nils-herrmann/clean_scirepeval huggingface dataset:
Investigating the utility of clinical assessments to predict success with presbyopic contact lens correction.
Venue: Contact lens anterior eye the journal of the British Contact Lens Association

To determine the utility of a range of clinical and non clinical indicators to aid the initial selection of the optimum presbyopic contact lens. In addition, to assess whether lens preference was influenced by the visual performance compared to the other designs trialled (intra subject) or compared to participants who preferred other designs (inter subject)  A double masked randomised crossover trial of Air Optix Aqua multifocal, PureVision 2 for Presbyopia, Acuvue OASYS for Presbyopia, Biofinity multifocal and monovision was conducted on 35 presbyopes (54.3+ 6.2years) Participant lifestyle, personality, pupil characteristics and aberrometry were assessed prior to lens fitting. After 4 weeks of wear, high and low contrast visual acuity (VA) under photopic and mesopic conditions, reading speed, Near Activity Visual Questionnaire (NAVQ) rating, subjective quality of vision scoring, defocus curves, stereopsis, halometry, aberrometry and ocular physiology were quantified.  After trialling all the lenses, preference was mixed (n=12 Biofinity, n=10 monovision, n=7 Purevision, n=4 Air Optix Aqua, n=2 Oasys) Lens preference was not dependent on personality (F=1.182, p=0.323) or the hours spent working at near (p=0.535) or intermediate (p=0.759) distances. No intersubject or strong intrasubject relationships emerged between lens preference and reading speed, NAVQ rating, halo size, aberrometry or ocular physiology (p>0.05) CONCLUSIONS Participant lifestyle and personality, ocular optics, contact lens visual performance and ocular physiology provided poor indicators of the preferred lens type after 4 weeks of wear. This is confounded by the wide range of task visual demands of presbyopes and the limited optical differences between current multifocal contact lens designs.
Nonlinear Finite Elements For Continua And Structures

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Are the new starting block facilities beneficial for backstroke start performance?
Venue: Journal of sports sciences

We aimed to analyse the handgrip positioning and the wedge effects on the backstroke start performance and technique. Ten swimmers completed randomly eight 15 m backstroke starts (four with hands on highest horizontal and four on vertical handgrip) performed with and without wedge. One surface and one underwater camera recorded kinematic data. Standardised mean difference (SMD) and 95% confidence intervals (CI) were used. Handgrip positioning did not affect kinematics with and without wedge use. Handgrips horizontally positioned and feet over wedge displayed greater knee angular velocity than without it (SMD 0.82; 95% CI: 1.56, 0.08) Hands vertically positioned and feet over wedge presented greater take off angle (SMD 0.81; 95% CI: 1.55, 0.07) centre of mass (CM) vertical positioning at first water contact (SMD 0.97; 95% CI: 1.87, 0.07) and CM vertical velocity at CM immersion (SMD 1.03; 95% CI: 0.08, 1.98) when comparing without wedge use. Swimmers extended the hip previous to the knee and ankle joints, except for the variant with hands vertically positioned without wedge (SMD 0.75; 95% CI: 0.03, 1.53) Swimmers should preserve biomechanical advantages achieved during flight with variant with hands vertically positioned and wedge throughout entry and underwater phase.
Synthesis and Characterization of HZSM 5 Catalyst for Methanol to Propylene (MTP) Reaction

The effects of different templates on HZSM 5 catalyst morphology and activity in the conversion of methanol to propylene (MTP) were investigated. High silica HZSM 5 catalysts (Si/Al 200) were synthesized successfully using Ludox as silica source and different structure directing agents (SDAs) XRD, SEM, FT IR, NH3 TPD, and N2 adsorption desorption analytical techniques characterized the catalysts. Morpholine shaped nano plate like morphology. TPAOH as template resulted in the high BET surface area (369.4 m2/g) and crystallinity. The HZSM 5 catalysts showed significant catalytic activity. The catalyst with TPABr as template represented the highest propylene selectivity (45% methanol conversion (99.8% and long lifetime (72 h)
Electron beam welding Techniques and trends Review

Electron beam welding, despite a long history and widespread arc and laser technology, is still widely used in industry. The main applications for this high efficiency welding process are: automotive, electronics, electrical engineering, aerospace and mechanical engineering industry. The technology ensures high quality welded joints in structural metals in a wide range of thickness from 0.025 mm to 300 mm. It is also used for the production of films and coatings by deposition and surface modification. In the paper approximated examples of the use of the electron beam are given by the welding, rapid prototyping, texturing surface, cladding with wire and powder as well as alloying. It also provides information about the possible techniques that can be used during these processes and the trends in electron beam welding.
GaN based emissive microdisplays: A very promising technology for compact, ultra high brightness display systems

High brightness GaN based emissive microdisplays can be fabricated with different approaches that are listed and described. They consist either of hybridizing a GaN LED array on a CMOS circuit or building a monolithic component on a single substrate. Using the hybridization approach, two types of 10 mm pixel pitch GaN microdisplay prototypes were developed: (1) directly driven, 300 x 252 pixels and (2) active matrix, 873 x 500 pixels. Brightness as high as 1 x 106 and 1 x 107 cd/m2 for blue and green arrays, respectively, were reached. GaN based emissive microdisplays are suitable for augmented reality systems or head up displays, but some challenges remain before they can be put in production.
The Development of Multimodal Lexical Resources

Human communication is a multimodal activity, involving not only speech and written expressions, but intonation, images, gestures, visual clues, and the interpretation of actions through perception. In this paper, we describe the design of a multimodal lexicon that is able to accommodate the diverse modalities that present themselves in NLP applications. We have been developing a multimodal semantic representation, VoxML, that integrates the encoding of semantic, visual, gestural, and action based features associated with linguistic expressions. 1  The primary focus of lexical resource development in computational linguistics has traditionally been on the syntactic and semantic encoding of word forms for monolingual and multilingual language applications. Recently, however, several factors have motivated researchers to look more closely at the relationship between both spoken and written language and the expression of meaning through other modalities. Specifically, there are at least three areas of CL research that have emerged as requiring significant cross modal or multimodal lexical resource support. These are: Language visualization and simulation generation: Creating images from linguistic input; generating dynamic narratives in simulation environments from action oriented expressions;(Chang et al. 2015; Coyne and Sproat, 2001; Siskind, 2001; Pustejovsky and Krishnaswamy, 2016; Krishnaswamy and Pustejovsky, 2016) Visual Question Answering and image content interpretation: QA and querying over image datasets, based on the vectors associated with the image, but trained on caption image pairings in the data; (Antol et al. 2015; Chao et al. 2015a; Chao et al. 2015b)  Understanding integrated spoken language with human or avatargenerated gestures; generating gesture in dialogue to supplement linguistic expressions;(Rautaray and Agrawal, 2015; Jacko, 2012; Turk, 2014; Bunt et al. 1998) To meet the demands for a lexical resource that can help drive such diverse applications, we have been pursuing a new approach to modeling the semantics of natural language, Multimodal Semantic Simulations (MSS) This framework assumes both a richer formal model of events and their participants, as well as a modeling language for constructing 3D visualizations of objects and events denoted by natural language expressions. The Dynamic Event Model (DEM) encodes events as programs in a dynamic logic with an operational semantics, while the language VoxML, Visual Object Concept Modeling Language, is being used as the platform for multimodal semantic simulations in the context of human computer communication, as well as for imageand video related content based querying. Prior work in visualization from natural language has largely focused on object placement and orientation in static scenes (Coyne and Sproat, 2001; Siskind, 2001; Chang et al. 2015) In previous work (Pustejovsky and Krishnaswamy, 2014; Pustejovsky, 2013a) we introduced a method for modeling natural language expressions within a 3D simulation environment, Unity. The goal of that work was to
Transmision de Conocimiento y Analisis de Redes Sociales

Este articulo discute consideraciones metodologicas referidas a los disenos de investigacion que integran metodologias cualitativas y cuantitativas que consolidan el proceso de analisis. Se presenta un estudio que articula la metodologia etnografica con el desarrollo de un modelo reticular que aborda la transmision de conocimiento de la produccion textil comunitaria. Se propone un analisis comparativo en dos comunidades indigenas en Argentina, con una trayectoria de actividad textil rastreable al siglo XIX. El modelo se construye a partir del mapeo de integrantes de las comunidades vinculados por lazos de ensenanza y se indaga en 1) indices de centralidad y su distribucion, 2) caracteristicas de los nodos (generacion y unidad domestica) en relacion con el indice de out degree y, 3) sub grupos, puntos de corte y puentes. La interpretacion conjunta de los datos y su visualizacion, integra distintos niveles del proceso de transmision comunitaria a traves del tiempo. El modelo propuesto abre nuevos aproximaciones sobre la articulacion entre los resultados de las metricas del Analisis de Redes Sociales y la profundizacion en el estudio de los circuitos de transmision de informacion, considerando las caracteristicas estructurales de los grafos y la dinamica interrelacion entre niveles de analisis.
Roles of CD48 in regulating immunity and tolerance.
Venue: Clinical immunology

CD48, a member of the signaling lymphocyte activation molecule family, participates in adhesion and activation of immune cells. Although constitutively expressed on most hematopoietic cells, CD48 is upregulated on subsets of activated cells. CD48 can have activating roles on T cells, antigen presenting cells and granulocytes, by binding to CD2 or bacterial FimH, and through cell intrinsic effects. Interactions between CD48 and its high affinity ligand CD244 are more complex, with both stimulatory and inhibitory outcomes. CD48 interactions regulate target cell lysis by NK cells and CTLs, which are important for viral clearance and regulation of effector/memory T cell generation and survival. Here we review roles of CD48 in infection, tolerance, autoimmunity, and allergy, as well as the tools used to investigate this receptor. We discuss stimulatory and regulatory roles for CD48, its potential as a therapeutic target in human disease, and current challenges to investigation of this immunoregulatory receptor.
The role genes encoding of killer cell immunoglobulin like receptors (KIRs) and their ligands in susceptibility to and progression of HIV infection.
Venue: Postepy higieny i medycyny doswiadczalnej

NK cells are a part of the innate antiviral response. Their activity is regulated by signals from the surface receptors. Some of them, known as killer cell immunoglobulin like receptors (KIRs) determine the quality and intensity of the immunological response, together with their ligands (HLA class I) KIR genes are very polymorphic, and this is reflected in the NK activity modulation. The stimulation of NK cells, especially in the early stages of the infection, can reduce the transmission of HIV or slow down the progression of infection. The varied KIR/HLA repertoire is a limiting factor for the risk of HIV infection and disease progression. Such diversity enables optimal regulation of NK cells and maintenance of the balance between activation to eliminate infected cells and inhibition. The control of NK cell activity via KIR3DL1/3DS1 and HLA Bw4 (especially Bw4 80I) seems to be very important in the HIV context. With a few exceptions, it leads to a reduction of susceptibility to HIV infection and better viremia control, and slows down depletion of CD4+ T cells. Incompatibility of sexual partners for KIRs and HLA may oblige NK cells from the exposed partner to reject incoming cells from the HIV positive partner. The presence of the inhibitory KIR, in the absence of its ligand, results in a lower threshold of NK cell activation, which reduces the chance of infection. The presence of an inhibitory receptor with a low affinity to the ligand (KIR2DL3+HLA C1) is associated with lower susceptibility, and the effective NK cell inhibition (KIR2DL2+HLA C1) results in increased susceptibility to HIV infection. The advantage of activating KIRs, especially in the presence of their ligands, is associated with higher cytolytic abilities, and thus reduced risk of HIV infection. If the virus is not eliminated in an early stage of infection, massive activation of NK is unfavorable due to the excessive stimulation of the immune system.
AndroZoo: Collecting Millions of Android Apps for the Research Community
Venue: 2016 IEEE/ACM 13th Working Conference on Mining Software Repositories (MSR)

We present a growing collection of Android Applications col lected from several sources, including the official GooglePlay app market. Our dataset, AndroZoo, currently contains more than three million apps, each of which has beenanalysed by tens of different AntiVirus products to knowwhich applications are detected as Malware. We provide thisdataset to contribute to ongoing research efforts, as well asto enable new potential research topics on Android Apps.By releasing our dataset to the research community, we alsoaim at encouraging our fellow researchers to engage in reproducible experiments.
ITEM2VEC: Neural item embedding for collaborative filtering
Venue: 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP)

Many Collaborative Filtering (CF) algorithms are item based in the sense that they analyze item item relations in order to produce item similarities. Recently, several works in the field of Natural Language Processing (NLP) suggested to learn a latent representation of words using neural embedding algorithms. Among them, the Skip gram with Negative Sampling (SGNS) also known as word2vec, was shown to provide state of the art results on various linguistics tasks. In this paper, we show that item based CF can be cast in the same framework of neural word embedding. Inspired by SGNS, we describe a method we name item2vec for item based CF that produces embedding for items in a latent space. The method is capable of inferring item item relations even when user information is not available. We present experimental results that demonstrate the effectiveness of the item2vec method and show it is competitive with SVD.
Modular Robotic Systems: Characteristics and Applications
Venue: J. Intell. Robotic Syst.

Employing Modular Robotic Systems (MRS) in different application domains confronts a large number of challenging problems in design, optimization, and planning, and so identifying characteristics of such problems is an important step toward finding proper solution approaches for them. In this paper, we address this issue and provide a comprehensive study on MRS through a structured survey about MRS characteristics and their applications. A novel framework called MITE is proposed to characterize both the properties and applications of MRS from four perspectives of Module, Information, Task, and Environment, based on more than 120 domain specific features, supplemented by a mapping scheme for describing the interrelations of the four basic aspects of the Task component, namely, Application (for describing high level tasks such as navigation and rescue) Behavior (for referring to constitutive behaviors like locomotion and manipulation which bring about Applications) Goal (for characterizing the way Behaviors are accomplished) and Operation (for designating activities specific to modular robots, such as self reconfiguration and gait control) Also, by providing a methodical review on modular robotics, the paper deals with some analyses on recent trends, research gaps and challenges, as well as open problems in the field of MRS.
Survey on research and development of reconfigurable modular robots

This article presents a comprehensive survey of reconfigurable modular robots, which covers the origin, history, the state of the art, key technologies, challenges, and applications of reconfigurable modular robots. An elaborative classification of typical reconfigurable modular robots is proposed based on the characteristics of the modules and the reconfiguration mechanism. As the system characteristics of reconfigurable modular robots are mainly dependent on the functions of modules, the mechanical and electrical design features of modules of typical reconfigurable modular robots are discussed in detail. Furthermore, an in depth comparison analysis is conducted, which encompasses discussions of module shape, module degrees of freedom, module attribute, connection mechanisms, interface autonomy, locomotion modes, and workspace. Meanwhile, many reconfigurable modular robot researches focus on the study of self X capabilities (i.e. self reconfiguration, self assembly, self adaption, etc. which embodies autonomy performance of reconfigurable modular robots in certain extent. An evolutionary cobweb evaluation model is proposed in this article to evaluate the autonomy level of reconfigurable modular robots. Although various reconfigurable modular robots have been developed and some of them have been put into practical applications such as search and rescue missions, there still exist many open theoretical, technical, and practical challenges in this field. This work is hopefully to offer a reference for the further developments of reconfigurable modular robots.
Classification With an Edge: Improving Semantic Image Segmentation with Boundary Detection
Venue: ArXiv

We present an end to end trainable deep convolutional neural network (DCNN) for semantic segmentation with built in awareness of semantically meaningful boundaries. Semantic segmentation is a fundamental remote sensing task, and most state of the art methods rely on DCNNs as their workhorse. A major reason for their success is that deep networks learn to accumulate contextual information over very large windows (receptive fields) However, this success comes at a cost, since the associated loss of effecive spatial resolution washes out high frequency details and leads to blurry object boundaries. Here, we propose to counter this effect by combining semantic segmentation with semantically informed edge detection, thus making class boundaries explicit in the model, First, we construct a comparatively simple, memory efficient model by adding boundary detection to the Segnet encoder decoder architecture. Second, we also include boundary detection in FCN type models and set up a high end classifier ensemble. We show that boundary detection significantly improves semantic segmentation with CNNs. Our high end ensemble achieves 90% overall accuracy on the ISPRS Vaihingen benchmark.
Generating Natural Questions About an Image
Venue: ACL

There has been an explosion of work in the vision language community during the past few years from image captioning to video transcription, and answering questions about images. These tasks have focused on literal descriptions of the image. To move beyond the literal, we choose to explore how questions about an image are often directed at commonsense inference and the abstract events evoked by objects in the image. In this paper, we introduce the novel task of Visual Question Generation (VQG) where the system is tasked with asking a natural and engaging question when shown an image. We provide three datasets which cover a variety of images from object centric to event centric, with considerably more abstract training data than provided to state of the art captioning systems thus far. We train and test several generative and retrieval models to tackle the task of VQG. Evaluation results show that while such models ask reasonable questions for a variety of images, there is still a wide gap with human performance which motivates further work on connecting images with commonsense knowledge and pragmatics. Our proposed task offers a new challenge to the community which we hope furthers interest in exploring deeper connections between vision language.
Awareness of Oral Hygiene among Children in Chennai

The aim of this study was to assess the attitude and awareness of children towards oral health and dental care as well as to evaluate the factors that determine these variables.  To help spread awareness of oral hygiene among children  The foundation for healthy permanent teeth in children and teenagers is laid during the first years of life. Poor diet, poor habits of food intake and inadequate tooth brushing habits during the first 2 years of life have been shown in several studies to be related to tooth decay in children. The development of caries in primary teeth further increases the risk of developing caries in permanent teeth. Therefore it is essential to establish a proper oral hygiene routine early in life to help ensure the development of strong and healthy teeth.  To help children to lead a better life and healthy oral hygiene
Adaptive data augmentation for image classification
Venue: 2016 IEEE International Conference on Image Processing (ICIP)

Data augmentation is the process of generating samples by transforming training data, with the target of improving the accuracy and robustness of classifiers. In this paper, we propose a new automatic and adaptive algorithm for choosing the transformations of the samples used in data augmentation. Specifically, for each sample, our main idea is to seek a small transformation that yields maximal classification loss on the transformed sample. We employ a trust region optimization strategy, which consists of solving a sequence of linear programs. Our data augmentation scheme is then integrated into a Stochastic Gradient Descent algorithm for training deep neural networks. We perform experiments on two datasets, and show that that the proposed scheme outperforms random data augmentation algorithms in terms of accuracy and robustness, while yielding comparable or superior results with respect to existing selective sampling approaches.