nils-herrmann/clean_scirepeval huggingface dataset:
Recommender system for Persian blogs

With the rapid growth of the internet and the spread of the information contained therein, the volume of information available on the web is more than the ability of users to manage, capture and keep the information up to date. One solution to this problem are personalization and recommender systems. Recommender systems use the comments of the group of users so that, to help people in that group more effectively to identify their favorite items from a huge set of choices. In recent years, the web has seen very strong growth in the use of blogs. Considering the high volume of information in blogs, bloggers are in trouble to find the desired information and find blogs with similar thoughts and desires. Therefore, considering the mass of information for the blogs, a blog recommender system seems to be necessary. In this paper, by combining different methods of clustering and collaborative filtering, personalized recommender system for Persian blogs is suggested.
Neuroprotective potential of silymarin against CNS disorders: insight into the pathways and molecular mechanisms of action.

Silymarin, a C25 containing flavonoid from the plant Silybum marianum, has been the gold standard drug to treat liver disorders associated with alcohol consumption, acute and chronic viral hepatitis, and toxin-induced hepatic failures since its discovery in 1960. Apart from the hepatoprotective nature, which is mainly due to its antioxidant and tissue regenerative properties, Silymarin has recently been reported to be a putative neuroprotective agent against many neurologic diseases including Alzheimer's and Parkinson's diseases, and cerebral ischemia. Although the underlying neuroprotective mechanism of Silymarin is believed to be due to its capacity to inhibit oxidative stress in the brain, it also confers additional advantages by influencing pathways such as β-amyloid aggregation, inflammatory mechanisms, cellular apoptotic machinery, and estrogenic receptor mediation. In this review, we have elucidated the possible neuroprotective effects of Silymarin and the underlying molecular events, and suggested future courses of action for its acceptance as a CNS drug for the treatment of neurodegenerative diseases.
Learning Knowledge Graphs for Question Answering through Conversational Dialog

We describe how a question-answering system can learn about its domain from conversational dialogs. Our system learns to relate concepts in science questions to propositions in a fact corpus, stores new concepts and relations in a knowledge graph (KG), and uses the graph to solve questions. We are the first to acquire knowledge for question-answering from open, natural language dialogs without a fixed ontology or domain model that predetermines what users can say. Our relation-based strategies complete more successful dialogs than a query expansion baseline, our taskdriven relations are more effective for solving science questions than relations from general knowledge sources, and our method is practical enough to generalize to other domains.
The Dichotomy for Conservative Constraint Satisfaction is Polynomially Decidable

Given a fixed constraint language Γ , the conservative CSP over Γ (denoted by c-CSP( Γ )) is a variant of CSP( Γ ) where the domain of each variable can be restricted arbitrarily. In  a dichotomy has been prove n for conservative CSP: for every fixed language Γ , c-CSP( Γ ) is either in P or NP-complete. However, the characterization of conservatively tractable languages i s of algebraic nature and the recognition algorithm provided in  is super-exponen tial in the domain size. The main contribution of this paper is a polynomial-time alg orithm that, given a constraint language Γ as input, decides if c-CSP( Γ ) is tractable. In addition, if Γ is proven tractable the algorithm also outputs its coloured graph, which contains valuable information on the structure of Γ .
A survey of FPGA-based accelerators for convolutional neural networks

Deep convolutional neural networks (CNNs) have recently shown very high accuracy in a wide range of cognitive tasks, and due to this, they have received significant interest from the researchers. Given the high computational demands of CNNs, custom hardware accelerators are vital for boosting their performance. The high energy efficiency, computing capabilities and reconfigurability of FPGA make it a promising platform for hardware acceleration of CNNs. In this paper, we present a survey of techniques for implementing and optimizing CNN algorithms on FPGA. We organize the works in several categories to bring out their similarities and differences. This paper is expected to be useful for researchers in the area of artificial intelligence, hardware architecture and system design.
Outcome of patients who refuse transfusion after cardiac surgery: a natural experiment with severe blood conservation.

Jehovah's Witness patients (Witnesses) who undergo cardiac surgery provide a unique natural experiment in severe blood conservation because anemia, transfusion, erythropoietin, and antifibrinolytics have attendant risks. Our objective was to compare morbidity and long-term survival of Witnesses undergoing cardiac surgery with a similarly matched group of patients who received transfusions.



A total of 322 Witnesses and 87 453 non-Witnesses underwent cardiac surgery at our center from January 1, 1983, to January 1, 2011. All Witnesses prospectively refused blood transfusions. Among non-Witnesses, 38 467 did not receive blood transfusions and 48 986 did. We used propensity methods to match patient groups and parametric multiphase hazard methods to assess long-term survival. Our main outcome measures were postoperative morbidity complications, in-hospital mortality, and long-term survival.



Witnesses had fewer acute complications and shorter length of stay than matched patients who received transfusions: myocardial infarction, 0.31% vs 2.8% (P = . 01); additional operation for bleeding, 3.7% vs 7.1% (P = . 03); prolonged ventilation, 6% vs 16% (P < . 001); intensive care unit length of stay (15th, 50th, and 85th percentiles), 24, 25, and 72 vs 24, 48, and 162 hours (P < . 001); and hospital length of stay (15th, 50th, and 85th percentiles), 5, 7, and 11 vs 6, 8, and 16 days (P < . 001). Witnesses had better 1-year survival (95%; 95% CI, 93%-96%; vs 89%; 95% CI, 87%-90%; P = . 007) but similar 20-year survival (34%; 95% CI, 31%-38%; vs 32% 95% CI, 28%-35%; P = . 90).



Witnesses do not appear to be at increased risk for surgical complications or long-term mortality when comparisons are properly made by transfusion status. Thus, current extreme blood management strategies do not appear to place patients at heightened risk for reduced long-term survival.
Subpixel rendering for the pentile display based on the human visual system

Recently, with the increased demand for low-power displays regarding portable devices, the RGBG PenTile display is popularly utilized. However, unlike the traditional RGB-stripe display with its three color channels, each pixel of the RGBG PenTile display comprises only two color channels, thereby causing the color leakage image distortion. To cope with this problem, most of the conventional methods employ a preprocessing filter for subpixel rendering; however, these filters cannot remove the color leakage completely, and they also result in the blurring artifacts. In this paper, a novel approach to obtain the preprocessing filter is presented. We formulate a filter design method as a minimum mean square error problem and derive an optimal preprocessing filter that is based on the human visual system (HVS) as follows; first, two perceived images indicating how human recognizes the images on the RGB and RGBG displays are generated and then the difference between the two perceived images is minimized to derive the optimal filter. In addition, in order to prevent the blurring artifact, the proposed filter is applied to the previously detected color-leakage region only. Experimental results demonstrate that the proposed method outperforms the conventional methods in terms of both the subjective and objective image quality.
Hierarchical Threshold Secret Sharing

We consider the problem of threshold secret sharing in groups with hierarchical structure. In such settings, the secret is shared among a group of participants that is partitioned into levels. The access structure is then determined by a sequence of threshold requirements: a subset of participants is authorized if it has at least k0 0 members from the highest level, as well as at least k1 > k0 members from the two highest levels and so forth. Such problems may occur in settings where the participants differ in their authority or level of confidence and the presence of higher level participants is imperative to allow the recovery of the common secret. Even though secret sharing in hierarchical groups has been studied extensively in the past, none of the existing solutions addresses the simple setting where, say, a bank transfer should be signed by three employees, at least one of whom must be a department manager. We present a perfect secret sharing scheme for this problem that, unlike most secret sharing schemes that are suitable for hierarchical structures, is ideal. As in Shamir's scheme, the secret is represented as the free coefficient of some polynomial. The novelty of our scheme is the usage of polynomial derivatives in order to generate lesser shares for participants of lower levels. Consequently, our scheme uses Birkhoff interpolation, i.e., the construction of a polynomial according to an unstructured set of point and derivative values. A substantial part of our discussion is dedicated to the question of how to assign identities to the participants from the underlying finite field so that the resulting Birkhoff interpolation problem will be well posed. In addition, we devise an ideal and efficient secret sharing scheme for the closely related hierarchical threshold access structures that were studied by Simmons and Brickell.
Bortezomib mediates antiangiogenesis in multiple myeloma via direct and indirect effects on endothelial cells.

Bone marrow angiogenesis plays an important role in the pathogenesis and progression in multiple myeloma. Recent studies have shown that proteasome inhibitor bortezomib (Velcade, formerly PS-341) can overcome conventional drug resistance in vitro and in vivo; however, its antiangiogenic activity in the bone marrow milieu has not yet been defined. In the present study, we examined the effects of bortezomib on the angiogenic phenotype of multiple myeloma patient-derived endothelial cells (MMEC). At clinically achievable concentrations, bortezomib inhibited the proliferation of MMECs and human umbilical vein endothelial cells in a dose-dependent and time-dependent manner. In functional assays of angiogenesis, including chemotaxis, adhesion to fibronectin, capillary formation on Matrigel, and chick embryo chorioallantoic membrane assay, bortezomib induced a dose-dependent inhibition of angiogenesis. Importantly, binding of MM.1S cells to MMECs triggered multiple myeloma cell proliferation, which was also abrogated by bortezomib in a dose-dependent fashion. Bortezomib triggered a dose-dependent inhibition of vascular endothelial growth factor (VEGF) and interleukin-6 (IL-6) secretion by the MMECs, and reverse transcriptase-PCR confirmed drug-related down-regulation of VEGF, IL-6, insulin-like growth factor-I, Angiopoietin 1 (Ang1), and Ang2 transcription. These data, therefore, delineate the mechanisms of the antiangiogenic effects of bortezomib on multiple myeloma cells in the bone marrow milieu.
Studies Rethinking Bank Efficiency and Regulation : How Off-Balance-Sheet Activities Make a Difference

Financial Industry Studies is published by the Federal Reserve Bank of Dallas. The views expressed are those of the authors and should not be attributed to the Federal Reserve Bank of Dallas or the Federal Reserve System. Articles may be reprinted on the condition that the source is credited and a copy of the publication containing the reprinted article is provided to the Financial Industry Studies Department of the Federal Reserve Bank of Dallas.
The effectiveness of SMS as verification of flood early warning messages from users' perception

The aim of this paper is to research the effectiveness of SMS verification by understanding the correlation between notification and verification of flood early warning messages. This study contributes to the design of the dissemination techniques for SMS as an early warning messages. The metrics used in this study are using user perceptions of tasks, which include the ease of use (EOU) perception for using SMS and confidence with SMS skills perception, as well as, the users' positive perceptions, which include users' perception of usefulness and satisfaction perception towards using SMS as an early warning messages for floods. Experiments and surveys were conducted in flood-prone areas in Semarang, Indonesia. The results showed that the correlation is in users' perceptions of tasks for the confidence with skill.
Musical Training as a Framework for Brain Plasticity: Behavior, Function, and Structure

Musical training has emerged as a useful framework for the investigation of training-related plasticity in the human brain. Learning to play an instrument is a highly complex task that involves the interaction of several modalities and higher-order cognitive functions and that results in behavioral, structural, and functional changes on time scales ranging from days to years. While early work focused on comparison of musical experts and novices, more recently an increasing number of controlled training studies provide clear experimental evidence for training effects. Here, we review research investigating brain plasticity induced by musical training, highlight common patterns and possible underlying mechanisms of such plasticity, and integrate these studies with findings and models for mechanisms of plasticity in other domains.
Time-Domain Analysis of Graphene-Based Miniaturized Antennas for Ultra-Short-Range Impulse Radio Communications

Graphene is enabling a plethora of applications in a wide range of fields due to its unique electrical, mechanical, and optical properties. Among them, graphene-based plasmonic miniaturized antennas (or shortly named, graphennas) are garnering growing interest in the field of communications. In light of their reduced size, in the micrometric range, and an expected radiation frequency of a few terahertz, graphennas offer means for the implementation of ultra-short-range wireless communications. Motivated by their high radiation frequency and potentially wideband nature, this paper presents a methodology for the time-domain characterization and evaluation of graphennas. The proposed framework is highly vertical, as it aims to build a bridge between technological aspects, antenna design, and communications. Using this approach, qualitative and quantitative analyses of a particular case of graphenna are carried out as a function of two critical design parameters, namely, chemical potential and carrier mobility. The results are then compared to the performance of equivalent metallic antennas. Finally, the suitability of graphennas for ultra-short-range communications is briefly discussed.
Executive function deficits in autism spectrum disorders and attention-deficit/hyperactivity disorder: Examining profiles across domains and ages

Deficits in 'executive function' (EF) are characteristic of several clinical disorders, most notably Autism Spectrum Disorders (ASD) and Attention-Deficit/Hyperactivity Disorder (ADHD). In this study, age- and IQ-matched groups with ASD, ADHD, or typical development (TD) were compared on a battery of EF tasks tapping three core domains: response selection/inhibition, flexibility, and planning/working memory. Relations between EF, age and everyday difficulties (rated by parents and teachers) were also examined. Both clinical groups showed significant EF impairments compared with TD peers. The ADHD group showed greater inhibitory problems on a Go-no-Go task, while the ASD group was significantly worse on response selection/monitoring in a cognitive estimates task. Age-related improvements were clearer in ASD and TD than in ADHD. At older (but not younger) ages, the ASD group outperformed the ADHD group, performing as well as the TD group on many EF measures. EF scores were related to specific aspects of communicative and social adaptation, and negatively correlated with hyperactivity in ASD and TD. Within the present groups, the overall findings suggested less severe and persistent EF deficits in ASD (including Asperger Syndrome) than in ADHD.
Benchmarking Spatial Join Operations with Spatial Output

The spatial join operation is benchmarked using variants of well-known spatial data structures such as the R-tree, R -tree, R-tree, and the PMR quadtree. The focus is on a spatial join with spatial output because the result of the spatial join frequently serves as input to subsequent spatial operations (i.e., a cascaded spatial join as would be common in a spatial spreadsheet). Thus, in addition to the time required to perform the spatial join itself (whose output is not always required to be spatial), the time to build the spatial data structure also plays an important role in the benchmark. The studied quantities are the time to build the data structure and the time to do the spatial join in an application domain consisting of planar line segment data. Experiments reveal that spatial data structures based on a disjoint decomposition of space and bounding boxes (i.e., the R-tree and the PMR quadtree with bounding boxes) outperform the other structures that are based upon a non-disjoint decomposition (i.e., the R-tree and R -tree). As the size of the output of the spatial join increases with respect to the larger of the two inputs, the advantage of the bounding boxes used in methods based on a disjoint non-regular decomposition is no longer a factor and methods based on a disjoint regular decomposition (i.e., the PMR quadtree regardless of the presence of bounding boxes) perform signi cantly better. When the output of the spatial join is not required to be spatial (i.e., it is a list or table of tuples), then the performance of the best methods based on a non-disjoint decomposition (i.e., the R -tree) is comparable to those with a disjoint decomposition (i.e., the R-tree and the PMR quadtree with and without bounding boxes) as long as the size of the output is considerably smaller than that of the larger of the two inputs (e.g., 10%). However, as the output gets larger, the methods based on a regular decomposition (i.e., the PMR quadtree with and without bounding boxes) are much better than those based on an irregular decomposition (i.e, the R -tree and the R-tree) which have comparable performance in this case regardless of whether or not they are based on a disjoint or non-disjoint decomposition of space.
A national evaluation of treatment outcomes for cocaine dependence.

This national study focused on posttreatment outcomes of community treatments of cocaine dependence. Relapse to weekly (or more frequent) cocaine use in the first year after discharge from 3 major treatment modalities was examined in relation to patient problem severity at admission to the treatment program and length of stay.



We studied 1605 cocaine-dependent patients from 11 cities located throughout the United States using a naturalistic, nonexperimental evaluation design. They were sequentially admitted from November 1991 to December 1993 to 55 community-based treatment programs in the national Drug Abuse Treatment Outcome Studies. Included were 542 patients admitted to 19 long-term residential programs, 458 patients admitted to 24 outpatient drug-free programs, and 605 patients admitted to 12 short-term inpatient programs.



Of 1605 patients, 377 (23.5%) reported weekly cocaine use in the year following treatment (dropping from 73.1% in the year before admission). An additional 18.0% had returned to another drug treatment program. Higher severity of patient problems at program intake and shorter stays in treatment (<90 days) were related to higher cocaine relapse rates.



Patients with the most severe problems were more likely to enter long-term residential programs, and better outcomes were reported by those treated 90 days or longer. Dimensions of psychosocial problem severity and length of stay are, therefore, important considerations in the treatment of cocaine dependence. Cocaine relapse rates for patients with few problems at program intake were most favorable across all treatment conditions, but better outcomes for patients with medium- to high-level problems were dependent on longer treatment stays.
Microbiota and Mucosal Immunity in Amphibians

We know that animals live in a world dominated by bacteria. In the last 20 years, we have learned that microbes are essential regulators of mucosal immunity. Bacteria, archeas, and viruses influence different aspects of mucosal development and function. Yet, the literature mainly covers findings obtained in mammals. In this review, we focus on two major themes that emerge from the comparative analysis of mammals and amphibians. These themes concern: (i) the structure and functions of lymphoid organs and immune cells in amphibians, with a focus on the gut mucosal immune system; and (ii) the characteristics of the amphibian microbiota and its influence on mucosal immunity. Lastly, we propose to use Xenopus tadpoles as an alternative small-animal model to improve the fundamental knowledge on immunological functions of gut microbiota.
Building structures from classifiers for passage reranking

This paper shows that learning to rank models can be applied to automatically learn complex patterns, such as relational semantic structures occurring in questions and their answer passages. This is achieved by providing the learning algorithm with a tree representation derived from the syntactic trees of questions and passages connected by relational tags, where the latter are again provided by the means of automatic classifiers, i.e., question and focus classifiers and Named Entity Recognizers. This way effective structural relational patterns are implicitly encoded in the representation and can be automatically utilized by powerful machine learning models such as kernel methods.
 We conduct an extensive experimental evaluation of our models on well-known benchmarks from the question answer (QA) track of TREC challenges. The comparison with state-of-the-art systems and BM25 show a relative improvement in MAP of more than 14% and 45%, respectively. Further comparison on the task restricted to the answer sentence reranking shows an improvement in MAP of more than 8% over the state of the art.
Multi30K: Multilingual English-German Image Descriptions

We introduce the Multi30K dataset to stimulate multilingual multimodal research. Recent advances in image description have been demonstrated on Englishlanguage datasets almost exclusively, but image description should not be limited to English. This dataset extends the Flickr30K dataset with i) German translations created by professional translators over a subset of the English descriptions, and ii) German descriptions crowdsourced independently of the original English descriptions. We describe the data and outline how it can be used for multilingual image description and multimodal machine translation, but we anticipate the data will be useful for a broader range of tasks.
Decoupling Encoder and Decoder Networks for Abstractive Document Summarization

In this brief report we present an overview of the MultiLing 2017 effort and workshop, as implemented within EACL 2017. MultiLing is a community-driven initiative that pushes the state-of-the-art in Automatic Summarization by providing data sets and fostering further research and development of summarization systems. This year the scope of the workshop was widened, bringing together researchers that work on summarization across sources, languages and genres. We summarize the main tasks planned and implemented this year, also providing insights on next steps.