title: Robot Path Planning: An Object-Oriented Approach

abstract: Path planning has important applications in many areas, for example industrial robotics, autonomous systems, virtual prototyping, and computer-aided drug design. This thesis presents a new framework for developing and evaluating path planning algorithms. The framework is named CoPP (Components for Path Planning). It consists of loosely coupled and reusable components that are useful for building path planning applications. The framework is especially designed to make it easy to do fair comparisons between different path planning algorithms. CoPP is also designed to allow almost any user-defined moving system. The default type of moving system is a robot class, which is capable of describing tree-like kinematic chains. Additional features of this robot class are: joint couplings, numerical or closed-form inverse kinematics, and hierarchical robot representations. The last feature is useful when planning for complex systems like a mobile platform equipped with an arm and a hand. During the last six years, Rapidly-exploring Random Trees (RRTs) have become a popular framework for developing randomized path planning algorithms. This thesis presents a method for augmenting bidirectional RRT-planners with local trees. For problems where the solution trajectory has to pass through several narrow passages, local trees help to reduce the required planning time. To reduce the work needed for programming of industrial robots, it is desirable to allow task specifications at a very high level, leaving it up to the robot system to figure out what to do. Here we present a fast and flexible pick-and-place planner. Given an object that has to be moved to another position, the planner chooses a suitable grasp of the object and finds motions that bring the object to the desired position. The planner can also handle constraints on, e.g., the orientation of the manipulated object. For planning of pick-and-place tasks it is necessary to choose a grasp suitable to the task. Unless the grasp is given, some sort of grasp planning has to be performed. This thesis presents a fast grasp planner for a three- fingered robot hand. The grasp planner could be used in an industrial setting, where a robot is to pick up irregularly shaped objects from a conveyor belt. In conjunction with grasp planning, a new method for evaluating grasp stability is presented.


title: Perspectives on Cognitive Informatics and Cognitive Computing

abstract: Cognitive informatics is a transdisciplinary enquiry of computer science, information sciences, cognitive science, and intelligence science that investigates the internal information processing mechanisms and processes of the brain and natural intelligence, as well as their engineering applications in cognitive computing. Cognitive computing is an emerging paradigm of intelligent computing methodologies and systems based on cognitive informatics that implements computational intelligence by autonomous inferences and perceptions mimicking the mechanisms of the brain. This article presents a set of collective perspectives on cognitive informatics and cognitive computing, as well as their applications in abstract intelligence, computational intelligence, computational linguistics, knowledge representation, symbiotic computing, granular computing, semantic computing, machine learning, and social computing.


title: Comparative Evaluation of Spin-Transfer-Torque and Magnetoelectric Random Access Memory

abstract: Spin-transfer torque random access memory (STT-RAM), as a promising nonvolatile memory technology, faces challenges of high write energy and low density. The recently developed magnetoelectric random access memory (MeRAM) enables the possibility of overcoming these challenges by the use of voltage-controlled magnetic anisotropy (VCMA) effect and achieves high density, fast speed, and low energy simultaneously. As both STT-RAM and MeRAM suffer from the reliability problem of write errors, we implement a fast Landau–Lifshitz–Gilbert equation-based simulator to capture their write error rate (WER) under process and temperature variation. We utilize a multi-write peripheral circuit to minimize WER and design reliable STT-RAM and MeRAM. With the same acceptable WER, MeRAM shows advantages of 83% faster write speed, 67.4% less write energy, 138% faster read speed, and 28.2% less read energy compared with STT-RAM. Benefiting from the VCMA effect, MeRAM also achieves twice the density of STT-RAM with a 32 nm technology node, and this density difference is expected to increase with technology scaling down.


A comparison of Haar-like, LBP and HOG approaches to concrete and asphalt runway detection in high resolution imagery


title: Miniaturized 3-bit Phase Shifter for 60 GHz Phased-Array in 65 nm CMOS Technology

abstract: The letter presents a compact 3-bit 90 ° phase shifter for phased-array applications at the 60 GHz ISM band (IEEE 802.11ad standard). The designed phase shifter is based on reflective-type topology using the proposed reflective loads with binary-weighted digitally-controlled varactor arrays and the transformer-type directional coupler. The measured eight output states of the implemented phase shifter in 65 nm CMOS technology, exhibit phase-resolution of 11.25 ° with an RMS phase error of 5.2 °. The insertion loss is 5.69 ± 1.22 dB at 60 GHz and the return loss is better than 12 dB over 54-66 GHz. The chip demonstrates a compact size of only 0.034 mm2.


Compositional Neural Semantic Parsing for Compound Virtual


title: More than just a game: ethical issues in gamification

abstract: Gamification is the use of elements and techniques from video game design in non-game contexts. Amid the rapid growth of this practice, normative questions have been under-explored. The primary goal of this article is to develop a normatively sophisticated and descriptively rich account for appropriately addressing major ethical considerations associated with gamification. The framework suggests that practitioners and designers should be precautious about, primarily, but not limited to, whether or not their use of gamification practices: (1) takes unfair advantage of workers (e.g., exploitation); (2) infringes any involved workers' or customers' autonomy (e.g., manipulation); (3) intentionally or unintentionally harms workers and other involved parties; or (4) has a negative effect on the moral character of involved parties.


title: Behavior Informatics: A New Perspective

abstract: This installment of Trends & Controversies provides an array of perspectives on the latest research in behavior informatics. Longbing Cao introduces the work in "Behavior Informatics: A New Perspective." Then, in "Behavior Computing," Longbing Cao and Thorsten Joachims provide a basic overview of the topic. Next is "Coupled Behavior Representation, Modeling, Analysis, and Reasoning" by Can Wang, Longbing Cao, Eric Gaussier, Jinjiu Li, Yuming Ou, and Dan Luo. The fourth article is "Behavior Analysis in Social Media," by Reza Zafarani and Huan Liu. The fifth article is "Group Recommendation and Behavior," by Guandong Xu and Zhiang Wu. Gabriella Pasi wrote the sixth article, "Web Search and Behavior." The seventh article, "Behaviors of IPTV Users," is by Ya Zhang, Xiaokang Yang, and Hongyuan Zha. Finally, "Should Behavioral Models of Terror Groups Be Disclosed?" is by Edoardo Serra and V.S. Subrahmanian.


title: Evaluation by Citation: Trends in Publication Behavior, Evaluation Criteria, and the Strive for High Impact Publications

abstract: Criteria for the evaluation of most scholars' work have recently received wider attention due to high-profile cases of scientific misconduct which are perceived to be linked to these criteria. However, in the competition for career advancement and funding opportunities almost all scholars are subjected to the same criteria. Therefore these evaluation criteria act as 'switchmen', determining the tracks along which scholarly work is pushed by the dynamic interplay of interests of both scholars and their institutions. Currently one of the most important criteria is the impact of publications. In this research, the extent to which publish or perish, a long standing evaluation criterion, led to scientific misconduct is examined briefly. After this the strive for high impact publications will be examined, firstly by identifying the period in which this became an important evaluation criterion, secondly by looking at variables contributing to the impact of scholarly papers by means of a non-structured literature study, and lastly by combining these data into a quantitative analysis.


title: Gender Classification of Blog Authors: With Feature Engineering and Deep Learning using LSTM Networks

abstract: In this paper, we present two approaches to automatically classify the gender of blog authors: the first is a manual feature extraction based system incorporating two novel feature classes: variable length character sequence patterns and thirteen new word classes, along with an added class of surface features while the second is a first-ever application of a memory variant of Recurrent Neural Networks, i.e. Bidirectional Long Short Term Memory Networks (BLSTMs) on this task. We use two blog data sets to report our results: the first is a well-explored one used by the previous state-of-the-art model while the other is a 20 times larger corpus. For the first system, we use a voting of machine learning classifiers to obtain an improved accuracy with respect to the previous feature mining systems on the former data set. Using our second approach, we show that the accuracy obtained using such deep LSTMs is comparable to the current state-of-the-art deep learning system for the task of gender classification. Finally, we carry out a comparative study of performance of both the systems on the two data sets.


title: An information-theoretic approach to unsupervised feature selection for high-dimensional data

abstract: In this paper, we propose an information-theoretic approach to design the functional representations to extract the hidden common structure shared by a set of random variables. The main idea is to measure the common information between the random variables by Watanabe's total correlation, and then find the hidden attributes of these random variables such that the common information is reduced the most given these attributes. We show that these attributes can be characterized by an exponential family specified by the eigen-decomposition of some pairwise joint distribution matrix. Then, we adopt the log-likelihood functions for estimating these attributes as the desired functional representations of the random variables, and show that such representations are informative to describe the common structure. Moreover, we design both the multivariate alternating conditional expectation (MACE) algorithm to compute the proposed functional representations for discrete data, and a novel neural network training approach for continuous or high-dimensional data. Furthermore, we show that our approach has deep connections to existing techniques, such as Hirschfeld-Gebelein-Renyi (HGR) maximal correlation, linear principal component analysis (PCA), and consistent functional map, which establishes insightful connections between information theory and machine learning. Finally, the performances of our algorithms are validated by numerical simulations.


title: A New Fast and Robust Method Based on Head Detection for People-Flow Counting System

abstract: This paper presents an implementation method for the people counting system which detects and tracks moving people using a fixed single camera. The main contribution of this paper is the novel head detection method based on body's geometry. A novel body descriptor is proposed for finding people's head which is defined as Body Feature Rectangle (BFR). First, a vertical projection method is used to get the line which divides touching persons into individuals. Second, a special inscribed rectangle is found to locate the neck position which describes the torso area. Third, locations of people's heads can be got according to its neck-positions. Last, a robust counting method named MEA is proposed to get the real counts of walking people flows. The proposed method can divide the multiple-people image into individuals whatever people merge with each other or not. Moreover, the passing people can be counted accurately under the influence of wearing hats. Experimental results show that our proposed method can nearly reach to an accuracy of 100% if the number of a people-merging pattern is less than six. Keywords-People Counting; Head Detection; BFR; People-flow Tracking


title: Gender Differences in Personality Traits Across Cultures: Robust and Surprising Findings

abstract: Secondary analyses of Revised NEO Personality Inventory data from 26 cultures (N = 23,031) suggest that gender differences are small relative to individual variation within genders; differences are replicated across cultures for both college-age and adult samples, and differences are broadly consistent with gender stereotypes: Women reported themselves to be higher in Neuroticism, Agreeableness, Warmth, and Openness to Feelings, whereas men were higher in Assertiveness and Openness to Ideas. Contrary to predictions from evolutionary theory, the magnitude of gender differences varied across cultures. Contrary to predictions from the social role model, gender differences were most pronounced in European and American cultures in which traditional sex roles are minimized. Possible explanations for this surprising finding are discussed, including the attribution of masculine and feminine behaviors to roles rather than traits in traditional cultures. Gender differences in personality traits have been documented in many empirical studies.1 Maccoby and Jacklin (1974) conducted the first major review of research on sex-related differences in cognition, temperament, and social behavior in children and adults. They concluded that men are more assertive and less anxious than women; no differences were found for two other traits analyzed, locus of control and self-esteem. Feingold (1994) used meta-analysis to confirm the gender differences in adult personality traits reported by Maccoby and Jacklin (1974) and explored other gender differences in normative data from the most widely used personality inventories. He concluded that women scored lower than men on assertiveness and higher on gregariousness (extroversion), anxiety, trust, and tendermindedness (nurturance). Feingold (1994) organized his review in terms of the five broad factors and 30 specific facets of the Revised NEO Personality Inventory (NEO-PI-R; Costa & McCrae. 1992). As a comprehensive guide to personality traits, that model can provide the basis for a systematic examination of gender differences in personality. Unfortunately, from the available data, Feingold was only able to conduct reviews of nine traits. In this article, we provide new data that allow an examination of gender differences in all 30 traits assessed by the NEO-PI-R, and thus offer a more complete account of gender differences in personality.


title: Tweeting about sexism: The well-being benefits of a social media collective action

abstract: Although collective action has psychological benefits in non-gendered contexts (Drury et al., 2005, Br. J. Soc. Psychol., 44, 309), the benefits for women taking action against gender discrimination are unclear. This study examined how a popular, yet unexplored potential form of collective action, namely tweeting about sexism, affects women's well-being. Women read about sexism and were randomly assigned to tweet or to one of three control groups. Content analyses showed tweets exhibited collective intent and action. Analyses of linguistic markers suggested public tweeters used more cognitive complexity in their language than private tweeters. Profile analyses showed that compared to controls, only public tweeters showed decreasing negative affect and increasing psychological well-being, suggesting tweeting about sexism may serve as a collective action that can enhance women's well-being.


title: Time-series clustering for data analysis in Smart Grid

abstract: The increasingly pervasive deployment of networked sensors in the Smart Grid for monitoring energy consumption has resulted in an unprecedentedly large amount of data generation. Efficient methods are required to understand this high volume and high dimensional data on an embedded platform, which has many challenges due to memory, processing and power constraints. One of the popular methods to analyze time-series data is clustering. In this paper, we discuss D-Stream II, a common time-series clustering algorithm, and demonstrate that it fails to obtain clusters in sample Smart Grid applications. Then, we propose an enhanced version of this algorithm, which handles the scenarios where the original algorithm fails. We show the effectiveness of our algorithm using real residential power consumption data from Pecan Street database. Our enhanced algorithm efficiently handles the high volume energy consumption data and captures the everyday energy consumption patterns of each residential home, which allow the consumer to compare energy consumption with their neighbors or detect any abnormality such as a defective appliance. The consumers can also be clustered into different groups, which can be effectively used to enhance the demand response policies. Our algorithm can perform clustering on approximately half a million energy consumption data points using only 5KB max memory, 360J max overhead in energy consumption and can complete in 8 mins on a resource limited embedded platform (Raspberry Pi 2).


title: NEVESIM: event-driven neural simulation framework with a Python interface

abstract: NEVESIM is a software package for event-driven simulation of networks of spiking neurons with a fast simulation core in C++, and a scripting user interface in the Python programming language. It supports simulation of heterogeneous networks with different types of neurons and synapses, and can be easily extended by the user with new neuron and synapse types. To enable heterogeneous networks and extensibility, NEVESIM is designed to decouple the simulation logic of communicating events (spikes) between the neurons at a network level from the implementation of the internal dynamics of individual neurons. In this paper we will present the simulation framework of NEVESIM, its concepts and features, as well as some aspects of the object-oriented design approaches and simulation strategies that were utilized to efficiently implement the concepts and functionalities of the framework. We will also give an overview of the Python user interface, its basic commands and constructs, and also discuss the benefits of integrating NEVESIM with Python. One of the valuable capabilities of the simulator is to simulate exactly and efficiently networks of stochastic spiking neurons from the recently developed theoretical framework of neural sampling. This functionality was implemented as an extension on top of the basic NEVESIM framework. Altogether, the intended purpose of the NEVESIM framework is to provide a basis for further extensions that support simulation of various neural network models incorporating different neuron and synapse types that can potentially also use different simulation strategies.


title: New core loss measurement method for high frequency magnetic materials

abstract: Magnetic core loss is an important concern for power converters. As the switching frequency increases and converter size reduced, the core loss will have significant impact to the converter efficiency and temperature. Accurate evaluation is important for magnetic design and converter loss estimation. The classic two-winding method is limited to low frequencies (usually below 1 MHz) because it is sensitive to phase discrepancy. In this paper, a new method is proposed for high-frequency core loss measurement that utilizes capacitive cancellation, which is suitable for HF and VHF core loss measurement. The new method greatly reduces the sensitivity to phase discrepancy, which is the dominating error source in the conventional two-winding method. An experimental demonstration is performed at 10 MHz, and the possible errors are analyzed in detail. With the proposed method, the high-frequency core loss can be accurately measured.


title: europeana . eu The Europeana Linked Open Data Pilot

abstract: data.europeana.eu is an ongoing effort of making Europeana metadata available as Linked Open Data on the Web. It allows others to access metadata collected from Europeana data providers via standard Web technologies. The data are represented in the Europeana Data Model (EDM) and the described resources are addressable and dereferencable by their URIs. Links between Europeana resources and other resources in the Linked Data Web will enable the discovery of semantically related resources. We developed an approach that allows Europeana data providers to opt for their data to become Linked Data and converts their metadata to EDM, benefiting from Europeana efforts to link them to semantically related resources on the Web. With that approach, we produced a first Linked Data version of Europeana and published the resulting datasets on the Web. We also gained experiences with respect to EDM, HTTP URI design, and RDF store performance and report them in this paper.


title: Biometric Gait Authentication Using Accelerometer Sensor

abstract: This paper presents a biometric user authentication based on a person’s gait. Unlike most previous gait recognition approaches, which are based on machine vision techniques, in our approach gait patterns are extracted from a physical device attached to the lower leg. From the output of the device accelerations in three directions: vertical, forward-backward, and sideways motion of the lower leg are obtained. A combination of these accelerations is used for authentication. Applying two different methods, histogram similarity and cycle length, equal error rates (EER) of 5% and 9% were achieved, respectively.


title: Health Behaviours during Pregnancy in Women with Very Severe Obesity

abstract: The health behaviours of pregnant women with very severe obesity are not known, though these women are at high risk of pregnancy complications. We carried out a prospective case-control study including 148 very severely obese (BMI >40 kg/m2) and 93 lean (BMI <25 kg/m2) pregnant women. Diet, physical activity, smoking, alcohol and folic acid consumption were assessed by questionnaire in early and late (16 and 28 weeks gestation) pregnancy. Circulating levels of iron, vitamin B12 and folate and other essential trace elements and minerals were measured in a subset at each time point. The findings biochemically confirmed that very severely obese women consumed diets that were energy-rich but poor in essential micronutrients. A third of all women met physical activity recommendations for pregnancy. A third of very severely obese women and two thirds of lean women took folic acid supplements prior to pregnancy. Very severely obese women were more likely to smoke but less likely to drink alcohol than lean women (all p < 0.05). Women with very severe obesity have low self-reported intakes and circulating levels of essential micronutrients in pregnancy and few follow current recommendations for pregnancy nutrition and lifestyle. These high-risk women represent a group to target for education about health behaviours prior to and during pregnancy.


HIGH FIDELITY MODELING AND EXPERIMENTAL EVALUATION OF BINAURAL BONE CONDUCTION COMMUNICATION DEVICES


Attention-based LSTM Network for Cross-Lingual Sentiment Classification


title: The cost-efficient, common-ground, non-isolated three-port converter deduced from the single-inductor dual-output (SIDO) topology

abstract: In recent year, three-port converter (TPC) has been widely presented to connect the renewable source, energy-stored component and the load for renewable application. In this paper, a new non-isolated TPC deduced from a single-inductor-dual-output (SIDO) converter is proposed. The proposed TPC is obtained by introducing a coupling winding and a diode in the original SIDO converter. Such a derivation requires the minimal component amount and its three ports share the common ground. The derivation, operational principle and circuit analysis of the proposed TPC are presented in this paper, a prototype is also built to verify the analysis. The experimental results show the validity. Due to the compact-size, low-cost, common-ground and full-control features, the proposed TPC can be widely used in the low-power on-board applications.


title: The vertebrate ancestral repertoire of visual opsins, transducin alpha subunits and oxytocin/vasopressin receptors was established by duplication of their shared genomic region in the two rounds of early vertebrate genome duplications

abstract: BackgroundVertebrate color vision is dependent on four major color opsin subtypes: RH2 (green opsin), SWS1 (ultraviolet opsin), SWS2 (blue opsin), and LWS (red opsin). Together with the dim-light receptor rhodopsin (RH1), these form the family of vertebrate visual opsins. Vertebrate genomes contain many multi-membered gene families that can largely be explained by the two rounds of whole genome duplication (WGD) in the vertebrate ancestor (2R) followed by a third round in the teleost ancestor (3R). Related chromosome regions resulting from WGD or block duplications are said to form a paralogon. We describe here a paralogon containing the genes for visual opsins, the G-protein alpha subunit families for transducin (GNAT) and adenylyl cyclase inhibition (GNAI), the oxytocin and vasopressin receptors (OT/VP-R), and the L-type voltage-gated calcium channels (CACNA1-L).ResultsSequence-based phylogenies and analyses of conserved synteny show that the above-mentioned gene families, and many neighboring gene families, expanded in the early vertebrate WGDs. This allows us to deduce the following evolutionary scenario: The vertebrate ancestor had a chromosome containing the genes for two visual opsins, one GNAT, one GNAI, two OT/VP-Rs and one CACNA1-L gene. This chromosome was quadrupled in 2R. Subsequent gene losses resulted in a set of five visual opsin genes, three GNAT and GNAI genes, six OT/VP-R genes and four CACNA1-L genes. These regions were duplicated again in 3R resulting in additional teleost genes for some of the families. Major chromosomal rearrangements have taken place in the teleost genomes. By comparison with the corresponding chromosomal regions in the spotted gar, which diverged prior to 3R, we could time these rearrangements to post-3R.ConclusionsWe present an extensive analysis of the paralogon housing the visual opsin, GNAT and GNAI, OT/VP-R, and CACNA1-L gene families. The combined data imply that the early vertebrate WGD events contributed to the evolution of vision and the other neuronal and neuroendocrine functions exerted by the proteins encoded by these gene families. In pouched lamprey all five visual opsin genes have previously been identified, suggesting that lampreys diverged from the jawed vertebrates after 2R.


title: The Informatics Transform: Re-Engineering Libraries for the Data Decade

abstract: In this paper, Liz Lyon explores how libraries can re-shape to better reflect the requirements and challenges of today’s data-centric research landscape. The Informatics Transform presents five assertions as potential pathways to change, which will help libraries to re-position, re-profile, and re-structure to better address research data management challenges. The paper deconstructs the institutional research lifecycle and describes a portfolio of ten data support services which libraries can deliver to support the research lifecycle phases. Institutional roles and responsibilities for research data management are also unpacked, building on the framework from the earlier Dealing with Data Report. Finally, the paper examines critical capacity and capability challenges and proposes some innovative steps to addressing the significant skills gaps.


title: 32-bit cyclic redundancy codes for Internet applications

abstract: Standardized 32-bit cyclic redundancy codes provide fewer bits of guaranteed error detection than they could, achieving a Hamming Distance (HD) of only 4 for maximum-length Ethernet messages, whereas HD=6 is possible. Although research has revealed improved codes, exploring the entire design space has previously been computationally intractable, even for special-purpose hardware. Moreover, no CRC polynomial has yet been found that satisfies an emerging need to attain both HD=6 for 12K bit messages and HD=4 for message lengths beyond 64 Kbits. This paper presents results from the first exhaustive search of the 32-bit CRC design space. Results from previous research are validated and extended to include identifying all polynomials achieving a better HD than the IEEE 802.3 CRC-32 polynomial. A new class of polynomials is identified that provides HD=6 up to nearly 16K bit and HD=4 up to 114K bit message lengths, providing the best achievable design point that maximizes error detection for both legacy and new applications, including potentially iSCSI and application-implemented error checks.


title: Modeling temporal structure of decomposable motion segments for activity classification

abstract: Much recent research in human activity recognition has focused on the problem of recognizing simple repetitive (walking, running, waving) and punctual actions (sitting up, opening a door, hugging). However, many interesting human activities are characterized by a complex temporal composition of simple actions. Automatic recognition of such complex actions can benefit from a good understanding of the temporal structures. We present in this paper a framework for modeling motion by exploiting the temporal structure of the human activities. In our framework, we represent activities as temporal compositions of motion segments. We train a discriminative model that encodes a temporal decomposition of video sequences, and appearance models for each motion segment. In recognition, a query video is matched to the model according to the learned appearances and motion segment decomposition. Classification is made based on the quality of matching between the motion segment classifiers and the temporal segments in the query sequence. To validate our approach, we introduce a new dataset of complex Olympic Sports activities. We show that our algorithm performs better than other state of the art methods.


title: Research on Cross-border E-commerce platform selection in China small & medium-sized enterprises

abstract: This paper establishes the evaluation criterion system of Cross-border E-commerce platform selection based on DeLone & McLean's e-commerce success model and MA-OWA operator, using triangular fuzzy analytic hierarchy process to determine the weight of each level of criteria. Further, the fuzzy comprehensive evaluation model is established. Finally the case study proves scientificness and feasibility of the evaluation criterion systems and the validity of the evaluation method.


title: The Optimal inter-implant interval in pediatric sequential bilateral implantation

abstract: An increasing number of children receive bilateral cochlear implants (CIs) sequentially. Outcomes of bilateral implantation show high variability. This retrospective analysis investigates the optimal inter-implant interval. For this purpose, speech comprehension results of 250 children who underwent sequential bilateral cochlear implantation were evaluated. All individuals underwent periodic speech perception testing in quiet and noise. The most recent unilateral data for each side were statistically analyzed. Speech test outcomes were evaluated with reference to age at first implantation and interval between implantations. A statistically significant difference for speech test performance was obtained between the first-implanted ear and the second-implanted ear for all children (expressed as a mean). These outcomes were dependent on the inter-implant interval. There was a significant correlation (r = - 0.497; p = 0.000) between speech test results and the inter-implant interval. Nevertheless, one subgroup of 27 children had the same or better results for the second side as compared with the first. In conclusion, the evaluation of the inter-implant interval and age groups at first implantation showed a preferred interval of up to four years in children under the age of 4 at first implantation. The older the children were at first implantation, the shorter the inter-implant interval had to be. It is as a direct consequence of this interval that children for whom it was longer were also older.


title: Left ventricular torsional mechanics after left ventricular reconstruction surgery for ischemic cardiomyopathy.

abstract: OBJECTIVES ::: Surgical left ventricular reconstruction improves symptoms and potentially prognosis in patients with ischemic cardiomyopathy; however, the effects of reconstruction on myocardial mechanics are not well defined. Therefore, we have computed left ventricular rotation and torsion in patients undergoing left ventricular reconstruction to determine its effects on these quantitative measures of myocardial mechanics. ::: ::: ::: METHODS ::: Magnetic resonance imaging with tissue grid-tagging was performed in 26 patients (19 male/7 female, 62 +/- 11 years) (mean +/- standard deviation) before (23 +/- 29 days) and after (231 +/- 106 days) left ventricular reconstruction, as well as in 7 healthy volunteers (5 male/2 female, 34 +/- 7 years). Left ventricular rotation was computed at basal and apical short-axis levels; torsion was defined as the difference between apical and basal rotation. ::: ::: ::: RESULTS ::: Before left ventricular reconstruction, maximal apical rotation was significantly impaired relative to that of healthy volunteers (P = .001), although maximal basal rotation was preserved (P = .84). After reconstruction, maximal torsion did not change significantly: torsion was 6 degrees +/- 3 degrees both before and after reconstruction (P = .84). However, the rate of early diastolic untwist improved significantly after reconstruction (-18 degrees/s +/- 13 degrees/s vs -23 degrees/s +/- 14 degrees/s; P = .04). Furthermore, patients with relatively worse torsion before reconstruction demonstrated more improved function after reconstruction; patients with torsion of less than 6 degrees (n = 12) showed greater improvement in ejection fraction (15% vs 6%; P = .005), torsion (1 degrees vs -1 degrees; P = .01), and diastolic untwist (-9 degrees/s vs -25 degrees/s; P < .001) than did patients with torsion of 6 degrees or more (n = 14). ::: ::: ::: CONCLUSIONS ::: Torsional mechanics were severely impaired by ischemic cardiomyopathy. On average, left ventricular reconstruction did not affect systolic torsion generation significantly; however, patients with relatively worse torsion did show improvement. Furthermore, the rate of untwist improved after surgery, suggesting that diastolic function was improved.


New Position-Specific Movement Ability Test ( PoSMAT ) Protocol Suite and Norms for Talent Identification , Selection , and Personalized Training for Soccer Players


title: Time-dependent series variance learning with recurrent mixture density networks

abstract: This paper presents an improved nonlinear mixture density approach to modeling the time-dependent variance in time series. First, we elaborate a recurrent mixture density network for explicit modeling of the time conditional mixing coefficients, as well as the means and variances of its Gaussian mixture components. Second, we derive training equations with which all the network weights are inferred in the maximum likelihood framework. Crucially, we calculate temporal derivatives through time for dynamic estimation of the variance network parameters. Experimental results show that, when compared with a traditional linear heteroskedastic model, as well as with the nonlinear mixture density network trained with static derivatives, our dynamic recurrent network converges to more accurate results with better statistical characteristics and economic performance.


title: Time series forecasting using a hybrid ARIMA and neural network model

abstract: Abstract Autoregressive integrated moving average (ARIMA) is one of the popular linear models in time series forecasting during the past three decades. Recent research activities in forecasting with artificial neural networks (ANNs) suggest that ANNs can be a promising alternative to the traditional linear methods. ARIMA models and ANNs are often compared with mixed conclusions in terms of the superiority in forecasting performance. In this paper, a hybrid methodology that combines both ARIMA and ANN models is proposed to take advantage of the unique strength of ARIMA and ANN models in linear and nonlinear modeling. Experimental results with real data sets indicate that the combined model can be an effective way to improve forecasting accuracy achieved by either of the models used separately.


title: Analyzing the Training Processes of Deep Generative Models

abstract: Among the many types of deep models, deep generative models (DGMs) provide a solution to the important problem of unsupervised and semi-supervised learning. However, training DGMs requires more skill, experience, and know-how because their training is more complex than other types of deep models such as convolutional neural networks (CNNs). We develop a visual analytics approach for better understanding and diagnosing the training process of a DGM. To help experts understand the overall training process, we first extract a large amount of time series data that represents training dynamics (e.g., activation changes over time). A blue-noise polyline sampling scheme is then introduced to select time series samples, which can both preserve outliers and reduce visual clutter. To further investigate the root cause of a failed training process, we propose a credit assignment algorithm that indicates how other neurons contribute to the output of the neuron causing the training failure. Two case studies are conducted with machine learning experts to demonstrate how our approach helps understand and diagnose the training processes of DGMs. We also show how our approach can be directly used to analyze other types of deep models, such as CNNs.


title: A Course Based on Open Organization Principles

abstract: This poster presents a plan for a first-semester freshman computing course based on Open Organization principles using humanitarian organizations as the context. The goal of the course is to instill engagement with learning in students from the beginning of their academic careers when they are psychologically invested in their learning and more likely to internalize the learning. The anticipated outcome is students who are motivated, engaged, and able to handle the rapid rate of computing change in today's society.


title: An introduction to event-triggered and self-triggered control

abstract: Recent developments in computer and communication technologies have led to a new type of large-scale resource-constrained wireless embedded control systems. It is desirable in these systems to limit the sensor and control computation and/or communication to instances when the system needs attention. However, classical sampled-data control is based on performing sensing and actuation periodically rather than when the system needs attention. This paper provides an introduction to event- and self-triggered control systems where sensing and actuation is performed when needed. Event-triggered control is reactive and generates sensor sampling and control actuation when, for instance, the plant state deviates more than a certain threshold from a desired value. Self-triggered control, on the other hand, is proactive and computes the next sampling or actuation instance ahead of time. The basics of these control strategies are introduced together with a discussion on the differences between state feedback and output feedback for event-triggered control. It is also shown how event- and self-triggered control can be implemented using existing wireless communication technology. Some applications to wireless control in process industry are discussed as well.


title: Application of Data Mining to Network Intrusion Detection: Classifier Selection Model

abstract: As network attacks have increased in number and severity over the past few years, intrusion detection system (IDS) is increasingly becoming a critical component to secure the network. Due to large volumes of security audit data as well as complex and dynamic properties of intrusion behaviors, optimizing performance of IDS becomes an important open problem that is receiving more and more attention from the research community. The uncertainty to explore if certain algorithms perform better for certain attack classes constitutes the motivation for the reported herein. In this paper, we evaluate performance of a comprehensive set of classifier algorithms using KDD99 dataset. Based on evaluation results, best algorithms for each attack category is chosen and two classifier algorithm selection models are proposed. The simulation result comparison indicates that noticeable performance improvement and real-time intrusion detection can be achieved as we apply the proposed models to detect different kinds of network attacks.


title: FTIR time series of tropospheric HCN in eastern China: seasonality, interannual variability and source attribution

abstract: Abstract. We analyzed seasonality and interannual variability of tropospheric HCN column amounts in densely populated eastern China for the first time. The results were derived from solar absorption spectra recorded with ground-based high spectral resolution Fourier transform infrared (FTIR) spectrometer at Hefei (117°10′ E, 31°54′ N) between 2015 and 2018. The tropospheric HCN columns over Hefei, China showed significant seasonal variations with three monthly mean peaks throughout the year. The magnitude of the tropospheric HCN column peak in May > September > December. The tropospheric HCN column reached a maximum of (9.8 ± 0.78) × 1015 molecules/cm2 in May and a minimum of (7.16 ± 0.75) × 1015 molecules/cm2 in November. In most cases, the tropospheric HCN columns at Hefei (32° N) are higher than the FTIR observations at Ny Alesund (79° N), Kiruna (68° N), Bremen (53° N), Jungfraujoch (47° N), Toronto (44° N), Rikubetsu (43° N), Izana (28° N), Mauna Loa (20° N), La Reunion Maido (21° S), Lauder (45° S), and Arrival Heights (78° S) that are affiliated with the Network for Detection of Atmospheric Composition Change (NDACC). Enhancements of the tropospheric HCN columns were observed between September 2015 and July 2016 compared to the counterpart measurements in other years. The magnitude of the enhancement ranges from 5 to 46 % with an average of 22 %. Enhancement of tropospheric HCN (ΔHCN) is correlated with the coincident enhancement of tropospheric CO (ΔCO), indicating that enhancements of tropospheric CO and HCN were due to the same sources. The GEOS-Chem tagged CO simulation, the global fire maps and the PSCFs (Potential Source Contribution Function) calculated using back trajectories revealed that the seasonal maxima in May is largely due to the influence of biomass burning in South Eastern Asia (SEAS) (41 ± 13.1 %), Europe and Boreal Asia (EUBA) (21 ± 9.3 %) and Africa (AF) (22 ± 4.7 %). The seasonal maxima in September is largely due to the influence of biomass burnings in EUBA (38 ± 11.3 %), AF (26 ± 6.7 %), SEAS (14 ± 3.3 %), and Northern America (NA) (13.8 ± 8.4 %). For the seasonal maxima in December, dominant contributions are from AF (36 ± 7.1 %), EUBA (21 ± 5.2 %), and NA (18.7 ± 5.2 %). The tropospheric HCN enhancement between September 2015 and July 2016 at Hefei (32° N) were attributed to an elevated influence of biomass burnings in SEAS, EUBA, and Oceania (OCE) in this period. Particularly, an elevated fire number in OCE in the second half of 2015 dominated the tropospheric HCN enhancement in September–December 2015. An elevated fire number in SEAS in the first half of 2016 dominated the tropospheric HCN enhancement in January–July 2016.


title: A new ultra-wideband, ultra-short monocycle pulse generator with reduced ringing

abstract: We introduce a new ultra-wideband (UWB), ultra-short, step recovery diode monocycle pulse generator. This pulse generator uses a simple RC high-pass filter as a differentiator to generate the monocycle pulse directly. The pulse-shaping network employs a resistive circuit to achieve UWB matching and substantial removal of the pulse ringing, and rectifying and switching diodes to further suppress the ringing. An ultra-short monocycle pulse of 300-ps pulse duration, -17-dB ringing level, and good symmetry has been demonstrated. Good agreement between the measured and calculated results was achieved.


title: When 3D-Aided 2D Face Recognition Meets Deep Learning: An extended UR2D for Pose-Invariant Face Recognition

abstract: Most of the face recognition works focus on specific modules or demonstrate a research idea. This paper presents a pose-invariant 3D-aided 2D face recognition system (UR2D) that is robust to pose variations as large as 90? by leveraging deep learning technology. The architecture and the interface of UR2D are described, and each module is introduced in detail. Extensive experiments are conducted on the UHDB31 and IJB-A, demonstrating that UR2D outperforms existing 2D face recognition systems such as VGG-Face, FaceNet, and a commercial off-the-shelf software (COTS) by at least 9% on the UHDB31 dataset and 3% on the IJB-A dataset on average in face identification tasks. UR2D also achieves state-of-the-art performance of 85% on the IJB-A dataset by comparing the Rank-1 accuracy score from template matching. It fills a gap by providing a 3D-aided 2D face recognition system that has compatible results with 2D face recognition systems using deep learning techniques.


title: Blind Deconvolution Using Convex Programming

abstract: We consider the problem of recovering two unknown vectors, $\w$ and $\x$, of length $L$ from their circular convolution. We make the structural assumption that the two vectors are members of known subspaces, one with dimension $N$ and the other with dimension $K$. Although the observed convolution is nonlinear in both $\w$ and $\x$, it is linear in the rank-1 matrix formed by their outer product $\w\x^*$. This observation allows us to recast the deconvolution problem as low-rank matrix recovery problem from linear measurements, whose natural convex relaxation is a nuclear norm minimization program. We prove the effectiveness of this relaxation by showing that for"generic"signals, the program can deconvolve $\w$ and $\x$ exactly when the maximum of $N$ and $K$ is almost on the order of $L$. That is, we show that if $\x$ is drawn from a random subspace of dimension $N$, and $\w$ is a vector in a subspace of dimension $K$ whose basis vectors are"spread out"in the frequency domain, then nuclear norm minimization recovers $\w\x^*$ without error. We discuss this result in the context of blind channel estimation in communications. If we have a message of length $N$ which we code using a random $L\times N$ coding matrix, and the encoded message travels through an unknown linear time-invariant channel of maximum length $K$, then the receiver can recover both the channel response and the message when $L\gtrsim N+K$, to within constant and log factors.


title: A K-fold Method for Baseline Estimation in Policy Gradient Algorithms

abstract: The high variance issue in unbiased policy-gradient methods such as VPG and REINFORCE is typically mitigated by adding a baseline. However, the baseline fitting itself suffers from the underfitting or the overfitting problem. In this paper, we develop a K-fold method for baseline estimation in policy gradient algorithms. The parameter K is the baseline estimation hyperparameter that can adjust the bias-variance trade-off in the baseline estimates. We demonstrate the usefulness of our approach via two state-of-the-art policy gradient algorithms on three MuJoCo locomotive control tasks.


title: An analysis of 3D point cloud reconstruction from light field images

abstract: Current methodologies for the generation of 3D point cloud from real world scenes rely upon a set of 2D images capturing the scene from several points of view. Novel plenoptic cameras sample the light field crossing the main camera lens creating a light field image. The information available in a plenoptic image must be processed in order to render a view or create the depth map of the scene. This paper analyses a method for the reconstruction of 3D models. The reconstruction of the model is obtained from a single image shot. Exploiting the properties of plenoptic images, a point cloud is generated and compared with a point cloud of the same object but generated with a different plenoptic camera.


title: Fault experiments in a commercial-scale PV laboratory and fault detection using local outlier factor

abstract: The experiments of line-line faults in solar photovoltaic (PV) arrays are implemented in a 35kW commercial-scale PV laboratory. The paper discusses both the transient response and post-fault behavior of the PV system (DC side), including PV array, overcurrent protection devices (OCPD, i.e. fuses) and grid-connected PV inverter. The experimental results demonstrate that fault locations and the maximum power point tracking (MPPT) may significantly reduce the fault current magnitude so that the fault is not detectable by OCPD. To eliminate the protection “blind spot,” statistic outlier detection rules are applied on the PV-string monitoring system, but sometimes they may cause false alarms. To address this issue, the local outlier factor (LOF) is developed in PV installations that shows improved fault detection. In experimental results, the proposed method demonstrates several advantages over traditional PV monitoring systems, such as simplicity, quick response, easy implementation and no requirement of weather information. The limitations of the proposed method are also discussed.


Feelings-as-Information Theory


title: Inverse Kinematics solution for a 3DOF robotic structure using Denavit-Hartenberg Convention

abstract: Robotic manipulators are very common for different robotic applications. We designed and created a 3DOF robotic leg shaped structure. Then we deduced the Kinematics and Inverse Kinematics solution through Denavit-Hartenberg Convention and with that solution, simulation software is designed to predict the movement and also to control the robotic structure. This simulation software gives predicted results for the robotic structure. After that we attached two of the robotic legs with a small car and moved the car by pushing through them. With the robotic leg the car moved at about 0.48 feet/second velocity. Our further plan is to use the robotic leg to create a Hexapod robot.


title: Dynamic Pooling for Complex Event Recognition

abstract: The problem of adaptively selecting pooling regions for the classification of complex video events is considered. Complex events are defined as events composed of several characteristic behaviors, whose temporal configuration can change from sequence to sequence. A dynamic pooling operator is defined so as to enable a unified solution to the problems of event specific video segmentation, temporal structure modeling, and event detection. Video is decomposed into segments, and the segments most informative for detecting a given event are identified, so as to dynamically determine the pooling operator most suited for each sequence. This dynamic pooling is implemented by treating the locations of characteristic segments as hidden information, which is inferred, on a sequence-by-sequence basis, via a large-margin classification rule with latent variables. Although the feasible set of segment selections is combinatorial, it is shown that a globally optimal solution to the inference problem can be obtained efficiently, through the solution of a series of linear programs. Besides the coarse-level location of segments, a finer model of video structure is implemented by jointly pooling features of segment-tuples. Experimental evaluation demonstrates that the resulting event detector has state-of-the-art performance on challenging video datasets.


title: oASIS: Adaptive Column Sampling for Kernel Matrix Approximation

abstract: Kernel matrices (e.g. Gram or similarity matrices) are essential for many state-of-the-art approaches to classification, clustering, and dimensionality reduction. For large datasets, the cost of forming and factoring such kernel matrices becomes intractable. To address this challenge, we introduce a new adaptive sampling algorithm called Accelerated Sequential Incoherence Selection (oASIS) that samples columns without explicitly computing the entire kernel matrix. We provide conditions under which oASIS is guaranteed to exactly recover the kernel matrix with an optimal number of columns selected. Numerical experiments on both synthetic and real-world datasets demonstrate that oASIS achieves performance comparable to state-of-the-art adaptive sampling methods at a fraction of the computational cost. The low runtime complexity of oASIS and its low memory footprint enable the solution of large problems that are simply intractable using other adaptive methods.


title: Study of co-authorship network of papers in the Journal of Research in Medical Sciences using social network analysis

abstract: BACKGROUND ::: Co-authorship is one of the most tangible forms of research collaboration. A co-authorship network is a social network in which the authors through participation in one or more publication through an indirect path have linked to each other. The present research using the social network analysis studied co-authorship network of 681 articles published in Journal of Research in Medical Sciences (JRMS) during 2008-2012. ::: ::: ::: MATERIALS AND METHODS ::: The study was carried out with the scientometrics approach and using co-authorship network analysis of authors. The topology of the co-authorship network of 681 published articles in JRMS between 2008 and 2012 was analyzed using macro-level metrics indicators of network analysis such as density, clustering coefficient, components and mean distance. In addition, in order to evaluate the performance of each authors and countries in the network, the micro-level indicators such as degree centrality, closeness centrality and betweenness centrality as well as productivity index were used. The UCINET and NetDraw softwares were used to draw and analyze the co-authorship network of the papers. ::: ::: ::: RESULTS ::: The assessment of the authors productivity in this journal showed that the first ranks were belonged to only five authors, respectively. Furthermore, analysis of the co-authorship of the authors in the network demonstrated that in the betweenness centrality index, three authors of them had the good position in the network. They can be considered as the network leaders able to control the flow of information in the network compared with the other members based on the shortest paths. On the other hand, the key role of the network according to the productivity and centrality indexes was belonged to Iran, Malaysia and United States of America. ::: ::: ::: CONCLUSION ::: Co-authorship network of JRMS has the characteristics of a small world network. In addition, the theory of 6° separation is valid in this network was also true.


title: Tense and Aspect Error Correction for ESL Learners Using Global Context

abstract: As the number of learners of English is constantly growing, automatic error correction of ESL learners' writing is an increasingly active area of research. However, most research has mainly focused on errors concerning articles and prepositions even though tense/aspect errors are also important. One of the main reasons why tense/aspect error correction is difficult is that the choice of tense/aspect is highly dependent on global context. Previous research on grammatical error correction typically uses pointwise prediction that performs classification on each word independently, and thus fails to capture the information of neighboring labels. In order to take global information into account, we regard the task as sequence labeling: each verb phrase in a document is labeled with tense/aspect depending on surrounding labels. Our experiments show that the global context makes a moderate contribution to tense/aspect error correction.


title: Verifying (In-)Stability in Floating-Point Programs by Increasing Precision, Using SMT Solving

abstract: When computing with floating-point numbers, programmers choose a certain floating-point precision (like, for instance, float or double) upfront, for each variable. However, whether the chosen precision is appropriate for the computation at hand, and vice versa, is difficult to judge. One way is to increase the precision, and observe whether the result of the computation changes too much, in which case the computation with the original precisions is considered 'unstable'. This effect may be exhibited with certain inputs, and not with others. With a classical testing approach, inputs that show instability can be very difficult to find. Moreover, testing can only show instability, not stability. In this paper, we present an approach, and its implementation, which can formally prove that an increased precision causes only a limited (quantified) change of the result. Alternatively, if the computation is not stable, the method returns inputs that exhibit this. We use methods from program verification, connecting to a novel SMT (sat- isfiability modulo theories) solver for floating-point number constraints. The user augments the program P with asser- tions on the expected stability bound. The system then creates a new program P', a certain kind of merge of P with a higher precision copy of P, computes the weakest precondition of P' w.r.t. these assertions, and feeds the resulting formula to the SMT solver, which then proves stability or alternatively returns data for a test exhibiting unstability, to be used for further analysis. The implemen- tation of the system targets a toy language but supports the IEEE standard in a realistic manner. The paper describes the method and its implementation, reports experiments, and discusses the results.


title: The impact of Web quality and playfulness on user acceptance of online retailing

abstract: We investigated the effect of playfulness on user acceptance of online retailing and tested the relationship between Web quality factors and user acceptance behavior. A survey of 942 users of Web-based online retailing was conducted to test our model. The results showed that playfulness plays an important role in enhancing user attitude and behavioral intention to use a site. We also found that Web quality, categorized into system, information, and service quality, had a significant impact on the perceived ease of use, playfulness, and usefulness, and consequently, that it encouraged website use in the context of online retailing. Our study thus provided a balanced and integrative framework for determining Web quality. It enhanced our knowledge of the effect of playfulness, which should help Web practitioners and researchers better understand user behavior in Web-based online retailing.


title: Anatomic variations of the hepatic arteries in 604 selective celiac and superior mesenteric angiographies

abstract: In modern surgical and transplantation procedures the recognition of anatomic vascular abnormalities of the hepatic arteries is of greater importance than ever. The purpose of this study was to evaluate and classify these variations with respect to their impact on visceral surgery. A total of 604 selective celiac and superior mesenteric angiographies performed on patients with known or suspected liver cirrhosis or hepatic or pancreatic malignancies and on donors of partial liver grafts were analyzed retrospectively. The vascular anatomy of the liver was classified according to different established systems and with particular attention to rare variations. Hepatic arterial anatomy as considered normal in textbook descriptions was found in 79.1%, an aberrant or accessory left hepatic artery (LHA) arising from the left gastric artery in 3.0% and an aberrant or accessory right hepatic artery (RHA) branching off the superior mesenteric artery in 11.9% of the cases. In 1.4% of the cases there was a combination of anomalies of both the LHA and RHA. Variants of the celiac trunk, double hepatic arteries branching at the celiac trunk or hepatic arteries arising directly from the aorta, occurred in 4.1% of the cases. Further atypical branches of the LHA and RHA were found in 0.5% of the cases. Since the incidence and pattern of different types of hepatic arterial anatomy can require specialized preoperative diagnostic as well as intraoperative strategies, knowledge of these abnormalities and their frequency is of major importance for the surgeon as well as the radiologist.


title: NetSpam: a Network-based Spam Detection Framework for Reviews in Online Social Media

abstract: Nowadays, a big part of people rely on available content in social media in their decisions (e.g., reviews and feedback on a topic or product). The possibility that anybody can leave a review provides a golden opportunity for spammers to write spam reviews about products and services for different interests. Identifying these spammers and the spam content is a hot topic of research, and although a considerable number of studies have been done recently toward this end, but so far the methodologies put forth still barely detect spam reviews, and none of them show the importance of each extracted feature type. In this paper, we propose a novel framework, named NetSpam , which utilizes spam features for modeling review data sets as heterogeneous information networks to map spam detection procedure into a classification problem in such networks. Using the importance of spam features helps us to obtain better results in terms of different metrics experimented on real-world review data sets from Yelp and Amazon Web sites. The results show that NetSpam outperforms the existing methods and among four categories of features, including review-behavioral, user-behavioral, review-linguistic, and user-linguistic, the first type of features performs better than the other categories.


title: Performance of a Real-Time EtherCAT Master Under Linux

abstract: The adoption of open-source operating systems for the execution of real-time applications is gaining popularity, even in the networked control systems domain, due to cost and flexibility reasons. However, as opposed to their commercial counterparts, the actual performance level to be expected from them is still little known and may often depend on the kind of real-time extension being used, its configuration, and the overall software load of the system, including best-effort components. In this paper, an open-source EtherCAT master supported by a popular real-time extension for Linux, the RT Patch, is thoroughly evaluated with long-term measurements, which build confidence on the suitability of the proposed approach for real-world applications. Special attention is devoted to the unexpected, adverse effect that some best-effort components, for instance, graphics applications, may have on the overall real-time characteristics of the system. For reference, the proposed approach is also compared with RTAI, a more traditional and well-known real-time extension for Linux already in use for demanding applications.


title: Evaluation of alternative glyph designs for time series data in a small multiple setting

abstract: We present the results of a controlled experiment to investigate the performance of different temporal glyph designs in a small multiple setting. Analyzing many time series at once is a common yet difficult task in many domains, for example in network monitoring. Several visualization techniques have, thus, been proposed in the literature. Among these, iconic displays or glyphs are an appropriate choice because of their expressiveness and effective use of screen space. Through a controlled experiment, we compare the performance of four glyphs that use different combinations of visual variables to encode two properties of temporal data: a) the position of a data point in time and b) the quantitative value of this data point. Our results show that depending on tasks and data density, the chosen glyphs performed differently. Line Glyphs are generally a good choice for peak and trend detection tasks but radial encodings are more effective for reading values at specific temporal locations. From our qualitative analysis we also contribute implications for designing temporal glyphs for small multiple settings.


title: Distributed Processing of Similarity Queries

abstract: Many modern applications in diverse fields demand the efficient manipulation of very large multidimensional datasets. It is evident, that efficient and effective query processing techniques need to be developed, in order to provide acceptable response times in query processing. In this paper, we study the processing of similarity nearest neighbor queries in large distributed multidimensional databases, where objects are represented as vectors in a vector space, and are distributed in a multi-computer environment. The departure from the centralized case embodies a number of advantages and (unfortunately) a number of difficulties that need to be successfully overcome. In this perspective, four query evaluation strategies are presented, namely Concurrent Processing (CP), Selective Processing (SP), Two-Phase Processing (2PP) and Probabilistic Processing (PRP). The proposed techniques are compared analytically and experimentally, in order to discover the advantages of each one, as well as the best cases where each one should be applied. Experimental results are presented, demonstrating the performance of each method under different parameters values. Also, we investigate the impact of derived data that should be maintained in order to process similarity queries efficiently.


title: OpenSky report 2016: Facts and figures on SSR mode S and ADS-B usage

abstract: This paper provides up-to-date statistics on SSR Mode S and 1090ES ADS-B usage from the OpenSky Network. By analyzing the large host of real-world data collected by OpenSky, we provide insights on the current composition of Mode S downlink communications, aircraft equipage, interrogation patterns and much more. We publish up-to-date, empirically validated numbers on the status of the ongoing ADS-B deployment based on data collected over large areas in Europe, North America, and New Zealand, and categorize these aircraft by incorporating publicly available data sources.


title: DSGAN: Generative Adversarial Training for Distant Supervision Relation Extraction

abstract: Distant supervision can effectively label data for relation extraction, but suffers from the noise labeling problem. Recent works mainly perform soft bag-level noise reduction strategies to find the relatively better samples in a sentence bag, which is suboptimal compared with making a hard decision of false positive samples in sentence level. In this paper, we introduce an adversarial learning framework, which we named DSGAN, to learn a sentence-level true-positive generator. Inspired by Generative Adversarial Networks, we regard the positive samples generated by the generator as the negative samples to train the discriminator. The optimal generator is obtained until the discrimination ability of the discriminator has the greatest decline. We adopt the generator to filter distant supervision training dataset and redistribute the false positive instances into the negative set, in which way to provide a cleaned dataset for relation classification. The experimental results show that the proposed strategy significantly improves the performance of distant supervision relation extraction comparing to state-of-the-art systems.


Effectively Learning from Pedagogical Demonstrations.


title: Optimal Hyperparameters for Deep LSTM-Networks for Sequence Labeling Tasks

abstract: Selecting optimal parameters for a neural network architecture can often make the difference between mediocre and state-of-the-art performance. However, little is published which parameters and design choices should be evaluated or selected making the correct hyperparameter optimization often a "black art that requires expert experiences" (Snoek et al., 2012). In this paper, we evaluate the importance of different network design choices and hyperparameters for five common linguistic sequence tagging tasks (POS, Chunking, NER, Entity Recognition, and Event Detection). We evaluated over 50.000 different setups and found, that some parameters, like the pre-trained word embeddings or the last layer of the network, have a large impact on the performance, while other parameters, for example the number of LSTM layers or the number of recurrent units, are of minor importance. We give a recommendation on a configuration that performs well among different tasks.


title: Wireless Power Transfer in Loosely Coupled Links: Coil Misalignment Model

abstract: A novel analytical model of inductively coupled wireless power transfer is presented. For the first time, the effects of coil misalignment and geometry are addressed in a single mathematical expression. In the applications envisaged, such as radio frequency identification (RFID) and biomedical implants, the receiving coil is normally significantly smaller than the transmitting coil. Formulas are derived for the magnetic field at the receiving coil when it is laterally and angularly misaligned from the transmitting coil. Incorporating this magnetic field solution with an equivalent circuit for the inductive link allows us to introduce a power transfer formula that combines coil characteristics and misalignment factors. The coil geometries considered are spiral and short solenoid structures which are currently popular in the RFID and biomedical domains. The novel analytical power transfer efficiency expressions introduced in this study allow the optimization of coil geometry for maximum power transfer and misalignment tolerance. The experimental results show close correlation with the theoretical predictions. This analytic technique can be widely applied to inductive wireless power transfer links without the limitations imposed by numerical methods.


title: An I/O mechanism on a dynamically reconfigurable processor - which should be moved: data or configuration?

abstract: In some applications on dynamically reconfigurable processor (DRP), the input/output of data stream occupies about 20% of total execution time. In order to hide the overhead of input/output, the separation of the I/O context and double-buffering mechanism are proposed. Using the mechanism, I/O overhead of six streaming processing programs can be completely hidden. Based on the analysis of I/O performance, two alternatives for executing parallel processing with multiple DRP cores are compared and discussed.


End-to-end dental pathology detection in 3D cone-beam computed tomography images


title: A theory of transfer learning with applications to active learning

abstract: We explore a transfer learning setting, in which a finite sequence of target concepts are sampled independently with an unknown distribution from a known family. We study the total number of labeled examples required to learn all targets to an arbitrary specified expected accuracy, focusing on the asymptotics in the number of tasks and the desired accuracy. Our primary interest is formally understanding the fundamental benefits of transfer learning, compared to learning each target independently from the others. Our approach to the transfer problem is general, in the sense that it can be used with a variety of learning protocols. As a particularly interesting application, we study in detail the benefits of transfer for self-verifying active learning; in this setting, we find that the number of labeled examples required for learning with transfer is often significantly smaller than that required for learning each target independently.


title: Fine-Tuning CNN Image Retrieval with No Human Annotation

abstract: Image descriptors based on activations of Convolutional Neural Networks (CNNs) have become dominant in image retrieval due to their discriminative power, compactness of the representation, and the efficiency of search. Training of CNNs, either from scratch or fine-tuning, requires a large amount of annotated data, where high quality of the annotation is often crucial. In this work, we propose to fine-tune CNNs for image retrieval on a large collection of unordered images in a fully automatic manner. Reconstructed 3D models, obtained by the state-of-the-art retrieval and structure-from-motion methods, guide the selection of the training data. We show that both hard positive and hard negative examples, selected by exploiting the geometry and the camera positions available from the 3D models, enhance the performance in particular object retrieval. CNN descriptor whitening discriminatively learned from the same training data outperforms the commonly used PCA whitening. We propose a novel trainable Generalized-Mean (GeM) pooling layer that generalizes max and average pooling and show that it boosts retrieval performance. Applying the proposed method on VGG network achieves state-of-the-art performance on standard benchmarks: Oxford Buildings, Paris, and Holidays datasets.


title: High-voltage class-D direct-drive audio amplifier for electrostatic loudspeakers

abstract: Electrostatic loudspeakers (ESLs) are capable of low distortion and have desirably flat frequency response within the audio frequency range. In this paper, a novel approach is proposed for audio amplification using a high-voltage direct-digital solid-state class-D amplifier as a method that replaces impedance matching ESL audio transformers. Optical coupling is used to achieve isolation from the high-voltage side. The proposed stacked MOSFET output stage provides a solution to the voltage swing limitations of the available switching power MOSFETs. An example digital amplifier having 14-bit ADC precision PWM with ƒ c = 50 kHz was implemented at ± 375 V delivering 7W into R a =20kΩ. The digital modulator was realized on a Xilinx Virtex4 Sx35-10ff668 at F s = 100 MHz clock.


title: Haplogroup Prediction from Y-STR Values Using a Bayesian-Allele- Frequency Approach

abstract: A new Bayesian allele-frequency approach to predicting the Y-chromosome haplogroup from a set of Y-STR marker values is presented and compared to other approaches. The method has been implemented in an Excel-based program, where an arbitrary number of STR markers may be input and a “goodness of fit” score for 15 haplogroups (C, E3a, E3b, G, H, I1a, I1b1, I1b2, J, L, N, O, Q, R1a, and R1b) and the Bayesian probability for each haplogroup is returned. This method has been applied to 100 R1b haplotypes, 50 I1a haplotypes (all having 37 STR markers available), and 54 of the YCC sample set (having 20 STR markers available).


title: Computer Vision and Natural Language Processing: Recent Approaches in Multimedia and Robotics

abstract: Integrating computer vision and natural language processing is a novel interdisciplinary field that has received a lot of attention recently. In this survey, we provide a comprehensive introduction of the integration of computer vision and natural language processing in multimedia and robotics applications with more than 200 key references. The tasks that we survey include visual attributes, image captioning, video captioning, visual question answering, visual retrieval, human-robot interaction, robotic actions, and robot navigation. We also emphasize strategies to integrate computer vision and natural language processing models as a unified theme of distributional semantics. We make an analog of distributional semantics in computer vision and natural language processing as image embedding and word embedding, respectively. We also present a unified view for the field and propose possible future directions.


title: Cultural neuroscience of the self: understanding the social grounding of the brain

abstract: Cultural neuroscience is an interdisciplinary field of research that investigates interrelations among culture, mind and the brain. Drawing on both the growing body of scientific evidence on cultural variation in psychological processes and the recent development of social and cognitive neuroscience, this emerging field of research aspires to understand how culture as an amalgam of values, meanings, conventions, and artifacts that constitute daily social realities might interact with the mind and its underlying brain pathways of each individual member of the culture. In this article, following a brief review of studies that demonstrate the surprising degree to which brain processes are malleably shaped by cultural tools and practices, the authors discuss cultural variation in brain processes involved in self-representations, cognition, emotion and motivation. They then propose (i) that primary values of culture such as independence and interdependence are reflected in the compositions of cultural tasks (i.e. daily routines designed to accomplish the cultural values) and further (ii) that active and sustained engagement in these tasks yields culturally patterned neural activities of the brain, thereby laying the ground for the embodied construction of the self and identity. Implications for research on culture and the brain are discussed.


title: Learning Deep Belief Networks from Non-Stationary Streams

abstract: Deep learning has proven to be beneficial for complex tasks such as classifying images. However, this approach has been mostly applied to static datasets. The analysis of non-stationary (e.g., concept drift) streams of data involves specific issues connected with the temporal and changing nature of the data. In this paper, we propose a proof-of-concept method, called Adaptive Deep Belief Networks, of how deep learning can be generalized to learn online from changing streams of data. We do so by exploiting the generative properties of the model to incrementally re-train the Deep Belief Network whenever new data are collected. This approach eliminates the need to store past observations and, therefore, requires only constant memory consumption. Hence, our approach can be valuable for life-long learning from non-stationary data streams.


title: High Range-Resolution Monopulse Tracking Radar

abstract: High range-resolution monopulse (HRRM) tracking radar which maintains wide instantaneous bandwidth through both range and angle error sensing channels provides range, azimuth, elevation, and amplitude for each resolved part of the target. The three-dimensional target detail can be used to improve and extend radar performance in several ways: for improved precision of target location, for target classification and recognition, to counter repeater-type ECM, to improve low-angle multipath tracking, to resolve multiple targets, as a miss-distance measurement capability, and for improved tracking in chaff and clutter. These have been demonstrated qualitatively except for the ECCM to repeater ECM and low-altitude tracking improvement. Initial results from an experimental HRRM radar with 3-ns pulse length show resolution of aircraft into its major parts and precise location of each resolved part accurately in range and angle. Realtime closed-loop tracking is performed on aircraft in flight using high-speed sampled, digitized, and processed HRRM range and angle video data. Clutter rejection capability is also demonstrated.


title: Gremlin: scheduling interactions in vehicular computing

abstract: Vehicular applications must not demand too much of a driver's attention. They often run in the background and initiate interactions with the driver to deliver important information. We argue that the vehicular computing system must schedule interactions by considering their priority, the attention they will demand, and how much attention the driver currently has to spare. Based on these considerations, it should either allow a given interaction or defer it. We describe a prototype called Gremlin that leverages edge computing infrastructure to help schedule interactions initiated by vehicular applications. It continuously performs four tasks: (1) monitoring driving conditions to estimate the driver's available attention, (2) recording interactions for analysis, (3) generating a user-specific quantitative model of the attention required for each distinct interaction, and (4) scheduling new interactions based on the above data. Gremlin performs the third task on edge computing infrastructure. Offload is attractive because the analysis is too computationally demanding to run on vehicular platforms. Since recording size for each interaction can be large, it is preferable to perform the offloaded computation at the edge of the network rather than in the cloud, and thereby conserve wide-area network bandwidth. We evaluate Gremlin by comparing its decisions to those recommended by a vehicular UI expert. Gremlin's decisions agree with the expert's over 90% of the time, much more frequently than the coarse-grained scheduling policies used by current vehicle systems. Further, we find that offloading of analysis to edge platforms reduces use of wide-area networks by an average of 15MB per analyzed interaction.


title: Evaluation of Dense 3D Reconstruction from 2D Face Images in the Wild

abstract: This paper investigates the evaluation of dense 3D face reconstruction from a single 2D image in the wild. To this end, we organise a competition that provides a new benchmark dataset that contains 2000 2D facial images of 135 subjects as well as their 3D ground truth face scans. In contrast to previous competitions or challenges, the aim of this new benchmark dataset is to evaluate the accuracy of a 3D dense face reconstruction algorithm using real, accurate and high-resolution 3D ground truth face scans. In addition to the dataset, we provide a standard protocol as well as a Python script for the evaluation. Last, we report the results obtained by three state-of-the-art 3D face reconstruction systems on the new benchmark dataset. The competition is organised along with the 2018 13th IEEE Conference on Automatic Face & Gesture Recognition.


title: Adversarially Robust Malware Detection Using Monotonic Classification

abstract: We propose monotonic classification with selection of monotonic features as a defense against evasion attacks on classifiers for malware detection. The monotonicity property of our classifier ensures that an adversary will not be able to evade the classifier by adding more features. We train and test our classifier on over one million executables collected from VirusTotal. Our secure classifier has 62% temporal detection rate at a 1% false positive rate. In comparison with a regular classifier with unrestricted features, the secure malware classifier results in a drop of approximately 13% in detection rate. Since this degradation in performance is a result of using a classifier that cannot be evaded, we interpret this performance hit as the cost of security in classifying malware.


The Galois/Counter Mode of Operation (GCM)


title: Ontology-Based Information Extraction for Knowledge Enrichment and Validation

abstract: Ontology is widely used as a mean to represent and share common concepts and knowledge from a particular domain or specialisation. As a knowledge representation, the knowledge within an ontology must be able to evolve along with the recent changes and updates within the community practice. In this paper, we propose a new Ontology-based Information Extraction (OBIE) system that extends existing systems in order to enrich and validate an ontology. Our model enables the ontology to find related recent knowledge in the domain from communities, by exploiting their underlying knowledge as keywords. The knowledge extraction process uses ontology-based and pattern-based information extraction technique. Not only the extracted knowledge enriches the ontology, it also validates contradictory instance-related statements within the ontology that is no longer relevant to recent practices. We determine a confidence value during the enrichment and validation process to ensure the stability of the enriched ontology. We implement the model and present a case study in herbal medicine domain. The result of the enrichment and validation process shows promising results. Moreover, we analyse how our proposed model contributes to the achievement of a richer and stable ontology.


title: The Alzheimer's Disease Neuroimaging Initiative (ADNI): MRI methods.

abstract: The Alzheimer's Disease Neuroimaging Initiative (ADNI) is a longitudinal multisite observational study of healthy elders, mild cognitive impairment (MCI), and Alzheimer's disease. Magnetic resonance imaging (MRI), (18F)-fluorodeoxyglucose positron emission tomography (FDG PET), urine serum, and cerebrospinal fluid (CSF) biomarkers, as well as clinical/psychometric assessments are acquired at multiple time points. All data will be cross-linked and made available to the general scientific community. The purpose of this report is to describe the MRI methods employed in ADNI. The ADNI MRI core established specifications that guided protocol development. A major effort was devoted to evaluating 3D T(1)-weighted sequences for morphometric analyses. Several options for this sequence were optimized for the relevant manufacturer platforms and then compared in a reduced-scale clinical trial. The protocol selected for the ADNI study includes: back-to-back 3D magnetization prepared rapid gradient echo (MP-RAGE) scans; B(1)-calibration scans when applicable; and an axial proton density-T(2) dual contrast (i.e., echo) fast spin echo/turbo spin echo (FSE/TSE) for pathology detection. ADNI MRI methods seek to maximize scientific utility while minimizing the burden placed on participants. The approach taken in ADNI to standardization across sites and platforms of the MRI protocol, postacquisition corrections, and phantom-based monitoring of all scanners could be used as a model for other multisite trials.


title: Asymmetric self-learning for tackling Twitter Spam Drift

abstract: Spam has become a critical problem on Twitter. In order to stop spammers, security companies apply blacklisting services to filter spam links. However, over 90% victims will visit a new malicious link before it is blocked by blacklists. To eliminate the limitation of blacklists, researchers have proposed a number of statistical features based mechanisms, and applied machine learning techniques to detect Twitter spam. In our labelled large dataset, we observe that the statistical properties of spam tweets vary over time, and thus the performance of existing ML based classifiers are poor. This phenomenon is referred as “Twitter Spam Drift”. In order to tackle this problem, we carry out deep analysis of 1 million spam tweets and 1 million non-spam tweets, and propose an asymmetric self-learning (ASL) approach. The proposed ASL can discover new information of changed tweeter spam and incorporate it into classifier training process. A number of experiments are performed to evaluate the ASL approach. The results show that the ASL approach can be used to significantly improve the spam detection accuracy of using traditional ML algorithms.


title: Refine Item-Based Collaborative Filtering Algorithms with Skew Amplification

abstract: Case Amplification can improve the accuracy of a collaborative filtering (CF) algorithm with no extra space overhead by amplifying the effect of close candidates in the prediction. However, in a cold start scenario, the traditional Case Amplification on an item-based prediction can reduce accuracy. Given a small known set, Case Amplification can give a mediocre candidate an unsuitable amplification, by amplifying the numerator and the denominator in a predicting formula equally. We propose a skew amplification mechanism to address the problem: we amplify the numerator and the denominator differently. This reduces the effect of a mediocre but close item in the prediction. The balance between different amplifications is kept automatically by a controller, whose behavior depends on the size of the given set. Evaluation was carried out on four benchmarks, and results show that, in a cold-start scenario, skew amplification outperforms Case Amplification on boosting an item-based CF algorithm, especially when the given set becomes small.


Understanding Consumer Behavior with Recurrent Neural Networks


title: Efficient influence maximization in social networks

abstract: Influence maximization is the problem of finding a small subset of nodes (seed nodes) in a social network that could maximize the spread of influence. In this paper, we study the efficient influence maximization from two complementary directions. One is to improve the original greedy algorithm of [5] and its improvement [7] to further reduce its running time, and the second is to propose new degree discount heuristics that improves influence spread. We evaluate our algorithms by experiments on two large academic collaboration graphs obtained from the online archival database arXiv.org. Our experimental results show that (a) our improved greedy algorithm achieves better running time comparing with the improvement of [7] with matching influence spread, (b) our degree discount heuristics achieve much better influence spread than classic degree and centrality-based heuristics, and when tuned for a specific influence cascade model, it achieves almost matching influence thread with the greedy algorithm, and more importantly (c) the degree discount heuristics run only in milliseconds while even the improved greedy algorithms run in hours in our experiment graphs with a few tens of thousands of nodes. Based on our results, we believe that fine-tuned heuristics may provide truly scalable solutions to the influence maximization problem with satisfying influence spread and blazingly fast running time. Therefore, contrary to what implied by the conclusion of [5] that traditional heuristics are outperformed by the greedy approximation algorithm, our results shed new lights on the research of heuristic algorithms.


title: Fuzzy cognitive maps for artificial emotions forecasting

abstract: At the present, emotion is considered as a critical point of human behaviour, and thus it should be embedded within the reasoning module when an intelligent system or a autonomous robot aims to emulate or anticipate human reactions. Therefore, current research in Artificial Intelligence shows an increasing interest in artificial emotion research for developing human-like systems. Based on Thayer's emotion model and Fuzzy Cognitive Maps, this paper presents a proposal for forecasting artificial emotions. It provides an innovative method for forecasting artificial emotions and designing an affective decision system. This work includes an experiment with three simulated artificial scenarios for testing the proposal. Each scenario generate different emotions according to the artificial experimental model.


title: Usage of a mobile social learning platform with virtual badges in a primary school

abstract: This study presents and examines SamEx, a mobile learning system used by 305 students in formal and informal learning in a primary school in Singapore. Students use SamEx in situ to capture media such as pictures, video clips and audio recordings, comment on them, and share them with their peers. In this paper we report on the experiences of students in using the application throughout a one-year period with a focus on self-directedness, quality of contributions, and answers to contextual question prompts. We examine how the usage of tools such as SamEx predicts students' science examination results, discuss the role of badges as an extrinsic motivational tool, and explore how individual and collaborative learning emerge. Our research shows that the quantity and quality of contributions provided by the students in SamEx predict the end-year assessment score. With respect to specific system features, contextual answers given by the students and the overall likes received by students are also correlated with the end-year assessment score. Answers given and likes received predict the total assessment score.Introduction of more collaboration software features could impact students' results.Engaging in self-directed activities helps students in their learning.Badges can motivate students to learn when teachers provide learning contexts.Intentional teacher-directed tasks stimulate students to participate.


title: Collaborator recommendation in heterogeneous bibliographic networks using random walks

abstract: The increasingly growing popularity of the collaboration among researchers and the increasing information overload in big scholarly data make it imperative to develop a collaborator recommendation system for researchers to find potential partners. Existing works always study this task as a link prediction problem in a homogeneous network with a single object type (i.e., author) and a single link type (i.e., co-authorship). However, a real-world academic social network often involves several object types, e.g., papers, terms, and venues, as well as multiple relationships among different objects. This paper proposes a RWR-CR (standing for random walk with restart-based collaborator recommendation) algorithm in a heterogeneous bibliographic network towards this problem. First, we construct a heterogeneous network with multiple types of nodes and links with a simplified network structure by removing the citing paper nodes. Then, two importance measures are used to weight edges in the network, which will bias a random walker’s behaviors. Finally, we employ a random walk with restart to retrieve relevant authors and output an ordered recommendation list in terms of ranking scores. Experimental results on DBLP and hep-th datasets demonstrate the effectiveness of our methodology and its promising performance in collaborator prediction.


title: A Long Dependency Aware Deep Architecture for Joint Chinese Word Segmentation and POS Tagging.

abstract: Long-term context is crucial to joint Chinese word segmentation and POS tagging (S&T) task. However, most of machine learning based methods extract features from a window of characters. Due to the limitation of window size, these methods can not exploit the long distance information. In this work, we propose a long dependency aware deep architecture for joint S&T task. Specifically, to simulate the feature templates of traditional discrete feature based models, we use different filters to model the complex compositional features with convolutional and pooling layer, and then utilize long distance dependency information with recurrent layer. Experiment results on five different datasets show the effectiveness of our proposed model.


title: H.: To CNF or not to CNF ? An efficient yet presentable version of the CYK algorithm

abstract: The most familiar algorithm to decide the membership problem for context-free grammars is the one by Cocke, Younger and Kasami (CYK) using grammars in Chomsky normal form (CNF). We propose to teach a simple modification of the CYK algorithm that uses grammars in a much less restrictive binary normal form (2NF) and two precomputations: the set of nullable nonterminals and the inverse of the unit relation between symbols. The modified algorithm is equally simple as the original one, but highlights that the at most binary branching rules alone are responsible for the O(n 3 ) time complexity. Moreover, the simple transformation to 2NF comes with a linear increase in grammar size, whereas some transformations to CNF found in most prominent textbooks on formal languages may lead to an exponential increase.


title: Live migration of virtual machines

abstract: Migrating operating system instances across distinct physical hosts is a useful tool for administrators of data centers and clusters: It allows a clean separation between hard-ware and software, and facilitates fault management, load balancing, and low-level system maintenance. By carrying out the majority of migration while OSes continue to run, we achieve impressive performance with minimal service downtimes; we demonstrate the migration of entire OS instances on a commodity cluster, recording service downtimes as low as 60ms. We show that that our performance is sufficient to make live migration a practical tool even for servers running interactive loads. In this paper we consider the design options for migrating OSes running services with liveness constraints, focusing on data center and cluster environments. We introduce and analyze the concept of writable working set, and present the design, implementation and evaluation of high-performance OS migration built on top of the Xen VMM.


title: Deep Forest: Towards An Alternative to Deep Neural Networks

abstract: In this paper, we propose gcForest, a decision tree ensemble approach with performance highly competitive to deep neural networks in a broad range of tasks. In contrast to deep neural networks which require great effort in hyper-parameter tuning, gcForest is much easier to train; even when it is applied to different data across different domains in our experiments, excellent performance can be achieved by almost same settings of hyper-parameters. The training process of gcForest is efficient, and users can control training cost according to computational resource available. The efficiency may be further enhanced because gcForest is naturally apt to parallel implementation. Furthermore, in contrast to deep neural networks which require largescale training data, gcForest can work well even when there are only small-scale training data.


C ] 2 5 N ov 2 01 5 How predictable is technological progress ? ∗


Constructing the context through goals and schemata: top-down processes in comprehension and beyond