nils-herrmann/clean_scirepeval huggingface dataset:
Answer Sequence Learning with Neural Networks for Answer Selection in Community Question Answering

In this paper, the answer selection problem in community question answering (CQA) is regarded as an answer sequence labeling task, and a novel approach is proposed based on the recurrent architecture for this problem. Our approach applies convolution neural networks (CNNs) to learning the joint representation of questionanswer pair firstly, and then uses the joint representation as input of the long shortterm memory (LSTM) to learn the answer sequence of a question for labeling the matching quality of each answer. Experiments conducted on the SemEval 2015 CQA dataset shows the effectiveness of our approach.
Long-Term Outcome of Laparoscopic Surgery for Pancreatic Neuroendocrine Tumors

As most pancreatic neuroendocrine tumors (PNET) are relatively small and solitary, they may be considered well suited for removal by a minimally invasive approach. There are few large series that describe laparoscopic surgery for PNET. The primary aim of this study was to describe the feasibility, outcome, and histopathology associated with laparoscopic pancreatic surgery (LS) of PNET in a large series. All patients with PNET who underwent LS at a single hospital from March 1997 to April 2011 were included retrospectively in the study. A total of 72 patients with PNET underwent 75 laparoscopic procedures, out of which 65 were laparoscopic resections or enucleations. The median operative time of all patients who underwent resections or enucleations was 175 (60–520) min, the median blood loss was 300 (5–2,700) ml, and the median length of hospital stay was 7 (2–27) days. The overall morbidity rate was 42 %, with a surgical morbidity rate of 21 % and postoperative pancreatic fistula (POPF) formation in 21 %. Laparoscopic enucleations were associated with a higher rate of POPF than were laparoscopic resections. Five-year disease-specific survival rate was 90 %. The T stage, R stage, and a Ki-67 cutoff value of 5 % significantly predicted 5-year survival. LS of PNET is feasible with acceptable morbidity and a good overall disease-specific long-term prognosis.
Worm chips: microtools for C. elegans biology.

The study of small-size animal models, such as the roundworm C. elegans, has provided great insight into several in vivo biological processes, extending from cell apoptosis to neural network computing. The physical manipulation of this micron-sized worm has always been a challenging task. Here, we discuss the applications, capabilities and future directions of a new family of worm manipulation tools, the 'worm chips'. Worm chips are microfabricated devices capable of precisely manipulating single worms or a population of worms and their environment. Worm chips pose a paradigm shift in current methodologies as they are capable of handling live worms in an automated fashion, opening up a new direction in in vivo small-size organism studies.
Medical datamining with probabilistic classifiers

Statistical classifiers typically build (parametric) probabilistic models of the training data, and compute the probability that an unknown sample belongs to each of the possible classes using these models. We utilize two established measures to compare the performance of statistical classifiers namely; classification accuracy (or error rate) and the area under ROC. Naïve Bayes has obtained much relevance in data classification for machine learning and datamining. In our work, a comparative analysis of the accuracy performance of statistical classifiers namely Naïve Bayes (NB), MDL discretized NB, 4 different variants of NB and 8 popular non-NB classifiers was carried out on 21 medical datasets using classification accuracy and true positive rate. Our results indicate that the classification accuracy of Naïve Bayes (MDL discretized) on the average is the best performer. The significance of this work through the results of the comparative analysis, we are of the opinion that medical datamining with generative methods like Naïve Bayes is computationally simple yet effective and are to be used whenever possible as the benchmark for statistical classifiers.
A semi-Lagrangian contouring method for fluid simulation

In this article, we present a semi-Lagrangian surface tracking method for use with fluid simulations. Our method maintains an explicit polygonal mesh that defines the surface, and an octree data structure that provides both a spatial index for the mesh and a means for efficiently approximating the signed distance to the surface. At each timestep, a new surface is constructed by extracting the zero set of an advected signed-distance function. Semi-Lagrangian backward path tracing is used to advect the signed-distance function. One of the primary advantages of this formulation is that it enables tracking of surface characteristics, such as color or texture coordinates, at negligible additional cost. We include several examples demonstrating that the method can be effectively used as part of a fluid simulation to animate complex and interesting fluid behaviors.
New consumer behavior : A review of research on eWOM and hotels

This study aims to gather and analyze published articles regarding the influence of electronic word-ofmouth (eWOM) on the hotel industry. Articles published in the last five years appearing in six different academically recognized journals of tourism have been reviewed in the present study. Analysis of these articles has identified two main lines of research: review-generating factors (previous factors that cause consumers to write reviews) and impacts of eWOM (impacts caused by online reviews) from consumer perspective and company perspective. A summary of each study’s description, methodology and main results are outlined below, as well as an analysis of findings. This study also seeks to facilitate understanding and provide baseline information for future articles related to eWOM and hotels with the intention that researchers have a “snapshot” of previous research and the results achieved to date.
A novel wide-band envelope detector

In this paper, we present a novel wide-band envelope detector comprising a fully-differential operational transconductance amplifier (OTA), a full-wave rectifier and a peak detector. To enhance the frequency performance of the envelop detector, we utilize a gyrator-C active inductor load in the OTA for wider bandwidth. Additionally, it is shown that the high-speed rectifier of the envelope detector requires high bias current instead of the sub-threshold bias condition. The experimental results show that the proposed envelope detector can work from 100-Hz to 1.6-GHz with an input dynamic range of 50-dB at 100-Hz and 40-dB at 1.6-GHz, respectively. The envelope detector was fabricated on the TSMC 0.18-um CMOS process with an active area of 0.652 mm2.
Treatment of inflammatory bowel disease (IBD) in dogs and cats.

The treatment of inflammatory bowel disease (IBD) possesses numerous difficulties owing to the unclear etiology of the disease. This article overviews the drugs used in the treatment of IBD depending on the intensity of clinical symptoms (Canine Inflammatory Bowel Disease Activity Index and Canine Chronic Enterophaty Clinical Activity Index). Patients demonstrating mild symptoms of the disease are usually placed on an appropriate diet which may be combined with immunomodulative or probiotic treatment. In moderate progression of IBD, 5-aminosalicylic acid (mesalazine or olsalazine) derivatives may be administered. Patients showing severe symptoms of the disease are usually treated with immunosuppressive drugs, antibiotics and elimination diet. Since the immune system plays an important role in the pathogenesis of the disease, the advancements in biological therapy research will contribute to the progress in the treatment of canine and feline IBD in the coming years.
Melanoma exosomes educate bone marrow progenitor cells toward a pro-metastatic phenotype through MET

Tumor-derived exosomes are emerging mediators of tumorigenesis. We explored the function of melanoma-derived exosomes in the formation of primary tumors and metastases in mice and human subjects. Exosomes from highly metastatic melanomas increased the metastatic behavior of primary tumors by permanently 'educating' bone marrow progenitors through the receptor tyrosine kinase MET. Melanoma-derived exosomes also induced vascular leakiness at pre-metastatic sites and reprogrammed bone marrow progenitors toward a pro-vasculogenic phenotype that was positive for c-Kit, the receptor tyrosine kinase Tie2 and Met. Reducing Met expression in exosomes diminished the pro-metastatic behavior of bone marrow cells. Notably, MET expression was elevated in circulating CD45−C-KITlow/+TIE2+ bone marrow progenitors from individuals with metastatic melanoma. RAB1A, RAB5B, RAB7 and RAB27A, regulators of membrane trafficking and exosome formation, were highly expressed in melanoma cells. Rab27A RNA interference decreased exosome production, preventing bone marrow education and reducing, tumor growth and metastasis. In addition, we identified an exosome-specific melanoma signature with prognostic and therapeutic potential comprised of TYRP2, VLA-4, HSP70, an HSP90 isoform and the MET oncoprotein. Our data show that exosome production, transfer and education of bone marrow cells supports tumor growth and metastasis, has prognostic value and offers promise for new therapeutic directions in the metastatic process.
Decoupled current control with novel anti-windup for PMSM drives

This paper proposes a new current controller with modified decoupling and anti-windup schemes for a permanent magnet synchronous motor (PMSM) drive. In designing the controller, an improved voltage model, which is different from existing models in that it reflects all the nonlinear characteristics of the motor, is considered. In an actual PMSM, unintentional distortion occurs in inductance and flux due to magnetic saturation and structural asymmetry. In this paper, the effects of such distortion on voltage ripple are analyzed and the effect of voltage distortion on current control is analyzed in detail. Based on the voltage model, a decoupling controller is developed to effectively separate the d-q current regulators. The controller produces compensation voltages using the current error of the other axis. In addition, an anti-windup controller is designed that takes into account not only the integrator output in PI controllers but also the integrator output in decoupling controllers. The proposed current controller aimed at compensating all nonlinearities of PMSM enables high-performance operation of the motor. The feasibility of the proposed current control scheme is verified by experimental results.
Combining Two Perspectives on Classifying Multimodal Data for Recognizing Speaker Traits

Human communication involves conveying messages both through verbal and non-verbal channels (facial expression, gestures, prosody, etc.). Nonetheless, the task of learning these patterns for a computer by combining cues from multiple modalities is challenging because it requires effective representation of the signals and also taking into consideration the complex interactions between them. From the machine learning perspective this presents a two-fold challenge: a) Modeling the intermodal variations and dependencies; b) Representing the data using an apt number of features, such that the necessary patterns are captured but at the same time allaying concerns such as over-fitting. In this work we attempt to address these aspects of multimodal recognition, in the context of recognizing two essential speaker traits, namely passion and credibility of online movie reviewers. We propose a novel ensemble classification approach that combines two different perspectives on classifying multimodal data. Each of these perspectives attempts to independently address the two-fold challenge. In the first, we combine the features from multiple modalities but assume inter-modality conditional independence. In the other one, we explicitly capture the correlation between the modalities but in a space of few dimensions and explore a novel clustering based kernel similarity approach for recognition. Additionally, this work investigates a recent technique for encoding text data that captures semantic similarity of verbal content and preserves word-ordering. The experimental results on a recent public dataset shows significant improvement of our approach over multiple baselines. Finally, we also analyze the most discriminative elements of a speaker's non-verbal behavior that contribute to his/her perceived credibility/passionateness.
User-centered design methods in practice: a survey of the state of the art

This paper reports the results of a recent survey involving over one hundred leading professionals of user-centered design (UCD). The survey covered a broad range of issues ranging from the profile of a typical UCD project including the percentage of total budget on UCD, organizational impact of UCD, measures of UCD success, and the most widely used methods and techniques. Results show that cost-benefit tradeoffs are a key consideration in the adoption of UCD methods. Measures of UCD effectiveness are lacking and rarely applied. There is a major discrepancy between the commonly cited measures and what were actually applied.
Analysis of schizophrenia and hepatocellular carcinoma genetic network with corresponding modularity and pathways: novel insights to the immune system

Schizophrenic patients show lower incidences of cancer, implicating schizophrenia may be a protective factor against cancer. To study the genetic correlation between the two diseases, a specific PPI network was constructed with candidate genes of both schizophrenia and hepatocellular carcinoma. The network, designated schizophrenia-hepatocellular carcinoma network (SHCN), was analysed and cliques were identified as potential functional modules or complexes. The findings were compared with information from pathway databases such as KEGG, Reactome, PID and ConsensusPathDB. The functions of mediator genes from SHCN show immune system and cell cycle regulation have important roles in the eitology mechanism of schizophrenia. For example, the over-expressing schizophrenia candidate genes, SIRPB1, SYK and LCK, are responsible for signal transduction in cytokine production; immune responses involving IL-2 and TREM-1/DAP12 pathways are relevant for the etiology mechanism of schizophrenia. Novel treatments were proposed by searching the target genes of FDA approved drugs with genes in potential protein complexes and pathways. It was found that Vitamin A, retinoid acid and a few other immune response agents modulated by RARA and LCK genes may be potential treatments for both schizophrenia and hepatocellular carcinoma. This is the first study showing specific mediator genes in the SHCN which may suppress tumors. We also show that the schizophrenic protein interactions and modulation with cancer implicates the importance of immune system for etiology of schizophrenia.
Analysis Of Machine Learning Classifier Performance In Adding Custom Gestures To The Leap Motion

Analysis Of Machine Learning Classifier Performance In Adding Custom Gestures To The Leap Motion Eric Yun The use of supervised machine learning to extend the capabilities and overall viability of motion sensing input devices has been an increasingly popular avenue of research since the release of the Leap Motion in 2013. The device's optical sensors are capable of recognizing and tracking key features of a user's hands and fingers, which can be obtained and manipulated through a robust API. This makes statistical classification ideal for tackling the otherwise laborious and error prone nature of adding new programmer-defined gestures to the set of recognized gestures. Although a handful of studies have explored the effectiveness of machine learning with the Leap Motion, none to our knowledge have run a comparative performance analysis of classification algorithms or made use of more than several of them in their experiments. The aim of this study is to improve the reliability of detecting newly added gestures by identifying the classifiers that produce the best results. To this end, a formal analysis of the most popular classifiers used in the field of machine learning was performed to determine those most appropriate to the requirements of the Leap Motion. A recording and
Finger gaits planning for multifingered manipulation

A robotic hand may change its grasp status and relocate some of its fingers in order to perform a large scale manipulation. Such a strategy is called a finger gait. In this paper, a randomized manipulation planning algorithm is proposed to solving the finger gait planning problem. One of the most used finger gaiting primitives, finger substitution, is introduced. Because of its discrete-continuous characteristics, the kinematics model of a finger substitution is formulated into a hybrid automaton. Considering the discrete and continuous topology of the automaton, both the discrete metric and continuous metric are defined on the state space. An improved RRT based planner is proposed to find a feasible finger substitution. Finally, simulation results verify the validity of the proposed finger gait planner.
Towards Automatic Personalized Content Generation for Platform Games

In this paper, we show that personalized levels can be automatically generated for platform games. We build on previous work, where models were derived that predicted player experience based on features of level design and on playing styles. These models are constructed using preference learning, based on questionnaires administered to players after playing different levels. The contributions of the current paper are (1) more accurate models based on a much larger data set; (2) a mechanism for adapting level design parameters to given players and playing style; (3) evaluation of this adaptation mechanism using both algorithmic and human players. The results indicate that the adaptation mechanism effectively optimizes level design parameters for particular players.
Manga and non-photorealistic rendering

In focusing my efforts on non-photorealistic computer graphics (CG) imagery, I have been struggling to create pictures that are, in a sense, more realistJc than those rendered using so-called photorealisric techniques. In the past, when directing television programs, I felt something distinctly lacking in CG visuals CG tends to look monolithic and excessively clean-cut I don't deny that the perfectly integrated monsters and super-real ist ic special effects are qui te capt ivat ing fo r viewers, but most of them are l i t t le more than just beautiful visuals on the two-dimensional screen o r moni tor . As technology improves and the visuals created by CG tend towards seamless reality, the more lifeless seem these pictures.The pervading opinion of late seems to be that CG should be used such that an audience can't detect its presence a mundane goal in my opinion. When we produce a film based on a wellknown story, inevitably some viewers will find the original more to their liking. Readers of the original text have the freedom to inter° pret characters' movements and the author's viewpoint themselves, an aspect not unique to, but inherent in, the process of reading. Thus, even the most complete visuals will fall short of providing an audience with this most satisfying dimension. Filmed imagery and hyper-realistic CG seldom allow the degree of i n te rp re ta t i on a more pa r t i c i pa to ry medium such as the printed word does to the viewer. This might explain why I feel so little for most of the CG imagery we see in popular media.At any rate, there can be little doubt that we collect information with our five senses and our brain interprets it-What is "real" for us is what is interpreted, not what is reflected on our retinae. Perhaps then CG can facilitate this aspect oF interpretation. Interpretation is how we arrange the world in order to make sense of it. That is, it is symbolization of the world performed by our brains. Much CG, though, aims at visual s imulat ion exclusively and ignores the act of interpretation as an important component of our enjoyment of visual art. For example, when creating a realistic image of a glass through CG, a common step is to calculate how the light falling on the CG glass refracts and reflects. I think, however, that in trying to create images meaningful to our audiences, we might be better oft starting with the question "how best co draw this F~ure I: P~-~re scm/I. See page 9S for co/or image.
Layer-Wise Floorplan Extraction for Automatic Urban Building Reconstruction

Urban building reconstruction is an important step for urban digitization and realisticvisualization. In this paper, we propose a novel automatic method to recover urban building geometry from 3D point clouds. The proposed method is suitable for buildings composed of planar polygons and aligned with the gravity direction, which are quite common in the city. Our key observation is that the building shapes are usually piecewise constant along the gravity direction and determined by several dominant shapes. Based on this observation, we formulate building reconstruction as an energy minimization problem under the Markov Random Field (MRF) framework. Specifically, point clouds are first cutinto a sequence of slices along the gravity direction. Then, floorplans are reconstructed by extracting boundaries of these slices, among which dominant floorplans are extracted and propagated to other floors via MRF. To guarantee correct propagation, a new distance measurement for floorplans is designed, which first encodes floorplans into strings and then calculates distances between their corresponding strings. Additionally, an image based editing method is also proposed to recover detailed window structures. Experimental results on both synthetic and real data sets have validated the effectiveness of our method.
Representation Learning

Learning latent representations from long text sequences is an important first step in many natural language processing applications. Recurrent Neural Networks (RNNs) have become a cornerstone for this challenging task. However, the quality of sentences during RNN-based decoding (reconstruction) decreases with the length of the text. We propose a sequence-to-sequence, purely convolutional and deconvolutional autoencoding framework that is free of the above issue, while also being computationally efficient. The proposed method is simple, easy to implement and can be leveraged as a building block for many applications. We show empirically that compared to RNNs, our framework is better at reconstructing and correcting long paragraphs. Quantitative evaluation on semi-supervised text classification and summarization tasks demonstrate the potential for better utilization of long unlabeled text data.