nils-herrmann/clean_scirepeval huggingface dataset:
The role of descending inhibitory pathways on chronic pain modulation and clinical implications.

The treatment and management of chronic pain is a major challenge for clinicians. Chronic pain is often underdiagnosed and undertreated, and there is a lack of awareness of the pathophysiologic mechanisms that contribute to chronic pain. Chronic pain involves peripheral and central sensitization, as well as the alteration of the pain modulatory pathways. Imbalance between the descending facilitatory systems and the descending inhibitory systems is believed to be involved in chronic pain in pathological conditions. A pharmacological treatment that could restore the balance between these 2 pathways by diminishing the descending facilitatory pain pathways and enhancing the descending inhibitory pain pathways would be a valuable therapeutic option for patients with chronic pain. Due to the lack of evidence for pharmacological options that act on descending facilitation pathways, in this review we summarize the role of the descending inhibitory pain pathways in pain perception. This review will focus primarily on monoaminergic descending inhibitory pain pathways and their contribution to the mechanism of chronic pain and several pharmacological treatment options that enhance these pathways to reduce chronic pain. We describe anatomical structures and neurotransmitters of the descending inhibitory pain pathways that are activated in response to nociceptive pain and altered in response to sustained and persistent pain which leads to chronic pain in various pathological conditions.
Feature Detection and Tracking with Constrained Local Models

We present an efficient and robust model matching method which uses a joint shape and texture appearance model to generate a set of region template detectors. The model is fitted to an unseen image in an iterative manner by generating templates using the joint model and the current parameter estimates, correlating the templates with the target image to generate response images and optimising the shape parameters so as to maximise the sum of responses. The appearance model is similar to that used in the AAM . However in our approach the appearance model is used to generate likely feature templates, instead of trying to approximate the image pixels directly. We show that when applied to human faces, our Constrained Local Model (CLM) algorithm is more robust and more accurate than the original AAM search method, which relies on the image reconstruction error to update the model parameters. We demonstrate improved localisation accuracy on two publicly available face data sets and improved tracking on a challenging set of in-car face sequences.
DuReader: a Chinese Machine Reading Comprehension Dataset from Real-world Applications

This paper introduces DuReader, a new large-scale, open-domain Chinese machine reading comprehension (MRC) dataset, designed to address real-world MRC. DuReader has three advantages over previous MRC datasets: (1) data sources: questions and documents are based on Baidu Search and Baidu Zhidao1; answers are manually generated. (2) question types: it provides rich annotations for more question types, especially yes-no and opinion questions, that leaves more opportunity for the research community. (3) scale: it contains 200K questions, 420K answers and 1M documents; it is the largest Chinese MRC dataset so far. Experiments show that human performance is well above current state-of-the-art baseline systems, leaving plenty of room for the community to make improvements. To help the community make these improvements, both DuReader2 and baseline systems3 have been posted online. We also organize a shared competition to encourage the exploration of more models. Since the release of the task, there are significant improvements over the baselines.
Nutrition labels: a survey of use, understanding and preferences among ethnically diverse shoppers in New Zealand.

Effective nutrition labels are part of a supportive environment that encourages healthier food choices. The present study examined the use, understanding and preferences regarding nutrition labels among ethnically diverse shoppers in New Zealand.



A survey was carried out at twenty-five supermarkets in Auckland, New Zealand, between February and April 2007. Recruitment was stratified by ethnicity. Questions assessed nutrition label use, understanding of the mandatory Nutrition Information Panel (NIP), and preference for and understanding of four nutrition label formats: multiple traffic light (MTL), simple traffic light (STL), NIP and percentage of daily intake (%DI).



In total 1525 shoppers completed the survey: 401 Maori, 347 Pacific, 372 Asian and 395 New Zealand European and Other ethnicities (ten did not state ethnicity).



Reported use of nutrition labels (always, regularly, sometimes) ranged from 66% to 87% by ethnicity. There was little difference in ability to obtain information from the NIP according to ethnicity or income. However, there were marked ethnic differences in ability to use the NIP to determine if a food was healthy, with lesser differences by income. Of the four label formats tested, STL and MTL labels were best understood across all ethnic and income groups, and MTL labels were most frequently preferred.



There are clear ethnic and income disparities in ability to use the current mandatory food labels in New Zealand (NIP) to determine if foods are healthy. Conversely, MTL and STL label formats demonstrated high levels of understanding and acceptance across ethnic and income groups.
Tracking control of ball and plate system using a improved PSO on-line training PID neural network

In this paper, an on-line training PIDNN controller using an improved DEPSO algorithm for trajectory tracking of the ball and plate system is proposed. Since the ball and plate system is a typical under-actuated system with inherent nonlinearity and coupling between its parameters, the accurate mathematical model is difficult to be derived, so that a lot of nonlinear control and intelligent control methods are used for the ball and plate system control. The control method using a PID neural network is one of the intelligent methods. In this paper, an improved particle swarm optimization method based on differential evolution algorithm (DEPSO) is used to train the weighting factors of multilayered forward neural network. This PIDNN control method based on DEPSO algorithm can overcome the shortcoming of the BP algorithm which is easy to get into local minimum. At the same time, the simulation results of tracking control for ball and plate system show that the proposed PIDNN controller has simple structure, nice static and dynamic characteristics.
Engineering opportunities in cancer immunotherapy.

Immunotherapy has great potential to treat cancer and prevent future relapse by activating the immune system to recognize and kill cancer cells. A variety of strategies are continuing to evolve in the laboratory and in the clinic, including therapeutic noncellular (vector-based or subunit) cancer vaccines, dendritic cell vaccines, engineered T cells, and immune checkpoint blockade. Despite their promise, much more research is needed to understand how and why certain cancers fail to respond to immunotherapy and to predict which therapeutic strategies, or combinations thereof, are most appropriate for each patient. Underlying these challenges are technological needs, including methods to rapidly and thoroughly characterize the immune microenvironment of tumors, predictive tools to screen potential therapies in patient-specific ways, and sensitive, information-rich assays that allow patient monitoring of immune responses, tumor regression, and tumor dissemination during and after therapy. The newly emerging field of immunoengineering is addressing some of these challenges, and there is ample opportunity for engineers to contribute their approaches and tools to further facilitate the clinical translation of immunotherapy. Here we highlight recent technological advances in the diagnosis, therapy, and monitoring of cancer in the context of immunotherapy, as well as ongoing challenges.
CCCFNet: A Content-Boosted Collaborative Filtering Neural Network for Cross Domain Recommender Systems

To overcome data sparsity problem, we propose a cross domain recommendation system named CCCFNet which can combine collaborative filtering and content-based filtering in a unified framework. We first introduce a factorization framework to tie CF and content-based filtering together. Then we find that the MAP estimation of this framework can be embedded into a multi-view neural network. Through this neural network embedding the framework can be further extended by advanced deep learning techniques.
Accuracy-based Curriculum Learning in Deep Reinforcement Learning

In this paper, we investigate a new form of automated curriculum learning based on adaptive selection of accuracy requirements, called accuracybased curriculum learning. Using a reinforcement learning agent based on the Deep Deterministic Policy Gradient algorithm and addressing the Reacher environment, we first show that an agent trained with various accuracy requirements sampled randomly learns more efficiently than when asked to be very accurate at all times. Then we show that adaptive selection of accuracy requirements, based on a local measure of competence progress, automatically generates a curriculum where difficulty progressively increases, resulting in a better learning efficiency than sampling randomly.
Sentiment Analysis in the News

Recent years have brought a significant growth in the volume of research in sentiment analysis, mostly on highly subjective text types (movie or product reviews). The main difference these texts have with news articles is that their target is clearly defined and unique across the text. Following different annotation efforts and the analysis of the issues encountered, we realised that news opinion mining is different from that of other text types. We identified three subtasks that need to be addressed: definition of the target; separation of the good and bad news content from the good and bad sentiment expressed on the target; and analysis of clearly marked opinion that is expressed explicitly, not needing interpretation or the use of world knowledge. Furthermore, we distinguish three different possible views on newspaper articles – author, reader and text, which have to be addressed differently at the time of analysing sentiment. Given these definitions, we present work on mining opinions about entities in English language news, in which (a) we test the relative suitability of various sentiment dictionaries and (b) we attempt to separate positive or negative opinion from good or bad news. In the experiments described here, we tested whether or not subject domain-defining vocabulary should be ignored. Results showed that this idea is more appropriate in the context of news opinion mining and that the approaches taking this into consideration produce a better performance.
Concept Transfer Learning for Adaptive Language Understanding

Concept definition is important in language understanding (LU) adaptation since literal definition difference can easily lead to data sparsity even if different data sets are actually semantically correlated. To address this issue, in this paper, a novel concept transfer learning approach is proposed. Here, substructures within literal concept definition are investigated to reveal the relationship between concepts. A hierarchical semantic representation for concepts is proposed, where a semantic slot is represented as a composition of atomic concepts. Based on this new hierarchical representation, transfer learning approaches are developed for adaptive LU. The approaches are applied to two tasks: value set mismatch and domain adaptation, and evaluated on two LU benchmarks: ATIS and DSTC 2&3. Thorough empirical studies validate both the efficiency and effectiveness of the proposed method. In particular, we achieve state-ofthe-art performance (F1-score 96.08%) on ATIS by only using lexicon features.
Eletronic Health Records using Blockchain Technology

Data privacy refers to ensuring that users keep control over access to information, whereas data accessibility refers to ensuring that information access is unconstrained. Conflicts between privacy and accessibility of data are natural to occur, and healthcare is a domain in which they are particularly relevant. In the present article, we discuss how blockchain technology, and smart contracts, could help in some typical scenarios related to data access, data management and data interoperability for the specific healthcare domain. We then propose the implementation of a large-scale information architecture to access Electronic Health Records (EHRs) based on Smart Contracts as information mediators. Our main contribution is the framing of data privacy and accessibility issues in healthcare and the proposal of an integrated blockchain based architecture.
MIXED CLASSIFICATION REGRESSION FRAMEWORK 3 CNN 3 D Pose ( a ) Overview of the problem Feature Network Pose NetworksImage 3 D Pose Object

3D pose estimation from a single 2D image is an important and challenging task in computer vision with applications in autonomous driving, robot manipulation and augmented reality. Since 3D pose is a continuous quantity, a natural formulation for this task is to solve a pose regression problem. However, since pose regression methods return a single estimate of the pose, they have difficulties handling multimodal pose distributions (e.g. in the case of symmetric objects). An alternative formulation, which can capture multimodal pose distributions, is to discretize the pose space into bins and solve a pose classification problem. However, pose classification methods can give large pose estimation errors depending on the coarseness of the discretization. In this paper, we propose a mixed classification-regression framework that uses a classification network to produce a discrete multimodal pose estimate and a regression network to produce a continuous refinement of the discrete estimate. The proposed framework can accommodate different architectures and loss functions, leading to multiple classification-regression models, some of which achieve state-of-the-art performance on the challenging Pascal3D+ dataset.
Data-driven Summarization of Scientific Articles

Data-driven approaches to sequence-to-sequence modelling have been successfully applied to short text summarization of news articles. Such models are typically trained on input-summary pairs consisting of only a single or a few sentences, partially due to limited availability of multi-sentence training data. Here, we propose to use scientific articles as a new milestone for text summarization: large-scale training data come almost for free with two types of high-quality summaries at different levels the title and the abstract. We generate two novel multi-sentence summarization datasets from scientific articles and test the suitability of a wide range of existing extractive and abstractive neural network-based summarization approaches. Our analysis demonstrates that scientific papers are suitable for data-driven text summarization. Our results could serve as valuable benchmarks for scaling sequence-to-sequence models to very long sequences.
Signature Verification Using a Siamese Time Delay Neural Network

This paper describes an algorithm for verification of signatures written on a pen-input tablet. The algorithm is based on a novel, artificial neural network, called a "Siamese" neural network. This network consists of two identical sub-networks joined at their outputs. During training the two sub-networks extract features from two signatures, while the joining neuron measures the distance between the two feature vectors. Verification consists of comparing an extracted feature vector ~ith a stored feature vector for the signer. Signatures closer to this stored representation than a chosen threshold are accepted, all other signatures are rejected as forgeries.
Collaborative Filtering With User-Item Co-Autoregressive Models

Deep neural networks have shown promise in collaborative filtering (CF). However, existing neural approaches are either user-based or item-based, which cannot leverage all the underlying information explicitly. We propose CF-UIcA, a neural co-autoregressive model for CF tasks, which exploits the structural correlation in the domains of both users and items. The co-autoregression allows extra desired properties to be incorporated for different tasks. Furthermore, we develop an efficient stochastic learning algorithm to handle large scale datasets. We evaluate CF-UIcA on two popular benchmarks: MovieLens 1M and Netflix, and achieve state-of-the-art performance in both rating prediction and top-N recommendation tasks, which demonstrates the effectiveness of CF-UIcA.
Benefits , impediments and critical success factors in B 2 C E-business adoption

This paper reports the results of a study carried out to assess the benefits, impediments and major critical success factors in adopting business to consumer e-business solutions. A case study method of investigation was used, and the experiences of six online companies and two bricks and mortar companies were documented. The major impediments identified are: leadership issues, operational issues, technology, and ineffective solution design. The critical success factors in the adoption of e-business are identified as: combining e-business knowledge, value proposition and delivery measurement, customer satisfaction and retention, monitoring internal processes and competitor activity, and finally building trust. Findings suggest that above all, adoption of e-business should be appropriate, relevant, value adding, and operationally as well as strategically viable for an organization instead of being a result of apprehensive compliance. q 2004
Denial-of-service attacks in OpenFlow SDN networks

Software-Defined Networking (SDN) has recently gained significant momentum. However, before any large scale deployments, it is important to understand security issues arising from this new technology. This paper discusses two types of Denial-of-Service (DoS) attacks specific to OpenFlow SDN networks. We emulate them on Mininet and provide an analysis on the effect of these attacks. We find that the timeout value of a flow rule, and the control plane bandwidth have a significant impact on the switch's capability. If not configured appropriately, they may allow successful DoS attacks. Finally, we highlight possible mitigation strategies to address such attacks.
Bone remodeling during fracture repair: The cellular picture.

Fracture healing is a complex event that involves the coordination of a variety of different processes. Repair is typically characterized by four overlapping stages: the initial inflammatory response, soft callus formation, hard callus formation, initial bony union and bone remodeling. However, repair can also be seen to represent a juxtaposition of two distinct forces: anabolism or tissue formation, and catabolism or remodeling. These anabolic/catabolic concepts are useful for understanding bone repair without giving the false impression of temporally distinct stages that operate independently. They are also relevant when considering intervention. In normal bone development, bone remodeling conventionally refers to the removal of calcified bone tissue by osteoclasts. However, in the context of bone repair there are two phases of tissue catabolism: the removal of the initial cartilaginous soft callus, followed by the eventual remodeling of the bony hard callus. In this review, we have attempted to examine catabolism/remodeling in fractures in a systematic fashion. The first section briefly summarizes the traditional four-stage view of fracture repair in a physiological manner. The second section highlights some of the limitations of using a temporal rather than process-driven model and summarizes the anabolic/catabolic paradigm of fracture repair. The third section examines the cellular participants in soft callus remodeling and in particular the role of the osteoclast in endochondral ossification. Finally, the fourth section examines the effects of delaying osteoclast-dependent hard callus remodeling and also poses questions regarding the crosstalk between anabolism and catabolism in the latter stages of fracture repair.
Scalable Generative Models for Multi-label Learning with Missing Labels

We present a scalable, generative framework for multi-label learning with missing labels. Our framework consists of a latent factor model for the binary label matrix, which is coupled with an exposure model to account for label missingness (i.e., whether a zero in the label matrix is indeed a zero or denotes a missing observation). The underlying latent factor model also assumes that the low-dimensional embeddings of each label vector are directly conditioned on the respective feature vector of that example. Our generative framework admits a simple inference procedure, such that the parameter estimation reduces to a sequence of simple weighted leastsquare regression problems, each of which can be solved easily, efficiently, and in parallel. Moreover, inference can also be performed in an online fashion using mini-batches of training examples, which makes our framework scalable for large data sets, even when using moderate computational resources. We report both quantitative and qualitative results for our framework on several benchmark data sets, comparing it with a number of state-of-the-art methods.