title: Domains of effective teaching process students perspectives in two medical schools.

abstract: BACKGROUND ::: There has been little systematic investigation of student belief about the characteristics of the optimum process for clinical bedside teaching. ::: ::: ::: AIMS ::: The intent was to identify the most important characteristics of the bedside teaching experience from the perspective of two groups of students, one from Oman and the other from Canada. ::: ::: ::: METHOD ::: Students were asked to complete a questionnaire about their concept of the ideal process of bedside teaching. Their answers were analyzed using factor analysis. ::: ::: ::: RESULTS ::: Answers provided by the students was consistent with the presence of six domains. These corresponded to Preparation, Introduction, Experience, Summary, Explanation, and Conclusion. 'Preparation' involves consideration of the patient and the knowledge level of the learners, 'Introduction' involves effective communication, and 'Experience' means the need for the students to be actively involved in the history and physical examination. The remaining three domains deal with the need to provide a summary and elaboration as well as advice and feedback. These 6 factors explained 60% of the total variance. ::: ::: ::: CONCLUSIONS ::: While these areas still need to be defined more closely, the application of these six domains to the structure of the bedside teaching experience is likely to result in improved student learning.


title: Metacognitive Facilitation of Spontaneous Thought Processes: When Metacognition Helps the Wandering Mind Find Its Way

abstract: Mind wandering (MW) and metacognition may give the impression of lying at the opposite poles of the spectrum of human cognition. MW involves undirected, spontaneous thought processes that often occur without our volition and sometimes despite our intentions. Metacognition, by contrast, involves the conscious, often intentional monitoring and evaluation of our own mental processes and behaviors. The neural correlates of MW and metacognition may also appear strictly distinct at first, considering the almost exclusive focus on default network regions’ involvement in MW, in contrast to the emphasis on higher order prefrontal regions’ role in metacognitive processing. In this chapter, we will argue that despite the apparent gulf between MW and metacognition, some of the most intriguing mental phenomena we humans are capable of experiencing involve an intimate, dynamic interplay between MW and metacognition. According to the standard view of their interaction, metacognition serves to correct the wandering mind, suppressing spontaneous thoughts and bringing attention back to more "worthwhile" tasks. In this chapter, we argue that this "negative" or suppressant view of their interactions represents only a part of the whole picture. Instead, we outline and discuss three examples of positive, facilitative interactions: creative thinking, mindfulness meditation, and lucid dreaming (being aware that one is dreaming while dreaming). We argue that at both the cognitive and neural levels, these phenomena appear to involve an intricate balance whereby spontaneous thought is allowed to arise naturally while at the same time accompanied by metacognitive monitoring of one’s mental content and state of awareness. In ideal cases, this symbiotic relationship results in metacognition facilitating or optimizing spontaneous thought processes, so that they become more creative, less intrusive, and more likely to lead to novel conclusion and realizations.


title: Autism, hypersystemizing, and truth

abstract: Evidence is reviewed suggesting that, in the general population, empathizing and systemizing show strong sex differences. The function of systemizing is to predict lawful events, including lawful change, or patterns in data. Also reviewed is the evidence that individuals on the autistic spectrum have degrees of empathizing difficulties alongside hypersystemizing. The hypersystemizing theory of autism spectrum conditions (ASC) proposes that people with ASC have an unusually strong drive to systemize. This can explain their preference for systems that change in highly lawful or predictable ways; why they become disabled when faced with systems characterized by less lawful change; and their “need for sameness” or “resistance to change”. If “truth” is defined as lawful patterns in data then, according to the hypersystemizing theory, people with ASC are strongly driven to discover the “truth”.


title: Introducing Expected Returns into Risk Parity Portfolios: A New Framework for Tactical and Strategic Asset Allocation ∗

abstract: Risk parity is an allocation method used to build diversified portfolios that does not rely on any assumptions of expected returns, thus placing risk management at the heart of the strategy. This explains why risk parity became a popular investment model after the global financial crisis in 2008. However, risk parity has also been criticized because it focuses on managing risk concentration rather than portfolio performance, and is therefore seen as being closer to passive management than active management. In this article, we show how to introduce assumptions of expected returns into risk parity portfolios. To do this, we consider a generalized risk measure that takes into account both the portfolio return and volatility. However, the trade-off between performance and volatility contributions creates some difficulty, while the risk budgeting problem must be clearly defined. After deriving the theoretical properties of such risk budgeting portfolios, we apply this new model to asset allocation. First, we consider long-term investment policy and the determination of strategic asset allocation. We then consider dynamic allocation and show how to build risk parity funds that depend on expected returns.


title: Radio Interface Technologies for Cooperative Transmission in 3GPP LTE-Advanced

abstract: This paper presents an overview of radio interface technologies for cooperative transmission in 3GPP LTE-Advanced, i.e., coordinated multi-point (CoMP) transmission, enhanced inter-cell interference coordination (eICIC) for heterogeneous deployments, and relay transmission techniques. This paper covers not only the technical components in the 3GPP specifications that have already been released, but also those that were discussed in the Study Item phase of LTE-Advanced, and those that are currently being discussed in 3GPP for potential specification in future LTE releases.


title: Strengths of character, orientations to happiness, and life satisfaction

abstract: Why are certain character strengths more associated with life satisfaction than others? A sample of US adults (N = 12,439) completed online surveys in English measuring character strengths, orientations to happiness (engagement, pleasure, and meaning), and life satisfaction, and a sample of Swiss adults (N = 445) completed paper-and-pencil versions of the same surveys in German. In both samples, the character strengths most highly linked to life satisfaction included love, hope, curiosity, and zest. Gratitude was among the most robust predictors of life satisfaction in the US sample, whereas perseverance was among the most robust predictors in the Swiss sample. In both samples, the strengths of character most associated with life satisfaction were associated with orientations to pleasure, to engagement, and to meaning, implying that the most fulfilling character strengths are those that make possible a full life.


title: MIMO Broadcast Channels with Finite Rate Feedback

abstract: Multiple transmit antennas in a downlink channel can provide tremendous capacity (i.e., multiplexing) gains, even when receivers have only single antennas. However, receiver and transmitter channel state information is generally required. In this correspondence, a system where each receiver has perfect channel knowledge, but the transmitter only receives quantized information regarding the channel instantiation is analyzed. The well-known zero-forcing transmission technique is considered, and simple expressions for the throughput degradation due to finite-rate feedback are derived. A key finding is that the feedback rate per mobile must be increased linearly with the signal-to-noise ratio (SNR) (in decibels) in order to achieve the full multiplexing gain. This is in sharp contrast to point-to-point multiple-input multiple-output (MIMO) systems, in which it is not necessary to increase the feedback rate as a function of the SNR


DataStories at SemEval-2017 Task 6: Siamese LSTM with Attention for Humorous Text Comparison.


Multiple-implementation testing of supervised learning software


title: Maximum Sustainable throughput Prediction for Data Stream Processing over Public Clouds

abstract: In cloud-based stream processing services, the maximum sustainable throughput (MST) is defined as the maximum throughput that a system composed of a fixed number of virtual machines (VMs) can ingest indefinitely. If the incoming data rate exceeds the system's MST, unprocessed data accumulates, eventually making the system inoperable. Thus, it is important for the service provider to keep the MST always larger than the incoming data rate by dynamically changing the number of VMs used by the system. In this paper, we identify a common data processing environment used by modern data stream processing systems, and we propose MST prediction models for this environment. We train the models using linear regression with samples obtained from a few VMs and predict MST for a larger number of VMs. To minimize the time and cost for model training, we statistically determine a set of training samples using Intel's Storm benchmarks with representative resource usage patterns. Using typical use-case benchmarks on Amazon's EC2 public cloud, our experiments show that, training with up to 8 VMs, we can predict MST for streaming applications with less than 4% average prediction error for 12 VMs, 9% for 16 VMs, and 32% for 24 VMs. Further, we evaluate our prediction models with simulation based elastic VM scheduling on a realistic workload. These simulation results show that with 10% over provisioning, our proposed models' cost efficiency is on par with the cost of an optimal scaling policy without incurring any service level agreement violations.


Fault detection using Principal Component Analysis ( PCA ) in a Wastewater Treatment Plant ( WWTP ) D . Garcı́a


title: Generating Distractors for Reading Comprehension Questions from Real Examinations

abstract: We investigate the task of distractor generation for multiple choice reading comprehension questions from examinations. In contrast to all previous works, we do not aim at preparing words or short phrases distractors, instead, we endeavor to generate longer and semantic-rich distractors which are closer to distractors in real reading comprehension from examinations. Taking a reading comprehension article, a pair of question and its correct option as input, our goal is to generate several distractors which are somehow related to the answer, consistent with the semantic context of the question and have some trace in the article. We propose a hierarchical encoder-decoder framework with static and dynamic attention mechanisms to tackle this task. Specifically, the dynamic attention can combine sentence-level and word-level attention varying at each recurrent time step to generate a more readable sequence. The static attention is to modulate the dynamic attention not to focus on question irrelevant sentences or sentences which contribute to the correct option. Our proposed framework outperforms several strong baselines on the first prepared distractor generation dataset of real reading comprehension questions. For human evaluation, compared with those distractors generated by baselines, our generated distractors are more functional to confuse the annotators.


title: An Intelligent Broker for Context-Aware Systems

abstract: We describe Context Broker Architecture (CoBrA) – a new architecture for supporting context-aware systems in smart spaces. Our architecture explores the use of Semantic Web languages for defining and publishing a context ontology, for sharing information about a context and for reasoning over such information. Central to our architecture is a broker agent that maintains a shared model of the context for all computing entities in the space and enforces the privacy policies defined by the users and devices. We also describe the use of CoBrA in prototyping an intelligent meeting room.


title: 3D shape reconstruction of Mooney faces

abstract: Two-tone (ldquoMooneyrdquo) images seem to arouse vivid 3D percept of faces, both familiar and unfamiliar, despite their seemingly poor content. Recent psychological and fMRI studies suggest that this percept is guided primarily by top-down procedures in which recognition precedes reconstruction. In this paper we investigate this hypothesis from a mathematical standpoint. We show that indeed, under standard shape from shading assumptions, a Mooney image can give rise to multiple different 3D reconstructions even if reconstruction is restricted to the Mooney transition curve (the boundary curve between black and white) alone. We then use top-down reconstruction methods to recover the shape of novel faces from single Mooney images exploiting prior knowledge of the structure of at least one face of a different individual. We apply these methods to thresholded images of real faces and compare the reconstruction quality relative to reconstruction from gray level images.


title: Opportunities for Evolutionary Music Composition

abstract: Traditional compositional techniques and many computer-assisted composition systems have been focused on the production of linear musical products. In an age where non-linear media are increasingly prominent there is a need to reassess these technologies in the light of new opportunities for making music with non-linear outcomes. This paper examines the current state of music making for non-linear media with a particular emphasis on evolutionary music and how and where it might be applied. In addition, some of the implications for computer-based tool design will be outlined.


title: Product Recommendations Enhanced with Reviews

abstract: User-written product reviews contain rich information about user preferences for product features and provide helpful explanations that are often used by shoppers to make their purchase decisions. E-commerce recommender systems can benefit enormously by also exploiting experiences of multiple customers captured in product reviews. In this tutorial, we present a range of techniques that allow recommender systems in e-commerce websites to take full advantage of reviews. This includes text mining methods for feature-specific sentiment analysis of products, topic models and distributed representations that bridge the vocabulary gap between user reviews and product descriptions. We present recommender algorithms that use review information to address the cold-start problem and generate recommendations with explanations. We discuss examples and experiences from an online marketplace (i.e., Flipkart).


title: Prediction of protein solvent accessibility using fuzzy k-nearest neighbor method

abstract: MOTIVATION ::: The solvent accessibility of amino acid residues plays an important role in tertiary structure prediction, especially in the absence of significant sequence similarity of a query protein to those with known structures. The prediction of solvent accessibility is less accurate than secondary structure prediction in spite of improvements in recent researches. The k-nearest neighbor method, a simple but powerful classification algorithm, has never been applied to the prediction of solvent accessibility, although it has been used frequently for the classification of biological and medical data. ::: ::: ::: RESULTS ::: We applied the fuzzy k-nearest neighbor method to the solvent accessibility prediction, using PSI-BLAST profiles as feature vectors, and achieved high prediction accuracies. With leave-one-out cross-validation on the ASTRAL SCOP reference dataset constructed by sequence clustering, our method achieved 64.1% accuracy for a 3-state (buried/intermediate/exposed) prediction (thresholds of 9% for buried/intermediate and 36% for intermediate/exposed) and 86.7, 82.0, 79.0 and 78.5% accuracies for 2-state (buried/exposed) predictions (thresholds of each 0, 5, 16 and 25% for buried/exposed), respectively. Our method also showed slightly better accuracies than other methods by about 2-5% on the RS126 dataset and a benchmarking dataset with 229 proteins. ::: ::: ::: AVAILABILITY ::: Program and datasets are available at http://biocom1.ssu.ac.kr/FKNNacc/ ::: ::: ::: CONTACT ::: jul@ssu.ac.kr.


title: Affective and Content Analysis of Online Depression Communities

abstract: A large number of people use online communities to discuss mental health issues, thus offering opportunities for new understanding of these communities. This paper aims to study the characteristics of online depression communities (CLINICAL) in comparison with those joining other online communities (CONTROL). We use machine learning and statistical methods to discriminate online messages between depression and control communities using mood, psycholinguistic processes and content topics extracted from the posts generated by members of these communities. All aspects including mood, the written content and writing style are found to be significantly different between two types of communities. Sentiment analysis shows the clinical group have lower valence than people in the control group. For language styles and topics, statistical tests reject the hypothesis of equality on psycholinguistic processes and topics between two groups. We show good predictive validity in depression classification using topics and psycholinguistic clues as features. Clear discrimination between writing styles and contents, with good predictive power is an important step in understanding social media and its use in mental health.


A Latent Representation of Users, Sessions, and Songs for Listening Behavior Analysis


title: EventMiner: Mining Events from Annotated Documents

abstract: Events are central in human history and thus also in Web queries, in particular if they relate to history or news. However, ambiguity issues arise as queries may refer to ambiguous events differing in time, geography, or participating entities. Thus, users would greatly benefit if search results were presented along different events. In this paper, we present EventMiner, an algorithm that mines events from top-k pseudo-relevant documents for a given query. It is a probabilistic framework that leverages semantic annotations in the form of temporal expressions, geographic locations, and named entities to analyze natural language text and determine important events. Using a large news corpus, we show that using semantic annotations, EventMiner detects important events and presents documents covering the identified events in the order of their importance.


title: A novel approach for resolving differences in single-cell gene expression patterns from zygote to blastocyst

abstract: Motivation: Single-cell experiments of cells from the early mouse embryo yield gene expression data for different developmental stages from zygote to blastocyst. To better understand cell fate decisions during differentiation, it is desirable to analyse the high-dimensional gene expression data and assess differences in gene expression patterns between different developmental stages as well as within developmental stages. Conventional methods include univariate analyses of distributions of genes at different stages or multivariate linear methods such as principal component analysis (PCA). However, these approaches often fail to resolve important differences as each lineage has a unique gene expression pattern which changes gradually over time yielding different gene expressions both between different developmental stages as well as heterogeneous distributions at a specific stage. Furthermore, to date, no approach taking the temporal structure of the data into account has been presented. ::: ::: Results: We present a novel framework based on Gaussian process latent variable models (GPLVMs) to analyse single-cell qPCR expression data of 48 genes from mouse zygote to blastocyst as presented by (Guo et al., 2010). We extend GPLVMs by introducing gene relevance maps and gradient plots to provide interpretability as in the linear case. Furthermore, we take the temporal group structure of the data into account and introduce a new factor in the GPLVM likelihood which ensures that small distances are preserved for cells from the same developmental stage. Using our novel framework, it is possible to resolve differences in gene expressions for all developmental stages. Furthermore, a new subpopulation of cells within the 16-cell stage is identified which is significantly more trophectoderm-like than the rest of the population. The trophectoderm-like subpopulation was characterized by considerable differences in the expression of Id2, Gata4 and, to a smaller extent, Klf4 and Hand1. The relevance of Id2 as early markers for TE cells is consistent with previously published results. ::: ::: Availability: The mappings were implemented based on Prof. Neil Lawrence's FGPLVM toolbox1; extensions for relevance analysis and including the structure of the data can be obtained from one of the authors' homepage.2 ::: ::: Contact: f.buettner@helmholtz-muenchen.de


title: When Brain Beats Behavior: Neuroforecasting Crowdfunding Outcomes

abstract: textabstractAlthough traditional economic and psychological theories imply that individual choice best scales to aggregate choice, primary components of choice reflected in neural activity may support even more generalizable forecasts. Crowdfunding represents a significant and growing platform for funding new and unique projects, causes, and products. To test whether neural activity could forecast market-level crowdfunding outcomes weeks later, 30 human subjects (14 female) decided whether to fund proposed projects described on an Internet crowdfunding website while undergoing scanning with functional magnetic resonance imaging. Although activity in both the nucleus accumbens (NAcc) and medial prefrontal cortex predicted individual choices to fund on atrial-to-trial basis in the neuroimaging sample, only NAcc activity generalized to forecast market funding outcomes weeks later on the Internet. Behavioral measures from the neuroimaging sample, however, did not forecast market funding outcomes. This pattern of associations was replicated in a second study. These findings demonstrate that a subset of the neural predictors of individual choice can generalize to forecast market-level crowdfunding outcomes—even better than choice itself.


Transfer Learning : From a Translation Model to a Dense Sentence Representation with Application to Paraphrase Detection


title: Hardware design of an NTT-based polynomial multiplier

abstract: This paper presents the design of a parameterizable n-coefficient polynomial multiplier based on an n-point NTT-core, which uses a systolic array. The designed NTT-based polynomial multiplier is described in generic structural VHDL; synthesized on the Stratix EP4SGX230KF40C2 using Quartus II V. 13; verified using Modelsim; and performs the product of two polynomials of degree 4095 in 95.64 μs. The hardware synthesis and performance results show that the designed polynomial multiplier presents a good area-time trade-off and it is suitable for hardware implementations of lattice-based cryptosystems.


title: Broadband multifunction AESA front-ends: New requirements and emerging technologies

abstract: Multifunction RF systems which can operate with different RF functions like radar, communication and electronic warfare offer a variety of benefits beyond the well-known AESA (Active Electronically Steerable Antenna) features. To reach a sufficient technology maturity to start product development a series of challenges most stemming from the large required operation bandwidth have to be resolved. Dual-polarised low-loss apertures with array grids designed for a grating lobe and scan blindness free ±60° conical Field of View (FoV) at the highest operational frequency have to be considered. The active components of the AESA and especially the T/R-M are required to be power efficient over the whole multifunction bandwidth and they shall be miniaturised to fit the array grid. RF functions like high speed datalink require linear high power amplification with low harmonics generation and interference suppression in receive is essential in order to maintain performance. Enablers for such broadband AESA are the GaN technology for T/R-M front-end components and SiGe BiCMOS technology for core chip and digital control of the T/R-M. The paper shows the state-of-the-art technology in Europe for multifunction RF Systems.


title: Attention-Based Guided Structured Sparsity of Deep Neural Networks

abstract: Network pruning is aimed at imposing sparsity in a neural network architecture by increasing the portion of zero-valued weights for reducing its size regarding energy-efficiency consideration and increasing evaluation speed. In most of the conducted research efforts, the sparsity is enforced for network pruning without any attention to the internal network characteristics such as unbalanced outputs of the neurons or more specifically the distribution of the weights and outputs of the neurons. That may cause severe accuracy drop due to uncontrolled sparsity. In this work, we propose an attention mechanism that simultaneously controls the sparsity intensity and supervised network pruning by keeping important information bottlenecks of the network to be active. On CIFAR-10, the proposed method outperforms the best baseline method by 6% and reduced the accuracy drop by 2.6x at the same level of sparsity.


title: Seamless kernel updates

abstract: Kernel patches are released frequently to fix bugs and security vulnerabilities. However, users and system administrators often delay installing these updates because they require a system reboot, which results in disruption of service and the loss of application state. Unfortunately, the longer a system remains out-of-date, the higher is the likelihood of system failure or a successful attack. Approaches, such as dynamic patching and hot swapping, have been proposed for updating the kernel. All of them either limit the types of updates that are supported, or require significant programming effort to manage. We have designed a system that checkpoints application-visible state, updates the kernel, and restores the application state thus minimizing disruption of service. By checkpointing high-level state, our system no longer depends on the precise implementation of a patch and can apply all backward compatible patches. Our results show that updates to major releases of the Linux kernel can be applied with minimal effort and no observable overhead.


CRCTOL: A semantic-based domain ontology learning system


title: The evolution of architectural forms through computer visualisation: muqarnas example

abstract: Computers have been used for the reconstruction of historical buildings in architecture and archaeology in order to visualise forms that existed before. The goal of this paper is to push further the evolution of an architectural form of a high cultural significance in the history of Islamic architecture; muqarnas. ::: ::: Muqarnas is a three-dimensional form whose visual function is to provide the gradual transition between two levels, two sizes, and or two shapes. It evolved over a period of eight centuries and, later, was neglected. Nowadays, it is being copied and introduced into the architecture of the Islamic world, but only as a rigid copy and not as a living evolving form. A long research on the computerisation of muqarnas over the last two decades aimed at pushing further the evolution of this form into new possibilities. ::: ::: The basic theory behind this research revolves around typology. Defining a type requires stating all its perceived qualities, both of fixed values 'characteristics', and of variable values 'attributes'. Creating new forms would be, then, giving new values for the attributes. A true evolution of the form happens when a characteristic is turned into an attribute and given new values. When this theory was applied in muqarnas research at many levels, it produced many new evolutionary steps which will be presented. The theory could be applied in other fields where there is a need for the evolution of traditional forms.


title: Planning Multi-Fingered Grasps as Probabilistic Inference in a Learned Deep Network

abstract: We propose a novel approach to multi-fingered grasp planning leveraging learned deep neural network models. We train a convolutional neural network to predict grasp success as a function of both visual information of an object and grasp configuration. We can then formulate grasp planning as inferring the grasp configuration which maximizes the probability of grasp success. We efficiently perform this inference using a gradient-ascent optimization inside the neural network using the backpropagation algorithm. Our work is the first to directly plan high quality multi-fingered grasps in configuration space using a deep neural network without the need of an external planner. We validate our inference method performing both multi-finger and two-finger grasps on real robots. Our experimental results show that our planning method outperforms existing planning methods for neural networks; while offering several other benefits including being data-efficient in learning and fast enough to be deployed in real robotic applications.


Dynamic Action Repetition for Deep Reinforcement Learning.


title: CAR CRASHES AND NON-HEAD IMPACT CERVICAL SPINE INJURIES IN INFANTS AND CHILDREN

abstract: The effects of child safety seats have been well documented in the medical literature. Scattered throughout the medical literature are individual case reports of cervical injury to children restrained in child restraint systems. A review of the literature is provided identifying previous documented cases. The authors also provide new case details of children with cervical spine injury without head contact. An overview of the growth of the infant and specific details in the cervical spine that may contribute to significant cervical injury without head impact is presented.


title: Detecting the time-dependent coherence between non-stationary electrophysiological signals -- A combined statistical and time-frequency approach

abstract: Abstract Various time–frequency methods have been used to study time-varying properties of non-stationary neurophysiological signals. In the present study, a time–frequency coherence estimate using continuous wavelet transform (CWT) together with its confidence intervals are proposed to evaluate the correlation between two non-stationary processes. The approach is based on averaging over repeat trials. A systematic comparison between approaches using CWT and short-time Fourier transform (STFT) is carried out. Simulated data are generated to test the performance of these methods when estimating time–frequency based coherence. In contrast to some recent studies, we find that CWT based coherence estimates do not supersede STFT based estimates. We suggest that a combination of STFT and CWT would be most suitable for analysing non-stationary neural data. Tests are presented to investigate the time and frequency discrimination capabilities of the two approaches. The methods are applied to two experimental data sets: electroencephalogram (EEG) and surface electromyogram (EMG) during wrist movements in a healthy subject, and local field potential (LFP) and surface EMG recordings during resting tremor in a Parkinsonian patient. Supporting software is available at http://www.dcs.warwick.ac.ukffeng/software/COD and http://www.neurospec.org .


Polygonal boundary approximation for a 2D general sweep based on envelope and boolean operations


title: Database-Assisted Spectrum Sharing in Satellite Communications: A Survey

abstract: This survey paper discusses the feasibility of sharing the spectrum between satellite telecommunication networks and terrestrial and other satellite networks on the basis of a comprehensive study carried out as part of the European Space Agency’s Advanced Research in Telecommunications Systems programme. The main area of investigation is the use of spectrum databases to enable a controlled sharing environment. Future satellite systems can largely benefit from the ability to access spectrum bands other than the dedicated licensed spectrum band. Potential spectrum sharing scenarios are classified as: a) secondary use of the satellite spectrum by terrestrial systems, b) satellite system as a secondary user of spectrum c) extension of a terrestrial network by using the satellite network; and d) two satellite systems sharing the same spectrum. We define practical use cases for each scenario and identify suitable techniques. The proposed scenarios and use cases cover several frequency bands and satellite orbits. Out of all the scenarios reviewed, owing to the announcement of many different mega-constellation satellite networks, we focus on analyzing the feasibility of spectrum sharing between geostationary orbit (GSO) and non-geostationary orbit (NGSO) satellite systems. The performance is primarily analyzed on the basis of widely accepted recommendations of the Radiocommunications Sector of the International Telecommunications Union. Finally, future research directions are identified.


title: Leech Collections from Washington State, with the Description of Two New Species of Placobdella (Annelida: Glossiphoniidae)

abstract: ABSTRACT An assessment of the hirudinifauna of Washington State is presented. In total, 11 distinct leech species were found representing two new records for the state and two new species of the genus Placobdella, both described herein. Placobdella kwetlumye, n. sp., and Placobdella sophieae, n. sp., both collected in Squires Lake, Whatcom County, are morphologically similar to P. burresonae and P. pediculata respectively, but exhibit morphological features that readily separate them from congeners. In addition to the descriptions of the new species, here we provide a brief summary of the morphological traits possessed by each species found.


title: Data Mining Techniques and Applications in Speech Processing - A Review

abstract: Data mining can be defined as an activity that automatically extracts hidden knowledge from large set of data. Data mining research has emerged in the areas of speech, audio processing and spoken language dialog. The research has gained interest due to audio data that are available in plenty. In this paper, we have discussed about different kinds of mining with speech, voice and audio processing and tasks of data mining in speech processing. Also a brief summary about existing data mining applications with speech and audio processing is given.


title: Level Set Segmentation With Multiple Regions

abstract: The popularity of level sets for segmentation is mainly based on the sound and convenient treatment of regions and their boundaries. Unfortunately, this convenience is so far not known from level set methods when applied to images with more than two regions. This communication introduces a comparatively simple way how to extend active contours to multiple regions keeping the familiar quality of the two-phase case. We further suggest a strategy to determine the optimum number of regions as well as initializations for the contours.


title: Segmentation of the Proximal Femur from MR Images using Deep Convolutional Neural Networks

abstract: Magnetic resonance imaging (MRI) has been proposed as a complimentary method to measure bone quality and assess fracture risk. However, manual segmentation of MR images of bone is time-consuming, limiting the use of MRI measurements in the clinical practice. The purpose of this paper is to present an automatic proximal femur segmentation method that is based on deep convolutional neural networks (CNNs). This study had institutional review board approval and written informed consent was obtained from all subjects. A dataset of volumetric structural MR images of the proximal femur from 86 subjects were manually-segmented by an expert. We performed experiments by training two different CNN architectures with multiple number of initial feature maps, layers and dilation rates, and tested their segmentation performance against the gold standard of manual segmentations using four-fold cross-validation. Automatic segmentation of the proximal femur using CNNs achieved a high dice similarity score of 0.95 ± 0.02 with precision = 0.95 ± 0.02, and recall = 0.95 ± 0.03. The high segmentation accuracy provided by CNNs has the potential to help bring the use of structural MRI measurements of bone quality into clinical practice for management of osteoporosis.


title: Environment-Scale Fabrication: Replicating Outdoor Climbing Experiences

abstract: Despite rapid advances in 3D printing, fabricating large, durable and robust artifacts is impractical with current technology. We focus on a particularly challenging environment-scale artifact: rock climbing routes. We propose a prototype fabrication method to replicate part of an outdoor climbing route and enable the same sensorimotor experience in an indoor gym. We start with 3D reconstruction of the rock wall using multi-view stereo and use reference videos of a climber in action to identify localized rock features that are necessary for ascent. We create 3D models akin to traditional indoor climbing holds, fabricated using rapid prototyping, molding and casting techniques. This results in robust holds accurately replicating the features and configuration of the original rock route. Validation was performed on two rock climbing sites in New Hampshire and Utah. We verified our results by comparing climbers' moves on the indoor replicas and original outdoor routes.


title: Graininess-Aware Deep Feature Learning for Pedestrian Detection

abstract: In this paper, we propose a graininess-aware deep feature learning method for pedestrian detection. Unlike most existing pedestrian detection methods which only consider low resolution feature maps, we incorporate fine-grained information into convolutional features to make them more discriminative for human body parts. Specifically, we propose a pedestrian attention mechanism which efficiently identifies pedestrian regions. Our method encodes fine-grained attention masks into convolutional feature maps, which significantly suppresses background interference and highlights pedestrians. Hence, our graininess-aware features become more focused on pedestrians, in particular those of small size and with occlusion. We further introduce a zoom-in-zoom-out module, which enhances the features by incorporating local details and context information. We integrate these two modules into a deep neural network, forming an end-to-end trainable pedestrian detector. Comprehensive experimental results on four challenging pedestrian benchmarks demonstrate the effectiveness of the proposed approach.


title: Quantitative Overhead Analysis for Python

abstract: Dynamic programming languages such as Python are becoming increasingly more popular, yet often show a significant performance slowdown compared to static languages such as C. This paper provides a detailed quantitative analysis of the overhead in Python without and with just-in-time (JIT) compilation. The study identifies a new major source of overhead, C function calls, for the Python interpreter. Additionally, we study the interaction of the run-time with the underlying processor hardware and find that the performance of Python with JIT depends heavily on the cache hierarchy and memory system. We find that proper nursery sizing is necessary for each application to optimize the trade-off between cache performance and garbage collection overhead. Although our studies focuses on Python, we show that our key findings can also apply to other dynamic languages such as Javascript.


title: Reconstruction-Based Pairwise Depth Dataset for Depth Image Enhancement Using CNN

abstract: Raw depth images captured by consumer depth cameras suffer from noisy and missing values. Despite the success of CNN-based image processing on color image restoration, similar approaches for depth enhancement have not been much addressed yet because of the lack of raw-clean pairwise dataset. In this paper, we propose a pairwise depth image dataset generation method using dense 3D surface reconstruction with a filtering method to remove low quality pairs. We also present a multi-scale Laplacian pyramid based neural network and structure preserving loss functions to progressively reduce the noise and holes from coarse to fine scales. Experimental results show that our network trained with our pairwise dataset can enhance the input depth images to become comparable with 3D reconstructions obtained from depth streams, and can accelerate the convergence of dense 3D reconstruction results.


title: Spatio-Temporal Transformer Network for Video Restoration

abstract: State-of-the-art video restoration methods integrate optical flow estimation networks to utilize temporal information. However, these networks typically consider only a pair of consecutive frames and hence are not capable of capturing long-range temporal dependencies and fall short of establishing correspondences across several timesteps. To alleviate these problems, we propose a novel Spatio-temporal Transformer Network (STTN) which handles multiple frames at once and thereby manages to mitigate the common nuisance of occlusions in optical flow estimation. Our proposed STTN comprises a module that estimates optical flow in both space and time and a resampling layer that selectively warps target frames using the estimated flow. In our experiments, we demonstrate the efficiency of the proposed network and show state-of-the-art restoration results in video super-resolution and video deblurring.


title: What Good are Models?

abstract: Models are central to engineering. They are used for analysis, synthesis, and communication between humans. A given artifact or process may have multiple models with different purposes, modeling different aspects, or modeling at varying levels of abstraction. In this paper, we give a general overview of how models are used, with the goal of making the concepts clearer for different communities. We focus on the domain of track-based flow management of automated systems, and identify two different modeling styles, Eulerian and Lagrangian. Eulerian models focus on regions of space, whereas Lagrangian models focus on entities moving through space. We discuss how the features of the system, like having centralized or decentralized control or the ability to install fixed infrastructure, influence the choice between these styles. Although the choice between modeling styles is rarely made consciously, it affects modeling efficiency, and one style may be far better suited for certain modeling problems than another. For problems with a more global nature concerning the physical space, an Eulerian model is likely to be a better match. For problems that concern the moving objects specifically, where the identity of the individual objects is important, a Lagrangian view is the one to choose. In many cases, combining the two styles is the most effective approach. We illustrate the two styles using an example of an automated quarry.


title: Domain Confusion with Self Ensembling for Unsupervised Adaptation

abstract: Data collection and annotation are time-consuming in machine learning, expecially for large scale problem. A common approach for this problem is to transfer knowledge from a related labeled domain to a target one. There are two popular ways to achieve this goal: adversarial learning and self training. In this article, we first analyze the training unstablity problem and the mistaken confusion issue in adversarial learning process. Then, inspired by domain confusion and self-ensembling methods, we propose a combined model to learn feature and class jointly invariant representation, namely Domain Confusion with Self Ensembling (DCSE). The experiments verified that our proposed approach can offer better performance than empirical art in a variety of unsupervised domain adaptation benchmarks.


Cross-Platform Differences in Self-disclosure and Trait Prediction


Efficiently Mining High Quality Phrases from Texts


title: Semantic Web for the Working Ontologist: Effective Modeling in RDFS and OWL

abstract: Semantic Web models and technologies provide information in machine-readable languages that enable computers to access the Web more intelligently and perform tasks automatically without the direction of users. These technologies are relatively recent and advancing rapidly, creating a set of unique challenges for those developing applications. ::: ::: Semantic Web for the Working Ontologist is the essential, comprehensive resource on semantic modeling, for practitioners in health care, artificial intelligence, finance, engineering, military intelligence, enterprise architecture, and more. Focused on developing useful and reusable models, this market-leading book explains how to build semantic content (ontologies) and how to build applications that access that content. ::: ::: New in this edition: ::: ::: ::: Coverage of the latest Semantic Web tools for organizing, querying, and processing information - see details in TOC below ::: ::: Detailed information on the latest ontologies used in key web applications including ecommerce, social networking, data mining, using government data, and more ::: ::: ::: ::: ::: Updated with the latest developments and advances in Semantic Web technologies for organizing, querying, and processing information, including SPARQL, RDF and RDFS, OWL 2.0, and SKOS ::: Detailed information on the ontologies used in today's key web applications, including ecommerce, social networking, data mining, using government data, and more ::: Even more illustrative examples and case studies that demonstrate what semantic technologies are and how they work together to solve real-world problems ::: ::: Table of Contents ::: ::: 1?What Is The Semantic Web? ::: 2?Semantic Modeling ::: 3?RDF - The Basis of the Semantic Web ::: 4?SPARQL - The Query Language for RDF ::: 5?Semantic Web Application Architecture ::: 6?RDF And Inferencing ::: 7?RDF Schema Language ::: 8?RDFS-Plus ::: 9?SKOS - the Simple Knowledge Organization System ::: 10?Ontologies in the Wild: Linked Open Data and the Open Graph Project ::: 11?Basic OWL ::: 12?Counting and Sets In OWL ::: 13?MORE Ontologies in the Wild: QUDT, GoodRelations, and OBO Foundry ::: 14?Good and Bad Modeling Practices ::: 15?OWL 2.0 Levels and Logic ::: 16?Conclusions ::: 17?Frequently Asked Questions


title: Quantum Spectral Clustering through a Biased Phase Estimation Algorithm

abstract: In this brief paper, we go through the theoretical steps of the spectral clustering on quantum computers by employing the phase estimation and the amplitude amplification algorithms. We discuss circuit designs for each step and show how to obtain the clustering solution from the output state. In addition, we introduce a biased version of the phase estimation algorithm which significantly speeds up the amplitude amplification process. The complexity of the whole process is analyzed: it is shown that when the circuit representation of a data matrix of order $N$ is produced through an ancilla based circuit in which the matrix is written as a sum of $L$ number of Householder matrices; the computational complexity is bounded by $O(2^mLN)$ number of quantum gates. Here, $m$ represents the number of qubits (e.g., 6) involved in the phase register of the phase estimation algorithm.


title: Review of Electromagnetic Techniques for Breast Cancer Detection

abstract: Breast cancer is anticipated to be responsible for almost 40,000 deaths in the USA in 2011. The current clinical detection techniques suffer from limitations which motivated researchers to investigate alternative modalities for the early detection of breast cancer. This paper focuses on reviewing the main electromagnetic techniques for breast cancer detection. More specifically, this work reviews the cutting edge research in microwave imaging, electrical impedance tomography, diffuse optical tomography, microwave radiometry, biomagnetic detection, biopotential detection, and magnetic resonance imaging (MRI). The goal of this paper is to provide biomedical researchers with an in-depth review that includes all main electromagnetic techniques in the literature and the latest progress in each of these techniques.


title: Rotor Flux-Barrier Geometry Design to Reduce Stator Iron Losses in Synchronous IPM Motors Under FW Operations

abstract: The interior permanent-magnet (IPM) synchronous motor is characterized by a high rotor anisotropy. Such an anisotropy is the cause of a high harmonic content of the air-gap flux density distribution, almost independent of the main flux. As a consequence, there are fluctuations of the flux density in the stator iron and, consequently, eddy-current iron losses. This aspect is prominently evident during flux-weakening operations, when the armature current weakens the permanent magnet flux and the motor runs above the base speed. This paper presents a complete study of such a phenomenon, including an analytical model and a finite element validation, as well as an experimental confirmation of the predicted stator tooth flux waveforms. Finally, some suggestions are given in order to design an IPM motor exhibiting reduced iron losses during all operating conditions.


title: The Sopranos Meets EverQuest - Social Networking in Massively Multiplayer Online Games

abstract: This article explores the ways social interaction plays an integral role in the game EverQuest. Through our research we argue that social networks form a powerful component of the gameplay and the gaming experience, one that must be seriously considered to understand the nature of massively multiplayer online games. We discuss the discrepancy between how the game is portrayed and how it is actually played. By examining the role of social networks and interactions we seek to explore how the friendships between the players could be considered the ultimate exploit of the game.


title: Deep Learning Approach for Short-Term Stock Trends Prediction Based on Two-Stream Gated Recurrent Unit Network

abstract: Financial news has been proven to be a crucial factor which causes fluctuations in stock prices. However, previous studies heavily relied on analyzing shallow features and ignored the structural relation among words in a sentence. Several sentiment analysis studies have tried to point out the relationship between investors’ reaction and news events. However, the sentiment dataset was usually constructed from the lingual dataset which is unrelated to the financial sector and led to poor performance. This paper proposes a novel framework to predict the directions of stock prices by using both financial news and sentiment dictionary. The original contributions of this paper include the proposal of a novel two-stream gated recurrent unit network and Stock2Vec—a sentiment word embedding trained on financial news dataset and Harvard IV-4. Two main experiments are conducted: the first experiment predicts SP 2) Stock2Vec is more efficient in dealing with financial datasets; and 3) applying the model, a simulation scenario proves that our model is effective for the stock sector.


title: Distributed Learning over Unreliable Networks

abstract: Most of today's distributed machine learning systems assume {\em reliable networks}: whenever two machines exchange information (e.g., gradients or models), the network should guarantee the delivery of the message. At the same time, recent work exhibits the impressive tolerance of machine learning algorithms to errors or noise arising from relaxed communication or synchronization. In this paper, we connect these two trends, and consider the following question: {\em Can we design machine learning systems that are tolerant to network unreliability during training?} With this motivation, we focus on a theoretical problem of independent interest---given a standard distributed parameter server architecture, if every communication between the worker and the server has a non-zero probability $p$ of being dropped, does there exist an algorithm that still converges, and at what speed? The technical contribution of this paper is a novel theoretical analysis proving that distributed learning over unreliable network can achieve comparable convergence rate to centralized or distributed learning over reliable networks. Further, we prove that the influence of the packet drop rate diminishes with the growth of the number of \textcolor{black}{parameter servers}. We map this theoretical result onto a real-world scenario, training deep neural networks over an unreliable network layer, and conduct network simulation to validate the system improvement by allowing the networks to be unreliable.


title: Deep Learning for Encrypted Traffic Classification: An Overview

abstract: Traffic classification has been studied for two decades and applied to a wide range of applications from QoS provisioning and billing in ISPs to security-related applications in firewalls and intrusion detection systems. Port-based, data packet inspection, and classical machine learning methods have been used extensively in the past, but their accuracy has declined due to the dramatic changes in Internet traffic, particularly the increase in encrypted traffic. With the proliferation of deep learning methods, researchers have recently investigated these methods for traffic classification and reported high accuracy. In this article, we introduce a general framework for deep-learning-based traffic classification. We present commonly used deep learning methods and their application in traffic classification tasks. Then we discuss open problems, challenges, and opportunities for traffic classification.


title: CT diagnosis of spontaneous dissection of the superior mesenteric artery.

abstract: We report two cases of spontaneous superior mesenteric artery dissection diagnosed by CT. In both cases lamination of the arterial wall and enhancement of both true and false lumina were the key to the diagnosis.


title: Enhancement of latent fingerprints on banknotes using monogenic wavelets

abstract: Latent fingerprints on banknotes provide valuable information in forensic applications to identify suspects of robbery cases and counterfeiters. The image quality of latent fingerprints is poor due to the complex background of banknotes compared to live-scan images acquired in a controlled environment. An effective orientation field estimation algorithm is necessary for enhancing poor quality latents in the presence of background noise for reliable extraction of minutia features. There have been attempts in the literature to characterize a fingerprint using an AM-FM model. Motivated by the ability of the monogenic signal representation to demodulate such a signal to extract its features, this paper proposes a novel algorithm of latent fingerprint multiresolution orientation estimation with a modified Simoncelli isotropic wavelet design and by an appropriate use of the multiresolution orientation field. Experimental results on synthetic noisy images and real latents from our created latent fingerprint database on banknotes show that the enhanced image quality by using the proposed algorithm is better than the other existing methods for estimating the fingerprint orientation.


title: Snooping Keystrokes with mm-level Audio Ranging on a Single Phone

abstract: This paper explores the limits of audio ranging on mobile devices in the context of a keystroke snooping scenario. Acoustic keystroke snooping is challenging because it requires distinguishing and labeling sounds generated by tens of keys in very close proximity. Existing work on acoustic keystroke recognition relies on training with labeled data, linguistic context, or multiple phones placed around a keyboard --- requirements that limit usefulness in an adversarial context. In this work, we show that mobile audio hardware advances can be exploited to discriminate mm-level position differences and that this makes it feasible to locate the origin of keystrokes from only a single phone behind the keyboard. The technique clusters keystrokes using time-difference of arrival measurements as well as acoustic features to identify multiple strokes of the same key. It then computes the origin of these sounds precise enough to identify and label each key. By locating keystrokes this technique avoids the need for labeled training data or linguistic context. Experiments with three types of keyboards and off-the-shelf smartphones demonstrate scenarios where our system can recover $94\%$ of keystrokes, which to our knowledge, is the first single-device technique that enables acoustic snooping of passwords.


title: Three-phase Infinite Level Inverter

abstract: Pulse width modulated voltage source inverters (PWM-VSI) have many advantages over the traditional bridge inverters; yet there are some challenges encountered by PWM inverters. As power and voltage rating increases; device constraint as well ashigh switching loss creates serious problems. Various pulse width modulated strategies like sinusoidal; 3rd harmonic injection and space vector PWM (SVPWM) employs high frequency switching to obtain quality output voltage with less ripple. Increasing number of voltage levels in a multi-level inverter requires an increase in number of active switches and passive elements. The problem of dead time requirement and shoot-through in PWM inverters distorts the output waveform and transfers less energy to load. A new type of three-phase VSI called three-phase Infinite Level Inverter (ILI) is proposed to mitigate all the above problems to a large extent. The device constraint and switching loss at high frequency is substantially reduced due to lower input DC supply. To get same output voltage; the proposed inverter requires only half of the DC link voltage compared to sine PWM inverter. Also; ILI takes only 0.576 times the input DC voltage compared to 3rd harmonic-injection or SVPWM. This shows that the proposed inverter has the maximum DC link utilization. The number of active switches and passive elements are same for any number of voltage levels. This topology is a multi-level inverter as there are many levels in the output voltage. Shoot-through problem is avoided and thus the proposed inverter doesn't need a dead time; leading to a better system reliability. Hysteresis current control is used for closed loop control. Results obtained from experimental setup and computer simulation using MATLAB/Simulink are presented to verify the above scheme.


title: Presence of somatostatin in normal human epidermis

abstract: Somatostatin (SOM) is a ubiquitous peptide which is responsible for the inhibition of numerous biological functions. SOM is described as an antiproliferative molecule and an inhibitor of exocrine or endocrine secretion from a variety of tissues, including pancreas, gastrointestinal tract, central and peripheral nervous system. Mediation of SOM effects can be indirect or direct, respectively, through other molecules or receptors on target cells. We have searched for the presence of SOM in the epidermis using immunofluorescence, confocal laser scanning microscopy, radioimmunoassay, and chromatography. Immunofluorescence and confocal laser scanning microscopy studies were performed using rabbit antiserum anti-SOM and mouse monoclonal antibody directed to CD1a Langerhans cell (LC) marker disclosed with fluorescein or tetramethylrhodamine isothiocyanate conjugates. SOM was extracted from whole skin or epidermal cell suspension or LC-enriched suspensions and analysed by radioimmunoassay. We used an antiserum which was reactive for the 6-11 portion of native SOM. Chromatographic columns were performed on extracts from whole skin. The epidermis was SOM immunoreactive. LC were immunoreactive for SOM and the staining was membranous. SOM was extracted from the whole skin at about 0.13 +/- 0.02 fmol/mg of tissue (mean +/- SEM). The SOM concentration in epidermal cell suspensions was 1.5 +/- 0.9 fmol/10(6) cells. Data obtained with LC-enriched suspensions showed large variations between donors. Extracts from skin showed one peak with an elution profile like that of 14 amino acid SOM. This study demonstrates that 14 amino acid SOM is expressed in normal human epidermis.


title: Review: The role of communication systems in smart grids: Architectures, technical solutions and research challenges

abstract: The purpose of this survey is to present a critical overview of smart grid concepts, with a special focus on the role that communication, networking and middleware technologies will have in the transformation of existing electric power systems into smart grids. First of all we elaborate on the key technological, economical and societal drivers for the development of smart grids. By adopting a data-centric perspective we present a conceptual model of communication systems for smart grids, and we identify functional components, technologies, network topologies and communication services that are needed to support smart grid communications. Then, we introduce the fundamental research challenges in this field including communication reliability and timeliness, QoS support, data management services, and autonomic behaviors. Finally, we discuss the main solutions proposed in the literature for each of them, and we identify possible future research directions.


title: "Constant, constant, multi-tasking craziness": managing multiple working spheres

abstract: Most current designs of information technology are based on the notion of supporting distinct tasks such as document production, email usage, and voice communication. In this paper we present empirical results that suggest that people organize their work in terms of much larger and thematically connected units of work. We present results of fieldwork observation of information workers in three different roles: analysts, software developers, and managers. We discovered that all of these types of workers experience a high level of discontinuity in the execution of their activities. People average about three minutes on a task and somewhat more than two minutes using any electronic tool or paper document before switching tasks. We introduce the concept of working spheres to explain the inherent way in which individuals conceptualize and organize their basic units of work. People worked in an average of ten different working spheres. Working spheres are also fragmented; people spend about 12 minutes in a working sphere before they switch to another. We argue that design of information technology needs to support people's continual switching between working spheres.


title: Teaching self-care to caregivers: Effects of mindfulness-based stress reduction on the mental health of therapists in training. Training and Education

abstract: Preparation for the role of therapist can occur on both professional and personal levels. Research has found that therapists are at risk for occupationally related psychological problems. It follows that self-care may be a useful complement to the professional training of future therapists. The present study examined the effects of one approach to self-care, Mindfulness-Based Stress Reduction (MBSR), for therapists in training. Using a prospective, cohort-controlled design, the study found participants in the MBSR program reported significant declines in stress, negative affect, rumination, state and trait anxiety, and significant increases in positive affect and self-compassion. Further, MBSR participation was associated with increases in mindfulness, and this enhancement was related to several of the beneficial effects of MBSR participation. Discussion highlights the potential for future research addressing the mental health needs of therapists and therapist trainees.


title: Project communication management patterns

abstract: In present, dynamically developing organizations, that often realize business tasks using the project-based approach, effective project management is of paramount importance. Numerous reports and scientific papers present lists of critical success factors in project management, and communication management is usually at the very top of the list. But even though the communication practices are found to be associated with most of the success dimensions, they are not given enough attention and the communication processes and practices formalized in the company's project management methodology are neither followed nor prioritized by project managers. This paper aims at supporting project managers and teams in more effective implementation of best practices in communication management by proposing a set of communication management patterns, which promote a context-problem-solution approach to communication management in projects.


Rule-based Reinforcement Learning augmented by External Knowledge


title: Biomedical question answering: A survey

abstract: Objectives: In this survey, we reviewed the current state of the art in biomedical QA (Question Answering), within a broader framework of semantic knowledge-based QA approaches, and projected directions for the future research development in this critical area of intersection between Artificial Intelligence, Information Retrieval, and Biomedical Informatics. Materials and methods: We devised a conceptual framework within which to categorize current QA approaches. In particular, we used ''semantic knowledge-based QA'' as a category under which to subsume QA techniques and approaches, both corpus-based and knowledge base (KB)-based, that utilize semantic knowledge-informed techniques in the QA process, and we further classified those approaches into three subcategories: (1) semantics-based, (2) inference-based, and (3) logic-based. Based on the framework, we first conducted a survey of open-domain or non-biomedical-domain QA approaches that belong to each of the three subcategories. We then conducted an in-depth review of biomedical QA, by first noting the characteristics of, and resources available for, biomedical QA and then reviewing medical QA approaches and biological QA approaches, in turn. The research articles reviewed in this paper were found and selected through online searches. Results: Our review suggested the following tasks ahead for the future research development in this area: (1) Construction of domain-specific typology and taxonomy of questions (biological QA), (2) Development of more sophisticated techniques for natural language (NL) question analysis and classification, (3) Development of effective methods for answer generation from potentially conflicting evidences, (4) More extensive and integrated utilization of semantic knowledge throughout the QA process, and (5) Incorporation of logic and reasoning mechanisms for answer inference. Conclusion: Corresponding to the growth of biomedical information, there is a growing need for QA systems that can help users better utilize the ever-accumulating information. Continued research toward development of more sophisticated techniques for processing NL text, for utilizing semantic knowledge, and for incorporating logic and reasoning mechanisms, will lead to more useful QA systems.


title: TNM staging of foregut (neuro)endocrine tumors: a consensus proposal including a grading system

abstract: The need for standards in the management of patients with endocrine tumors of the digestive system prompted the European Neuroendocrine Tumor Society (ENETS) to organize a first Consensus Conference, which was held in Frascati (Rome) and was based on the recently published ENETS guidelines on the diagnosis and treatment of digestive neuroendocrine tumors (NET). Here, we report the tumor–node–metastasis proposal for foregut NETs of the stomach, duodenum, and pancreas that was designed, discussed, and consensually approved at this conference. In addition, we report the proposal for a working formulation for the grading of digestive NETs based on mitotic count and Ki-67 index. This proposal, which needs to be validated, is meant to help clinicians in the stratification, treatment, and follow-up of patients.


title: Neural Semantic Personalized Ranking for item cold-start recommendation

abstract: Recommender systems help users deal with information overload and enjoy a personalized experience on the Web. One of the main challenges in these systems is the item cold-start problem which is very common in practice since modern online platforms have thousands of new items published every day. Furthermore, in many real-world scenarios, the item recommendation tasks are based on users' implicit preference feedback such as whether a user has interacted with an item. To address the above challenges, we propose a probabilistic modeling approach called Neural Semantic Personalized Ranking (NSPR) to unify the strengths of deep neural network and pairwise learning. Specifically, NSPR tightly couples a latent factor model with a deep neural network to learn a robust feature representation from both implicit feedback and item content, consequently allowing our model to generalize to unseen items. We demonstrate NSPR's versatility to integrate various pairwise probability functions and propose two variants based on the Logistic and Probit functions. We conduct a comprehensive set of experiments on two real-world public datasets and demonstrate that NSPR significantly outperforms the state-of-the-art baselines.


title: An Integrated UAV Navigation System Based on Aerial Image Matching

abstract: The aim of this paper is to explore the possibility of using geo-referenced satellite or aerial images to augment an Unmanned Aerial Vehicle (UAV) navigation system in case of GPS failure. A vision based navigation system which combines inertial sensors, visual odometer and registration of a UAV on-board video to a given geo-referenced aerial image has been developed and tested on real flight-test data. The experimental results show that it is possible to extract useful position information from aerial imagery even when the UAV is flying at low altitude. It is shown that such information can be used in an automated way to compensate the drift of the UAV state estimation which occurs when only inertial sensors and visual odometer are used.


title: Real-time Incremental Speech-to-Speech Translation of Dialogs

abstract: In a conventional telephone conversation between two speakers of the same language, the interaction is real-time and the speakers process the information stream incrementally. In this work, we address the problem of incremental speech-to-speech translation (S2S) that enables cross-lingual communication between two remote participants over a telephone. We investigate the problem in a novel real-time Session Initiation Protocol (SIP) based S2S framework. The speech translation is performed incrementally based on generation of partial hypotheses from speech recognition. We describe the statistical models comprising the S2S system and the SIP architecture for enabling real-time two-way cross-lingual dialog. We present dialog experiments performed in this framework and study the tradeoff in accuracy versus latency in incremental speech translation. Experimental results demonstrate that high quality translations can be generated with the incremental approach with approximately half the latency associated with non-incremental approach.


title: Riemannian manifold learning for nonlinear dimensionality reduction

abstract: In recent years, nonlinear dimensionality reduction (NLDR) techniques have attracted much attention in visual perception and many other areas of science. We propose an efficient algorithm called Riemannian manifold learning (RML). A Riemannian manifold can be constructed in the form of a simplicial complex, and thus its intrinsic dimension can be reliably estimated. Then the NLDR problem is solved by constructing Riemannian normal coordinates (RNC). Experimental results demonstrate that our algorithm can learn the data's intrinsic geometric structure, yielding uniformly distributed and well organized low-dimensional embedding data.


title: Multi-attribute combined mutual information (MACMI): An image registration framework for leveraging multiple data channels

abstract: We present a novel methodological framework for leveraging multiple image sources, including different modalities, acquisition protocols or image features, in the registration of more than two images via information theoretic data fusion. The technique, referred to as multi-attribute combined mutual information (MACMI), adopts a multivariate application of mutual information (MI) to allow several coregistered images to be represented as a single high dimensional multi-attribute image. Our approach improves scenarios involving registration of multiple images as it, (1) utilizes all aligned images obtained in earlier registration steps, (2) improves alignment accuracy compared with pairwise approaches that only consider two images (and hence a fraction of the available data) at a time, and (3) avoids complex optimization problems often associated with fully-groupwise methods. For example, if two coregistered volumes such as T2-weighted and PD-weighted MRI are to be aligned with PET, it is intuitively better to use information from both MR protocols instead of choosing one for registration with PET. In the automated elastic registration of 20 corresponding multiprotocol (T1, T2, PD) synthetic MRI images of the brain with known misalignment of PD MRI, MACMI showed significant improvement in terms of deformation field error over conventional MI-based pairwise registration (p ≪ 0.05). For a total of 108 corresponding whole-mount histology (WMH), T2 MRI, and DCE (T1) MRI images obtained from 17 prostate specimens with cancer, elastic registration of WMH to bothMRI protocols simultaneously was performed viaMACMI. Improved alignment in terms of prostate overlap and cancer localization was observed using MACMI, compared to pairwise registration of WMH to the individual T2 and DCE MR protocols.


title: Framework of Problem-Based Research: A Guide for Novice Researchers on the Development of a Research-Worthy Problem

abstract: This paper introduces the importance of a well-articulated, research-worthy problem statement as the centerpiece for any viable research. The aim of this work is to help novice researchers understand the value of problem-based research by providing a practical guide on the development of a well articulated and viable statement of a research-worthy problem as the starting point for all research. Additionally, this paper explores the interaction of the problem with the other fundamental elements of scholarly research including the research topic, goals, research questions, methodology, results, and conclusions. Scaffolding for articulating a ‘research-worthy problem’ is provided in the form of a deconstruction of the expression into definitions of its component terms, followed by a discussion of what is not a research-worthy problem. A roadmap on locating problems that could support scholarly research is provided. The theoretical foundation is placed into practice by examining some problem statements and proposing a template for crafting an effective statement.


title: The Defense Advanced Research Projects Agency (DARPA) Tactical Mobile Robotics Program

abstract: Today’s regional military conflicts are increasingly likely to occur in populated urban areas, in, around, over, and under unknown buildings. This places land forces in dangerous and unpredictable situations, and in many instances the conflicts also threaten noncombatants. To address this need, the Defense Advanced Research Projects Agency (DARPA) Tactical Mobile Robotics program is researching and developing the capability to perform urban reconnaissance with teams of small, low-cost, semiautonomous mobile robots. Easily transportable by individuals, these robot teams will be capable of working together to perform a variety of reconnaissance functions. The program develops enabling technologies (machine perception, autonomous operation, and robotic locomotion) and integrates them into tactical systems for urban operations.


title: Resilience in the face of stress: emotion regulation as a protective factor

abstract: Introduction Everyone experiences stress at one time or another – from major events such as the death of a loved one, to more minor stressors such as financial difficulties. Not surprisingly, exposure to stress is generally associated with a wide range of negative outcomes, including decreased well-being, increased incidence of disease, post-traumatic stress disorder, generalized anxiety disorder, and major depressive disorder (Dohrenwend & Dohrenwend, 1974; Kendler et al., 1999; Monat et al., 2007). However, not all individuals who are exposed to even high levels of stress develop such negative outcomes. In fact, recent evidence suggests that a considerable number of individuals exhibit resilience, which is commonly defined as maintained or improved mental health in the face of stress, after short disruptions (if any) to normal functioning (Freitas & Downey, 1998; Rutter, 1999; Luthar et al., 2000; Bonanno, 2005). Note that this definition, which we adapt here, conceptualizes resilience as a potential outcome after exposure to stress rather than a psychological trait that leads to positive outcomes (c.f. Norris et al., 2008). It, therefore, appears that, in the face of comparable stressors, some individuals exhibit significantly impaired functioning while others show impressive resilience. Understanding the factors that govern the great individual variance in outcomes after stress is important for understanding mental health and for developing interventions and prevention programs that foster resilience. What factors, then, might predict resilience? One key to this question might lie in the fact that stressful events are inherently highly emotional (Sarason et al., 1978; Lazarus, 1999). For this reason, people’s ability to regulate emotions may be a critically important factor in determining resilience (Figure 2.1). The present chapter will review relevant literatures and suggest that there is indeed evidence to support this thesis. More specifically, we will propose that a specific type of emotion regulation, cognitive emotion regulation, holds particular promise for contributing to resilience.


title: Hope and Hopelessness: The Role of Hope in Buffering the Impact of Hopelessness on Suicidal Ideation

abstract: Objectives ::: The present study investigated whether hope and hopelessness are better conceptualized as a single construct of bipolar spectrum or two distinct constructs and whether hope can moderate the relationship between hopelessness and suicidal ideation. ::: ::: ::: Methods ::: Hope, hopelessness, and suicidal ideation were measured in a community sample of 2106 participants through a population-based household survey. ::: ::: ::: Results ::: Confirmatory factor analyses showed that a measurement model with separate, correlated second-order factors of hope and hopelessness provided a good fit to the data and was significantly better than that of the model collapsing hope and hopelessness into a single second-order factor. Negative binomial regression showed that hope and hopelessness interacted such that the effect of hopelessness on suicidal ideation was lower in individuals with higher hope than individuals with lower hope. ::: ::: ::: Conclusions ::: Hope and hopelessness are two distinct but correlated constructs. Hope can act as a resilience factor that buffers the impact of hopelessness on suicidal ideation. Inducing hope in people may be a promising avenue for suicide prevention.


title: Distributed file systems: concepts and examples

abstract: The purpose of a distributed file system (DFS) is to allow users of physically distributed computers to share data and storage resources by using a common file system. A typical configuration for a DFS is a collection of workstations and mainframes connected by a local area network (LAN). A DFS is implemented as part of the operating system of each of the connected computers. This paper establishes a viewpoint that emphasizes the dispersed structure and decentralization of both data and control in the design of such systems. It defines the concepts of transparency, fault tolerance, and scalability and discusses them in the context of DFSs. The paper claims that the principle of distributed operation is fundamental for a fault tolerant and scalable DFS design. It also presents alternatives for the semantics of sharing and methods for providing access to remote files. A survey of contemporary UNIX-based systems, namely, UNIX United, Locus, Sprite, Sun's Network File System, and ITC's Andrew, illustrates the concepts and demonstrates various implementations and design alternatives. Based on the assessment of these systems, the paper makes the point that a departure from the extending centralized file systems over a communication network is necessary to accomplish sound distributed file system design.


title: Addressing Function Approximation Error in Actor-Critic Methods

abstract: In value-based reinforcement learning methods such as deep Q-learning, function approximation errors are known to lead to overestimated value estimates and suboptimal policies. We show that this problem persists in an actor-critic setting and propose novel mechanisms to minimize its effects on both the actor and critic. Our algorithm takes the minimum value between a pair of critics to restrict overestimation and delays policy updates to reduce per-update error. We evaluate our method on the suite of OpenAI gym tasks, outperforming the state of the art in every environment tested.


title: Real time drowsiness detection using eye blink monitoring

abstract: According to analysis reports on road accidents of recent years, it's renowned that the main cause of road accidents resulting in deaths, severe injuries and monetary losses, is due to a drowsy or a sleepy driver. Drowsy state may be caused by lack of sleep, medication, drugs or driving continuously for long time period. An increase rate of roadside accidents caused due to drowsiness during driving indicates a need of a system that detects such state of a driver and alerts him prior to the occurrence of any accident. During the recent years, many researchers have shown interest in drowsiness detection. Their approaches basically monitor either physiological or behavioral characteristics related to the driver or the measures related to the vehicle being used. A literature survey summarizing some of the recent techniques proposed in this area is provided. To deal with this problem we propose an eye blink monitoring algorithm that uses eye feature points to determine the open or closed state of the eye and activate an alarm if the driver is drowsy. Detailed experimental findings are also presented to highlight the strengths and weaknesses of our technique. An accuracy of 94% has been recorded for the proposed methodology.


A Processing-in-Memory Taxonomy and a Case for Studying Fixed-function PIM


title: Precocious Puberty (Complete, Partial)

abstract: Two decades ago, the diagnosis of premature sexual development was considered to be simple; the conditions that were recognised were either central precocious puberty, isolated premature thelarche or an adrenal aetiology. The use of pelvic ultrasound and gonadotrophin-releasing hormone (GnRH) analogue treatment was to completely alter our understanding of these disorders. Pelvic ultrasound led to differentiating ovarian appearances in conditions such as McCune-Albright syndrome and the variation in ovarian maturation in premature thelarche and central precocious puberty. Failure to respond to GnRH analogue therapy in children with precocious puberty led to the concept of gonadotrophin-independent precocious puberty (GIPP). During the 1980s, several variants of premature sexual maturation were described and this was important, both for the natural history of these conditions and the requirement for therapy. Adrenal lesions causing sexual maturation are included in this chapter for completeness. Their diagnosis and management is considerably simpler than premature sexual maturation of a gonadal aetiology.


title: Walking on Partial Footholds Including Line Contacts with the Humanoid Robot Atlas

abstract: We present a method for humanoid robot walking on partial footholds such as small stepping stones and rocks with sharp surfaces. Our algorithm does not rely on prior knowledge of the foothold, but information about an expected foothold can be used to improve the stepping performance. After a step is taken, the robot explores the new contact surface by attempting to shift the center of pressure around the foot. The available foothold is inferred by the way in which the foot rotates about contact edges and/or by the achieved center of pressure locations on the foot during exploration. This estimated contact area is then used by a whole body momentum-based control algorithm. To walk and balance on partial footholds, we combine fast, dynamic stepping with the use of upper body angular momentum to regain balance. We applied this method to the Atlas humanoid designed by Boston Dynamics to walk over small contact surfaces, such as line and point contacts. We present experimental results and discuss performance limitations.


title: Semi-automated map creation for fast deployment of AGV fleets in modern logistics

abstract: Abstract Today, Automated Guided Vehicles (AGVs) still have a low market share in logistics, compared to manual forklifts. We identified one of the main bottlenecks in the rather long deployment time which involves precise 2D mapping of the plant, 3D geo-referencing of pick-up/ drop positions and the manual design of the roadmap. The long deployment time has various reasons: in state-of-the-art plant installations, designated infrastructure is still necessary for the localization; the mapping process requires highly skilled personnel; in many cases unavailable or inappropriate position information of drop points for goods must be corrected on site. Finally, the design of the roadmap, performed by expert technicians is manually optimised in a tedious process to achieve maximum flow of goods for the plant operator. In total the setup of a plant to be ready for AGV operation is taking several months, binding highly skilled technicians and involves very time-consuming and costly on-site procedures. Therefore, we present an approach to AGV deployment which aims to drastically reduce the time, cost and involved personnel. First, we propose the employment of a novel, industrial-ready SICK 3D laser scanning technology in order to build an accurate and consistent virtual representation of the whole warehouse environment. By utilizing suitable segmentation and processing a semantic map is generated that contains 3D geo-referenced positions as well as a 2D localization map eliminating the need for dedicated solution to 2D mapping. Second, the semantic map provides a free space map which is used as a basis for the automatic roadmap creation in order to achieve optimal flow. So, this paper proposes an innovative methodology for obtaining, in a semi-automated manner, the map of an industrial environment where a system of multiple AGVs will be installed with less time and cost.


title: Parallel simulation of software defined networks

abstract: Existing network architectures fall short when handling networking trends, e.g., mobility, server virtualization, and cloud computing, as well as market requirements with rapid changes. Software-defined networking (SDN) is designed to transform network architectures by decoupling the control plane from the data plane. Intelligence is shifted to the logically centralized controller with direct programmability, and the underlying infrastructures are abstracted from applications. The wide adoption of SDN in network industries has motivated development of large-scale, high-fidelity testbeds for evaluation of systems that incorporate SDN. We leverage our prior work on a hybrid network testbed with a parallel network simulator and a virtual-machine-based emulation system. In this paper, we extend the testbed to support OpenFlow-based SDN simulation and emulation; show how to exploit typical SDN controller behavior to deal with potential performance issues caused by the centralized controller in parallel discrete-event simulation; and investigate methods for improving the model scalability, including an asynchronous synchronization algorithm for passive controllers and a two-level architecture for active controllers. The techniques not only improve the simulation performance, but also are valuable for designing scalable SDN controllers.


title: A linked list run-length-based single-pass connected component analysis for real-time embedded hardware

abstract: Conventional connected component analysis (CCA) algorithms render a slow performance in real-time embedded applications due to multiple passes to resolve label equivalences. As this fundamental task becomes crucial for stream processing, single-pass algorithms were introduced to enable a stream-oriented hardware design. However, most single-pass CCA algorithms in the literature inhibit maximum streaming throughput as additional time such as horizontal blanking period is required to resolve label equivalence. This paper proposes a novel single-pass CCA algorithm, using a combination of linked list and run-length-based techniques to label and resolve equivalences as well as extracting the object features in a single raster scan. The proposed algorithm involves a label recycling scheme which attains low memory requirement design. Experimental results show the implementation of the proposed CCA achieves one cycle per pixel throughput and surpasses the most memory-efficient state-of-the-art work up to 25 % reduction in memory usage for \(7680\times 4320\) pixels image.


title: Shape reconstruction from gradient data

abstract: We present a generalized method for reconstructing the shape of an object from measured gradient data. A certain class of optical sensors does not measure the shape of an object but rather its local slope. These sensors display several advantages, including high information efficiency, sensitivity, and robustness. For many applications, however, it is necessary to acquire the shape, which must be calculated from the slopes by numerical integration. Existing integration techniques show drawbacks that render them unusable in many cases. Our method is based on an approximation employing radial basis functions. It can be applied to irregularly sampled, noisy, and incomplete data, and it reconstructs surfaces both locally and globally with high accuracy.


title: Cosolver2B: An efficient local search heuristic for the Travelling Thief Problem

abstract: Real-world problems are very difficult to optimize. However, many researchers have been solving benchmark problems that have been extensively investigated for the last decades even if they have very few direct applications. The Traveling Thief Problem (TTP) is a NP-hard optimization problem that aims to provide a more realistic model. TTP targets particularly routing problem under packing/loading constraints which can be found in supply chain management and transportation. In this paper, TTP is presented and formulated mathematically. A combined local search algorithm is proposed and compared with Random Local Search (RLS) and Evolutionary Algorithm (EA). The obtained results are quite promising since new better solutions were found.


title: FAULT DIAGNOSIS OF AN INDUSTRIAL GAS TURBINE USING NEURO-FUZZY METHODS

abstract: Abstract The paper focuses on the application of neuro-fuzzy techniques in fault detection and isolation. The objective of this paper is to detect and isolate faults to an industrial gas turbine, with emphasis on faults occurred in the actuator part of the gas turbine. A neuro-fuzzy based learning and adaptation of TSK fuzzy models is used for residual generation, while for residual evaluation a neuro-fuzzy classifier for Mamdani models is used. The paper is concerned on how to obtain an interpretable fault classifier as well as interpretable models for residual generation.


title: Unsupervised Human Action Detection by Action Matching

abstract: We propose a new task of unsupervised action detection by action matching. Given two long videos, the objective is to temporally detect all pairs of matching video segments. A pair of video segments are matched if they share the same human action. The task is category independent—it does not matter what action is being performed—and no supervision is used to discover such video segments. Unsupervised action detection by action matching allows us to align videos in a meaningful manner. As such, it can be used to discover new action categories or as an action proposal technique within, say, an action detection pipeline. Moreover, it is a useful pre-processing step for generating video highlights, e.g., from sports videos.,,,,,, We present an effective and efficient method for unsupervised action detection. We use an unsupervised temporal encoding method and exploit the temporal consistency in human actions to obtain candidate action segments. We evaluate our method on this challenging task using three activity recognition benchmarks, namely, the MPII Cooking activities dataset, the THUMOS15 action detection benchmark and a new dataset called the IKEA dataset. On the MPII Cooking dataset we detect action segments with a precision of 21.6% and recall of 11.7% over 946 long video pairs and over 5000 ground truth action segments. Similarly, on THUMOS dataset we obtain 18.4% precision and 25.1% recall over 5094 ground truth action segment pairs.


title: Multi-chip gang bonding technology using the thermo-compression bonder for Si substrate

abstract: Gang bonding process using TCB (thermo-compression bonder) has been developed in recent years as one of the methods to mount advanced packages fast [1, 2]. In the gang bonding process, we can improve the throughput considerably by post-bonding multi pre-bonded chips at a time. However, when a conventional elastic film was used to reduce the variance of bonding load to each chip, we found that the position shift of the chips occurred by deformation of the film especially in narrow pitch chip bonding. In addition, it is difficult to supply enough heat for the solder joint, because the elastic film generally has low thermal conductivity and low heat resistance. In particular, heat capacity necessary for solder fusion is insufficient in the case of chip on wafer (CoW) using Si substrate. In this presentation, we will report about novel gang bonding head (GBH) that is able to reduce the position shift and has both of high thermal conductivity and low elasticity.


title: X-band FMCW radar system with variable chirp duration

abstract: For application in a short range ground based surveillance radar a combination between frequency modulated continuous wave (FMCW) transmit signals and a receive antenna array system is considered in this paper. The target echo signal is directly down converted by the instantaneous transmit frequency. The target range R will be estimated based on the measured frequency shift f B between transmit and receive signal. Due to an extremely short chirp duration T chirp , the target radial velocity υ τ has only a very small influence to the measured frequency shift f B . Therefore the radial velocity υ τ will not be measured inside a single FMCW chirp but in a sequence of chirp signals and inside each individual range gate. Finally, the target azimuth angle is calculated utilizing the receive antenna array and applying a digital beamforming scheme. Furthermore, in order to unambiguously measure even high radial velocities, a variable chirp duration is proposed on a dwell to dwell basis.


title: Alliances in Industrial Purchasing: The Determinants of Joint Action in Buyer-Supplier Relationships

abstract: Recent trends in industrial markets indicate that buyers and sellers are increasingly supplanting conventional “arm's length” arrangements with “alliances” involving closer ties. The authors develo...


Detection of Lung Cancer Using Backpropagation Neural Networks and Genetic Algorithm


title: Precise and robust enable control circuitry for LDO voltage regulators

abstract: This paper presents novel circuit solutions for two issues related to the Enable control of low-dropout (LDO) voltage regulators: setting precise voltage thresholds for the ON/OFF states of the LDO and ensuring that in the OFF state the LDO output is not affected by fast variations of the supply voltage. First, an Enable circuit with hysteresis and temperature compensated thresholds is described: the accuracy of its threshold voltages - including their low temperature coefficients - are predicted by analytical analysis and validated by measurements performed on a silicon implementation. Second, a simple yet effective comparator is proposed, able to significantly reduce the effect the supply voltage variations have on the output voltage when the regulator is in OFF state. Simulation results show that, when the supply voltage varies from 0 to 28V in 28µs, the overshoot of the output voltage is reduced from 5.5V to under 200mV, that is by a factor of 35.