nils-herrmann/clean_scirepeval huggingface dataset:
Unrestricted Vocabulary Keyword Spotting Using LSTM-CTC

Keyword spotting (KWS) aims to detect predefined keywords in continuous speech. Recently, direct deep learning approaches have been used for KWS and achieved great success. However, these approaches mostly assume fixed keyword vocabulary and require significant retraining efforts if new keywords are to be detected. For unrestricted vocabulary, HMM based keywordfiller framework is still the mainstream technique. In this paper, a novel deep learning approach is proposed for unrestricted vocabulary KWS based on Connectionist Temporal Classification (CTC) with Long Short-Term Memory (LSTM). Here, an LSTM is trained to discriminant phones with the CTC criterion. During KWS, an arbitrary keyword can be specified and it is represented by one or more phone sequences. Due to the property of peaky phone posteriors of CTC, the LSTM can produce a phone lattice. Then, a fast substring matching algorithm based on minimum edit distance is used to search the keyword phone sequence on the phone lattice. The approach is highly efficient and vocabulary independent. Experiments showed that the proposed approach can achieve significantly better results compared to a DNN-HMM based keyword-filler decoding system. In addition, the proposed approach is also more efficient than the DNN-HMM KWS baseline.
PEN: Parallel English-Persian news corpus

Parallel corpora are the necessary resources in many multilingual natural language processing applications, including machine translation and cross-lingual information retrieval. Manual preparation of a large scale parallel corpus is a very time consuming and costly procedure. In this paper, the work towards building a sentence-level aligned EnglishPersian corpus in a semi-automated manner is presented. The design of the corpus, collection, and alignment process of the sentences is described. Two statistical similarity measures were used to find the similarities of sentence pairs. To verify the alignment process automatically, Google Translator was used. The corpus is based on news resources available online and consists of about 30,000 formal sentence pairs.
A Brief Tutorial on Distributed and Concurrent Machine Learning

The area of machine learning has made considerable progress over the past decade, enabled by the widespread availability of large datasets, as well as by improved algorithms and models. Given the large computational demands of machine learning workloads, parallelism, implemented either through single-node concurrency or through multi-node distribution, has been a third key ingredient to advances in machine learning.
 The goal of this tutorial is to provide the audience with an overview of standard distribution techniques in machine learning, with an eye towards the intriguing trade-offs between synchronization and communication costs of distributed machine learning algorithms, on the one hand, and their convergence, on the other.The tutorial will focus on parallelization strategies for the fundamental stochastic gradient descent (SGD) algorithm, which is a key tool when training machine learning models, from classical instances such as linear regression, to state-of-the-art neural network architectures.
 The tutorial will describe the guarantees provided by this algorithm in the sequential case, and then move on to cover both shared-memory and message-passing parallelization strategies, together with the guarantees they provide, and corresponding trade-offs. The presentation will conclude with a broad overview of ongoing research in distributed and concurrent machine learning. The tutorial will assume no prior knowledge beyond familiarity with basic concepts in algebra and analysis.
Effect of irrigation with magnetically treated water on faba bean growth and composition

*Corresponding author Email: ahmed_shabaka@yahoo.com Phone: +20643381839 A pot experiment was conducted to investigate the effect of irrigation with different magnetized water on faba bean (Vicia faba L) growth and composition. Prepared sandy soil was packed in plastic pots (5 kg capacity) at a rate of 4 kg. Faba bean seeds were cultivated at rate of 4 seeds/pot. After the germination,faba bean plants were thinned into 2 plants/pot. Both sewage sludge compost (SSC) and tricalcium phosphate (TCP) were added to the soil at a rate of 25 tones ha-1 and 720 kg ha-1,respectively. Irrigation water sources were magnetized by passing throws a magnetic field 1000 gauss magnetron unit of 0.5 inch diameter. Plant length, shoot and root fresh and dry weights of faba bean were significantly increased by using the different magnetized irrigation water sources compared with the non-magnetized water. Only root fresh and dry weights of faba bean plant were significantly increased by using magnetized irrigation water and the different soil organic and inorganic treatments. On the other, plant length, shoot and fresh and dry weights were not significantly affected by the combined effect of magnetism and soil treatments. Shoot N, P and K contents and uptake of faba bean were significantly increased by the individual and the combined application of SSC and TCP to soil compared with untreated soil. Shoot N, P and K contents and uptake of faba bean was significantly increased by using magnetized irrigation water compared with the non-magnetized water.Generally, using different magnetized irrigation water sources, soil salinity, soluble cations and anions were significantly decreased by using magnetized water. Soil salinity, soluble cations and anions were significantly increased by adding both the individual and the combined SSC and TCP. Available soil N, P and K were significantly increased by adding both the individual and the combined SSC and TCP. Using different magnetized water sources, available soil N, P and K were significantly increased.
Automatic Population of a Pharmaceutical Company Ontology

We have implemented and evaluated a method to populate a company ontology, focusing on hierarchical relations such as acquisitions or subsidiaries. Our method searches for information about user-specified companies on the Internet using a search engine API (Google Custom Search API). From the resulted snippets we identify companies using machine learning and extract relations between them using a set of manually defined semantic patterns. We developed filtering methods both for companies and unlikely relations and from the set of company and relation instances we build this way, we construct an ontology addressing identity matching and consistency problems in a company-specific manner. We achieved a precision of 77 to 93 percent, depending on the evaluated relations.
DOES ORGANIC WINE TASTE BETTER ? AN ANALYSIS OF EXPERTS ’ RATINGS

Eco-labels are part of a new wave of environmental policy that emphasizes information disclosure as a tool to induce environmentally friendly behavior by both firms and consumers. Little consensus exists as to whether eco-certified products are actually better than their conventional counterparts. This paper seeks to understand the link between eco-certification and product quality. We use data from three leading wine rating publications (Wine Advocate, Wine Enthusiast, and Wine Spectator) to assess quality for 74,148 wines produced in California between 1998 and 2009. Our results indicate that eco-certification is associated with a statistically significant increase in wine quality rating.
Hamstring Injury Prevention Practices in Elite Sport: Evidence for Eccentric Strength vs. Lumbo-Pelvic Training.

Hamstring strain injuries are endemic in running-based sports. Given the economic and performance implications of these injuries, a significant body of research has emerged in recent years in an attempt to identify risk factors and develop or optimise injury prevention strategies. Surveys of injury prevention practices among medical and conditioning staff in elite sport suggest that many sporting clubs invest significant efforts in eccentric hamstring conditioning and lumbo-pelvic or trunk stability programmes. The purpose of this narrative review was to critically evaluate the evidence underpinning these practices. Single-exercise eccentric training interventions have proven effective in the prevention of primary and recurrent hamstring strains, when compliance is adequate. However, despite its almost universal acceptance, the authors are aware of only one, very recent, prospective risk factor study examining the effect of lumbo-pelvic motion during sprinting on hamstring injury risk. Furthermore, the interventions exploring the effect of lumbo-pelvic training on hamstring injury rates have not measured stability in any way. An improved understanding of the evidence underpinning commonly employed hamstring injury prevention practices may enable clinicians and coaches to better prioritise effective strategies in the increasingly complex environment of elite sport.
Cybersecurity Systems for Human Cognition Augmentation

Why should wait for some days to get or receive the cybersecurity systems for human cognition augmentation book that you order? Why should you take it if you can get the faster one? You can find the same book that you order right here. This is it the book that you can receive directly after purchasing. This cybersecurity systems for human cognition augmentation is well known book in the world, of course many people will try to own it. Why don't you become the first? Still confused with the way?
Structural plasticity and the evolution of antibody affinity and specificity.

The germline precursor to the ferrochelatase antibody 7G12 was found to bind the polyether jeffamine in addition to its cognate hapten N-methylmesoporphyrin. A comparison of the X-ray crystal structures of the ligand-free germline Fab and its complex with either hapten or jeffamine reveals that the germline antibody undergoes significant conformational changes upon the binding of these two structurally distinct ligands, which lead to increased antibody-ligand complementarity. The five somatic mutations introduced during affinity maturation lead to enhanced binding affinity for hapten and a loss in affinity for jeffamine. Moreover, a comparison of the crystal structures of the germline and affinity-matured antibodies reveals that somatic mutations not only fix the optimal binding site conformation for the hapten, but also introduce interactions that interfere with the binding of non-hapten molecules. The structural plasticity of this germline antibody and the structural effects of the somatic mutations that result in enhanced affinity and specificity for hapten likely represent general mechanisms used by the immune response, and perhaps primitive proteins, to evolve high affinity, selective receptors for so many distinct chemical structures.
A double-blind placebo-controlled study of lamotrigine monotherapy in outpatients with bipolar I depression. Lamictal 602 Study Group.

More treatment options for bipolar depression are needed. Currently available antidepressants may increase the risk of mania and rapid cycling, and mood stabilizers appear to be less effective in treating depression than mania. Preliminary data suggest that lamotrigine, an established antiepileptic drug, may be effective for both the depression and mania associated with bipolar disorder. This is the first controlled multicenter study evaluating lamotrigine monotherapy in the treatment of bipolar I depression.



Outpatients with bipolar I disorder experiencing a major depressive episode (DSM-IV, N = 195) received lamotrigine (50 or 200 mg/day) or placebo as monotherapy for 7 weeks. Psychiatric evaluations, including the Hamilton Rating Scale for Depression (HAM-D), the Montgomery-Asberg Depression Rating Scale (MADRS), Mania Rating Scale, and the Clinical Global Impressions scale for Severity (CGI-S) and Improvement (CGI-I) were completed at each weekly visit.



Lamotrigine 200 mg/day demonstrated significant antidepressant efficacy on the 17-item HAM-D, HAM-D Item 1, MADRS, CGI-S, and CGI-I compared with placebo. Improvements were seen as early as week 3. Lamotrigine 50 mg/day also demonstrated efficacy compared with placebo on several measures. The proportions of patients exhibiting a response on CGI-I were 51%, 41%, and 26% for lamotrigine 200 mg/day, lamotrigine 50 mg/day, and placebo groups, respectively. Adverse events and other safety results were similar across treatment groups, except for a higher rate of headache in the lamotrigine groups.



Lamotrigine monotherapy is an effective and well-tolerated treatment for bipolar depression.
Design and modeling of synthetic test circuit to generate four parameter TRV envelope

Short circuit current and transient recovery voltage arising in power systems under fault conditions can develop thermal and dielectric failures in the system and may create severe damage to the critical components. Therefore, main devices in our power system especially like circuit breaker extremely need to be tested first. Testing can be done by two ways; direct testing, and synthetic testing. For testing high voltage circuit breakers, direct testing is not economical because of high power generating capability requirement of laboratory, high installation cost, and more space. Synthetic testing is an economical method for testing of high voltage circuit breakers. In synthetic test circuit, it is quite complex to choose the circuit components value for a desired transient recovery voltage (TRV) envelope. It is because, modification of any component value may cause change in all parameters of output waveform. This paper proposes a synthesis process to design synthetic test circuit to generate four-parameter transient recovery voltage (TRV) envelope for circuit breaker testing. A synthetic test circuit has been simulated in PSCAD to generate four-parameter TRV envelope for 145kV rating of circuit breaker.
A literature review on emotion recognition system using various facial expression

Recognition and extracting various emotions and then validating those emotions from the facial expressions has become important for improving the overall human computer interaction. So this paper also describe about Emotion Recognition Techniques. Emotion Recognition has become a progressive research area and it plays a major role in Human-Computer-Interaction. For any Facial Expression Recognition, it is necessary to extract the features of face that can be possibly used to detect the expression. For Feature Extraction the Principal Component Analysis will be used. A survey of various techniques used in emotion recognition like PCA, LBP etc. is done in this paper and listing their performance. The goal of this paper is to show the comparison with Other Recognition Techniques with different approaches. And also describe the general step of the how to recognize emotion from various facial expression Keyword: -. Emotion Recognition, Facial Expression
Developmental and regional expression in the rat brain and functional properties of four NMDA receptors

An in situ study of mRNAs encoding NMDA receptor subunits in the developing rat CNS revealed that, at all stages, the NR1 gene is expressed in virtually all neurons, whereas the four NR2 transcripts display distinct expression patterns. NR2B and NR2D mRNAs occur prenatally, whereas NR2A and NR2C mRNAs are first detected near birth. All transcripts except NR2D peak around P20. NR2D mRNA, present mainly in midbrain structures, peaks around P7 and thereafter decreases to adult levels. Postnatally, NR2B and NR2C transcript levels change in opposite directions in the cerebellar internal granule cell layer. In the adult hippocampus, NR2A and NR2B mRNAs are prominent in CA1 and CA3 pyramidal cells, but NR2C and NR2D mRNAs occur in different subsets of interneurons. Recombinant binary NR1-NR2 channels show comparable Ca2+ permeabilities, but marked differences in voltage-dependent Mg2+ block and in offset decay time constants. Thus, the distinct expression profiles and functional properties of NR2 subunits provide a basis for NMDA channel heterogeneity in the brain.
Mean-square H∞ filtering for stochastic systems: Application to a 2DOF helicopter

This paper designs the central finite-dimensional H1 filter for linear stochastic systems with integral-quadratically bounded deterministic disturbances, that is suboptimal for a given threshold g with respect to a modified Bolza–Meyer quadratic criterion including the attenuation control term with the opposite sign. The original H1 filtering problem for a linear stochastic system is reduced to the corresponding mean-square H2 filtering problem, using the technique proposed in Doyle (1989) . In the example, the designed filter is applied to estimation of the pitch and yaw angles of a two degrees of freedom (2DOF) helicopter. & 2011 Elsevier B.V. All rights reserved.
Adjuvant capecitabine and oxaliplatin for gastric cancer after D2 gastrectomy (CLASSIC): a phase 3 open-label, randomised controlled trial

D2 gastrectomy is recommended in US and European guidelines, and is preferred in east Asia, for patients with resectable gastric cancer. Adjuvant chemotherapy improves patient outcomes after surgery, but the benefits after a D2 resection have not been extensively investigated in large-scale trials. We investigated the effect on disease-free survival of adjuvant chemotherapy with capecitabine plus oxaliplatin after D2 gastrectomy compared with D2 gastrectomy only in patients with stage II-IIIB gastric cancer.



The capecitabine and oxaliplatin adjuvant study in stomach cancer (CLASSIC) study was an open-label, parallel-group, phase 3, randomised controlled trial undertaken in 37 centres in South Korea, China, and Taiwan. Patients with stage II-IIIB gastric cancer who had had curative D2 gastrectomy were randomly assigned to receive adjuvant chemotherapy of eight 3-week cycles of oral capecitabine (1000 mg/m(2) twice daily on days 1 to 14 of each cycle) plus intravenous oxaliplatin (130 mg/m(2) on day 1 of each cycle) for 6 months or surgery only. Block randomisation was done by a central interactive computerised system, stratified by country and disease stage. Patients, and investigators giving interventions, assessing outcomes, and analysing data were not masked. The primary endpoint was 3 year disease-free survival, analysed by intention to treat. This study reports a prespecified interim efficacy analysis, after which the trial was stopped after a recommendation by the data monitoring committee. 
1035 patients were randomised (520 to receive chemotherapy and surgery, 515 surgery only). Median follow-up was 34·2 months (25·4-41·7) in the chemotherapy and surgery group and 34·3 months (25·6-41·9) in the surgery only group. 3 year disease-free survival was 74% (95% CI 69-79) in the chemotherapy and surgery group and 59% (53-64) in the surgery only group (hazard ratio 0·56, 95% CI 0·44-0·72; p<0·0001). Grade 3 or 4 adverse events were reported in 279 of 496 patients (56%) in the chemotherapy and surgery group and in 30 of 478 patients (6%) in the surgery only group. The most common adverse events in the intervention group were nausea (n=326), neutropenia (n=300), and decreased appetite (n=294).



Adjuvant capecitabine plus oxaliplatin treatment after curative D2 gastrectomy should be considered as a treatment option for patients with operable gastric cancer.



F Hoffmann-La Roche and Sanofi-Aventis.
FishNet: A Versatile Backbone for Image, Region, and Pixel Level Prediction

The basic principles in designing convolutional neural network (CNN) structures for predicting objects on different levels, e.g., image-level, region-level, and pixellevel, are diverging. Generally, network structures designed specifically for image classification are directly used as default backbone structure for other tasks including detection and segmentation, but there is seldom backbone structure designed under the consideration of unifying the advantages of networks designed for pixellevel or region-level predicting tasks, which may require very deep features with high resolution. Towards this goal, we design a fish-like network, called FishNet. In FishNet, the information of all resolutions is preserved and refined for the final task. Besides, we observe that existing works still cannot directly propagate the gradient information from deep layers to shallow layers. Our design can better handle this problem. Extensive experiments have been conducted to demonstrate the remarkable performance of the FishNet. In particular, on ImageNet-1k, the accuracy of FishNet is able to surpass the performance of DenseNet and ResNet with fewer parameters. FishNet was applied as one of the modules in the winning entry of the COCO Detection 2018 challenge. The code is available at https://github.com/kevin-ssy/FishNet.
Graph similarity and matching

Measures of graph similarity have a broad array of applications, including comparing chemical structures, navigating complex networks like the World Wide Web, and more recently, analyzing different kinds of biological data. This thesis surveys several different notions of similarity, then focuses on an interesting class of iterative algorithms that use the structural similarity of local neighborhoods to derive pairwise similarity scores between graph elements. We have developed a new similarity measure that uses a linear update to generate both node and edge similarity scores and has desirable convergence properties. This thesis also explores the application of our similarity measure to graph matching. We attempt to correctly position a subgraph GB within a graph GA using a maximum weight matching algorithm applied to the similarity scores between GA and GB. Significant performance improvements are observed when the topological information provided by the similarity measure is combined with additional information about the attributes of the graph elements and their local neighborhoods. Matching results are presented for subgraph matching within randomly-generated graphs; an appendix briefly discusses matching applications in the yeast interactome, a graph representing protein-protein interactions within yeast.  George Verghese Title: Professor of Electrical Engineering and Computer Science
Design and Fabrication of Double-Focused Ultrasound Transducers to Achieve Tight Focusing

Beauty treatment for skin requires a high-intensity focused ultrasound (HIFU) transducer to generate coagulative necrosis in a small focal volume (e.g., 1 mm³) placed at a shallow depth (3-4.5 mm from the skin surface). For this, it is desirable to make the F-number as small as possible under the largest possible aperture in order to generate ultrasound energy high enough to induce tissue coagulation in such a small focal volume. However, satisfying both conditions at the same time is demanding. To meet the requirements, this paper, therefore, proposes a double-focusing technique, in which the aperture of an ultrasound transducer is spherically shaped for initial focusing and an acoustic lens is used to finally focus ultrasound on a target depth of treatment; it is possible to achieve the F-number of unity or less while keeping the aperture of a transducer as large as possible. In accordance with the proposed method, we designed and fabricated a 7-MHz double-focused ultrasound transducer. The experimental results demonstrated that the fabricated double-focused transducer had a focal length of 10.2 mm reduced from an initial focal length of 15.2 mm and, thus, the F-number changed from 1.52 to 1.02. Based on the results, we concluded that the proposed double-focusing method is suitable to decrease F-number while maintaining a large aperture size.