nils-herrmann/clean_scirepeval huggingface dataset:
Involvement of the cerebellar cortex and nuclei in verbal and visuospatial working memory: A 7<ce:hsp sp=0.25></ce:hsp>T fMRI study

The first aim of the present study was to extend previous findings of similar cerebellar cortical areas being involved in verbal and spatial n-back working memory to the level of the cerebellar nuclei. The second aim was to investigate whether different areas of the cerebellar cortex and nuclei contribute to different working memory tasks (n-back vs. Sternberg tasks). Young and healthy subjects participated in two functional magnetic resonance imaging (fMRI) studies using a 7 T MR scanner with its increased signal-to-noise ratio. One group of subjects (n=21) performed an abstract and a verbal version of an n-back task contrasting a 2-back and 0-back condition. Another group of subjects (n=23) performed an abstract and a verbal version of a Sternberg task contrasting a high load and a low load condition. A block design was used. For image processing of the dentate nuclei, a recently developed region of interest (ROI) driven normalization method of the dentate nuclei was applied (Diedrichsen et al., 2011). Whereas activated areas of the cerebellar cortex and dentate nuclei were not significantly different comparing the abstract and verbal versions of the n-back task, activation in the abstract and verbal Sternberg tasks was significantly different. In both n-back tasks activation was most prominent at the border of lobules VI and Crus I, within lobule VII, and within the more caudal parts of the dentate nucleus bilaterally. In Sternberg tasks the most prominent activations were found in lobule VI extending into Crus I on the right. In the verbal Sternberg task activation was significantly larger within right lobule VI compared to the abstract Sternberg task and compared to the verbal n-back task. Activations of rostral parts of the dentate were most prominent in the verbal Sternberg task, whereas activation of caudal parts predominated in the abstract Sternberg task. On the one hand, the lack of difference between abstract and verbal n-back tasks and the lack of significant lateralization suggest a more general contribution of the cerebellum to working memory regardless of the modality. On the other hand, the focus of activation in right lobule VI in the verbal Sternberg task suggests specific cerebellar contributions to verbal working memory. The verbal Sternberg task emphasizes maintenance of stimuli via phonological rehearsal, whereas central executive demands prevail in n-back tasks. Based on the model of working memory by Baddeley and Hitch (1974), the present results show that different regions of the cerebellum support functions of the central executive system and one of the subsidiary systems, the phonological loop.
Calibration Requirements and Procedures for Augmented Reality

Augmented reality entails the use of models and their associated renderings to supplement information in a real scene. In order for this information to be relevant or meaningful, the models must be positioned and displayed in such a way that they blend into the real world in terms of alignments, perspectives, illuminations, etc. For practical reasons the information necessary to obtain this realistic blending cannot be known a priori, and cannot be hard-wired into a system. Instead a number of calibration procedures are necessary so that the location and parameters of each of the system components are known. In this paper we identify the calibration steps necessary to build a complete computer model of the real world and then, using the augmented reality system developed at ECRC (Grasp) as an example, we describe each of the calibration processes.
Visual Object Recognition

The visual recognition problem is central to computer vision research. From robotics to information retrieval, many desired applications demand the ability to identify and localize categories, places, and objects. This tutorial overviews computer vision algorithms for visual object recognition and image classification. We introduce primary representations and learning approaches, with an emphasis on recent advances in the field. The target audience consists of researchers or students working in AI, robotics, or vision who would like to understand what methods and representations are available for these problems. This lecture summarizes what is and isn’t possible to do reliably today, and overviews key concepts that could be employed in systems requiring visual categorization.
Effect of temporal envelope smearing on speech reception.

The effect of smearing the temporal envelope on the speech-reception threshold (SRT) for sentences in noise and on phoneme identification was investigated for normal-hearing listeners. For this purpose, the speech signal was split up into a series of frequency bands (width of 1/4, 1/2, or 1 oct) and the amplitude envelope for each band was low-pass filtered at cutoff frequencies of 0, 1/2, 1, 2, 4, 8, 16, 32, or 64 Hz. Results for 36 subjects show (1) a severe reduction in sentence intelligibility for narrow processing bands at low cutoff frequencies (0-2 Hz); and (2) a marginal contribution of modulation frequencies above 16 Hz to the intelligibility of sentences (provided that lower modulation frequencies are completely present). For cutoff frequencies above 4 Hz, the SRT appears to be independent of the frequency bandwidth upon which envelope filtering takes place. Vowel and consonant identification with nonsense syllables were studied for cutoff frequencies of 0, 2, 4, 8, or 16 Hz in 1/4-oct bands. Results for 24 subjects indicate that consonants are more affected than vowels. Errors in vowel identification mainly consist of reduced recognition of diphthongs and of confusions between long and short vowels. In case of consonant recognition, stops appear to suffer most, with confusion patterns depending on the position in the syllable (initial, medial, or final).
End-to-End Learning of Latent Deformable Part-Based Representations for Object Detection

Object detection methods usually represent objects through rectangular bounding boxes from which they extract features, regardless of their actual shapes. In this paper, we apply deformations to regions in order to learn representations better fitted to objects. We introduce DP-FCN, a deep model implementing this idea by learning to align parts to discriminative elements of objects in a latent way, i.e. without part annotation. This approach has two main assets: it builds invariance to local transformations, thus improving recognition, and brings geometric information to describe objects more finely, leading to a more accurate localization. We further develop both features in a new model named DP-FCN2.0 by explicitly learning interactions between parts. Alignment is done with an in-network joint optimization of all parts based on a CRF with custom potentials, and deformations are influencing localization through a bilinear product. We validate our models on PASCAL VOC and MS COCO datasets and show significant gains. DP-FCN2.0 achieves state-of-the-art results of 83.3 and 81.2% on VOC 2007 and 2012 with VOC data only.
Fuzzy memoization for floating-point multimedia applications

Instruction memoization is a promising technique to reduce the power consumption and increase the performance of future low-end/mobile multimedia systems. Power and performance efficiency can be improved by reusing instances of an already executed operation. Unfortunately, this technique may not always be worth the effort due to the power consumption and area impact of the tables required to leverage an adequate level of reuse. In this paper, we introduce and evaluate a novel way of understanding multimedia floating-point operations based on the fuzzy computation paradigm: performance and power consumption can be improved at the cost of small precision losses in computation. By exploiting this implicit characteristic of multimedia applications, we propose a new technique called tolerant memoization. This technique expands the capabilities of classic memoization by associating entries with similar inputs to the same output. We evaluate this new technique by measuring the effect of tolerant memoization for floating-point operations in a low-power multimedia processor and discuss the trade-offs between performance and quality of the media outputs. We report energy improvements of 12 percent for a set of key multimedia applications with small LUT of 6 Kbytes, compared to 3 percent obtained using previously proposed techniques.
CoSIP: A Constrained Session Initiation Protocol for the Internet of Things

The Internet of Things (IoT) refers to the interconnection of billions of constrained devices, denoted as “smart objects” (SO), in an Internet-like structure. SOs typically feature limited capabilities in terms of computation and memory and operate in constrained environments, such low-power lossy networks. As IP has been foreseen as the standard for smart-object communication, an e↵ort to bring IP connectivity to SOs and define suitable communication protocols (i.e. CoAP) is being carried out within standardization organisms, such as IETF. In this paper, we propose a constrained version of the Session Initiation Protocol (SIP), named “CoSIP”, whose intent is to allow constrained devices to instantiate communication sessions in a lightweight and standard fashion. Session instantiation can include a negotiation phase of some parameters which will be used for all subsequent communication. CoSIP can be adopted in several application scenarios, such as service discovery and publish/subscribe applications, which are detailed. An evaluation of the proposed protocol is also presented, based on a Java implementation of CoSIP, to show the benefits that its adoption can bring about, in terms of compression rate with the existing SIP protocol and message overhead compared with the use of CoAP.
Path-guided artificial potential fields with stochastic reachable sets for motion planning in highly dynamic environments

Highly dynamic environments pose a particular challenge for motion planning due to the need for constant evaluation or validation of plans. However, due to the wide range of applications, an algorithm to safely plan in the presence of moving obstacles is required. In this paper, we propose a novel technique that provides computationally efficient planning solutions in environments with static obstacles and several dynamic obstacles with stochastic motions. Path-Guided APF-SR works by first applying a sampling-based technique to identify a valid, collision-free path in the presence of static obstacles. Then, an artificial potential field planning method is used to safely navigate through the moving obstacles using the path as an attractive intermediate goal bias. In order to improve the safety of the artificial potential field, repulsive potential fields around moving obstacles are calculated with stochastic reachable sets, a method previously shown to significantly improve planning success in highly dynamic environments. We show that Path-Guided APF-SR outperforms other methods that have high planning success in environments with 300 stochastically moving obstacles. Furthermore, planning is achievable in environments in which previously developed methods have failed.
Requirements, Traceability and DSLs in Eclipse with the Requirements Interchange Format (ReqIF)

Requirements engineering (RE) is a crucial aspect in systems development and is the area of ongoing research and process improvement. However, unlike in modelling, there has been no established standard that activities could converge on. In recent years, the emerging Requirements Interchange Format (RIF/ReqIF) gained more and more visibility in industry, and research projects start to investigate these standards. To avoid redundant efforts in implementing the standard, the VERDE and Deploy projects cooperate to provide a stable common basis for RIF/ReqIF that could be leveraged by other research projects too. In this paper, we present an Eclipse-based extensible implementation of a RIF/ReqIF-based requirements editing platform. In addition, we are concerned with two related aspects of RE that take advantage of the common platform. First, how can the quality of requirements be improved by replacing or complementing natural language requirements with formal approaches such as domain specific languages or models. Second, how can we establish robust traceability that links requirements and model constructs and other artefacts of the development process. We present two approaches to traceability and two approaches to modelling. We believe that our research represents a significant contribution to the existing tooling landscape, as it is the first clean-room implementation of the RIF/ReqIF standard. We believe that it will help reduce gaps in often heterogeneous tool chains and inspire new conceptual work and new tools.
Joint torques during sit-to-stand in healthy subjects and people with Parkinson's disease.

To compare lower limb joint torques during sit-to-stand in normal elderly subjects and people with Parkinson's disease, using a developed biomechanical model simulating all phases of sit-to-stand. A cross-sectional study utilizing a Parkinsonian and a control group.



Subjects with Parkinson's disease were observed to experience difficulty in performing sit-to-stand. The developed model was used to calculate the lower limb joint torques in normal elderly subjects and subjects with Parkinson's disease, to delineate possible causes underlying difficulties in initiating sit-to-stand task.



Six normal elderly subjects and seven age-matched subjects with Parkinson's disease performed five sit-to-stand trials at their self-selected speed. Anthropometric data, two-dimensional kinematic and foot-ground and thigh-chair reactive forces were used to calculate, via inverse dynamics, the joint torques during sit-to-stand in both before and after seat-off phases. The difference between the control and Parkinson's disease group was analysed using independent t-tests.



Both control and Parkinson's disease groups had a similar joint kinematic pattern, although the Parkinson's disease group demonstrated a slower angular displacement. The latter subjects produced significantly smaller normalized hip flexion torque and presented a slower torque build-up rate than the able-bodied subjects (P<0.05).



Slowness of sit-to-stand in people with Parkinson's disease could be due to a reduced hip flexion joint torque and a prolonged rate of torque production.
Capturing the Semantics of Key Phrases Using Multiple Languages for Question Retrieval

In the age of Web 2.0, community user contributed questions and answers provide an important alternative for knowledge acquisition through web search. Question retrieval in current community-based question answering (CQA) services do not, in general, work well for long and complex queries, such as the questions. The main reasons are the verboseness in natural language queries and the word mismatch between the queries and the candidate questions in the CQA archive during retrieval. To address these two problems, existing solutions try to refine the search queries by distinguishing the key concepts in the queries and expanding the queries with relevant content. However, using the existing query refinement approaches can only identify the key and non-key concepts, while the differences between the key concepts are overlooked. Moreover, the existing query expansion approaches, not only overlook the weights of key concepts in the queries, but also fail to consider concept level expansion for them. In this paper, we explore a key concept identification approach for query refinement and a pivot language translation based approach to explore key concept paraphrasing. We further propose a new question retrieval model which can seamlessly integrate the key concepts and their paraphrases. The experimental results demonstrate that the integrated retrieval model significantly outperforms the state-of-the-art models in question retrieval.
Developing a biodata measure and situational judgment inventory as predictors of college student performance.

This article describes the development and validation of a biographical data (biodata) measure and situational judgment inventory (SJI) as useful predictors of broadly defined college student performance outcomes. These measures provided incremental validity when considered in combination with standardized college-entrance tests (i.e., SAT/ACT) and a measure of Big Five personality constructs. Racial subgroup mean differences were much smaller on the biodata and SJI measures than on the standardized tests and college grade point average. Female students tended to outperform male students on most predictors and outcomes with the exception of the SAT/ACT. The biodata and SJI measures show promise for student development contexts and for selecting students on a wide range of outcomes with reduced adverse impact.
A Generic Toolkit for the Successful Management of Delphi Studies

This paper presents the case of a non-traditional use of the Delphi method for theory evaluation. On the basis of experience gained through secondary and primary research, a generic decision toolkit for Delphi studies is proposed, comprising of taxonomy of Delphi design choices, a stage model and critical methodological decisions. These research tools will help to increase confidence when adopting the Delphi alternative and allow for a wider and more comprehensive recognition of the method within both scientific and interpretivist studies.
Collaborative matrix factorization mechanism for group recommendation in big data-based library systems

Academic groups are designed specifically for researchers. A group recommendation procedure is essential to support scholars’ research-based social activities. However, group recommendation methods are rarely applied in online libraries and they often suffer from scalability problem in big data context. The purpose of this paper is to facilitate academic group activities in big data-based library systems by recommending satisfying articles for academic groups.  The authors propose a collaborative matrix factorization (CoMF) mechanism and implement paralleled CoMF under Hadoop framework. Its rationale is collaboratively decomposing researcher-article interaction matrix and group-article interaction matrix. Furthermore, three extended models of CoMF are proposed.  Empirical studies on CiteULike data set demonstrate that CoMF and three variants outperform baseline algorithms in terms of accuracy and robustness. The scalability evaluation of paralleled CoMF shows its potential value in scholarly big data environment.  The proposed methods fill the gap of group-article recommendation in online libraries domain. The proposed methods have enriched the group recommendation methods by considering the interaction effects between groups and members. The proposed methods are the first attempt to implement group recommendation methods in big data contexts.  The proposed methods can improve group activity effectiveness and information shareability in academic groups, which are beneficial to membership retention and enhance the service quality of online library systems. Furthermore, the proposed methods are applicable to big data contexts and make library system services more efficient.  The proposed methods have potential value to improve scientific collaboration and research innovation.  The proposed CoMF method is a novel group recommendation method based on the collaboratively decomposition of researcher-article matrix and group-article matrix. The process indirectly reflects the interaction between groups and members, which accords with actual library environments and provides an interpretable recommendation result.
Automatic Language Identification from Written Texts - An Overview

Language Identification is the task of automatically identifying the language(s) in which the content is written in a document (web page, text document). Due to the widespread use of internet, identification of languages has become an important preprocessing step for a number of applications such as machine translation, Part-of-Speech tagging, linguistic corpus creation, supporting low-density languages, accessibility of social media or user-generated content, search engines and information extraction in addition to processing multilingual documents. In a multilingual country like India, Language Identification has wider scope to bridge the digital divide between different language users. This paper presents a brief overview of the challenges involved in automatic language identification, existing methodologies and some of the tools available for language identification.
Approximate Inference for Deep Latent Gaussian Mixtures

Deep latent Gaussian models (DLGMs) composed of density and inference networks [14]—the pipeline that defines a Variational Autoencoder [8]—have achieved notable success on tasks ranging from image modeling  to semi-supervised classification [6, 11]. However, the approximate posterior in these models is usually chosen to be a factorized Gaussian, thereby imposing strong constraints on the posterior form and its ability to represent the true posterior, which is often multimodal. Recent work has attempted to improve the quality of the posterior approximation by altering the Stochastic Gradient Variational Bayes (SGVB) optimization objective. Burda et al.  proposed an importance weighted objective, and Li and Turner  then generalized the importance sampling approach to a family of α-divergences. Yet, changing the optimization objective is not the only way to attenuate posterior restrictions. Instead, the posterior form itself can be made richer. For instance, Kingma et al.  employ full-covariance Gaussian posteriors, and Nalisnick & Smyth [13] use (truncated) GEM random variables. This paper continues this later line of work by using a Gaussian mixture latent space. We describe learning and inference for not only the traditional mixture model but also Dirichlet Process mixtures  (with posterior truncation). Our deep Latent Gaussian mixture model (DLGMM) generalizes previous work such as Factor Mixture Analysis  and Deep Gaussian Mixtures  to arbitrary differentiable inter-layer transformations.
Quantum Field Theory in Curved Spacetime

We review the mathematically rigorous formulation of the quantum theory of a linear field propagating in a globally hyperbolic spacetime. This formulation is accomplished via the algebraic approach, which, in essence, simultaneously admits all states in all possible (unitarily inequivalent) Hilbert space constructions. The physically nonsingular states are restricted by the requirement that their two-point function satisfy the Hadamard condition, which insures that the ultra-violet behavior of the state be similar to that of the vacuum state in Minkowski spacetime, and that the expected stress-energy tensor in the state be finite. We briefly review the Unruh and Hawking effects from the perspective of the theoretical framework adopted here. A brief discussion also is given of several open issues and questions in quantum field theory in curved spacetime regarding the treatment of “backreaction”, the validity of some version of the “averaged null energy condition”, and the formulation and properties of quantum field theory in causality violating