nils-herrmann/clean_scirepeval huggingface dataset:
Learning Abductive Reasoning Using Random Examples

We consider a new formulation of abduction. Our formulation differs from the existing approaches in that it does not cast the “plausibility” of explanations in terms of either syntactic minimality or an explicitly given prior distribution. Instead, “plausibility,” along with the rules of the domain, is learned from concrete examples (settings of attributes). Our version of abduction thus falls in the “learning to reason” framework of Khardon and Roth. Such approaches enable us to capture a natural notion of “plausibility” in a domain while avoiding the problem of specifying an explicit representation of what is “plausible,” a task that humans find extremely difficult. In this work, we specifically consider the question of which syntactic classes of formulas have efficient algorithms for abduction. It turns out that while the representation of the query is irrelevant to the computational complexity of our problem, the representation of the explanation critically affects its tractability. We find that the class of k-DNF explanations can be found in polynomial time for any fixed k; but, we also find evidence that even very weak versions of our abduction task are intractable for the usual class of conjunctive explanations. This evidence is provided by a connection to the usual, inductive PAC-learning model proposed by Valiant. We also briefly consider an exception-tolerant variant of abduction. We observe that it is possible for polynomial-time algorithms to tolerate a few adversarially chosen exceptions, again for the class of kDNF explanations. All of the algorithms we study are particularly simple, and indeed are variants of a rule proposed by Mill.
Deep vein thrombosis using noncontrast-enhanced MR venography with electrocardiographically gated three-dimensional half-Fourier FSE: preliminary experience.

Three noncontrast-enhanced MR venography techniques are presented for assessing deep vein thrombosis (DVT) at 0.5 T in patients with metallic implants. Two cardiac-gated 3D half-Fourier FSE fresh blood imaging sequences with flow-refocusing pulses (FR-FBI) in the read-out (RO) direction and without FR pulses (non-FR-FBI) were developed for slower-flowing blood. For faster flowing blood, a swap phase-encode arterial double-subtraction elimination (SPADE) technique was developed. The three techniques were assessed both quantitatively using signal-to-noise (SNR) and contrast-noise-ratio (CNR) measurements and qualitatively by subjective image analysis in 15 volunteers. SPADE was compared to FR-FBI in the pelvic veins and FR-FBI was compared to non-FR-FBI in the thigh and calf veins. Both SPADE and FR-FBI techniques produced significantly higher SNRs, CNRs, and image quality in each comparative study (P<0.001). Five patients with metallic implants and confirmed DVT underwent SPADE (pelvic veins) and FR-FBI (thigh and calf veins) examinations and the results were compared to conventional venography. The SPADE and FR-FBI images showed all DVTs from all five patients without interference from implant susceptibility artifacts. The excellent image quality produced by both SPADE and FR-FBI throughout peripheral vasculature demonstrates their promise for detecting DVT in postsurgery patients.
Neglected Time: Impaired Temporal Perception of Multisecond Intervals in Unilateral Neglect

Recent neuroimaging and neuropsychological studies have suggested that the right hemisphere, particularly frontal regions, is important for the perception of the passage of time. We examined the ability to estimate durations of up to 60 sec in a group of eight patients with unilateral neglect. When estimating multisecond intervals, neglect patients grossly underestimated all durations. On average, healthy controls (HC) demonstrated reasonably accurate estimates of all durations tested. Although the right hemisphere lesioned control patients without neglect also tended to underestimate durations, these underestimations were significantly better than the performance of the neglect group. These findings suggest a pivotal role for a right hemisphere fronto-parietal network in the accurate perception of multisecond durations. Furthermore, these findings add to a growing body of literature suggesting that neglect cannot be understood simply in terms of a bias in orienting attention to one side of space. Additional deficits of the kind demonstrated here are likely to be crucial in determining the nature and extent of the loss of conscious awareness for contralesional events.
3D region segmentation using topological persistence

A `region' is an important concept in interpreting 3D point cloud data since regions may correspond to objects in a scene. To correctly interpret 3D point cloud data, we need to partition the dataset into regions that correspond to objects or parts of an object. In this paper, we present a region growing approach that combines global (topological) and local (color, surface normal) information to segment 3D point cloud data. Using ideas from persistent homology theory, our algorithm grows a simplicial complex representation of the point cloud dataset. At each step in the growth process we compute the zeroth homology group of the complex, which corresponds to the number of connected components, and use color and surface normal statistics to build regions. Lastly, we extract out the segmented regions of the dataset. We show that this method provides a stable segmentation of point cloud data in the presence of noise and poorly sampled data, thus providing advantages over contemporary region-based segmentation techniques.
Using time topic modeling for semantics-based dynamic research interest finding

Researchers interests finding has been an active area of investigation for different recommendation tasks. Previous approaches for finding researchers interests exploit writing styles and links connectivity by considering time of documents, while semantics-based intrinsic structure of words is ignored. Consequently, a topic model named Author-Topic model is proposed, which exploits semantics-based intrinsic structure of words present between the authors of research papers. It ignores simultaneous modeling of time factor which results in exchangeability of topics problem, which is important factor to deal with when finding dynamic research interests. For example, in many real world applications, like finding reviewers for papers and finding taggers in the social tagging systems one need to consider different time periods. In this paper, we present time topic modeling approach named Temporal-Author-Topic (TAT) which can simultaneously model text, researchers and time of research papers to overcome the exchangeability of topics problem. The mixture distribution over topics is influenced by both co-occurrences of words and timestamps of the research papers. Consequently, topics occurrence and their related researchers change over time, while the meaning of particular topic almost remains unchanged. Proposed approach is used to discover topically related researchers for different time periods. We also show how their interests and relationships change over a time period. Empirical results on large research papers corpus show the effectiveness of our proposed approach and dominance over Author-Topic (AT) model, by handling the exchangeability of topics problem, which enables it to obtain similar meaning of particular topic overtime.
The Effect of Climbing Ability and Slope Inclination on Vertical Foot Loading Using a Novel Force Sensor Instrumentation System

The aim of the study was to assess the effects of climbing ability and slope inclination on vertical loading both in terms the forces involved and physiological responses. Five novice and six intermediate female climbers completed a climbing route at three slope inclinations (85°, 90°, and 98°). The vertical loading during the climb was assessed by force-time integral using a Novel Pedar-X insole and physiological responses via oxygen uptake and heart rate. The novice climbers had a significantly lower (p < 0.05) vertical loading on foot holds and higher oxygen uptake and heart rate compared to intermediate climbers. A significant negative correlation was identified between the force-time integral and oxygen uptake (R = -0.72), and with heart rate (R = -0.64), respectively. The time-force integral decreased across the ascents with increasing slope inclination (p < 0.001). The results indicate that more advanced ability climbers make greater use of foot holds, with associated lowering in physiological response (oxygen uptake and heart rate) across all slope inclinations.
ZLIB Compressed Data Format Specification version 3.3

Status of This Memo This memo provides information for the Internet community. This memo does not specify an Internet standard of any kind. Distribution of this memo is unlimited. Permission is granted to copy and distribute this document for any purpose and without charge, including translations into other languages and incorporation into compilations, provided that the copyright notice and this notice are preserved, and that any substantive changes or deletions from the original are clearly marked.  This specification defines a lossless compressed data format. The data can be produced or consumed, even for an arbitrarily long sequentially presented input data stream, using only an a priori bounded amount of intermediate storage. The format presently uses the DEFLATE compression method but can be easily extended to use other compression methods. It can be implemented readily in a manner not covered by patents. This specification also defines the ADLER-32 checksum (an extension and improvement of the Fletcher checksum), used for detection of data corruption, and provides an algorithm for computing it.
Latent Structure Preserving Hashing

Aiming at efficient similarity search, hash functions are designed to embed high-dimensional feature descriptors to low-dimensional binary codes such that similar descriptors will lead to binary codes with a short distance in the Hamming space. It is critical to effectively maintain the intrinsic structure and preserve the original information of data in a hashing algorithm. In this paper, we propose a novel hashing algorithm called Latent Structure Preserving Hashing (LSPH), with the target of finding a well-structured low-dimensional data representation from the original high-dimensional data through a novel objective function based on Nonnegative Matrix Factorization (NMF) with their corresponding Kullback-Leibler divergence of data distribution as the regularization term. Via exploiting the joint probabilistic distribution of data, LSPH can automatically learn the latent information and successfully preserve the structure of high-dimensional data. To further achieve robust performance with complex and nonlinear data, in this paper, we also contribute a more generalized multi-layer LSPH (ML-LSPH) framework, in which hierarchical representations can be effectively learned by a multiplicative up-propagation algorithm. Once obtaining the latent representations, the hash functions can be easily acquired through multi-variable logistic regression. Experimental results on three large-scale retrieval datasets, i.e., SIFT 1M, GIST 1M and 500 K TinyImage, show that ML-LSPH can achieve better performance than the single-layer LSPH and both of them outperform existing hashing techniques on large-scale data.
Alternatives to the k-means algorithm that find better clusterings

We investigate here the behavior of the standard k-means clustering algorithm and several alternatives to it: the k-harmonic means algorithm due to Zhang and colleagues, fuzzy k-means, Gaussian expectation-maximization, and two new variants of k-harmonic means. Our aim is to find which aspects of these algorithms contribute to finding good clusterings, as opposed to converging to a low-quality local optimum. We describe each algorithm in a unified framework that introduces separate cluster membership and data weight functions. We then show that the algorithms do behave very differently from each other on simple low-dimensional synthetic datasets and image segmentation tasks, and that the k-harmonic means method is superior. Having a soft membership function is essential for finding high-quality clusterings, but having a non-constant data weight function is useful also.
Integrating illness concerns into cognitive behavioral therapy for children and adolescents with inflammatory bowel disease and co-occurring anxiety.

To examine the feasibility and preliminary benefits of an integrative cognitive behavioral therapy (CBT) with adolescents with inflammatory bowel disease and anxiety.



Nine adolescents participated in a CBT program at their gastroenterologist's office. Structured diagnostic interviews, self-report measures of anxiety and pain, and physician-rated disease severity were collected pretreatment and post-treatment.



Postintervention, 88% of adolescents were treatment responders, and 50% no longer met criteria for their principal anxiety disorder. Decreases were demonstrated in anxiety, pain, and disease severity.



Anxiety screening and a mental health referral to professionals familiar with medical management issues is important.
Fluctuations of spontaneous EEG topographies predict disease state in relapsing-remitting multiple sclerosis

Spontaneous fluctuations of neuronal activity in large-scale distributed networks are a hallmark of the resting brain. In relapsing-remitting multiple sclerosis (RRMS) several fMRI studies have suggested altered resting-state connectivity patterns. Topographical EEG analysis reveals much faster temporal fluctuations in the tens of milliseconds time range (termed "microstates"), which showed altered properties in a number of neuropsychiatric conditions. We investigated whether these microstates were altered in patients with RRMS, and if the microstates' temporal properties reflected a link to the patients' clinical features. We acquired 256-channel EEG in 53 patients (mean age 37.6 years, 45 females, mean disease duration 9.99 years, Expanded Disability Status Scale ≤ 4, mean 2.2) and 49 healthy controls (mean age 36.4 years, 33 females). We analyzed segments of a total of 5 min of EEG during resting wakefulness and determined for both groups the four predominant microstates using established clustering methods. We found significant differences in the temporal dynamics of two of the four microstates between healthy controls and patients with RRMS in terms of increased appearance and prolonged duration. Using stepwise multiple linear regression models with 8-fold cross-validation, we found evidence that these electrophysiological measures predicted a patient's total disease duration, annual relapse rate, disability score, as well as depression score, and cognitive fatigue measure. In RRMS patients, microstate analysis captured altered fluctuations of EEG topographies in the sub-second range. This measure of high temporal resolution provided potentially powerful markers of disease activity and neuropsychiatric co-morbidities in RRMS.
Recurrent Pixel Embedding for Instance Grouping

We introduce a differentiable, end-to-end trainable framework for solving pixel-level grouping problems such as instance segmentation consisting of two novel components. First, we regress pixels into a hyper-spherical embedding space so that pixels from the same group have high cosine similarity while those from different groups have similarity below a specified margin. We analyze the choice of embedding dimension and margin, relating them to theoretical results on the problem of distributing points uniformly on the sphere. Second, to group instances, we utilize a variant of mean-shift clustering, implemented as a recurrent neural network parameterized by kernel bandwidth. This recurrent grouping module is differentiable, enjoys convergent dynamics and probabilistic interpretability. Backpropagating the group-weighted loss through this module allows learning to focus on correcting embedding errors that won't be resolved during subsequent clustering. Our framework, while conceptually simple and theoretically abundant, is also practically effective and computationally efficient. We demonstrate substantial improvements over state-of-the-art instance segmentation for object proposal generation, as well as demonstrating the benefits of grouping loss for classification tasks such as boundary detection and semantic segmentation.
Determination of inositol hexanicotinate in rat plasma by high performance liquid chromatography with UV detection.

A HPLC method with UV detection at 262nm was developed to analyze inositol hexanicotinate in rat plasma. Plasma samples were extracted with an equal volume of acetonitrile, followed by dilution with mobile phase buffer (5mM phosphate buffer, pH 6.0) to eliminate any solvent effects. Inositol hexanicotinate and the internal standard (mebendazole) were separated isocratically using a mobile phase of acetonitrile/phosphate buffer (35:65, v/v, pH 6.0) at a flow rate of 1.0mL/min and a reverse-phase XTerra MS C(18) column (4.6mmx150mm, 3.5microm). The standard curve was linear over a concentration range of 1.5-100.0microg/mL of inositol hexanicotinate in rat plasma. The HPLC method was validated with intra- and inter-day precisions of 1.55-4.30% and 2.69-21.5%, respectively. The intra- and inter-day biases were -0.75 to 19.8% and 2.58-22.0%, respectively. At plasma concentrations of 1.5-100microg/mL, the mean recovery of inositol hexanicotinate was 99.6%. The results of a stability study indicated that inositol hexanicotinate was unstable in rat plasma samples, but was stable in acetonitrile extracts of rat plasma for up to 24h at 4 degrees C. The assay is simple, rapid, specific, sensitive, and reproducible and has been used successfully to analyze inositol hexanicotinate plasma concentrations in a pharmacokinetic study using the rat as an animal model.
An Organ of Zuckerkandl tumour shown by MRA (2007: 12b)

Pheochromocytomas are catecholamine-releasing tumors that can be found in an extraadrenal location in 10% of the cases. Almost half of all pheochromocytomas are now discovered incidentally during cross-sectional imaging for unrelated causes. We present a case of a paragaglioma of the organ of Zuckerkandl that was discovered incidentally during a magnetic resonance angiogram performed for intermittent claudication. Subsequent investigation with computed tompgraphy and I-123 metaiodobenzylguanine scintigraphy as well as an overview of the literature are also presented.
We Need Numbers!: Heuristic Evaluation during Demonstrations (HED) for Measuring Usability in IT System Procurement

We introduce a new usability inspection method called HED (heuristic evaluation during demonstrations) for measuring and comparing usability of competing complex IT systems in public procurement. The method presented enhances traditional heuristic evaluation to include the use context, comprehensive view of the system, and reveals missing functionality by using user scenarios and demonstrations. HED also quantifies the results in a comparable way. We present findings from a real-life validation of the method in a large-scale procurement project of a healthcare and social welfare information system. We analyze and compare the performance of HED to other usability evaluation methods used in procurement. Based on the analysis HED can be used to evaluate the level of usability of an IT system during procurement correctly, comprehensively and efficiently.
Building Efficient Query Engines in a High-Level Language

Abstraction without regret refers to the vision of using high-level programming languages for systems development without experiencing a negative impact on performance. A database system designed according to this vision offers both increased productivity and high performance instead of sacrificing the former for the latter as is the case with existing, monolithic implementations that are hard to maintain and extend.
 In this article, we realize this vision in the domain of analytical query processing. We present LegoBase, a query engine written in the high-level programming language Scala. The key technique to regain efficiency is to apply generative programming: LegoBase performs source-to-source compilation and optimizes database systems code by converting the high-level Scala code to specialized, low-level C code. We show how generative programming allows to easily implement a wide spectrum of optimizations, such as introducing data partitioning or switching from a row to a column data layout, which are difficult to achieve with existing low-level query compilers that handle only queries. We demonstrate that sufficiently powerful abstractions are essential for dealing with the complexity of the optimization effort, shielding developers from compiler internals and decoupling individual optimizations from each other.
 We evaluate our approach with the TPC-H benchmark and show that (a) with all optimizations enabled, our architecture significantly outperforms a commercial in-memory database as well as an existing query compiler. (b) Programmers need to provide just a few hundred lines of high-level code for implementing the optimizations, instead of complicated low-level code that is required by existing query compilation approaches.
Principle and Performance of Semi-Persistent Scheduling for VoIP in LTE System

This paper presents an effective scheduling scheme called semi-persistent scheduling for VoIP service in LTE system. The main challenges of effectively supporting VoIP service in LTE system are 1) the tight delay requirement combined with the frequent arrival of small packets of VoIP traffic and 2) the scarcity of radio resources along with control channel restriction in LTE system. Simulation results show that semi-persistent scheduling can support high system capacity while at the same time guaranteeing the QoS requirements such as packet delay and packet loss rate of VoIP. Furthermore, semi- persistent scheduling requires less control signaling overhead which is very important for efficient resources utilization in a practical system.
Preclinical and clinical effects of Nigella sativa and its constituent, thymoquinone: A review.

ETHNOPHARMACOLOGICAL RELEVANCE
Nigella sativa (N. sativa) L. (Ranunculaceae), well known as black cumin, has been used as a herbal medicine that has a rich historical background. It has been traditionally and clinically used in the treatment of several diseases. Many reviews have investigated this valuable plant, but none of them focused on its clinical effects. Therefore, the aim of the present review is to provide a comprehensive report of clinical studies on N. sativa and some of its constituents.



Studies on the clinical effects of N. sativa and its main constituent, thymoquinone, which were published between 1979 and 2015, were searched using various databases.



During the last three decades, several in vivo and in vitro animal studies revealed the pharmacological properties of the plant, including its antioxidant, antibacterial, antiproliferative, proapoptotic, anti-inflammatory, and antiepileptic properties, and its effect on improvement in atherogenesis, endothelial dysfunction, glucose metabolism, lipid profile dysfunction, and prevention of hippocampus pyramidal cell loss. In clinical studies, antimicrobial, antioxidant, anti-inflammatory, antitumor, and antidiabetic properties as well as therapeutic effects on metabolic syndrome, and gastrointestinal, neuronal, cardiovascular, respiratory, urinary, and reproductive disorders were found in N. sativa and its constituents.



Extensive basic and clinical studies on N. sativa seed powder, oil, extracts (aqueous, ethanolic, and methanolic), and thymoquinone showed valuable therapeutic effects on different disorders with a wide range of safe doses. However, there were some confounding factors in the reviewed clinical trials, and a few of them presented data about the phytochemical composition of the plant. Therefore, a more standard clinical trial with N. sativa supplementation is needed for the plant to be used as an inexpensive potential biological adjuvant therapy.