Query: Enrollment Prediction through Data Mining

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Learning with kernels <=SEP=> All rights reserved. No part of this book may be reproduced in any form by any electronic or mechanical means (including photocopying, recording, or information storage and retrieval) without permission in writing from the publisher.

The Nature of Statistical Learning Theory <=SEP=> 

Statistical Decision Theory and Bayesian Analysis, 2nd Edition <=SEP=> 

Approximation by superpositions of a sigmoidal function <=SEP=> Abstr,,ct. In this paper we demonstrate that finite linear combinations of compositions of a fixed, univariate function and a set ofaffine functionals can uniformly approximate any continuous function of n real variables with support in the unit hypercube; only mild conditions are imposed on the univariate function. Our results settle an open question about representability in the class of single bidden layer neural networks. In particular, we show that arbitrary decision regions can be arbitrarily well approximated by continuous feedforward neural networks with only a single internal, hidden layer and any continuous sigmoidal nonlinearity. The paper discusses approximation properties of other possible types of nonlinearities that might be implemented by artificial neural networks.

Estimating the dimension of a linear model <=SEP=> 

Satellite Image Analysis for Disaster and Crisis-Management Support <=SEP=> This paper describes how multisource satellite data and efficient image analysis may successfully be used to conduct rapid-mapping tasks in the domain of disaster and crisis-management support. The German Aerospace Center (DLR) has set up a dedicated crosscutting service, which is the so-called "Center for satellite-based Crisis Information" (ZKI), to facilitate the use of its Earth-observation capacities in the service of national and international response to major disaster situations, humanitarian relief efforts, and civil security issues. This paper describes successful rapid satellite mapping campaigns supporting disaster relief and demonstrates how this technology can be used for civilian crisis-management purposes. During the last years, various international coordination bodies were established, improving the disaster-response-related cooperation within the Earth-observation community worldwide. DLR/ZKI operates in this context, closely networking with public authorities (civil security), nongovernmental organizations (humanitarian relief organizations), satellite operators, and other space agencies. This paper reflects on several of these international activities, such as the International Charter Space and Major Disasters, describes mapping procedures, and reports on rapid-mapping experiences gained during various disaster-response applications. The example cases presented cover rapid impact assessment after the Indian Ocean Tsunami, forest fires mapping for Portugal, earthquake-damage assessment for Pakistan, and landslide extent mapping for the Philippines

Toward a Science of Cyber–Physical System Integration <=SEP=> System integration is the elephant in the china store of large-scale cyber-physical system (CPS) design. It would be hard to find any other technology that is more undervalued scientifically and at the same time has bigger impact on the presence and future of engineered systems. The unique challenges in CPS integration emerge from the heterogeneity of components and interactions. This heterogeneity drives the need for modeling and analyzing cross-domain interactions among physical and computational/networking domains and demands deep understanding of the effects of heterogeneous abstraction layers in the design flow. To address the challenges of CPS integration, significant progress needs to be made toward a new science and technology foundation that is model based, precise, and predictable. This paper presents a theory of composition for heterogeneous systems focusing on stability. Specifically, the paper presents a passivity-based design approach that decouples stability from timing uncertainties caused by networking and computation. In addition, the paper describes cross-domain abstractions that provide effective solution for model-based fully automated software synthesis and high-fidelity performance analysis. The design objectives demonstrated using the techniques presented in the paper are group coordination for networked unmanned air vehicles (UAVs) and high-confidence embedded control software design for a quadrotor UAV. Open problems in the area are also discussed, including the extension of the theory of compositional design to guarantee properties beyond stability, such as safety and performance.

An evaluation of alternative architectures for transaction processing in the cloud <=SEP=> Cloud computing promises a number of advantages for the deployment of data-intensive applications. One important promise is reduced cost with a pay-as-you-go business model. Another promise is (virtually) unlimited throughput by adding servers if the workload increases. This paper lists alternative architectures to effect cloud computing for database applications and reports on the results of a comprehensive evaluation of existing commercial cloud services that have adopted these architectures. The focus of this work is on transaction processing (i.e., read and update workloads), rather than analytics or OLAP workloads, which have recently gained a great deal of attention. The results are surprising in several ways. Most importantly, it seems that all major vendors have adopted a different architecture for their cloud services. As a result, the cost and performance of the services vary significantly depending on the workload.

Integrated Dual-Channel X-Band Offset-Transmitter for Phase Steering and DDMA Arrays <=SEP=> Multiple-Input and Multiple-Output (MIMO) arrays provide more degrees of freedom than conventional phased arrays. They require that every transmitting element of the array can be identified when received. One way to achieve this is to give every element its own unique frequency. Offset-transmitters may be used to introduce MIMO or Doppler Division Multiple Access (DDMA) into phased-arrays without an excessive increase in waveform-generating hardware. Our dual-channel demonstrator IC can obtain a phase accuracy better than 1 degree and an spurious level of better than -65dBc for a single on-chip channel. This work investigates at X-band the effects of the limited on-chip isolation of 35dB, when multiple offset outputs are generated on a single chip for both beam steering and DDMA. In case of beam steering, the requirements on channel-to-channel isolation are less strict, making it well within reach. In the case of DDMA, we recommend increasing the channel-to-channel isolation by implementing multiple chips, in which case independent signals can be generated.

Semi-supervised Semantic Pattern Discovery with Guidance from Unsupervised Pattern Clusters <=SEP=> We present a simple algorithm for clustering semantic patterns based on distributional similarity and use cluster memberships to guide semi-supervised pattern discovery. We apply this approach to the task of relation extraction. The evaluation results demonstrate that our novel bootstrapping procedure significantly outperforms a standard bootstrapping. Most importantly, our algorithm can effectively prevent semantic drift and provide semi-supervised learning with a natural stopping criterion.

A Theoretical Basis for a Biopharmaceutic Drug Classification: The Correlation of in Vitro Drug Product Dissolution and in Vivo Bioavailability <=SEP=> A biopharmaceutics drug classification scheme for correlating in vitro drug product dissolution and in vivo bioavailability is proposed based on recognizing that drug dissolution and gastrointestinal permeability are the fundamental parameters controlling rate and extent of drug absorption. This analysis uses a transport model and human permeability results for estimating in vivo drug absorption to illustrate the primary importance of solubility and permeability on drug absorption. The fundamental parameters which define oral drug absorption in humans resulting from this analysis are discussed and used as a basis for this classification scheme. These Biopharmaceutic Drug Classes are defined as: Case 1. High solubility-high permeability drugs, Case 2. Low solubility-high permeability drugs, Case 3. High solubility-low permeability drugs, and Case 4. Low solubility-low permeability drugs. Based on this classification scheme, suggestions are made for setting standards for in vitro drug dissolution testing methodology which will correlate with the in vivo process. This methodology must be based on the physiological and physical chemical properties controlling drug absorption. This analysis points out conditions under which no in vitro-in vivo correlation may be expected e.g. rapidly dissolving low permeability drugs. Furthermore, it is suggested for example that for very rapidly dissolving high solubility drugs, e.g. 85% dissolution in less than 15 minutes, a simple one point dissolution test, is all that may be needed to insure bioavailability. For slowly dissolving drugs a dissolution profile is required with multiple time points in systems which would include low pH, physiological pH, and surfactants and the in vitro conditions should mimic the in vivo processes. This classification scheme provides a basis for establishing in vitro-in vivo correlations and for estimating the absorption of drugs based on the fundamental dissolution and permeability properties of physiologic importance.

Improved ID3 algorithm <=SEP=> as the classical algorithm of the decision tree classification algorithm, ID3 is famous for the merits of high classifying speed easy, strong learning ability and easy construction. But when use it to classify, there does exist the problem of inclining to chose attributions which has many values, which affects its practicality. This paper for solving the problem a decision tree algorithm based on attribute-importance is proposed. The improved algorithm uses attribute-importance to increase information gain of attribution which has fewer attributions and compares ID3 with improved ID3 by an example. The experimental analysis of the data show that the improved ID3 algorithm can get more reasonable and more effective rules.

A Survey of Enabling Technologies of Low Power and Long Range Machine-to-Machine Communications <=SEP=> Low power and long range machine-to-machine (M2M) communication techniques are expected to provide ubiquitous connections for the wireless devices. In this paper, three major low power and long range M2M solutions are surveyed. The first type of solutions is referred to as the low power wide area (LPWA) network. The design of the LPWA techniques features low cost, low data rate, long communication range, and low power consumption. The second type of solutions is the IEEE 802.11ah which features higher data rates using a wider bandwidth than the LPWA-based solutions. The third type of solutions is operated under the cellular network infrastructure. Based on the analysis of the pros and cons of the enabling technologies of the surveyed M2M solutions, as well as the corresponding deployment strategies, the gaps in knowledge are identified. The paper also presents a summary of the research directions for improving the performance of the surveyed low power and long range M2M communication technologies.

A New Bit-Serial Architecture for Field Multiplication Using Polynomial Bases <=SEP=> Multiplication is the main finite field arithmetic operation in elliptic curve cryptography and its bit-serial hardware implementation is attractive in resource constrained environments such as smart cards, where the chip area is limited. In this paper, a new serial-output bitserial multiplier using polynomial bases over binary extension fields is proposed. It generates a bit of the multiplication in each clock cycle with the latency of one cycle. To the best of our knowledge, this is the first time that such a serial-output bit-serial multiplier architecture using polynomial bases for general irreducible polynomials is proposed.

DBpedia SPARQL Benchmark - Performance Assessment with Real Queries on Real Data <=SEP=> Triple stores are the backbone of increasingly many Data Web applications. It is thus evident that the performance of those stores is mission critical for individual projects as well as for data integration on the Data Web in general. Consequently, it is of central importance during the implementation of any of these applications to have a clear picture of the weaknesses and strengths of current triple store implementations. In this paper, we propose a generic SPARQL benchmark creation procedure, which we apply to the DBpedia knowledge base. Previous approaches often compared relational and triple stores and, thus, settled on measuring performance against a relational database which had been converted to RDF by using SQL-like queries. In contrast to those approaches, our benchmark is based on queries that were actually issued by humans and applications against existing RDF data not resembling a relational schema. Our generic procedure for benchmark creation is based on query-log mining, clustering and SPARQL feature analysis. We argue that a pure SPARQL benchmark is more useful to compare existing triple stores and provide results for the popular triple store implementations Virtuoso, Sesame, Jena-TDB, and BigOWLIM. The subsequent comparison of our results with other benchmark results indicates that the performance of triple stores is by far less homogeneous than suggested by previous benchmarks.

Bioinspired engineering of thermal materials. <=SEP=> In the development of next-generation materials with enhanced thermal properties, biological systems in nature provide many examples that have exceptional structural designs and unparalleled performance in their thermal or nonthermal functions. Bioinspired engineering thus offers great promise in the synthesis and fabrication of thermal materials that are difficult to engineer through conventional approaches. In this review, recent progress in the emerging area of bioinspired advanced materials for thermal science and technology is summarized. State-of-the-art developments of bioinspired thermal-management materials, including materials for efficient thermal insulation and heat transfer, and bioinspired materials for thermal/infrared detection, are highlighted. The dynamic balance of bioinspiration and practical engineering, the correlation of inspiration approaches with the targeted applications, and the coexistence of molecule-based inspiration and structure-based inspiration are discussed in the overview of the development. The long-term outlook and short-term focus of this critical area of advanced materials engineering are also presented.

Empirical Study on the Healing Nature of Mandalas <=SEP=> Mandalas were first used in therapy by Carl Jung, who found that the act of drawing mandalas had a calming effect on patients while at the same time facilitating psychic integration. There is a scarcity of controlled empirical studies of the healing impact of mandalas on mental health. Based on the efficacy of James Pennebaker’s written disclosure paradigm in promoting mental well-being (Pennebaker, 1997a, 1997b), the purpose of our study was to examine the benefits for those suffering from post traumatic stress disorder (PTSD) of processing traumatic events through the creation of mandalas. Benefits to participants were measured in terms of changes in the variables of PTSD symptoms, depressive symptoms, anxiety, spiritual meaning, and the frequency of physical symptoms and illness. Relative to those in the control condition, individuals assigned to the experimental mandala-creation group reported greater decreases in symptoms of trauma at the 1-month follow up. There were no other statistically significant outcome differences. Alternative modes of processing traumatic events (e.g., visually symbolically) may serve individuals who are either reluctant or unable to write about their experiences.

Improving the Performance of the FFT-based Parallel Code-phase Search Acquisition of GNSS Signals by Decomposition of the Circular Correlation <=SEP=> Dr. Cyril Botteron leads the GNSS and UWB groups in the electronics and signal processing laboratory at EPFL. He received his PhD degree from the University of Calgary, Canada, in 2003. His current research interests comprise the development of low power radio frequency (RF) integrated circuits and advanced signal processing techniques for ultra-low power communications and global and local positioning applications.

ButterflyNet: a mobile capture and access system for field biology research <=SEP=> Through a study of field biology practices, we observed that biology fieldwork generates a wealth of heterogeneous information, requiring substantial labor to coordinate and distill. To manage this data, biologists leverage a diverse set of tools, organizing their effort in paper notebooks. These observations motivated ButterflyNet, a mobile capture and access system that integrates paper notes with digital photographs captured during field research. Through ButterflyNet, the activity of leafing through a notebook expands to browsing all associated digital photos. ButterflyNet also facilitates the transfer of captured content to spreadsheets, enabling biologists to share their work. A first-use study with 14 biologists found this system to offer rich data capture and transformation, in a manner felicitous with current practice.

An extra low-power 1Tbit/s bandwidth PLL/DLL-less eDRAM PHY using 0.3V low-swing IO for 2.5D CoWoS application <=SEP=> A 1Tbit/s bandwidth PHY is demonstrated through 2.5D CoWoS platform. Two chips: SOC and eDRAM have been fabricated in TSMC 40nm CMOS technology and stacked on another silicon interposer chip in 65nm technology. Total 1024 DQ bus operating in 1.1Gbit/s with Vmin=0.3V are proven in experimental results. A novel timing compensation mechanism is presented to achieve a low-power and small area eDRAM PHY that excludes PLL/DLL but retains good timing margin. Another data sampling alignment training approach is reserved to enhance timing robustness. A compact low-swing IO also achieves great power efficiency of 0.105mW/Gbps.

Closed-Loop Control of a Three-Phase Neutral-Point-Clamped Inverter Using an Optimized Virtual-Vector-Based Pulsewidth Modulation <=SEP=> This paper presents a closed-loop control scheme for the three-level three-phase neutral-point-clamped dc-ac converter using the optimized nearest three virtual-space-vector pulsewidth modulation, which is a modulation that produces low output-voltage distortion with a significant reduction of the dc-link capacitance. A new specific loop modifying the modulating waveforms is proposed to rapidly control possible perturbations in the neutral-point voltage balance. An online estimation of the load displacement angle and load linear/nonlinear nature is introduced at no extra cost. The remaining part of the control is analogous to the control for a two-level converter with an appropriate interfacing to the selected modulation. The closed-loop control is designed for the case of a renewable-energy source connected to the ac mains, and its performance is analyzed through simulation and experiments.

From single cells to deep phenotypes in cancer <=SEP=> In recent years, major advances in single-cell measurement systems have included the introduction of high-throughput versions of traditional flow cytometry that are now capable of measuring intracellular network activity, the emergence of isotope labels that can enable the tracking of a greater variety of cell markers and the development of super-resolution microscopy techniques that allow measurement of RNA expression in single living cells. These technologies will facilitate our capacity to catalog and bring order to the inherent diversity present in cancer cell populations. Alongside these developments, new computational approaches that mine deep data sets are facilitating the visualization of the shape of the data and enabling the extraction of meaningful outputs. These applications have the potential to reveal new insights into cancer biology at the intersections of stem cell function, tumor-initiating cells and multilineage tumor development. In the clinic, they may also prove important not only in the development of new diagnostic modalities but also in understanding how the emergence of tumor cell clones harboring different sets of mutations predispose patients to relapse or disease progression.

Sequence Covering for Efficient Host-Based Intrusion Detection <=SEP=> This paper introduces a new similarity measure, the covering similarity, which we formally define for evaluating the similarity between a symbolic sequence and a set of symbolic sequences. A pairwise similarity can also be directly derived from the covering similarity to compare two symbolic sequences. An efficient implementation to compute the covering similarity is proposed which uses a suffix-tree data structure, but other implementations, based on suffix array for instance, are possible and are possibly necessary for handling very large-scale problems. We have used this similarity to isolate attack sequences from normal sequences in the scope of host-based intrusion detection. We have assessed the covering similarity on two well-known benchmarks in the field. In view of the results reported on these two datasets for the state-of-the-art methods, according to the comparative study, we have carried out based on three challenging similarity measures commonly used for string processing, or in bioinformatics, we show that the covering similarity is particularly relevant to address the detection of anomalies in sequences of system calls.

Qualitative clinical evaluation of scapular dysfunction: a reliability study. <=SEP=> The purpose of this study was to determine the intrarater and interrater reliability of a clinical evaluation system for scapular dysfunction. No commonly accepted terminology presently exists for describing the abnormal dynamic scapular movement patterns that are commonly associated with shoulder injury. A method of observation was devised for clinical evaluation of scapular dysfunction. Blinded evaluators (2 physicians and 2 physical therapists) were familiarized with the evaluation method of scapular movement patterns before viewing a videotape of 26 subjects with and without scapular dysfunction. Each evaluator was asked to categorize the predominant scapular movement pattern observed during bilateral humeral scaption and abduction motions. Reliability was assessed by a kappa coefficient. Intertester reliability (kappa = 0.4) was found to be slightly lower than intratester reliability (kappa = 0.5). These results indicate that, with refinement, this qualitative evaluation method may allow clinicians to standardize the categorization of dynamic scapular dysfunction patterns.

DART: a Dataset of Arguments and their Relations on Twitter <=SEP=> The problem of understanding the stream of messages exchanged on social media such as Facebook and Twitter is becoming a major challenge for automated systems. The tremendous amount of data exchanged on these platforms as well as the specific form of language adopted by social media users constitute a new challenging context for existing argument mining techniques. In this paper, we describe a resource of natural language arguments called DART (Dataset of Arguments and their Relations on Twitter) where the complete argument mining pipeline over Twitter messages is considered: (i) we identify which tweets can be considered as arguments and which cannot, and (ii) we identify what is the relation, i.e., support or attack, linking such tweets to each other.

I feel your pain: emotional closeness modulates neural responses to empathically experienced rejection. <=SEP=> Empathy is generally thought of as the ability to share the emotional experiences of others. In scientific terms, this is usually operationalized as an ability to vicariously feel others' mental and emotional experiences. Supporting this account, research demonstrates that watching others experience physical pain activates similar brain regions to the actual experience of pain itself. First-hand experience of social rejection also activates this network. The current work extends these findings by examining whether the "pain" network is similarly implicated in witnessing rejection, and whether emotional closeness modulates this response. We provide evidence for each of these suppositions, demonstrating: (a) that the pain network is activated when watching a friend suffer social rejection, and (b) that interpersonal closeness with that friend modulates this response. Further, we found that the inferior frontal gyrus, critical for representing others' mental and emotional states, mediates the relationship between emotional closeness and neural responses to watching the rejection of a friend.

HPI Question Answering System in BioASQ 2016 <=SEP=> Question answering (QA) systems are crucial when searching for exact answers for natural language questions in the biomedical domain. Answers to many of such questions can be extracted from the 26 millions biomedical publications currently included in MEDLINE when relying on appropriate natural language processing (NLP) tools. In this work we describe our participation in the task 4b of the BioASQ challenge using two QA systems that we developed for biomedicine. Preliminary results show that our systems achieved first and second positions in the snippet retrieval sub-task and for the generation of ideal answers.

The acceptance and use of customer relationship management (CRM) systems: An empirical study of distribution service industry in Taiwan <=SEP=> With the rapid change of business competitive environment, enterprise resource integration and innovative issues of business operation have gradually become the most important issues for businesses. Furthermore, many enterprises have implemented novel information technology and developing the innovative e-business applications systems such as enterprise resource planning (ERP), customer relationship management (CRM), knowledge management (KM) and supply chain management (SCM) to enhance their competitive advantages. CRM systems can help organizations to gain the potential new customers, promote the existing customers’ purchase, maintain good relationship with customers as well as to enhance the customer value, thus can improve the enterprise images. Moreover, the development and applications of CRM systems have also been considered as important issues for researchers and practitioners in recent years. For Taiwan’s industry, it has been gradually transferred from manufacturing-oriented to a service-oriented. Thus, the service industry has higher percentage in the GDP and in which the distribution service industry is the biggest one and be a core industry in the whole of service industry. The main purpose of this study is to explore the factors affecting the acceptance and use of CRM systems. Furthermore, the proposed research model was built on the basis of unified theory of acceptance and use of technology (UTAUT) and task-technology fit (TTF) framework as well as technological and managerial theories. The implications of findings for practice will be discussed. 2010 Elsevier Ltd. All rights reserved.

Spam filtering email classification (SFECM) using gain and graph mining algorithm <=SEP=> This paper proposes a hybrid solution of spam email classifier using context based email classification model as main algorithm complimented by information gain calculation to increase spam classification accuracy. Proposed solution consists of three stages email pre-processing, feature extraction and email classification. Research has found that LingerIG spam filter is highly effective at separating spam emails from cluster of homogenous work emails. Also experiment result proved the accuracy of spam filtering is 100% as recorded by the team of developers at University of Sydney. The study has shown that implementing the spam filter in the context-based email classification model is feasible. Experiment of the study has confirmed that spam filtering aspect of context-based classification model can be improved.

Efficient parallel translating embedding for knowledge graphs <=SEP=> Knowledge graph embedding aims to embed entities and relations of knowledge graphs into low-dimensional vector spaces. Translating embedding methods regard relations as the translation from head entities to tail entities, which achieve the state-of-the-art results among knowledge graph embedding methods. However, a major limitation of these methods is the time consuming training process, which may take several days or even weeks for large knowledge graphs, and result in great difficulty in practical applications. In this paper, we propose an efficient parallel framework for translating embedding methods, called ParTrans-X, which enables the methods to be paralleled without locks by utilizing the distinguished structures of knowledge graphs. Experiments on two datasets with three typical translating embedding methods, i.e., TransE [3], TransH [19], and a more efficient variant TransE- AdaGrad [11] validate that ParTrans-X can speed up the training process by more than an order of magnitude.