nils-herrmann/clean_scirepeval huggingface dataset:
Neural Networks with Few Multiplications
Venue: ICLR

For most deep learning algorithms training is notoriously time consuming. Since most of the computation in training neural networks is typically spent on floating point multiplications, we investigate an approach to training that eliminates the need for most of these. Our method consists of two parts: First we stochastically binarize weights to convert multiplications involved in computing hidden states to sign changes. Second, while back propagating error derivatives, in addition to binarizing the weights, we quantize the representations at each layer to convert the remaining multiplications into binary shifts. Experimental results across 3 popular datasets (MNIST, CIFAR10, SVHN) show that this approach not only does not hurt classification performance but can result in even better performance than standard stochastic gradient descent training, paving the way to fast, hardware friendly training of neural networks.
The Potential Impact of a Hepatitis C Vaccine for People Who Inject Drugs: Is a Vaccine Needed in the Age of Direct Acting Antivirals?
Venue: PloS one

The advent of highly effective hepatitis C (HCV) treatments has questioned the need for a vaccine to control HCV amongst people who inject drugs (PWID) However, high treatment costs and ongoing reinfection risk suggest it could still play a role. We compared the impact of HCV vaccination amongst PWID against providing HCV treatment.  Dynamic HCV vaccination and treatment models among PWID were used to determine the vaccination and treatment rates required to reduce chronic HCV prevalence or incidence in the UK over 20 or 40 years. Projections considered a low (50% protection for 5 years) moderate (70% protection for 10 years) or high (90% protection for 20 years) efficacy vaccine. Sensitivities to various parameters were examined.  To halve chronic HCV prevalence over 40 years, the low, moderate and high efficacy vaccines required annual vaccination rates (coverage after 20 years) of 162 (72% 77 (56% and 44 (38% per 1000 PWID, respectively. These vaccination rates were 16, 7.6 and 4.4 times greater than corresponding treatment rates. To halve prevalence over 20 years nearly doubled these vaccination rates (moderate and high efficacy vaccines only) and the vaccination to treatment ratio increased by 20% For all scenarios considered, required annual vaccination rates and vaccination to treatment ratios were at least a third lower to reduce incidence than prevalence. Baseline HCV prevalence had little effect on the vaccine's impact on prevalence or incidence, but substantially affected the vaccination to treatment ratios. Behavioural risk heterogeneity only had an effect if we assumed no transitions between high and low risk states and vaccinations were targeted or if PWID were high risk for their first year.  Achievable coverage levels of a low efficacy prophylactic HCV vaccine could greatly reduce HCV transmission amongst PWID. Current high treatment costs ensure vaccination could still be an important intervention option.
Stacked Sparse Autoencoder in PolSAR Data Classification Using Local Spatial Information
Venue: IEEE Geoscience and Remote Sensing Letters

Terrain classification is an important topic in polarimetric synthetic aperture radar (PolSAR) image processing. Among various classification techniques, the stacked sparse autoencoder (SSAE) is a kind of deep learning method that can automatically learn useful features layer by layer in an unsupervised manner. However, the scattering measurements of individual pixels in PolSAR images are affected by the speckle; hence, the performance of pixel based classification approaches would be poor. In this situation, a novel framework is proposed to learn robust features of PolSAR data. The local spatial information is introduced into SSAE to learn the deep spatial sparse features automatically for the first time. Furthermore, the influences of the neighbor pixels on the central pixel are controlled depending on the spatial distances from the neighbor pixels to the central pixel. Experimental results with fully PolSAR data indicate that the proposed method provides a competitive solution.
Soft Skills The Need of the Hour for Professional Competence A Review on Interpersonal Skills and Intrapersonal Skills Theories

Professional competence in the job needs a serious attention as the last decade has seen an increase in the number of industries like the IT Sectors, Retail, Travel, Transport, Financial Services, Hospitality, Beauty and Health care. Even though job opportunities have increased to considerable extent in recent years, it is difficult to find a suitable candidate and this merely indicates that we have a shortage of individuals who are employable. The growth in the number of students waiting to be employed versus those who could be employed requires urgent attention. Educational institutions have realized the need for inculcating soft skills to their students along with the academic qualification (the hard skills) to ensure their professional competence. Soft skills are distinct from the hard skills and are recognized as the key to bridge the talent gap we face in India today. It is essential to interact with the clients and to work in a collaborative manner within the team. This reflects on the work quality and productivity. Soft skills are more to do with who we are than what we know. The hard skills may be the foundation to a successful career. However, they need to be cemented with soft skills. Soft skills can be categorised as interpersonal and intrapersonal skills. Interpersonal skills are very much noticeable to others and they are the competence of an individual to interpret and manage his/her own feelings, actions, motivations and that of others in the social contexts. Intrapersonal skills deal with feelings, thoughts and emotions that are stirred up within an individual. This skill is not apparent because it is within a person and the individual next to them may not be aware of it. This paper aims at explaining the need for soft skills for the professional competence and reviews the theories of interpersonal and intrapersonal skills where in need for acquiring those skills are considered as the need of the hour by many experts.
Learning dexterous manipulation for a soft robotic hand from human demonstrations
Venue: 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Dexterous multi fingered hands can accomplish fine manipulation behaviors that are infeasible with simple robotic grippers. However, sophisticated multi fingered hands are often expensive and fragile. Low cost soft hands offer an appealing alternative to more conventional devices, but present considerable challenges in sensing and actuation, making them difficult to apply to more complex manipulation tasks. In this paper, we describe an approach to learning from demonstration that can be used to train soft robotic hands to perform dexterous manipulation tasks. Our method uses object centric demonstrations, where a human demonstrates the desired motion of manipulated objects with their own hands, and the robot autonomously learns to imitate these demonstrations using reinforcement learning. We propose a novel algorithm that allows us to blend and select a subset of the most feasible demonstrations, which we use with an extension of the guided policy search framework that learns generalizable neural network policies. We demonstrate our approach on the RBO Hand 2, with learned motor skills for turning a valve, manipulating an abacus, and grasping.
Recurrent neural network based language models with variation in net topology, language, and granularity
Venue: 2016 International Conference on Asian Language Processing (IALP)

In this paper, we study language models based on recurrent neural networks on three databases in two languages. We implement basic recurrent neural networks (RNN) and refined RNNs with long short term memory (LSTM) cells. We use the corpora of Penn Tree Bank (PTB) and AMI in English, and the Academia Sinica Balanced Corpus (ASBC) in Chinese. On ASBC, we investigate word based and character based language models. For character based language models, we look into the cases where the inter word space is treated or not treated as a token. In summary, we report and comment on the performance of RNN language models with different databases, network topology, language, and granularity.
Iterative closest point based 3D object reconstruction using RGB D acquisition devices
Venue: 2016 24th Signal Processing and Communication Application Conference (SIU)

This paper introduces, and verifies the applicability of, a practical algorithm for creating 3D points clouds resembling objects, based on multiple RGB D frames having been taken from different viewpoints, by the Kinect 2 camera. The experimental set up is described, along with a certain variant of the process referred to as iterative closest point, being utilized for the latter purpose. Moreover, in order to achieve reliable and realistic representations of the objects, the noise involved in the initial result of the reconstruction procedure is removed through employing a high pass filter mask, being aimed at detecting and excluding the outliers. The proposed method, based on the experiments whose results are reported in the paper, is computationally less costly than the relevant alternatives suggested in the literature heretofore, which is one of its fundamental contributions when dealing with real time scenarios. The strengths and weaknesses of the proposed algorithm are discussed according to the results, where objects of various sorts, i.e. with different colors and types of materials and surface, are taken into account and investigated as case studies.
Muon reconstruction performance of the ATLAS detector in proton proton collision data at s =13 TeV

This article documents the performance of the ATLAS muon identification and reconstruction using the LHC dataset recorded at s 13 TeV in 2015. Using a large sample of J/ps mm and Z mm decays from 3.2 fb 1 of pp collision data, measurements of the reconstruction efficiency, as well as of the momentum scale and resolution, are presented and compared to Monte Carlo simulations. The reconstruction efficiency is measured to be close to 99 over most of the covered phase space |e| 2.5 and 5 pT 100 GeV) The isolation efficiency varies between 93 and 100 depending on the selection applied and on the momentum of the muon. Both efficiencies are well reproduced in simulation. In the central region of the detector, the momentum resolution is measured to be 1.7 (2.3 for muons from J/ps mm (Z mm) decays, and the momentum scale is known with an uncertainty of 0.05 In the region |e| 2.2, the pT resolution for muons from Z mm decays is 2.9 while the precision of the momentum scale for low pT muons from J/ps mm decays is about 0.2
Collaborative representation based fine grained species recognition
Venue: 2016 International Conference on Image and Vision Computing New Zealand (IVCNZ)

Fine grained Visual Categorization (FGVC) is an open problem in Computer Vision due to subtle differences between categories. The present paper demonstrates that Collaborative Representation based Classification (CRC) can address this problem successfully. Instead of the traditional discriminative approach of classification, CRC takes a co operative approach by representing the query image as a weighted collaboration of training images across all classes in the feature space. The superior performance of CRC compared to some other modern classifiers including SVM is shown in this work using several popular descriptors like GIST+Color, SIFT and CNN features with Species Recognition chosen as the representative FGVC problem. Besides experiments on the Oxford 102 Flowers and CUB200 2011 Bird benchmarks, the present work also introduces a new challenging dataset NZ Birds v1.0 with 600 images of 30 New Zealand endemic and native bird species.
Argumentative texts and clause types
Venue: ArgMining@ACL

Argumentative texts have been thoroughly analyzed for their argumentative structure, and recent efforts aim at their automatic classification. This work investigates linguistic properties of argumentative texts and text passages in terms of their semantic clause types. We annotate argumentative texts with Situation Entity (SE) classes, which combine notions from lexical aspect (states, events) with genericity and habituality of clauses. We analyse the correlation of SE classes with argumentative text genres, components of argument structures, and some functions of those components. Our analysis reveals interesting relations between the distribution of SE types and the argumentative text genre, compared to other genres like fiction or report. We also see tendencies in the correlations between argument components (such as premises and conclusions) and SE types, as well as between argumentative functions (such as support and rebuttal) and SE types. The observed tendencies can be deployed for automatic recognition and fine grained classification of argumentative text passages.
Using Pre Trained Models for Fine Grained Image Classification in Fashion Field

Fashion image classification offers online stores a fast and effective way to manage the volume of their products. It could be used in product categorization by their brands, types, styles, etc. It could be also used to make fashion ensembles of clothes and even to create product recommendations. The results of apparel image classification are also useful for such purposes as product filtering and search. A wide range of different models have been successfully applied to coarse level image classification, but only a few of them could be applied to fashion images due to the nature of the data: such images could belong to the one high level topic such as 't shirt' 'skirt' or 'sport shoes' but at the lower level be fine grained (e.g. 'high heels' 'medium heels' Each apparel shop can have its own fine grained hierarchy leading to difficulties to acquire a big dataset. This work shows necessity of pre trained model usage to achieve a good predictive quality. In addition, the article contains a description and an experimental study of an approach to stack features from pretrained model and features learned directly from an image pixel representation. This approach significantly outperforms baseline methods in fine grained image classification [1, 2, 3] and a classification based only on a pre trained model.
Revision of the EU Green Public Procurement Criteria for Food and Catering Services

This report forms the basis to revise the existing EU Green Public Procurement (GPP) criteria for Food and Catering services. This will assist in the reduction of negative impacts of the, public procurement of these services, on the environment, human health and natural resources. The revision of EU GPP requires in depth information about the technical and environmental performance of the service group as well as about the current procurement processes. The scientific body of evidence gathered will be cross checked with sector experienced stakeholders, to develop consensus on how the criteria should be revised to deliver optimum environmental improvements. This Report formed a basis for producing the draft EU GPP criteria set proposals discussed in the Ad Hoc Working Group (AHWG) meetings. Chapter 1 focuses on the definition for and scoping of Food and Catering Services to overview of the different scope and definitions and to narrow down the services in order to obtain a homogenous scope. Chapter 2, market analysis, presents the market overview and the market structure by focusing on the contract catering market. The environmental analysis (Chapter 3) provides a detail analysis of the main environmental hotspots for the food product categories by reviewing a number of LCA studies. Other non LCA aspects including ethical and health consideration are also summarised. The technical analysis focus on the current schemes and labels identified as important for the revision of the current EU GPP criteria. Chapter 4 provide improvement options, based on the best sector practices and national cases studies which could assist the revision of the EU GPP, and reduce the sectors environmental impacts, by drawing on findings from the market and technical analysis.
Characteristics of polycyclic aromatic hydrocarbons in food oils in Beijing catering services
Venue: Environmental Science and Pollution Research

The concentrations and characteristics of 16 polycyclic aromatic hydrocarbons (PAHs) in 48 oil samples randomly collected from 30 catering services that employ six cooking methods were quantified via high performance liquid chromatography (HPLC) These 16 PAHs were detected in almost all of the samples. The levels of S16PAHs, S4PAHs, benzo[a]pyrene (BaP) and total BaP equivalents (SBaPeq) for the six cooking methods exceeded the legal limit. The concentrations of S4PAHs were approximately 9.5 to 16.4 times the legal limit proposed by the European Commission (Off J Eur Union 215:4 8, 2011) and the level of BaP exceeded the national standard in China by 4.7 to 10.6 fold, particularly in oil from fried foods. Low molecular weight PAHs (LMW PAHs) were predominant in fried food oil from different catering services and accounted for 94.8 of these oils, and the SBaPeq of the high molecular weight PAHs (HMW PAHs) was 11.5 fold higher than that of the LMW PAHs. The concentrations of S16PAHs (3751.9 7585.8 mg/kg) S4PAHs (144.6 195.7 mg/kg) BaP (79.7 135.8 mg/kg) and SBaPeq (231.0 265.4 mg/kg) were highest in the samples from fast food restaurants/buffets (FB) followed by those from fried food stalls (FS) and then cooking restaurants/cafeterias (RC) The results of this study suggest that the government should strengthen control and supervision of PAH contamination in food and edible oils.
Analysing risk factors for urinary tract infection based on automated monitoring of hospital acquired infection.
Venue: The Journal of hospital infection

Urinary tract infections account for as much as one third of all nosocomial infections. The aim of this study was to examine previously reported characteristics of patients with hospital acquired urinary tract infections (HA UTI) using an automated infection monitoring system (Hospital Acquired Infection Registry: HAIR) A matched case control study was conducted to investigate the association of risk factors with HA UTI. Patients with HA UTI more frequently had indwelling urinary catheters or a disease in the genitourinary or nervous system than the controls. Automated hospital acquired infection monitoring enables documentation of key risk factors to better evaluate infection control interventions in general or for selected groups of patients.
Models and Algorithms of Information Retrieval in a Multilingual Environment on the Basis of Thematic and Dynamic Text Corpora

Models and algorithms of information retrieval in the global and local computer networks on the basis of thematic and dynamic text corpora are proposed in this article. The developed algorithms provide the effectiveness of documents information retrieval and notable for universality, i. e. for the independence from topics. Information system adjustment to a particular data domain can be fully automated, it adds up to the creation of a respective thematic text corpus and an actualization of dictionaries of the knowledge database.
Characterization of coronary atherosclerosis by intravascular imaging modalities.
Venue: Cardiovascular diagnosis and therapy

Coronary artery disease (CAD) is highly prevalent in Western countries and is associated with morbidity, mortality, and a significant economic burden. Despite the development of anti atherosclerotic medical therapies, many patients still continue to suffer from coronary events. This residual risk indicates the need for better risk stratification and additional therapies to achieve more reductions in cardiovascular risk. Recent advances in imaging modalities have contributed to visualizing atherosclerotic plaques and defining lesion characteristics in vivo. This innovation has been applied to refining revascularization procedure, assessment of anti atherosclerotic drug efficacy and the detection of high risk plaques. As such, intravascular imaging plays an important role in further improvement of cardiovascular outcomes in patients with CAD. The current article reviews available intravascular imaging modalities with regard to its method, advantage and disadvantage.
Clinical Characterization of Coronary Atherosclerosis With Dual Modality OCT and Near Infrared Autofluorescence Imaging.
Venue: JACC. Cardiovascular imaging

The authors present the clinical imaging of human coronary arteries in vivo using a multimodality optical coherence tomography (OCT) and near infrared autofluorescence (NIRAF) intravascular imaging system and catheter.  Although intravascular OCT is capable of providing microstructural images of coronary atherosclerotic lesions, it is limited in its capability to ascertain the compositional/molecular features of plaque. A recent study in cadaver coronary plaque showed that endogenous NIRAF is elevated in necrotic core lesions. The combination of these 2 technologies in 1 device may therefore provide synergistic data to aid in the diagnosis of coronary pathology in vivo.  We developed a dual modality intravascular imaging system and 2.6 F catheter that can simultaneously acquire OCT and NIRAF data from the same location on the artery wall. This technology was used to obtain volumetric OCT NIRAF images from 12 patients with coronary artery disease undergoing percutaneous coronary intervention. Images were acquired during a brief, nonocclusive 3 to 4 ml/s contrast purge at a speed of 100 frames/s and a pullback rate of 20 or 40 mm/s. OCT NIRAF data were analyzed to determine the distribution of the NIRAF signal with respect to OCT delineated plaque morphological features.  High quality intracoronary OCT and NIRAF image data >50 mm pullback length) were successfully acquired without complication in all patients (17 coronary arteries) The maximum NIRAF signal intensity of each plaque was compared with OCT defined type, showing a statistically significant difference between plaque types (1 way analysis of variance, p 0.0001) Interestingly, coronary arterial NIRAF intensity was elevated only focally in plaques with a high risk morphological phenotype (p 0.05) including OCT fibroatheroma, plaque rupture, and fibroatheroma associated with in stent restenosis.  This OCT NIRAF study demonstrates that dual modality microstructural and fluorescence intracoronary imaging can be safely and effectively conducted in human patients. Our findings show that NIRAF is associated with a high risk morphological plaque phenotype. The focal distribution of NIRAF in these lesions furthermore suggests that this endogenous imaging biomarker may provide complementary information to that obtained by structural imaging alone.