking tasks. The tracking problem is caste as finding constrained dominant sets from a graph. In addition to having a unified framework that simultaneously solves within- and across-camera tracking, the third layer helps link broken tracks of the same person occurring during within-camera tracking. In this work, we propose a fast algorithm, based on dynamics from evolutionary game theory, which is efficient and salable to large-scale real-world applications.


title: Medial Knee Injury: Part 1, Static Function of the Individual Components of the Main Medial Knee Structures

abstract: BackgroundThere is a lack of knowledge on the primary and secondary static stabilizing functions of the posterior oblique ligament (POL), the proximal and distal divisions of the superficial medial collateral ligament (sMCL), and the meniscofemoral and meniscotibial portions of the deep medial collateral ligament (MCL).HypothesisIdentification of the primary and secondary stabilizing functions of the individual components of the main medial knee structures will provide increased knowledge of the medial knee ligamentous stability.Study DesignDescriptive laboratory study.MethodsTwenty-four cadaveric knees were equally divided into 3 groups with unique sequential sectioning sequences of the POL, sMCL (proximal and distal divisions), and deep MCL (meniscofemoral and meniscotibial portions). A 6 degree of freedom electromagnetic tracking system monitored motion after application of valgus loads (10 N·m) and internal and external rotation torques (5 N·m) at 0°, 20°, 30°, 60°, and 90° of knee flexion.ResultsThe ...


title: ISANA: Wearable Context-Aware Indoor Assistive Navigation with Obstacle Avoidance for the Blind

abstract: This paper presents a novel mobile wearable context-aware indoor maps and navigation system with obstacle avoidance for the blind. The system includes an indoor map editor and an App on Tango devices with multiple modules. The indoor map editor parses spatial semantic information from a building architectural model, and represents it as a high-level semantic map to support context awareness. An obstacle avoidance module detects objects in front using a depth sensor. Based on the ego-motion tracking within the Tango, localization alignment on the semantic map, and obstacle detection, the system automatically generates a safe path to a desired destination. A speech-audio interface delivers user input, guidance and alert cues in real-time using a priority-based mechanism to reduce the user’s cognitive load. Field tests involving blindfolded and blind subjects demonstrate that the proposed prototype performs context-aware and safety indoor assistive navigation effectively.


title: Online Clustering of Bandits

abstract: We introduce a novel algorithmic approach to content recommendation based on adaptive clustering of exploration-exploitation "bandit") strategies. We provide a sharp regret analysis of this algorithm in a standard stochastic noise setting, demonstrate its scalability properties, and prove its effectiveness on a number of artificial and real-world datasets. Our experiments show a significant increase in prediction performance over state-of-the-art methods for bandit problems.


title: Topic evolution in a stream of documents

abstract: Abstract Document collections evolve over time, new topics emerge and old ones decline. At the same time, the terminology evolves as well. Much literature is devoted to topic evolution in nite document sequences assuming a xed vocabulary. In this study, we propose \Topic Monitor" for the monitoring and understanding of topic and vocabulary evolution over an in nite document sequence, i.e. a stream. We use Probabilistic Latent Semantic Analysis (PLSA) for topic modeling and propose new folding-in techniques for topic adaptation under an evolving vocabulary. We extract a series of models, on which we detect index-based topic threads as human-interpretable descriptions of topic evolution.