nils-herrmann/clean_scirepeval huggingface dataset:
Maps and Globes in Virtual Reality

This paper explores different ways to render world-wide geographic maps in virtual reality (VR). We compare: (a) a 3D exocentric globe, where the user’s viewpoint is outside the globe; (b) a flat map (rendered to a plane in VR); (c) an egocentric 3D globe, with the viewpoint inside the globe; and (d) a curved map, created by projecting the map onto a section of a sphere which curves around the user. In all four visualisations the geographic centre can be smoothly adjusted with a standard handheld VR controller and the user, through a head-tracked headset, can physically move around the visualisation. For distance comparison exocentric globe is more accurate than egocentric globe and flat map. For area comparison more time is required with exocentric and egocentric globes than with flat and curved maps. For direction estimation, the exocentric globe is more accurate and faster than the other visual presentations. Our study participants had a weak preference for the exocentric globe. Generally the curved map had benefits over the flat map. In almost all cases the egocentric globe was found to be the least effective visualisation. Overall, our results provide support for the use of exocentric globes for geographic visualisation in mixed-reality. CCS Concepts •Human-centered computing → Virtual reality; Geographic visualization; Empirical studies in HCI;
Formation of primary amines on silicon nitride surfaces: a direct, plasma-based pathway to functionalization.

Silicon nitride is the most commonly used passivation layer in biosensor applications where electronic components must be interfaced with ionic solutions. Unfortunately, the predominant method for functionalizing silicon nitride surfaces, silane chemistry, suffers from a lack of reproducibility. As an alternative, we have developed a silane-free pathway that allows for the direct functionalization of silicon nitride through the creation of primary amines formed by exposure to a radio frequency glow discharge plasma fed with humidified air. The aminated surfaces can then be further functionalized by a variety of methods; here we demonstrate using glutaraldehyde as a bifunctional linker to attach a robust NeutrAvidin (NA) protein layer. Optimal amine formation, based on plasma exposure time, was determined by labeling treated surfaces with an amine-specific fluorinated probe and characterizing the coverage using X-ray photoelectron spectroscopy (XPS). XPS and radiolabeling studies also reveal that plasma-modified surfaces, as compared with silane-modified surfaces, result in similar NA surface coverage, but notably better reproducibility.
The Transition to Minimal Consciousness through the Evolution of Associative Learning

The minimal state of consciousness is sentience. This includes any phenomenal sensory experience - exteroceptive, such as vision and olfaction; interoceptive, such as pain and hunger; or proprioceptive, such as the sense of bodily position and movement. We propose unlimited associative learning (UAL) as the marker of the evolutionary transition to minimal consciousness (or sentience), its phylogenetically earliest sustainable manifestation and the driver of its evolution. We define and describe UAL at the behavioral and functional level and argue that the structural-anatomical implementations of this mode of learning in different taxa entail subjective feelings (sentience). We end with a discussion of the implications of our proposal for the distribution of consciousness in the animal kingdom, suggesting testable predictions, and revisiting the ongoing debate about the function of minimal consciousness in light of our approach.
The validity of interpersonal skills assessment via situational judgment tests for predicting academic success and job performance.

This study provides conceptual and empirical arguments why an assessment of applicants' procedural knowledge about interpersonal behavior via a video-based situational judgment test might be valid for academic and postacademic success criteria. Four cohorts of medical students (N = 723) were followed from admission to employment. Procedural knowledge about interpersonal behavior at the time of admission was valid for both internship performance (7 years later) and job performance (9 years later) and showed incremental validity over cognitive factors. Mediation analyses supported the conceptual link between procedural knowledge about interpersonal behavior, translating that knowledge into actual interpersonal behavior in internships, and showing that behavior on the job. Implications for theory and practice are discussed.
Refining non-taxonomic relation labels with external structured data to support ontology learning

This paper presents a method to integrate external knowledge sources such as DBpedia and OpenCyc into an ontology learning system that automatically suggests labels for unknown relations in domain ontologies based on large corpora of unstructured text. The method extracts and aggregates verb vectors from semantic relations identified in the corpus. It composes a knowledge base which consists of (i) verb centroids for known relations between domain concepts, (ii) mappings between concept pairs and the types of known relations, and (iii) ontological knowledge retrieved from external sources. Applying semantic inference and validation to this knowledge base yields a refined relation label suggestion. A formal evaluation compares the accuracy and average ranking precision of this hybrid method with the performance of methods that solely rely on corpus data and those that are only based on reasoning and external data sources.
CAST: Using neural networks to improve trading systems based on technical analysis by means of the RSI financial indicator

Stock price predictions have been a field of study from several points of view including, among others, artificial intelligence and expert systems. For shortterm predictions, the technical indicator Relative Strength Indicator (RSI) has been published in many papers and used worldwide. CAST is presented in this paper. CAST can be seen as a set of solutions for calculating the RSI using artificial intelligence techniques. The improvement is based on the use of feedforward neural networks to calculate the RSI in a more accurate way, which we call the iRSI. This new tool will be used in two scenarios. In the first, it will predict a market-in our case, the Spanish IBEX 35 stock market. In the second, it will predict single-company values pertaining to the IBEX 35. The results are very encouraging and reveal that the CAST can predict the given market as a whole along with individual stock pertaining to the IBEX 35 index.
Aligning Gaussian-Topic with Embedding Network for Summarization Ranking

Query-oriented summarization addresses the problem of information overload and help people get the main ideas within a short time. Summaries are composed by sentences. So, the basic idea of composing a salient summary is to construct quality sentences both for user specific queries and multiple documents. Sentence embedding has been shown effective in summarization tasks. However, these methods lack of the latent topic structure of contents. Hence, the summary lies only on vector space can hardly capture multi-topical content. In this paper, our proposed model incorporates the topical aspects and continuous vector representations, which jointly learns semantic rich representations encoded by vectors. Then, leveraged by topic filtering and embedding ranking model, the summarization can select desirable salient sentences. Experiments demonstrate outstanding performance of our proposed model from the perspectives of prominent topics and semantic coherence.
Expanding and reprogramming the genetic code

Nature uses a limited, conservative set of amino acids to synthesize proteins. The ability to genetically encode an expanded set of building blocks with new chemical and physical properties is transforming the study, manipulation and evolution of proteins, and is enabling diverse applications, including approaches to probe, image and control protein function, and to precisely engineer therapeutics. Underpinning this transformation are strategies to engineer and rewire translation. Emerging strategies aim to reprogram the genetic code so that noncanonical biopolymers can be synthesized and evolved, and to test the limits of our ability to engineer the translational machinery and systematically recode genomes.
Coverage Path Planning for Surveying Disjoint Areas

Terrain surveying using unmanned aerial vehicles (UAV) is being applied to many areas such as precision agriculture or battlefield surveillance. A core problem of the surveying task is the coverage path planning problem (CPP); it is defined as the task of determining a path for an unmanned aerial vehicle so that it observes all points of a target area. State-of-the-art planners solve the problem for a unique region. However, in some operations, for example search and rescue, it is important to plan a path that covers more than a single area. We propose a method for solving the CPP for disjoint areas. The general problem is modeled as a rural postman problem which has been demonstrated to be NP-Hard. Our solution relies on dividing the problem into two steps: optimization of the visiting order and optimization of the flight lines orientation. The method is validated through several simulations using real parameters. In addition, it is fast enough for being implemented onboard.
Extraction and Processing of Rich Semantics from Medical Texts

Important information is captured in medical documents. To make use of this information and intepret the semantics, technologies are required for extracting, analysing and interpreting it. As a result, rich semantics including relations among events, subjectivity or polarity of events, become available. The First Workshop on Extraction and Processing of Rich Semantics from Medical Texts, is devoted to the technologies for dealing with clinical documents for medical information gathering and application in knowledge based systems. New approaches for identifying and analysing rich semantics are presented. In this paper, we introduce the topic and summarize the workshop contributions.
Effect of antispasmodic agents, alone or in combination, in the treatment of Irritable Bowel Syndrome: systematic review and meta-analysis.

Irritable bowel syndrome (IBS) is characterized by recurrent abdominal pain, bloating, and changes in bowel habit.



To determine the clinical effectiveness of the antispasmodic agents available in Mexico for the treatment of IBS.



We carried out a systematic review and meta-analysis of randomized controlled clinical trials on antispasmodic agents for IBS treatment. Clinical trials identified from January 1960 to May 2011 were searched for in MEDLINE, the Cochrane Library, and in the ClinicalTrials.gov registry. Treatment response was evaluated by global improvement of symptoms or abdominal pain, abdominal distention/bloating, and frequency of adverse events. The effect of antispasmodics vs placebo was expressed in OR and 95% CI.



Twenty-seven studies were identified, 23 of which fulfilled inclusion criteria. The studied agents were pinaverium bromide, mebeverine, otilonium, trimebutine, alverine, hyoscine, alverine/simethicone, pinaverium/simethicone, fenoverine, and dicyclomine. A total of 2585 patients were included in the meta-analysis. Global improvement was 1.55 (CI 95%: 1.33 to 1.83). Otilonium and the alverine/simethicone combination produced significant values in global improvement while the pinaverium/simethicone combination showed improvement in bloating. As for pain, 2394 patients were included with an OR of 1.52 (IC 95%: 1.28 a 1.80), favoring antispasmodics.



Antispasmodics were more effective than placebo in IBS, without any significant adverse events. The addition of simethicone improved the properties of the antispasmodic agents, as seen with the alverine/simethicone and pinaverium/simethicone combinations.
Automatically learning shape specifications

This paper presents a novel automated procedure for discovering expressive shape specifications for sophisticated functional data structures. Our approach extracts potential shape predicates based on the definition of constructors of arbitrary user-defined inductive data types, and combines these predicates within an expressive first-order specification language using a lightweight data-driven learning procedure. Notably, this technique requires no programmer annotations, and is equipped with a type-based decision procedure to verify the correctness of discovered specifications. Experimental results indicate that our implementation is both efficient and effective, capable of automatically synthesizing sophisticated shape specifications over a range of complex data types, going well beyond the scope of existing solutions.
Varieties of developmental dyslexia

This paper reviews and evaluates the evidence for the existence of distinct varieties of developmental dyslexia, analogous to those found in the acquired dyslexic population. Models of the normal adult reading process and of the development of reading in children are used to provide a framework for considering the issues. Data from a large-sample study of the reading patterns of developmental dyslexics are then reported. The lexical and sublexical reading skills of 56 developmental dyslexics were assessed through close comparison with the skills of 56 normally developing readers. The results indicate that there are at least two varieties of developmental dyslexia, the first of which is characterised by a specific difficulty using the lexical procedure, and the second by a difficulty using the sublexical procedure. These subtypes are apparently not rare, but are relatively prevalent in the developmental dyslexic population. The results of a second experiment, which suggest that neither of these reading patterns can be accounted for in terms of a general language disorder, are then reported.
The distal radial decompression osteotomy for ulnar impingement syndrome

The decompression of the distal radioulnar joint (DRUJ) is performed by ulnar translation of the radial shaft proximal to the sigmoid notch, i.e. detensioning of the distal part of the interosseous membrane (DIOM) while containment of the DRUJ is achieved by closed wedge osteotomy of the radius. The osteotomy shortens the radius which entails detensioning of the triangular fibrocartilage complex (TFCC). Facilitating the modified Henry approach to the distal palmar radius a radial based wedge osteotomy is applied. The proximal osteotomy is proximal to the ulnar head and distal osteotomy is proximal to the sigmoid notch to prevent iatrogenic impingement. Ulnar translation of the radial shaft is performed to loosen the DIOM. The closed wedge osteotomy reduces radial inclination which will foster containment of the DRUJ. Distal radial decompression osteotomy of the DRUJ preserves DRUJ function while relieving painful impingement. Further surgical interventions are not compromised in case of failure.
Neural activity associated with metaphor comprehension: spatial analysis

Though neuropsychological data indicate that the right hemisphere (RH) plays a major role in metaphor processing, other studies suggest that, at least during some phases of this processing, a RH advantage may not exist. The present study explores, through a temporally agile neural signal--the event-related potentials (ERPs)--, and through source-localization algorithms applied to ERP recordings, whether the crucial phase of metaphor comprehension presents or not a RH advantage. Participants (n=24) were submitted to a S1-S2 experimental paradigm. S1 consisted of visually presented metaphoric sentences (e.g., "Green lung of the city"), followed by S2, which consisted of words that could (i.e., "Park") or could not (i.e., "Semaphore") be defined by S1. ERPs elicited by S2 were analyzed using temporal principal component analysis (tPCA) and source-localization algorithms. These analyses revealed that metaphorically related S2 words showed significantly higher N400 amplitudes than non-related S2 words. Source-localization algorithms showed differential activity between the two S2 conditions in the right middle/superior temporal areas. These results support the existence of an important RH contribution to (at least) one phase of metaphor processing and, furthermore, implicate the temporal cortex with respect to that contribution.
A General Method for Amortizing Variational Filtering

iterations with AVF on the TIMIT data set.  Output predictions and reconstructions.  Validation performance and inference improvement vs. inference iterations. We evaluate amortized variational filtering (AVF) on three dynamical latent variable models: VRNN (Chung et al., 2015), SRNN (Fraccaro et al., 2016), and SVG (Denton & Fergus, 2018). We train these models on speech, MIDI music, and video data sets. Deep latent variable models are often used for dynamical tasks, like reinforcement learning or time-series prediction. A central challenge is performing efficient online inference of the hidden states (filtering). In the static setting, amortized variational techniques are widely used for inference, but applying these techniques to dynamical problems has required hand-crafting an inference procedure for every new model. We propose a general purpose method for efficiently performing accurate inference in any dynamical latent variable model. A General Method for Amortizing Variational Filtering Joseph Marino, Milan Cvitkovic, Yisong Yue   California Institute of Technology (Caltech)
Frictionless Authentication Systems: Emerging Trends, Research Challenges and Opportunities

Authentication and authorization are critical security layers to protect a wide range of online systems, services and content. However, the increased prevalence of wearable and mobile devices, the expectations of a frictionless experience and the diverse user environments will challenge the way users are authenticated. Consumers demand secure and privacy-aware access from any device, whenever and wherever they are, without any obstacles. This paper reviews emerging trends and challenges with frictionless authentication systems and identifies opportunities for further research related to the enrollment of users, the usability of authentication schemes, as well as security and privacy tradeoffs of mobile and wearable continuous authentication systems. Frictionless authentication; Behaviometrics; Security; Privacy; Usability.
The state of peer-to-peer network simulators

Networking research often relies on simulation in order to test and evaluate new ideas. An important requirement of this process is that results must be reproducible so that other researchers can replicate, validate, and extend existing work. We look at the landscape of simulators for research in peer-to-peer (P2P) networks by conducting a survey of a combined total of over 280 papers from before and after 2007 (the year of the last survey in this area), and comment on the large quantity of research using bespoke, closed-source simulators. We propose a set of criteria that P2P simulators should meet, and poll the P2P research community for their agreement. We aim to drive the community towards performing their experiments on simulators that allow for others to validate their results.
Enterprise Risk Management : Theory and Practice July 2006

he past two decades have seen a dramatic change in the role of risk management in corporations. Twenty years ago, the job of the corporate risk manager—typically, a low-level position in the corporate treasury—involved mainly the purchase of insurance. At the same time, treasurers were responsible for the hedging of interest rate and foreign exchange exposures. Over the last ten years, however, corporate risk management has expanded well beyond insurance and the hedging of financial exposures to include a variety of other kinds of risk—notably operational risk, reputational risk, and, most recently, strategic risk. What’s more, at a large and growing number of companies, the risk management function is directed by a senior executive with the title of chief risk officer (CRO) and overseen by a board of directors charged with monitoring risk measures and setting limits for these measures. A corporation can manage risks in one of two fundamentally different ways: (1) one risk at a time, on a largely compartmentalized and decentralized basis; or (2) all risks viewed together within a coordinated and strategic framework. The latter approach is often called “enterprise risk management,” or “ERM” for short. In this article, we suggest that companies that succeed in creating an effective ERM have a long-run competitive advantage over those that manage and monitor risks individually. Our argument in brief is that, by measuring and managing its risks consistently and systematically, and by giving its business managers the information and incentives to optimize the tradeoff between risk and return, a company strengthens its ability to carry out its strategic plan. In the pages that follow, we start by explaining how ERM can give companies a competitive advantage and add value for shareholders. Next we describe the process and challenges involved in implementing ERM. We begin by discussing how a company should assess its risk “appetite,” an assessment that should guide management’s decision about how much and which risks to retain and which to lay off. Then we show how companies should measure their risks. Third, we discuss various means of laying off “non-core” risks, which, as we argue below, increases the firm’s capacity for bearing those “core” risks the firm chooses to retain. Though ERM is conceptually straightforward, its implementation is not. And in the last—and longest—section of the chapter, we provide an extensive guide to the major difficulties that arise in practice when implementing ERM.
A review on image segmentation techniques

-Many image segmentation techniques are available in the literature. Some of these techniques use only the gray level histogram, some use spatial details while others use fuzzy set theoretic approaches. Most of these techniques are not suitable for noisy environments. Some works have been done using the Markov Random Field (MRF) model which is robust to noise, but is computationally involved. Neural network architectures which help to get the output in real time because of their parallel processing ability, have also been used for segmentation and they work fine even when the noise level is very high. The literature on color image segmentation is not that rich as it is for gray tone images. This paper critically reviews and summarizes some of these techniques. Attempts have been made to cover both fuzzy and non-fuzzy techniques including color image segmentation and neural network based approaches. Adequate attention is paid to segmentation of range images and magnetic resonance images. It also addresses the issue of quantitative evaluation of segmentation results. Image segmentation Fuzzy sets Markov Random Field Thresholding Edge detection Clustering Relaxation