Miracle-dz/UniFAR-FTUs huggingface dataset:
The Study of FNN Control on Direct Torque Control for BLDCM

In order to improve the response speed of brushless DC motor (BLDCM) control system and simplify its structure, a novel scheme is proposed in this paper. Direct torque control (DTC) scheme and fuzzy neural network strategy are combined in the new scheme. DTC has the advantage of fast response and simple system structure without complex vector transform unit. However, its torque ripple is relatively high. In the fuzzy neural network DTC system, exact magnet flux linkage can be acquired by neural network method and the actuation duration of voltage vector is fuzzily adjusted according to both torque error and its variance ratio to reduce torque ripple. Simulation results prove that the proposed control strategy is feasible. At last, it is verified by experiment results that the fuzzy neural network strategy executes effective control on torque and current. Consequently, the experimental system operates with good steady and transient behavior
Mean-Square Error in Periodogram Approaches With Adaptive Windowing

Modified periodogram approaches are nonparametric power spectral density (PSD) estimators. Here, we present a method for estimating the mean-square error (MSE) of these PSD estimators. The proposed approach uses the observed data to estimate not only the PSD but also the associated MSE simultaneously. The MSE estimate from the Blackman-Tukey approach can be utilized for comparison and choice of the optimum window among a set of smoothing windows of possibly different lengths. For Bartlett and Welch methods, the MSE estimate can be used for quality evaluation, and also enables the use of an additional smooth windowing for these modified periodogram approaches. The optimum adaptive windowing improves the performance of these approaches in the MSE sense. Furthermore, the optimally windowed autocorrelation estimate can be used for extrapolation with the maximum entropy method (MEM). Our simulation results confirm that the proposed optimum smooth windowing approach effectively improves the performance of modified periodogram PSD estimates in the MSE sense.
Occupational risk factors for lung cancer: a case-control study in West Germany.

BACKGROUND The aim of this study is to evaluate carcinogens and occupations suspected to cause lung cancer and to generate new hypotheses about occupational risks. METHODS In a hospital-based study 1004 incident lung cancer cases and the same number of population controls matched for region, sex and age were interviewed between 1988 and 1993 for their smoking and occupational history. Exposure assessment was based on 33 job-specific supplementary questionnaires. Conditional logistic regression was used to calculate odds ratios (OR) and to control for smoking and occupational asbestos exposure. RESULTS Lifelong prevalence of exposure to asbestos was 20.5% for exposure of more than 940 lifetime working hours among controls, corresponding to an OR of 1.62 (95% confidence interval [CI] : 1.28-2.05) that was reduced to 1.45 after adjustment for smoking (P < 5%). Statistically elevated risks after adjustment for smoking and asbestos were seen in metal production and processing workers, transportation workers and freight handlers, in the rubber and plastics industry, in metal production, in engine and vehicle building, and installation. Significantly increased OR after adjustment for smoking and asbestos that deserve further attention were seen in plastics processing workers (OR = 3.49), and sheet and structural metal workers (OR = 2.01 and 2.37, respectively). CONCLUSIONS The results of the study confirm previously described occupational risks. Because of the possibility of controlling for occupational asbestos exposure, the study gives clear indications for prevention and further research.
Climate change and human health: what are the research trends? A scoping review protocol

Introduction For 28 years, the Intergovernmental Panel on Climate Change (IPCC) has been assessing the potential risks associated with anthropogenic climate change. Although interest in climate change and health is growing, the implications arising from their interaction remain understudied. Generating a greater understanding of the health impacts of climate change could be key step in inciting some of the changes necessary to decelerate global warming. A long-term and broad overview of the existing scientific literature in the field of climate change and health is currently missing in order to ensure that all priority areas are being adequately addressed. In this paper we outline our methods to conduct a scoping review of the published peer-reviewed literature on climate change and health between 1990 and 2015. Methods and analysis A detailed search strategy will be used to search the PubMed and Web of Science databases. Specific inclusion and exclusion criteria will be applied in order to capture the most relevant literature in the time frame chosen. Data will be extracted, categorised and coded to allow for statistical analysis of the results. Ethics and dissemination No ethical approval was required for this study. A searchable database of climate change and health publications will be developed and a manuscript will be complied for publication and dissemination of the findings. We anticipate that this study will allow us to map the trends observed in publications over the 25-year time period in climate change and health research. It will also identify the research areas with the highest volume of publications as well as highlight the research trends in climate change and health.
Wide-Baseline Image Mosaicing for Indoor Environments

We introduce an automated image mosaicing system using a limited number of cameras arranged along a wide baseline, which generates a perceptually acceptable panorama of an indoor environment, including human subjects at varying depths. The target application for this research is the production of a seamless, extra-wide display for videoconferencing. In such a case, we deliberately employ a conﬁguration of translated cameras in order to preserve sensible semantics of eye contact for a viewer located toward the sides of the display. This is preferable to co-locating the cameras at a common center position in an attempt to approximate the case of rotation-only camera movement. Our system applies feature-based and direct image alignment techniques to register the input images, and then creates the ﬁnal image mosaic through multi-perspective projections. The competitive results of our work with current state-of-the-art techniques are discussed.
BF Integrase Genes of HIV-1 Circulating in São Paulo, Brazil, with a Recurrent Recombination Region

Although some studies have shown diversity in HIV integrase (IN) genes, none has focused particularly on the gene evolving in epidemics in the context of recombination. The IN gene in 157 HIV-1 integrase inhibitor-naïve patients from the São Paulo State, Brazil, were sequenced tallying 128 of subtype B (23 of which were found in non-B genomes), 17 of subtype F (8 of which were found in recombinant genomes), 11 integrases were BF recombinants, and 1 from subtype C. Crucially, we found that 4 BF recombinant viruses shared a recurrent recombination breakpoint region between positions 4900 and 4924 (relative to the HXB2) that includes 2 gRNA loops, where the RT may stutter. Since these recombinants had independent phylogenetic origin, we argue that these results suggest a possible recombination hotspot not observed so far in BF CRF in particular, or in any other HIV-1 CRF in general. Additionally, 40% of the drug-naïve and 45% of the drug-treated patients had at least 1 raltegravir (RAL) or elvitegravir (EVG) resistance-associated amino acid change, but no major resistance mutations were found, in line with other studies. Importantly, V151I was the most common minor resistance mutation among B, F, and BF IN genes. Most codon sites of the IN genes had higher rates of synonymous substitutions (dS) indicative of a strong negative selection. Nevertheless, several codon sites mainly in the subtype B were found under positive selection. Consequently, we observed a higher genetic diversity in the B portions of the mosaics, possibly due to the more recent introduction of subtype F on top of an ongoing subtype B epidemics and a fast spread of subtype F alleles among the B population.
Robust highlight extraction using multi-stream hidden Markov models for baseball video

This paper proposes a robust statistical framework to extract highlights from a baseball broadcast video. We applied multi-stream hidden Markov models (HMMs) to control the weights among different features. To achieve robustness against new highlights, we used a common simple structure for all the HMMs. In addition, scene segmentation and unsupervised adaptation were applied to achieve more robustness against the differences of environmental conditions among games. The precision rate of high-light extracting experiments for eight kinds of highlights from 4.5 hours of digest data was 77.4% and was increased to 78.7% by applying scene segmentation. Furthermore, the unsupervised adaptation method improved precision by 2.7 points to 81.4%. These results confirm the effectiveness of our framework.
Relevance filtering meets active learning: improving web-based concept detectors

We address the challenge of training visual concept detectors on web video as available from portals such as YouTube. In contrast to high-quality but small manually acquired training sets, this setup permits us to scale up concept detection to very large training sets and concept vocabularies. On the downside, web tags are only weak indicators of concept presence, and web video training data contains lots of non-relevant content. So far, there are two general strategies to overcome this label noise problem, both targeted at discarding non-relevant training content: (1) a manual refinement supported by active learning sample selection, (2) an automatic refinement using relevance filtering. In this paper, we present a highly efficient approach combining these two strategies in an interleaved setup: manually refined samples are directly used to improve relevance filtering, which again provides a good basis for the next active learning sample selection. Our results demonstrate that the proposed combination -- called active relevance filtering -- outperforms both a purely automatic filtering and a manual one based on active learning. For example, by using 50 manual labels per concept, an improvement of 5% over an automatic filtering is achieved, and 6% over active learning. By annotating only 25% of weak positive samples in the training set, a performance comparable to training on ground truth labels is reached.
GangES: Gang error simulation for hardware resiliency evaluation

As technology scales, the hardware reliability challenge affects a broad computing market, rendering traditional redundancy based solutions too expensive. Software anomaly based hardware error detection has emerged as a low cost reliability solution, but suffers from Silent Data Corruptions (SDCs). It is crucial to accurately evaluate SDC rates and identify SDC producing software locations to develop software-centric low-cost hardware resiliency solutions.A recent tool, called Relyzer, systematically analyzes an entire application's resiliency to single bit soft-errors using a small set of carefully selected error injection sites. Relyzer provides a practical resiliency evaluation mechanism but still requires significant evaluation time, most of which is spent on error simulations. This paper presents a new technique called GangES (Gang Error Simulator) that aims to reduce error simulation time. GangES observes that a set or gang of error simulations that result in the same intermediate execution state (after their error injections) will produce the same error outcome; therefore, only one simulation of the gang needs to be completed, resulting in significant overall savings in error simulation time. GangES leverages program structure to carefully select when to compare simulations and what state to compare. For our workloads, GangES saves 57% of the total error simulation time with an overhead ofjust 1.6%. This paper also explores pure program analyses based techniques that could obviate the needfor tools such as GangES altogether. The availability of Relyzer+GangES allows us to perform a detailed evaluation of such techniques. We evaluate the accuracy of several previously proposed program metrics. We find that the metrics we considered and their various linear combinations are unable to adequately predict an instruction's vulnerability to SDCs, further motivating the use of Relyzer+GangES style techniques as valuable solutions for the hardware error resiliency evaluation problem.
A Study of Ontology-based Query Expansion

With enormous data emerging on the Web, traditional keyword searching is challenged by short queries posed by users to vaguely describe their information need. Query expansion has been researched for decades and a variety of expansion strategies have improved retrieval effectiveness. At present, knowledge-based query expansion approaches are popular as the Web becomes more semantic. This paper studies state-of-the-art in ontologybased query expansion approaches, and expands on practical strategies to exploit the rich semantics of domain ontologies. This paper, one the one hand, focuses on finding out the success factors for ontology-based query expansion; on the other hand, it emphasizes the tradeoff between the gained retrieval effectiveness and the incurred computation cost.
Emergent quantum criticality, Fermi surfaces, and AdS[subscript 2]

Citation Faulkner, Thomas et al. " Emergent quantum criticality, Fermi surfaces, and AdS[subscript 2]. " Physical Review D 83 (2011). Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. The MIT Faculty has made this article openly available. Please share how this access benefits you. Your story matters. Gravity solutions dual to d-dimensional field theories at finite charge density have a near-horizon region, which is AdS 2 Â R dÀ1. The scale invariance of the AdS 2 region implies that at low energies the dual field theory exhibits emergent quantum critical behavior controlled by a (0 þ 1)-dimensional conformal field theories (CFT). This interpretation sheds light on recently-discovered holographic descriptions of Fermi surfaces, allowing an analytic understanding of their low-energy excitations. For example, the scaling behavior near the Fermi surfaces is determined by conformal dimensions in the emergent IR CFT. In particular, when the operator is marginal in the IR CFT, the corresponding spectral function is precisely of the ''marginal Fermi liquid'' form, postulated to describe the optimally doped cuprates.
Dual Immersion Leadership: a Case Study of Three K-5 Principals Who Show Success with Emergent Bilinguals

i List of Tables.iv List of Figures.v Chapter 1 Problem Statement.1 Chapter 2 Literature Review.15 Chapter 3 Research Design and Methodology.60 Chapter 4 Results.75 Chapter 5 Discussion.118 References.139 Appendices.145 Appendix A. Informed Consent Form (Principals). 145 Appendix B. Informed Consent Form (Focus Groups). 147 Appendix C. Protocol for Document Analysis.149 Appendix D. Interview Protocol.150 Appendix E. Focus Group Protocol.153
Mechanical analysis of program complexity

There has been a great deal of research done which investigates the problem of evaluating the complexity of particular algorithms; little effort however has been applied to the mechanization of this evaluation. This paper presents the ACE (for Automatic Complexity Evaluator) system which is able to analyse reasonably large programs like sorting programs or numerical programs in a fully mechanical way. A complexity function is derived from the initial program. This function is then automatically transformed into its non-recursive equivalent according to MacCarthy's recursion induction principle, using a pre-defined library of recursive definitions (for example id, length, exponential .). As the execution time is not a decidable property, this transformation will not be possible in all cases. The richer the pre-defined library is, the more likely the system is to succeed. The paper presents the reasons for mechanizing complexity calculus and the problems involved. It describes the operations performed by ACE and its implementation; limitations and further improvements are discussed in conclusion.
Mean Squared Error Analysis of Quantizers With Error Feedback

Quantization is a fundamental process in digital signal processing. <inline-formula><tex-math notation="LaTeX"> $\Delta \Sigma$</tex-math></inline-formula> modulators are often utilized for quantization, which can be easily implemented with static uniform quantizers and error feedback filters. In this paper, we analyze the mean squared quantization error of the quantizer with error feedback including the <inline-formula><tex-math notation="LaTeX"> $\Delta \Sigma$</tex-math></inline-formula> modulators. First, we study the quantizer with an ideal optimal error feedback filter that minimizes the mean squared error (MSE) of quantization. We show that the amplitude response of the optimal error feedback filter can be parameterized by one parameter. This parameterization enables us to find the optimal error feedback filter numerically. Second, the relationship between the number of bits used for the quantizer and the achievable MSE is clarified by using the optimal error feedback filter. This makes it possible to investigate the efficiency of the quantizer with the optimal error feedback filter in terms of MSE. Then, ideal optimal error feedback filters are approximated by practically implementable filters using the Yule-Walker method and the linear matrix inequality-based method. Numerical examples are provided for demonstrating our analysis and synthesis.
Expected discovery potential and sensitivity of the ANTARES neutrino telescope to neutrino point-like sources

The ANTARES telescope is being built in the Mediterranean Sea. The detector consists of a 3D array of photomultipliers (PMTs) that detects the Cherenkov light induced by the muons produced in neutrino interactions. Other signatures can also be detected. Since the neutrino fluxes from point-like sources are expected to be small, it is of the utmost importance to take advantage of the ANTARES pointing accuracy (angular resolution better than 0.3 degrees for muon events above 10 TeV) to disentangle a possible signal from the unavoidable atmospheric neutrino background. In order to distinguish an excess of neutrino events from the background, several searching algorithms have been developed within the ANTARES collaboration. In this contribution, the discovery potential and sensitivity to point-like sources of the ANTARES neutrino telescope are presented.
Reservation-based resource scheduling and code partition in mobile cloud computing

Mobile cloudlet has shown great potential to extend the computational limitation of mobile devices. The overall performance of the mobile applications could be improved when the computational gain from the cloudlet overweights the transmission cost. Yet, resources scheduling on the cloudlet is a challenging problem when there are multiple mobile offloading requests at the same time. Since the offloading requests would compete with each other and each offloading decision would impact the others, finding the optimal execution layout is quite difficult. In this paper, we propose a joint resource scheduling and code partition scheme that can efficiently allocate cloudlet resources to multiple mobile users. Our goal is to maximize the cloudlet throughput as well as to reduce the mobile application's execution time. Unlike the current approach which considers an infinite resource limitation on the cloud, our algorithm dynamically allocate the cloudlet resource based on the current usage. Both real world experiments and trace-driven simulations show that our solution can improve the cloudlet throughput by 20%-35% in the meantime provide speedup to mobile devices.
Performance evaluation of a hybrid optical/electrical interconnect

In response to the need for higher-speed and affordable-cost datacenter interconnection networks, hybrid optical/electrical architectures have been proposed. Even still, a number of implementation issues remain open and little is known about the performance of real applications over such networks. To fill this gap, we present a hybrid network architecture, called HydRA, comprising commodity off-the-shelf equipment, calculate its total price using current list prices, and show its price competitiveness against fat-tree alternatives. We also report on a prototype implementation of our HydRA network that uses Ethernet switches and a custom-built software controller. We show that our HydRA network prototype manages to accelerate the execution of real parallel workloads, when compared to equal-cost electronic-only fat-tree networks.
Electronic Reprint Applied Crystallography Advanced Solution Scattering Data Analysis Methods and Their Applications Advanced Solution Scattering Data Analysis Methods and Their Applications † † † Advanced Data Analysis Methods

Author(s) of this paper may load this reprint on their own web site provided that this cover page is retained. Republication of this article or its storage in electronic databases or the like is not permitted without prior permission in writing from the IUCr. A method for ab initio low-resolution shape and internal structure retrieval from contrast variation solution scattering data is described. The method uses a multiphase model of a particle build from densely packed dummy atoms and employs simulated annealing to find a compact interconnected configuration of phases that fits the available experimental data. In the particular case of a single phase particle (shape determination) the method is compared to another ab initio method using low resolution envelope functions. Examples of the shape determination of several proteins from experimental X-ray scattering data are presented.
Psychophysiological measures for estimating attention

2.1 BRAIN. 1 2.2 HEART . 2 2.3 MUSCLES . 3 2.4 POSTURE. 3 2.5 EYE . 4 2.6 COMPOSITE MEASURES . 5
Sentence Ordering Driven by Local and Global Coherence for Summary Generation

In summarization, sentence ordering is conducted to enhance summary readability by accommodating text coherence. We propose a grouping-based ordering framework that integrates local and global coherence concerns. Summary sentences are grouped before ordering is applied on two levels: group-level and sentence-level. Different algorithms for grouping and ordering are discussed. The preliminary results on single-document news datasets demonstrate the advantage of our method over a widely accepted method.
Type I Interferon Contributes to CD4+ T Cell Depletion Induced by Infection with HIV-1 in the Human Thymus

ABSTRACT Persistent induction of type 1 interferon (IFN) is associated with human immunodeficiency virus type 1 (HIV-1) infection. We report here that the pathogenic HIV strain R3A (HIV-R3A) induced high levels of type 1 IFN, while the nonpathogenic HIV-R3B showed no significant induction in human fetal thymus organ culture (HFTOC). We found that IFN contributed to the depletion of human T cells by HIV-R3A in a fusion-independent fashion. The R3B recombinant with the R3A Env V1V2 domain (R3B/A-V1V2) was able to induce type 1 IFN, which contributed to the increased depletion of T cells. Therefore, type 1 IFN induction plays a significant role in HIV-induced T cell depletion in the human thymus.