nils-herrmann/clean_scirepeval huggingface dataset:
Central sensitization: Implications for the diagnosis and treatment of pain

Nociceptor inputs can trigger a prolonged but reversible increase in the excitability and synaptic efficacy of neurons in central nociceptive pathways, the phenomenon of central sensitization. Central sensitization manifests as pain hypersensitivity, particularly dynamic tactile allodynia, secondary punctate or pressure hyperalgesia, aftersensations, and enhanced temporal summation. It can be readily and rapidly elicited in human volunteers by diverse experimental noxious conditioning stimuli to skin, muscles or viscera, and in addition to producing pain hypersensitivity, results in secondary changes in brain activity that can be detected by electrophysiological or imaging techniques. Studies in clinical cohorts reveal changes in pain sensitivity that have been interpreted as revealing an important contribution of central sensitization to the pain phenotype in patients with fibromyalgia, osteoarthritis, musculoskeletal disorders with generalized pain hypersensitivity, headache, temporomandibular joint disorders, dental pain, neuropathic pain, visceral pain hypersensitivity disorders and post-surgical pain. The comorbidity of those pain hypersensitivity syndromes that present in the absence of inflammation or a neural lesion, their similar pattern of clinical presentation and response to centrally acting analgesics, may reflect a commonality of central sensitization to their pathophysiology. An important question that still needs to be determined is whether there are individuals with a higher inherited propensity for developing central sensitization than others, and if so, whether this conveys an increased risk in both developing conditions with pain hypersensitivity, and their chronification. Diagnostic criteria to establish the presence of central sensitization in patients will greatly assist the phenotyping of patients for choosing treatments that produce analgesia by normalizing hyperexcitable central neural activity. We have certainly come a long way since the first discovery of activity-dependent synaptic plasticity in the spinal cord and the revelation that it occurs and produces pain hypersensitivity in patients. Nevertheless, discovering the genetic and environmental contributors to and objective biomarkers of central sensitization will be highly beneficial, as will additional treatment options to prevent or reduce this prevalent and promiscuous form of pain plasticity.
Hierarchical classification of Web content

This paper explores the use of hierarchical structure for classifying a large, heterogeneous collection of web content. The hierarchical structure is initially used to train different second-level classifiers. In the hierarchical case, a model is learned to distinguish a second-level category from other categories within the same top level. In the flat non-hierarchical case, a model distinguishes a second-level category from all other second-level categories. Scoring rules can further take advantage of the hierarchy by considering only second-level categories that exceed a threshold at the top level.
We use support vector machine (SVM) classifiers, which have been shown to be efficient and effective for classification, but not previously explored in the context of hierarchical classification. We found small advantages in accuracy for hierarchical models over flat models. For the hierarchical approach, we found the same accuracy using a sequential Boolean decision rule and a multiplicative decision rule. Since the sequential approach is much more efficient, requiring only 14%-16% of the comparisons used in the other approaches, we find it to be a good choice for classifying text into large hierarchical structures.
Complex Concurrent Engineering and the Design Structure Matrix Method

Concurrent engineering (CE) principles have considerably matured over the last decade. However, many companies still face enormous challenges when implementing and managing CE practices. This is due to the increased complexity of engineering products and processes, on one hand, and the lack of corresponding CE models and tools, on the other hand. This paper focuses on four critical problems that challenge management while implementing CE in complex product development (PD) projects. We refer to these problems as: iteration, overlapping, decomposition and integration, and convergence problems. We describe these problems proposing a unified modeling and solution approach based on the design structure matrix (DSM) method, which is an information exchange model that allows managers to represent complex task relationships to better plan and manage CE initiatives.
Using BabelNet to Improve OOV Coverage in SMT

Out-of-vocabulary words (OOVs) are a ubiquitous and difficult problem in statistical machine translation (SMT). This paper studies different strategies of using BabelNet to alleviate the negative impact brought about by OOVs. BabelNet is a multilingual encyclopedic dictionary and a semantic network, which not only includes lexicographic and encyclopedic terms, but connects concepts and named entities in a very large network of semantic relations. By taking advantage of the knowledge in BabelNet, three different methods – using direct training data, domain-adaptation techniques and the BabelNet API – are proposed in this paper to obtain translations for OOVs to improve system performance. Experimental results on English–Polish and English–Chinese language pairs show that domain adaptation can better utilize BabelNet knowledge and performs better than other methods. The results also demonstrate that BabelNet is a really useful tool for improving translation performance of SMT systems.
Power Consumption Modeling of Skid-Steer Tracked Mobile Robots on Rigid Terrain

Power consumption is a key element in outdoor mobile robot autonomy. This issue is very relevant in skid-steer tracked vehicles on account of their large ground contact area. In this paper, the power losses due to dynamic friction have been modeled from two different perspectives: 1) the power drawn by the rigid terrain and 2) the power supplied by the motors. Comparison of both approaches has provided new insight on skid steering on hard flat terrains at walking speeds. Experimental power models, which also include traction resistance and other power losses, have been obtained for two different track widths over marble flooring and asphalt with Auriga- beta, which is a full-size mobile robot. To this end, various internal probes have been set at different points of the power stream. Furthermore, new energy implications for navigation of these kinds of vehicles have been deduced and tested.
Information Technology and the Structuring of Organizations

Recent work in social theory departs from prior traditions in proposing that social phenomena can be understood as comprising both subjective and objective elements. We apply this premise of duality to understanding the relationship between information technology and organizations. We construct a theoretical framework in which the development and deployment of information technology in organizations is a social phenomenon, and in which the organizational consequences of technology are products of both material and social dimensions. The framework is based on Giddens’ theory of structuration, and it allows us to progress beyond several of the false dichotomies (subjective vs objective, socially constructed vs material, macro vs micro, and qualitative vs quantitative) that persist in investigations of the interaction between organizations and information technology. The framework can be used to guide studies in two main areas of information systems researchsystems development and the organizational consequences of using information technology. Iafomutin systems-olgmizatic.luI chang~izs~ sbrtutsoeI8l theoly-F&Iwmm~
ReSet: Learning Recurrent Dynamic Routing in ResNet-like Neural Networks

Neural Network is a powerful Machine Learning tool that shows outstanding performance in Computer Vision, Natural Language Processing, and Artificial Intelligence. In particular, recently proposed ResNet architecture and its modifications produce state-of-the-art results in image classification problems. ResNet and most of the previously proposed architectures have a fixed structure and apply the same transformation to all input images. In this work, we develop a ResNet-based model that dynamically selects Computational Units (CU) for each input object from a learned set of transformations. Dynamic selection allows the network to learn a sequence of useful transformations and apply only required units to predict the image label. We compare our model to ResNet-38 architecture and achieve better results than the original ResNet on CIFAR-10.1 test set. While examining the produced paths, we discovered that the network learned different routes for images from different classes and similar routes for similar images.
International Accounting Standards and Accounting Quality

We compare characteristics of accounting amounts for firms that apply International Accounting Standards (IAS) to a matched sample of firms that do not to investigate whether applying IAS is associated with higher accounting quality and lower equity cost of capital. We find that firms applying IAS evidence less earnings management, more timely loss recognition, and more value relevance of accounting amounts than do those applying domestic GAAP. Firms applying IAS have higher variance of the change in net income, a higher ratio of the variances of the change in net income and change in cash flows, a significantly less negative correlation between accruals and cash flows, and a lower frequency of small positive net income. They have a significantly higher frequency of large negative net income and generally higher value relevance of accounting amounts. Differences between firms applying IAS and those applying domestic GAAP in the period before IAS firms adopt IAS do not explain the differences in accounting quality. Firms applying IAS generally exhibit higher accounting quality than when they previously applied domestic GAAP. The increase in accounting quality for IAS firms is generally greater than that for firms applying domestic GAAP throughout the sample period. We also find weak evidence suggesting that application of IAS is associated with a lower equity cost of capital. Overall, our results suggest improvement in accounting quality associated with applying IAS.
Deep Reinforcement Learning Autoencoder with Noisy Feedback

End-to-end learning of communication systems enables joint optimization of transmitter and receiver, implemented as deep neural network (NN)-based autoencoders, over any type of channel and for an arbitrary performance metric. Recently, an alternating training procedure was proposed which eliminates the need for an explicit channel model. However, this approach requires feedback of real-valued losses from the receiver to the transmitter during training. In this paper, we first show that alternating training works even with a noisy feedback channel. Then, we design a system that learns to transmit real numbers over an unknown channel without a preexisting feedback link. Once trained, this feedback system can be used to communicate losses during alternating training of autoencoders. Evaluations over additive white Gaussian noise (AWGN) and Rayleigh blockfading (RBF) channels show that end-to-end communication systems trained using the proposed feedback system achieve the same performance as when trained with a perfect feedback link.
Theme Based Clustering of Tweets

In this paper, we present overview of our approach for clustering tweets. Due to short text of tweets, traditional text clustering mechanisms alone may not produce optimal results. We believe that there is an underlying theme/topic present in majority of tweets which is evident in growing usage of hashtag feature in the Twitter network. Clustering tweets based on these themes seems a more natural way for grouping. We propose to use Wikipedia topic taxonomy to discover the themes from the tweets and use the themes along with traditional word based similarity metric for clustering. We show some of our initial results to demonstrate the effectiveness of our approach.
Ensemble Coordination Approach in Multi-AGV Systems Applied to Industrial Warehouses

This paper deals with a holistic approach to coordinate a fleet of automated guided vehicles (AGVs) in an industrial environment. We propose an ensemble approach based on a two layer control architecture and on an automatic algorithm for the definition of the roadmap. The roadmap is built by considering the path planning algorithm implemented on the hierarchical architecture and vice versa. In this way, we want to manage the coordination of the whole system in order to increase the flexibility and the global efficiency. Furthermore, the roadmap is computed in order to maximize the redundancy, the coverage and the connectivity. The architecture is composed of two layers. The low-level represents the roadmap itself. The high-level describes the topological relationship among different areas of the environment. The path planning algorithm works on both these levels and the subsequent coordination among AGVs is obtained exploiting shared resource (i.e., centralized information) and local negotiation (i.e., decentralized coordination). The proposed approach is validated by means of simulations and comparison using real plants. Note to Practitioners-The motivation of this work grows from the need to increase the flexibility and efficiency of current multi-AGV systems. In particular, in the current state-of-the-art the AGVs are guided through a manually defined roadmap with ad-hoc strategies for coordination. This is translated into high setup time requested for installation and the impossibility to easily respond to dynamic changes of the environment. The proposed method aims at managing all the design, setup and control process in an automatic manner, decreasing the time for setup and installation. The flexibility is also increased by considering a coordination strategy for the fleet of AGVs not based on manual ad-hoc rules. Simulations are performed in order to compare the proposed approach to the current industrial one. In the future, industrial aspects, as the warehouse management system, will be integrated in order to achieve a real and complete industrial functionality.
They Exist! Introducing Plural Mentions to Coreference Resolution and Entity Linking

This paper analyzes arguably the most challenging yet under-explored aspect of resolution tasks such as coreference resolution and entity linking, that is the resolution of plural mentions. Unlike singular mentions each of which represents one entity, plural mentions stand for multiple entities. To tackle this aspect, we take the character identification corpus from the SemEval 2018 shared task that consists of entity annotation for singular mentions, and expand it by adding annotation for plural mentions. We then introduce a novel coreference resolution algorithm that selectively creates clusters to handle both singular and plural mentions, and also a deep learning-based entity linking model that jointly handles both types of mentions through multi-task learning. Adjusted evaluation metrics are proposed for these tasks as well to handle the uniqueness of plural mentions. Our experiments show that the new coreference resolution and entity linking models significantly outperform traditional models designed only for singular mentions. To the best of our knowledge, this is the first time that plural mentions are thoroughly analyzed for these two resolution tasks.
End-to-End Offline Goal-Oriented Dialog Policy Learning via Policy Gradient

Learning a goal-oriented dialog policy is generally performed offline with supervised learning algorithms or online with reinforcement learning (RL). Additionally, as companies accumulate massive quantities of dialog transcripts between customers and trained human agents, encoder-decoder methods have gained popularity as agent utterances can be directly treated as supervision without the need for utterance-level annotations. However, one potential drawback of such approaches is that they myopically generate the next agent utterance without regard for dialog-level considerations. To resolve this concern, this paper describes an offline RL method for learning from unannotated corpora that can optimize a goal-oriented policy at both the utterance and dialog level. We introduce a novel reward function and use both on-policy and off-policy policy gradient to learn a policy offline without requiring online user interaction or an explicit state space definition.
Sentiment Analysis Using Deep Learning Techniques: A Review

The World Wide Web such as social networks, forums, review sites and blogs generate enormous heaps of data in the form of users views, emotions, opinions and arguments about different social events, products, brands, and politics. Sentiments of users that are expressed on the web has great influence on the readers, product vendors and politicians. The unstructured form of data from the social media is needed to be analyzed and well-structured and for this purpose, sentiment analysis has recognized significant attention. Sentiment analysis is referred as text organization that is used to classify the expressed mind-set or feelings in different manners such as negative, positive, favorable, unfavorable, thumbs up, thumbs down, etc. The challenge for sentiment analysis is lack of sufficient labeled data in the field of Natural Language Processing (NLP). And to solve this issue, the sentiment analysis and deep learning techniques have been merged because deep learning models are effective due to their automatic learning capability. This Review Paper highlights latest studies regarding the implementation of deep learning models such as deep neural networks, convolutional neural networks and many more for solving different problems of sentiment analysis such as sentiment classification, cross lingual problems, textual and visual analysis and product review analysis, etc. Sentiment analysis; recurrent neural network; deep neural network; convolutional neural network; recursive neural network; deep belief network
Design of Broadband Linear and Efficient Power Amplifier for Long-Term Evolution Applications

In this letter, a broadband linear and efficient power amplifier for base-stations in long-term evolution (LTE) applications is designed, fabricated, and experimentally verified, employing Cree's CGH40010F GaN HEMT. A novel method of baseband, fundamental and harmonic impedances control is used to provide high linearity and efficiency across a broad bandwidth. When producing a two-tone carrier-to-intermodulation ratio (C/I) of 30 dBc, the circuit has demonstrated a two-tone power added efficiency (PAE) between 45%-60% across the frequency range from 1.6 to 2.6 GHz while delivering 36.0-38.5 dBm average output power. For a single-carrier 20 MHz LTE signal with a peak-to-average ratio (PAR) of 6.5 dB, a measured high PAE of 40%-55% can be achieved at an average output power of 35.3-37.5 dBm with an adjacent channel leakage ratio (ACLR) of about -30 dBc from 1.6 to 2.6 GHz.
A Vector Space for Distributional Semantics for Entailment

Distributional semantics creates vectorspace representations that capture many forms of semantic similarity, but their relation to semantic entailment has been less clear. We propose a vector-space model which provides a formal foundation for a distributional semantics of entailment. Using a mean-field approximation, we develop approximate inference procedures and entailment operators over vectors of probabilities of features being known (versus unknown). We use this framework to reinterpret an existing distributionalsemantic model (Word2Vec) as approximating an entailment-based model of the distributions of words in contexts, thereby predicting lexical entailment relations. In both unsupervised and semi-supervised experiments on hyponymy detection, we get substantial improvements over previous results.
GENETAG: a tagged corpus for gene/protein named entity recognition

Named entity recognition (NER) is an important first step for text mining the biomedical literature. Evaluating the performance of biomedical NER systems is impossible without a standardized test corpus. The annotation of such a corpus for gene/protein name NER is a difficult process due to the complexity of gene/protein names. We describe the construction and annotation of GENETAG, a corpus of 20K MEDLINE® sentences for gene/protein NER. 15K GENETAG sentences were used for the BioCreAtIvE Task 1A Competition. To ensure heterogeneity of the corpus, MEDLINE sentences were first scored for term similarity to documents with known gene names, and 10K high- and 10K low-scoring sentences were chosen at random. The original 20K sentences were run through a gene/protein name tagger, and the results were modified manually to reflect a wide definition of gene/protein names subject to a specificity constraint, a rule that required the tagged entities to refer to specific entities. Each sentence in GENETAG was annotated with acceptable alternatives to the gene/protein names it contained, allowing for partial matching with semantic constraints. Semantic constraints are rules requiring the tagged entity to contain its true meaning in the sentence context. Application of these constraints results in a more meaningful measure of the performance of an NER system than unrestricted partial matching. The annotation of GENETAG required intricate manual judgments by annotators which hindered tagging consistency. The data were pre-segmented into words, to provide indices supporting comparison of system responses to the "gold standard". However, character-based indices would have been more robust than word-based indices. GENETAG Train, Test and Round1 data and ancillary programs are freely available at ftp://ftp.ncbi.nlm.nih.gov/pub/tanabe/GENETAG.tar.gz . A newer version of GENETAG-05, will be released later this year.
Efficient Voltage Regulation for Microprocessor Cores Stacked in Vertical Voltage Domains

Due to exponential (Moores law) scaling of advanced CMOS technologies, the challenges associated with delivering power to performance and mobile computing systems are outpacing the capabilities of conventional voltage regulator (VR) topologies. To continue to scale throughput at constant power density, the level of parallelism in microprocessor architectures is expected to increase substantially. In this paper, we present a power conversion topology to provide independent multicore regulation in the 0.8-1.4 V range from a 12-V dc bus. The topology uses a multistage ladder converter to manage power delivery to digital circuits stacked in vertical voltage domains. This approach has several advantages with regard to systems efficiency as it allows a more moderate conversion ratio of the main dc-dc converter. Moreover, the parallel converter only needs to process a fraction of the power of each core as the current can be “recycled” by adjacent cores in the stack. We develop a dynamical model for a multiple-input, multiple-output control scheme that uses a simple integral-control law, augmented with fast voltage- and current-mode feedforward. Measurement results of a discrete prototype verify the control scheme and demonstrate the potential advantages in system efficiency but also emphasize the remaining challenges in meeting stringent VR dynamic response requirements.