nils-herrmann/clean_scirepeval huggingface dataset:
Compassion: a scoping review of the healthcare literature.

Recent concerns about suboptimal patient care and a lack of compassion have prompted policymakers to question the preparedness of clinicians for the challenging environment in which they practice. Compassionate care is expected by patients and is a professional obligation of clinicians; however, little is known about the state of research on clinical compassion. The purpose of this scoping review was to map the literature on compassion in clinical healthcare.



Searches of eight electronic databases and the grey literature were conducted to identify empirical studies published over the last 25 years. Eligible studies explored perceptions or interventions of compassionate care in clinical populations, healthcare professionals, and healthcare students. Following the title and abstract review, two reviewers independently screened full-texts articles, and extracted study data. A narrative approach to synthesizing and mapping the literature was used.



Of 36,637 records, 648 studies were retrieved and 44 studies were included in the review. Less than one third of studies included patients. Six themes emerged from studies that explored perceptions of compassionate care: nature of compassion, development of compassion, interpersonal factors related to compassion, action and practical compassion, barriers and enablers of compassion, and outcomes of compassion. Intervention studies included two compassionate care trials with patients and eight educational programs that aimed to improve compassionate care in clinicians and students.



This review identifies the limited empirical understanding of compassion in healthcare, highlighting the lack of patient and family voices in compassion research. A deeper understanding of the key behaviors and attitudes that lead to improved patient-reported outcomes through compassionate care is necessary.
Being Negative but Constructively: Lessons Learnt from Creating Better Visual Question Answering Datasets

Visual question answering (Visual QA) has attracted a lot of attention lately, seen essentially as a form of (visual) Turing test that artificial intelligence should strive to achieve. In this paper, we study a crucial component of this task: how can we design good datasets for the task? We focus on the design of multiplechoice based datasets where the learner has to select the right answer from a set of candidate ones including the target (i.e. the correct one) and the decoys (i.e. the incorrect ones). Through careful analysis of the results attained by state-of-the-art learning models and human annotators on existing datasets, we show that the design of the decoy answers has a significant impact on how and what the learning models learn from the datasets. In particular, the resulting learner can ignore the visual information, the question, or both while still doing well on the task. Inspired by this, we propose automatic procedures to remedy such design deficiencies. We apply the procedures to re-construct decoy answers for two popular Visual QA datasets as well as to create a new Visual QA dataset from the Visual Genome project, resulting in the largest dataset for this task. Extensive empirical studies show that the design deficiencies have been alleviated in the remedied datasets and the performance on them is likely a more faithful indicator of the difference among learning models. The datasets are released and publicly available via http://www.teds.
DSP-Based Control of Grid-Connected Power Converters Operating Under Grid Distortions

Power electronic Grid-Connected Converters (GCCs) are widely applied as grid interface in renewable energy sources. This paper proposes an extended Direct Power Control with Space Vector Modulation (DPC-SVM) scheme with improved operation performance under grid distortions. The real-time operated DPC-SVM scheme has to execute several important tasks as: space vector pulse width modulation, active and reactive power feedback control, grid current harmonics and voltage dips compensation. Thus, development and implementation of the DPC-SVM algorithm using single chip floating-point microcontroller TMS320F28335 is described. It combines large peripheral equipment, typical for microcontrollers, with high computation capacity characteristic for Digital Signal Processors (DSPs). The novelty of the proposed system lies in extension of the generic DPC-SVM scheme by additional higher harmonic and voltage dips compensation modules and implementation of the whole algorithm in a single chip floating point microcontroller. Overview of the laboratory setup, description of basic algorithm subtasks sequence, software optimization as well as execution time of specific program modules on fixed-point and floating-point processors are discussed. Selected oscillograms illustrating operation and robustness of the developed algorithm used in 5 kVA laboratory model of the GCC are presented.
Cones of Matrices and Set-Functions and 0-1 Optimization

It has been recognized recently that to represent a polyhedron as the projection of a higher dimensional, but simpler, polyhedron, is a powerful tool in polyhedral combinatorics. We develop a general method to construct higher-dimensional polyhedra (or, in some cases, convex sets) whose projection approximates the convex hull of 0-1 valued solutions of a system of linear inequalities. An important feature of these approximations is that one can optimize any linear objective function over them in polynomial time. In the special case of the vertex packing polytope, we obtain a sequence of systems of inequalities, such that already the first system includes clique, odd hole, odd antihole, wheel, and orthogonality constraints. In particular, for perfect (and many other) graphs, this first system gives the vertex packing polytope. For various classes of graphs, including t-perfect graphs, it follows that the stable set polytope is the projection of a polytope with a polynomial number of facets. We also discuss an extension of the method, which establishes a connection with certain submodular functions and the Möbius function of a lattice.
Ontology-based models in pervasive computing systems

Pervasive computing is by its nature open and extensible, and must integrate the information from a diverse range of sources. This leads to a problem of information exchange, so sub-systems must agree on shared representations. Ontologies potentially provide a well-founded mechanism for the representation and exchange of such structured information. A number of ontologies have been developed specifically for use in pervasive computing, none of which appears to cover adequately the space of concerns applicable to application designers. We compare and contrast the most popular ontologies, evaluating them against the system challenges generally recognized within the pervasive computing community. We identify a number of deficiencies that must be addressed in order to apply the ontological techniques successfully to next-generation pervasive systems.
Analysis and optimization of a modular stator core with segmental teeth and solid back iron for pm electric machines

Compared with conventional overlapping windings, nonoverlapping windings offer an exclusive advantage of short end-windings, which leads to less copper loss, shorter axial length, and higher slot fill factor particularly when used with segmental stator core. However, it is not so easy to manufacture the stator with nonoverlapping windings, especially when a high slot fill factor with low cost is required. In most existing methods, laminated segments are connected directly to form a complete stator core, so-called laminated-joint core. This requires precise mechanical assembles, and usually results in restrict process with expensive equipments. The goal of this paper is to propose a new segmental stator structure without laminated-joint, where the laminated tooth modules are coupled with a solid back iron. In this paper, existing manufacturing methods of segmental stator core will be reviewed and analyzed. Subsequently, several models are developed and improved, step by step, to achieve the best target, especially taking the iron losses as an evaluation criterion. Some techniques to reduce iron losses in the solid back iron will be proposed and compared. Some practical issues for manufacturing the proposed segmental stator core are discussed. Influence of subsidiary air gap on the iron losses will also be analyzed. And, cost evaluation of the proposed method, as well as the realistic shapes of laminations-solid-joints, is presented.
Overview and comparison of OpenCL and CUDA technology for GPGPU

GPU (Graphics Processing Unit) has a great impact on computing field. To enhance the performance of computing systems, researchers and developers use the parallel computing architecture of GPU. On the other hand, to reduce the development time of new products, two programming models are included in GPU, which are OpenCL (Open Computing Language) and CUDA (Compute Unified Device Architecture). The benefit of involving the two programming models in GPU is that researchers and developers don't have to understand OpenGL,  OpenCL is an open standard API, which has the advantage of cross-platform. CUDA is a parallel computer architecture developed by NVIDIA, which includes Runtime API and Driver API. Compared with OpenCL, CUDA is with better performance. In this paper, we used plenty of similar kernels to compare the computing performance of C, OpenCL and CUDA, the two kinds of API's on NVIDIA Quadro 4000 GPU. The experimental result showed that, the executive time of CUDA Driver API was 94.9%~99.0% faster than that of C, while and the executive time of CUDA Driver API was 3.8%~5.4% faster than that of OpenCL. Accordingly, the cross-platform characteristic of OpenCL did not affect the performance of GPU.
Coherent Dialogue with Attention-based Language Models

We model coherent conversation continuation via RNNbased dialogue models equipped with a dynamic attention mechanism. Our attention-RNN language model dynamically increases the scope of attention on the history as the conversation continues, as opposed to standard attention (or alignment) models with a fixed input scope in a sequence-tosequence model. This allows each generated word to be associated with the most relevant words in its corresponding conversation history. We evaluate the model on two popular dialogue datasets, the open-domain MovieTriples dataset and the closed-domain Ubuntu Troubleshoot dataset, and achieve significant improvements over the state-of-the-art and baselines on several metrics, including complementary diversity-based metrics, human evaluation, and qualitative visualizations. We also show that a vanilla RNN with dynamic attention outperforms more complex memory models (e.g., LSTM and GRU) by allowing for flexible, long-distance memory. We promote further coherence via topic modeling-based reranking.
Do GANs actually learn the distribution? An empirical study

Do GANS (Generative Adversarial Nets) actually learn the target distribution? The foundational paper of (Goodfellow et al 2014) suggested they do, if they were given “sufficiently large” deep nets, sample size, and computation time. A recent theoretical analysis in Arora et al (to appear at ICML 2017) raised doubts whether the same holds when discriminator has finite size. It showed that the training objective can approach its optimum value even if the generated distribution has very low support —in other words, the training objective is unable to prevent mode collapse. The current note reports experiments suggesting that such problems are not merely theoretical. It presents empirical evidence that well-known GANs approaches do learn distributions of fairly low support, and thus presumably are not learning the target distribution. The main technical contribution is a new proposed test, based upon the famous birthday paradox, for estimating the support size of the generated distribution.
COMPACT RING MONOPOLE ANTENNA WITH DOUBLE MEANDER LINES FOR 2 . 4 / 5 GHZ DUAL-BAND OPERATION

A novel compact ring monopole antenna with double meander lines is proposed for wireless local area networks (WLAN) applications in IEEE 802.11b/g/a systems. The designed antenna, fed by a 50 Ω microstrip transmission line, is only 32 mm in height and 16 mm in width. By introducing a horizontal and a vertical branched strips to a closed rectangular strip ring, the proposed antenna can generate two separate impedance bandwidths. Prototypes of the proposed antenna have been constructed and tested. The obtained impedance bandwidths reach about 12% for the 2.4 GHz band and 45.3% for the 5 GHz band, which meet the required bandwidth specification of 2.4/5 GHz WLAN standard. Also, good radiation performance and antenna gain over the two frequency ranges have been obtained.
Attribute-controlled face photo synthesis from simple line drawing

Face photo synthesis from simple line drawing is a one-to-many task as simple line drawing merely contains the contour of human face. Previous exemplar-based methods are over-dependent on the datasets and are hard to generalize to complicated natural scenes. Recently, several works utilize deep neural networks to increase the generalization, but they are still limited in the controllability of the users. In this paper, we propose a deep generative model to synthesize face photo from simple line drawing controlled by face attributes such as hair color and complexion. In order to maximize the controllability of face attributes, an attribute-disentangled variational auto-encoder (AD-VAE) is firstly introduced to learn latent representations disentangled with respect to specified attributes. Then we conduct photo synthesis from simple line drawing based on AD-VAE. Experiments show that our model can well disentangle the variations of attributes from other variations of face photos and synthesize detailed photorealistic face images with desired attributes. Regarding background and illumination as the style and human face as the content, we can also synthesize face photos with the target style of a style photo.
Sentiment analysis on Italian tweets

We describe TWITA, the first corpus of Italian tweets, which is created via a completely automatic procedure, portable to any other language. We experiment with sentiment analysis on two datasets from TWITA: a generic collection and a topic-specific collection. The only resource we use is a polarity lexicon, which we obtain by automatically matching three existing resources thereby creating the first polarity database for Italian. We observe that albeit shallow, our simple system captures polarity distinctions matching reasonably well the classification done by human judges, with differences in performance across polarity values and on the two sets.
Octree-Based Progressive Geometry Coding of Point Clouds

We propose a generic point cloud encoder that compresses geometry data including positions and normals of point samples corresponding to 3D objects with arbitrary topology. In this work, the coding process is led by an iterative octree cell subdivision of the object space. At each level of subdivision, positions of point samples are approximated by the geometry centers of all tree-front cells while normals are approximated by their statistical average within each of the tree-front cells. With this framework, we employ attribute-dependent encoding techniques to exploit different characteristics of various attributes. As a result, significant improvement in the rate-distortion (R-D) performance has been obtained with respect to the prior art. Furthermore, the proposed point cloud encoder can be potentially used for lossless geometry coding of 3D point clouds, given sufficient levels of octree expansion and normal space partitioning.
Decentralized path planning for multi-agent teams with complex constraints

This paper presents a novel approach to address the challenge of planning paths for multi-agent systems subject to complex constraints. The technique, called the Decentralized Multi-Agent Rapidly-exploring Random Tree (DMA-RRT) algorithm, extends the Closed-loop RRT (CLRRT) algorithm to handle multiple agents while retaining its ability to plan quickly. A core component of the DMARRT algorithm is a merit-based token passing coordination strategy that makes use of the tree of feasible trajectories grown in the CL-RRT algorithm to dynamically update the order in which agents replan. The reordering is based on a measure of each agent’s incentive to change the plan and allows agents with a greater potential improvement to replan sooner, which is demonstrated to improve the team’s overall performance compared to a traditional, scripted replan order. The main contribution of the work is a version of the algorithm, called Cooperative DMARRT, which introduces a cooperation strategy that allows an agent to modify its teammates’ plans in order to select paths that reduce their combined cost. This modification further improves team performance and avoids certain common deadlock scenarios. The paths generated by both algorithms are proven to satisfy inter-agent constraints, such as collision avoidance, and numerous simulation and experimental results are presented to demonstrate their performance. V.R. Desaraju ( ) · J.P. How Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA e-mail: rajeswar@alum.mit.edu J.P. How e-mail: jhow@mit.edu
A Study of Lexical Distribution in Citation Contexts through the IMRaD Standard

In this paper we present a large-scale approach for the extraction of verbs in reference contexts. We analyze citation contexts in relation with the IMRaD structure of scientific articles and use rank correlation analysis to characterize the distances between the section types. The results show strong differences in the verb frequencies around citations between the sections in the IMRaD structure. This study is a ”one-more-step” towards the lexical and semantic analysis of citation contexts.
Analysis of an Indoor Biomedical Radar-Based System for Health Monitoring

Innovative technology approaches have been increasingly investigated for the last two decades aiming at human-being long-term monitoring. However, current solutions suffer from critical limitations. In this paper, a complete system for contactless health-monitoring in home environment is presented. For the first time, radar, wireless communications, and data processing techniques are combined, enabling contactless fall detection and tagless localization. Practical limitations are considered and properly dealt with. Experimental tests, conducted with human volunteers in a realistic room setting, demonstrate an adequate detection of the target's absolute distance and a success rate of 94.3% in distinguishing fall events from normal movements. The volunteers were free to move about the whole room with no constraints in their movements.
Speech Quality Estimation: Models and Trends

This article presents a tutorial overview of models for estimating the quality experienced by users of speech transmission and communication services. Such models can be classified as either parametric or signal based. Signal-based models use input speech signals measured at the electrical or acoustic interfaces of the transmission channel. Parametric models, on the other hand, depend on signal and system parameters estimated during network planning or at run time. This tutorial describes the underlying principles as well as advantages and limitations of existing models. It also presents new developments, thus serving as a guide to an appropriate usage of the multitude of current and emerging speech quality models.
0.18 µm BCD technology platform with best-in-class 6 V to 70 V power MOSFETs

This paper presents a single BCD technology platform with high performance power devices at a wide range of operating voltages. The platform offers 6 V to 70 V LDMOS devices. All devices offer best-in-class specific on-resistance of 20 to 40 % lower than that of the state-of-the-art IC-based LDMOS devices and robustness better than the square SOA (safe-operating-area). Fully isolated LDMOS devices, in which independent bias is capable for circuit flexibility, demonstrate superior specific on-resistance (e.g. 11.9 mΩ-mm2 for breakdown voltage of 39 V). Moreover, the unusual sudden current enhancement appeared in the ID-VD saturation region of most of the high voltage LDMOS devices is significantly suppressed.
T-Hoarder: A framework to process Twitter data streams

With the eruption of online social networks, like Twitter and Facebook, a series of new APIs have appeared to allow access to the data that these new sources of information accumulate. One of most popular online social networks is the micro-blogging site Twitter. Its APIs allow many machines to access the torrent simultaneously to Twitter data, listening to tweets and accessing other useful information such as user profiles. A number of tools have appeared for processing Twitter data with different algorithms and for different purposes. In this paper T-Hoarder is described: a framework that enables tweet crawling, data filtering, and which is also able to display summarized and analytical information about the Twitter activity with respect to a certain topic or event in a web-page. This information is updated on a daily basis. The tool has been validated with real use-cases that allow making a series of analysis on the performance one may expect from this type of infrastructure.
Orthomode transducer for millimeter-wave correlation receivers

This paper presents a novel orthomode-transducer (OMT) architecture,which is particularly suitable for correlation receivers at millimeter waves. By exploiting an on-axis reverse-coupling structure, a compact OMT configuration is obtained, which provides high levels of channel equalization. The Ka-band prototypes exhibit very good electric performances in terms of isolation, cross-polarization, return loss, and channel equalization.
Elixir: Effective object-oriented program repair

This work is motivated by the pervasive use of method invocations in object-oriented (OO) programs, and indeed their prevalence in patches of OO-program bugs. We propose a generate-and-validate repair technique, called ELIXIR designed to be able to generate such patches. ELIXIR aggressively uses method calls, on par with local variables, fields, or constants, to construct more expressive repair-expressions, that go into synthesizing patches. The ensuing enlargement of the repair space, on account of the wider use of method calls, is effectively tackled by using a machine-learnt model to rank concrete repairs. The machine-learnt model relies on four features derived from the program context, i.e., the code surrounding the potential repair location, and the bug report. We implement ELIXIR and evaluate it on two datasets, the popular Defects4J dataset and a new dataset Bugs.jar created by us, and against 2 baseline versions of our technique, and 5 other techniques representing the state of the art in program repair. Our evaluation shows that ELIXIR is able to increase the number of correctly repaired bugs in Defects4J by 85% (from 14 to 26) and by 57% in Bugs.jar (from 14 to 22), while also significantly out-performing other state-of-the-art repair techniques including ACS, HD-Repair, NOPOL, PAR, and jGenProg.
Iris waveguide bandpass filter using substrate integrated waveguide (SIW) for satellite communication

In this paper, an iris waveguide filter with substrate integrated waveguide (SIW) structure at satellite communication band is discussed. The SIW structure is realized by via-holes on the substrate and shows a possibility of integration with other circuits. The iris waveguide filter has the insertion loss of 3.36 dB, the return loss of about 20 dB, and bandwidth of 3 %. Also, the iris waveguide filter using SIW has a sharp skirt characteristic.