nils-herrmann/clean_scirepeval huggingface dataset:
Neuropeptidomic Components Generated by Proteomic Functions in Secretory Vesicles for Cell–Cell Communication

Diverse neuropeptides participate in cell–cell communication to coordinate neuronal and endocrine regulation of physiological processes in health and disease. Neuropeptides are short peptides ranging in length from ~3 to 40 amino acid residues that are involved in biological functions of pain, stress, obesity, hypertension, mental disorders, cancer, and numerous health conditions. The unique neuropeptide sequences define their specific biological actions. Significantly, this review article discusses how the neuropeptide field is at the crest of expanding knowledge gained from mass-spectrometry-based neuropeptidomic studies, combined with proteomic analyses for understanding the biosynthesis of neuropeptidomes. The ongoing expansion in neuropeptide diversity lies in the unbiased and global mass-spectrometry-based approaches for identification and quantitation of peptides. Current mass spectrometry technology allows definition of neuropeptide amino acid sequence structures, profiling of multiple neuropeptides in normal and disease conditions, and quantitative peptide measures in biomarker applications to monitor therapeutic drug efficacies. Complementary proteomic studies of neuropeptide secretory vesicles provide valuable insight into the protein processes utilized for neuropeptide production, storage, and secretion. Furthermore, ongoing research in developing new computational tools will facilitate advancements in mass-spectrometry-based identification of small peptides. Knowledge of the entire repertoire of neuropeptides that regulate physiological systems will provide novel insight into regulatory mechanisms in health, disease, and therapeutics.
Reflexes of Grammar in Patterns of Language Change †

A central methodological problem of historical studies, in linguistics as in other disciplines, is that data are limited to what happens to have survived the vicissitudes of time. In particular, we cannot perform experiments to broaden the range of facts available for analysis, to compensate for sampling biases in the preservation of data or to test the validity of hypotheses. In historical syntax, the domain of this study, the problem is particularly acute, since grammatical analysis depends on negative evidence, the knowledge that certain sentence types are unacceptable. When we study living languages, we obtain such information experimentally, usually by elicitation of judgments of acceptability from informants. Though the methodological difficulties inherent in the experimental method of contemporary syntactic investigation may be substantial (Labov, 1975b), the information it provides forms the necessary basis of grammatical analysis. Hence, syntacticians who wish to interrogate historical material find themselves in difficulty. The difficulty will be mitigated if two reasonable assumptions are made (see, for example, Adams, 1987b; Santorini, 1989): 1) The past is like the present and general principles derived from the study of living languages in the present will hold of archaic ones as well. This assumption allows the historical syntactician to, in the words of Labov, "use the present to explain the past (Labov, 1975a)." 2) For reasonably simple sentences, if a certain type does not occur in a substantial corpus, then it is not grammatically possible in the language of that corpus. Here the assumption is, of course, problematic since non-occurrence in a corpus may always be due to non-grammatical, contextual factors or even to chance. Still, for structurally simple cases, including those we will be considering in this paper, it is unlikely to lead us far astray.
Action Recognition using Visual Attention

Attention based models have been shown to achieve promising results on several challenging tasks, including caption generation , machine translation , game-playing and tracking . Attention based models can potentially infer the action happening in videos by focusing only on the relevant places in each video frame. Soft-attention models are deterministic and can be trained using backpropagation. We propose a soft-attention based model for action recognition in videos. We use multi-layered Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM). Our model tends to recognize important elements in video frames based on the activities it detects.
Estimation and Inference of Impulse Responses by Local Projections

This paper introduces methods to compute impulse responses without specification and estimation of the underlying multivariate dynamic system. The central idea consists in estimating local projections at each period of interest rather than extrapolating into increasingly distant horizons from a given model, as it is done with vector autoregressions (VAR). The advantages of local projections are numerous: (1) they can be estimated by simple regression techniques with standard regression packages; (2) they are more robust to misspecification; (3) joint or point-wise analytic inference is simple; and (4) they easily accommodate experimentation with highly non-linear and flexible specifications that may be impractical in a multivariate context. Therefore, these methods are a natural alternative to estimating impulse responses from VARs. Monte Carlo evidence and an application to a simple, closed-economy, new-Keynesian model clarify these numerous advantages. •
Filtering Event Type Clustering MEDICAL EVENT EXTRACTOR TEMPORAL RESOLVER Temporal Expression Resolution Temporal Linking to Events User

In this paper, we describe a system for automatic construction of user disease progression timelines from their posts in online support groups using minimal supervision. In recent years, several online support groups have been established which has led to a huge increase in the amount of patient-authored text available. Creating systems which can automatically extract important medical events and create disease progression timelines for users from such text can help in patient health monitoring as well as studying links between medical events and users’ participation in support groups. Prior work in this domain has used manually constructed keyword sets to detect medical events. In this work, our aim is to perform medical event detection using minimal supervision in order to develop a more general timeline construction system. Our system achieves an accuracy of 55.17%, which is 92% of the performance achieved by a supervised baseline system.
Vision-Based Online Adaptation of Motion Primitives to Dynamic Surfaces: Application to an Interactive Robotic Wiping Task

Elderly or disabled people usually need augmented nursing attention both in home and clinical environments, especially to perform bathing activities. The development of an assistive robotic bath system, which constitutes a central motivation of this letter, would increase the independence and safety of this procedure, ameliorating in this way the everyday life for this group of people. In general terms, the main goal of this letter is to enable natural, physical human–robot interaction, involving human-friendly and user-adaptive online robot motion planning and interaction control. For this purpose, we employ imitation learning using a leader–follower framework called coordinate change dynamic movement primitives (CC-DMP), in order to incorporate the expertise of professional carers for bathing sequences. In this letter, we propose a vision-based washing system, combining CC-DMP framework with a perception-based controller, to adapt the motion of robot's end effector on moving and deformable surfaces, such as a human body part. The controller guarantees globally uniformly asymptotic convergence to the leader movement primitive while ensuring avoidance of restricted areas, such as sensitive skin body areas. We experimentally tested our approach on a setup including the humanoid robot ARMAR-III and a Kinect v2 camera. The robot executes motions learned from the publicly available KIT whole-body human motion database, achieving good tracking performance in challenging interactive task scenarios.
Type 2 diabetes across generations: from pathophysiology to prevention and management

Type 2 diabetes is now a pandemic and shows no signs of abatement. In this Seminar we review the pathophysiology of this disorder, with particular attention to epidemiology, genetics, epigenetics, and molecular cell biology. Evidence is emerging that a substantial part of diabetes susceptibility is acquired early in life, probably owing to fetal or neonatal programming via epigenetic phenomena. Maternal and early childhood health might, therefore, be crucial to the development of effective prevention strategies. Diabetes develops because of inadequate islet β-cell and adipose-tissue responses to chronic fuel excess, which results in so-called nutrient spillover, insulin resistance, and metabolic stress. The latter damages multiple organs. Insulin resistance, while forcing β cells to work harder, might also have an important defensive role against nutrient-related toxic effects in tissues such as the heart. Reversal of overnutrition, healing of the β cells, and lessening of adipose tissue defects should be treatment priorities.
Extending and Formalizing UML 2.0 Activity Diagrams for the specification of time-constrained business processes

In real world, most business processes are subject to time constraints. Equally important as the execution of a process is the time in which it is to be performed. A time constrained business process can be defined as a process whose correct behavior depends not only on the results obtained, but also on the time it is carried out. UML has become an important tool for model-driven software development, perhaps partly thanks to its visually oriented modeling concepts, and it is increasingly being used in business process modeling too. In this paper, we extend UML 2.0 Activity Diagrams with time annotations and new modelling constructs inspired of the Timed CSP language. Furthermore, in order to overcome the lack of formal semantics that these diagrams suffer from, we introduce a set of mapping rules to derive formal specifications in terms of Timed CSP processes from models in the extended Activity Diagrams being proposed. Timed CSP has several features that make it well suited to give a formal semantics for extended Activity Diagrams (e.g., compositionality, well defined denotational and operational semantics, etc.). The proposed mapping rules enable checking whether the model described in an Activity Diagram satisfies such properties as timeliness, deadlock freeness, liveness, etc. In order to show the applicability of our approach, we present a case study from a manufacturing domain.
A 32-nm Subthreshold 7T SRAM Bit Cell With Read Assist

The implementation of the six-transistor (6T) static random access memory cell in deep submicrometer region has become difficult due to the compromise between area, power, and performance, with local and global variations only exacerbating the problem further. To impede the read–write conflict of the 6T cell, the seven-transistor (7T) cell with a noise-margin-free read operation has previously been proposed. But it severely lags in terms of its write ability at lower voltages due to its single-ended write operation. Its single-ended read operation also degrades severely in performance when operating in subthreshold (ST) region. To combat these problems, we propose a 7T cell which operates in the ST region down to 0.4 V with improved dynamic write ability. The novel topology also helps reduce power consumption by achieving a lower data retention voltage point. A read assist has been proposed to greatly enhance the performance of the single-ended read operation in ST region. Large improvements in various performance metrics of the proposed cell have been attained while simultaneously achieving a low area of  $0.254~\mu \text{m}^{2}$  per bit cell on the 32-nm technology node.
Automatic Generation of Personalized Chinese Handwriting Characters

This paper presents a novel algorithmic method for automatically generating personal handwriting styles of Chinese characters through an example-based approach. The method first splits a whole Chinese character into multiple constituent parts, such as strokes, radicals, and frequent character components. The algorithm then analyzes and learns the characteristics of character handwriting styles both defined in the Chinese national font standard and those exhibited in a person's own handwriting records. In such an analysis process, we adopt a parametric representation of character shapes and also examine the spatial relationships between multiple constituent components of a character. By imitating shapes of individual character components as well as the spatial relationships between them, the proposed method can automatically generate personalized handwritings following an example-based approach. To explore the quality of our automatic generation algorithm, we compare the computer generated results with the authentic human handwriting samples, which appear satisfying for entertainment or mobile applications as agreed by Chinese subjects in our user study.
Searching online book documents and analyzing book citations

Academic search engines and digital libraries provide convenient online search and access facilities for scientific publications. However, most existing systems do not include books in their collections although several books are freely available online. Academic books are different from papers in terms of their length, contents and structure. We argue that accounting for academic books is important in understanding and assessing scientific impact. We introduce an open-book search engine that extracts and indexes metadata, contents, and bibliography from online PDF book documents. To the best of our knowledge, no previous work gives a systematical study on building a search engine for books.
 We propose a hybrid approach for extracting title and authors from a book that combines results from CiteSeer, a rule based extractor, and a SVM based extractor, leveraging web knowledge. For "table of contents" recognition, we propose rules based on multiple regularities based on numbering and ordering. In addition, we study bibliography extraction and citation parsing for a large dataset of books. Finally, we use the multiple fields available in books to rank books in response to search queries. Our system can effectively extract metadata and contents from large collections of online books and provides efficient book search and retrieval facilities.
Genetic Architecture of Palm Oil Fatty Acid Composition in Cultivated Oil Palm (Elaeis guineensis Jacq.) Compared to Its Wild Relative E. oleifera (H.B.K) Cortés

We searched for quantitative trait loci (QTL) associated with the palm oil fatty acid composition of mature fruits of the oil palm E. guineensis Jacq. in comparison with its wild relative E. oleifera (H.B.K) Cortés. The oil palm cross LM2T x DA10D between two heterozygous parents was considered in our experiment as an intraspecific representative of E. guineensis. Its QTLs were compared to QTLs published for the same traits in an interspecific Elaeis pseudo-backcross used as an indirect representative of E. oleifera. Few correlations were found in E. guineensis between pulp fatty acid proportions and yield traits, allowing for the rather independent selection of both types of traits. Sixteen QTLs affecting palm oil fatty acid proportions and iodine value were identified in oil palm. The phenotypic variation explained by the detected QTLs was low to medium in E. guineensis, ranging between 10% and 36%. The explained cumulative variation was 29% for palmitic acid C16:0 (one QTL), 68% for stearic acid C18:0 (two QTLs), 50% for oleic acid C18:1 (three QTLs), 25% for linoleic acid C18:2 (one QTL), and 40% (two QTLs) for the iodine value. Good marker co-linearity was observed between the intraspecific and interspecific Simple Sequence Repeat (SSR) linkage maps. Specific QTL regions for several traits were found in each mapping population. Our comparative QTL results in both E. guineensis and interspecific materials strongly suggest that, apart from two common QTL zones, there are two specific QTL regions with major effects, which might be one in E. guineensis, the other in E. oleifera, which are independent of each other and harbor QTLs for several traits, indicating either pleiotropic effects or linkage. Using QTL maps connected by highly transferable SSR markers, our study established a good basis to decipher in the future such hypothesis at the Elaeis genus level.
Actions in the Eye: Dynamic Gaze Datasets and Learnt Saliency Models for Visual Recognition

Systems based on bag-of-words models from image features collected at maxima of sparse interest point operators have been used successfully for both computer visual object and action recognition tasks. While the sparse, interest-point based approach to recognition is not inconsistent with visual processing in biological systems that operate in `saccade and fixate' regimes, the methodology and emphasis in the human and the computer vision communities remains sharply distinct. Here, we make three contributions aiming to bridge this gap. First, we complement existing state-of-the art large scale dynamic computer vision annotated datasets like Hollywood-2  and UCF Sports  with human eye movements collected under the ecological constraints of visual action and scene context recognition tasks. To our knowledge these are the first large human eye tracking datasets to be collected and made publicly available for video, vision.imar.ro/eyetracking (497,107 frames, each viewed by 19 subjects), unique in terms of their (a) large scale and computer vision relevance, (b) dynamic, video stimuli, (c) task control, as well as free-viewing. Second, we introduce novel dynamic consistency and alignment measures, which underline the remarkable stability of patterns of visual search among subjects. Third, we leverage the significant amount of collected data in order to pursue studies and build automatic, end-to-end trainable computer vision systems based on human eye movements. Our studies not only shed light on the differences between computer vision spatio-temporal interest point image sampling strategies and the human fixations, as well as their impact for visual recognition performance, but also demonstrate that human fixations can be accurately predicted, and when used in an end-to-end automatic system, leveraging some of the advanced computer vision practice, can lead to state of the art results.
A high PSRR Class-D audio amplifier IC based on a self-adjusting voltage reference

In a wide range of applications, audio amplifiers require a large Power Supply Rejection Ratio (PSRR) that the current Class-D architecture cannot reach. This paper proposes a self-adjusting internal voltage reference scheme that sets the bias voltages of the amplifier without losing on output dynamics. This solution relaxes the constraints on gain and feedback resistors matching that were previously the limiting factor for the PSRR. Theory of operation, design and IC evaluation in a Class-D amplifier in CMOS 0.25µm will be shown in this paper. The use of this voltage reference increased the amplifier's PSRR by 15dB, with only a 140µA increase in current consumption.
Study on Maximum Receivable Distance for Radiated Emission of Information Technology Equipment Causing Information Leakage

This study has discussed the information leakage threat due to the radiated emission of information technology equipment (ITE). This paper focused on the theoretical estimation of the maximum receivable distance for the radiated emission causing the information leakage. The maximum receivable distance estimation formula was theoretically derived based on the reception capacity of a wideband receiver with a specific antenna, the limits of the electric field strength of the radiated emission of ITE regulated in CISPR 22 standard, and the attenuation by the distance in a free space for the radiated emission. In addition, the maximum receivable distance estimation in the considerable worst case was conducted by assuming some realistic parameters for a commercial high-performance wideband receiver and a commercial high-gain log-periodic antenna.
Real-time human motion tracking using multiple depth cameras

In this paper, we consider the problem of tracking human motion with a 22-DOF kinematic model from depth images. In contrast to existing approaches, our system naturally scales to multiple sensors. The motivation behind our approach, termed Multiple Depth Camera Approach (MDCA), is that by using several cameras, we can significantly improve the tracking quality and reduce ambiguities as for example caused by occlusions. By fusing the depth images of all available cameras into one joint point cloud, we can seamlessly incorporate the available information from multiple sensors into the pose estimation. To track the high-dimensional human pose, we employ state-of-the-art annealed particle filtering and partition sampling. We compute the particle likelihood based on the truncated signed distance of each observed point to a parameterized human shape model. We apply a coarse-to-fine scheme to recognize a wide range of poses to initialize the tracker. In our experiments, we demonstrate that our approach can accurately track human motion in real-time (15Hz) on a GPGPU. In direct comparison to two existing trackers (OpenNI, Microsoft Kinect SDK), we found that our approach is significantly more robust for unconstrained motions and under (partial) occlusions.
Adaptive Hybrid Queue Configuration for Supercomputer Systems

Supercomputers have batch queues to which parallel jobs with specific requirements are submitted. Commercial schedulers come with various configurable parameters for the queues which can be adjusted based on the requirements of the system. The employed configuration affects both system utilization and job response times. Often times, choosing an optimal configuration with good performance is not straightforward and requires good knowledge of the system behavior to various kinds of workloads. In this paper, we propose a dynamic scheme for setting queue configurations, namely, the number of queues, partitioning of the processor space and the mapping of the queues to the processor partitions, and the processor size and execution time limits corresponding to the queues based on the historical workload patterns. We use a novel non-linear programming formulation for partitioning and mapping of nodes to the queues for homogeneous HPC systems. We also propose a novel hybrid partitioned-nonpartitioned scheme for allocating processors to the jobs submitted to the queues. Our simulation results for a supercomputer system with 35,000+ CPU cores show that our hybrid scheme gives up to 74% reduction in queue waiting times and up to 12% higher utilizations than static queue configurations.
Atomicity via source-to-source translation

We present an implementation and evaluation of atomicity (also known as software transactions) for a dialect of Java. Our implementation is fundamentally different from prior work in three respects: (1) It is entirely a source-to-source translation, producing Java source code that can be compiled by any Java compiler and run on any Java Virtual Machine. (2) It can enforce "strong" atomicity without assuming special hardware or a uniprocessor. (3) The implementation uses locks rather than optimistic concurrency, but it cannot deadlock and requires inter-thread communication only when there is data contention.