nils-herrmann/clean_scirepeval huggingface dataset:
Regulation of low-density lipoprotein receptors: implications for pathogenesis and therapy of hypercholesterolemia and atherosclerosis.

Low-density lipoprotein (LDL) is the most abundant and the most atherogenic class of cholesterol-carrying lipoproteins in human plasma. The level of plasma LDL is regulated by the LDL receptor, a cell surface glycoprotein that removes LDL from plasma by receptor-mediated endocytosis. Defects in the gene encoding the LDL receptor, which occur in patients with familial hypercholesterolemia, elevate the plasma LDL level and produce premature coronary atherosclerosis. The physiologically important LDL receptors are located primarily in the liver, where their number is regulated by the cholesterol content of the hepatocyte. When the cholesterol content of hepatocytes is raised by ingestion of diets high in saturated fat and cholesterol, LDL receptors fall and plasma LDL levels rise. Conversely, maneuvers that lower the cholesterol content of hepatocytes, such as ingestion of drugs that inhibit cholesterol synthesis (mevinolin or compactin) or prevent the reutilization of bile acids (cholestyramine or colestipol), stimulate LDL receptor production and lower plasma LDL levels. The normal process of receptor regulation can therefore be exploited in powerful and novel ways so as to reverse hypercholesterolemia and prevent atherosclerosis.
HMM Based Approach for Handwritten Arabic Word Recognition Using the IFN/ENIT- Database

An offline recognition system for Arabic handwritten words is presented. The recognition system is based on a semi-continuous 1-dimensional HMM. From each binary word image normalization parameters were estimated. First height, length, and baseline skew are normalized, then features are collected using a sliding window approach. This paper presents these methods in more detail. Some parameters were modified and the consequent effect on the recognition results are discussed. Significant tests were performed using the new IFN/ENIT database of handwritten Arabic words. The comprehensive database consists of 26459 Arabic words (Tunisian town/village names) handwritten by 411 different writers and is free for non-commercial research. In the performed tests we achieved maximal recognition rates of about 89% on a word level.
Bearing Health Condition Prediction Using Deep Belief Network

Bearings play a critical role in maintaining safety and reliability of rotating machinery. Bearings health condition prediction aims to prevent unexpected failures and minimize overall maintenance costs since it provides decision making information for condition-based maintenance. This paper proposes a Deep Belief Network (DBN)-based data-driven health condition prediction method for bearings. In this prediction method, a DBN is used as the predictor, which includes stacked RBMs and regression output. Our main contributions include development of a deep leaning-based data-driven prognosis solution that does not rely on explicit model equations and prognostic expertise, and providing comprehensive prediction results on five representative runto-failure bearings. The IEEE PHM 2012 challenge dataset is used to demonstrate the effectiveness of the proposed method, and the results are compared with two existing methods. The results show that the proposed method has promising performance in terms of short-term health condition prediction and remaining useful life prediction for bearings.
[Myopathy with trabecular fibers associated with familiar autoimmune polyglandular syndrome type 1].

An association between limb-girdle muscular dystrophy and autoimmune polyglandular syndrome type 1 (APS1), in three sisters born to consanguineous parents, is presented. The components of APS1 in these patients were hypoparathyroidism, autoimmune adrenal insufficiency, primary hypogonadism and mucocutaneous candidiasis. A muscle biopsy performed on the first patient showed over 40 % of trabeculated fibers, suggesting the diagnosis of myopathy with trabeculated fibers (MTF). Intracranial calcification was found in the second patient; and epilepsy, and several other minor components of APS1, in the third; cataracts were found in the last two patients. The clinical manifestations and inheritance of MTF and APS1 are reviewed. While recessive mutations in the AIRE gene (21q22.3) cause APS1, genetic transmission of hereditary MTF has not been investigated in depth. Mutations in CRYAA, a gene that shares the same locus as AIRE, may cause recessive inheritance of cataracts. Thus, the proposal of this article is that linkage of contiguous genes that includes the AIRE gene, might be responsible for the association of both diseases in these three patients. Additional involvement of CRYAA, that possibly causes cataracts in two of the patients, might support this hypothesis, due to the proximity of this gene to AIRE. The genes COL6A1 and COL6A2, localized in 21q22.3, are discarded as transmitters of MTF in these cases, on clinical criteria. The authors wish to draw attention to the association between limb-girdle muscular dystrophy and APS1, since it has been very rarely reported in the medical literature.
Use of Fourier Transformation Infrared ( FTIR ) Spectroscopy for Analysis of Functional Groups in Peanut Oil Biodiesel and Its Blends

The paper studied how fourier transformation infra-red (FTIR) spectroscopy can be used to monitor homogenous alkali catalyzed alcoholysis of peanut oil and methanol to produce biodiesel. The spectrum generated was used to identify the functional groups in the fuel sample for qualitative analysis and associated type of vibrations. Analyses on biodiesel blends with fossil diesel were also carried out with FTIR spectroscopy. Place and Duration of Study: Department of Mechanical Engineering, Federal University of Technology, Akure, Ondo State, Nigeria, between December 2014 and March 2015.  Biodiesel was produced by transesterifying peanut oil with methanol in the presence of sodium hydroxide as catalyst. Molar ratio of 6:1 (methanol to oil) was followed to shift the reaction to product side for more yield of fatty acid methyl esters (FAME) and the use of catalyst enabled the reaction to proceed faster. The oil and biodiesel were characterized following ASTM standards. The biodiesel obtained was separated from glycerol, washed with distilled water Original Research Article Oyerinde and Bello; BJAST, 13(3): 1-14, 2016; Article no.BJAST.22178 2 and dried. Biodiesel blends with fossil diesel from 10% v/v to 90% v/v in a step of 10% were produced. Samples of biodiesel and biodiesel blends were scan within mid-infrared region of 4000 cm – 400 cm with fourier transform infrared spectrometer by agilent technologies. The spectra obtained were interpreted and analyzed with the aid of structure correlation chart. A visual basic computer program was developed based on the data analyzed and experience gained.  The results revealed that the biodiesel contained fatty acid methyl esters (FAME). The FTIR spectrum for the biodiesel revealed the functional groups with characteristics bands, C=O, -(CH2)n-, C-O, C=C and C-H in the spectrum. The absorbance intensity (peak), at a region where strong absorption of FAME occurred in each blend spectrum increases with biodiesel concentration when verified with Beer’s law and the R value of 0.992 was obtained to show good fitting. Changes of transmittance with concentration and absorbance of blend spectra explored showed that transmittance decreases with both concentrations and absorbance and the R values of 0.991 and 0.997 were obtained respectively.  Catalyzed complete transesterification was performed on the peanut oil and methanol. The biodiesel produced contained an ester functional group and showed that it can be used as a substitute for fossil diesel in diesel engines. The biodiesel is miscible with fossil diesel and blending of any biodiesel concentration can be obtained. The computer program developed can help to identify the functional groups of similar fuels and associated type of vibrations at a given wavenumber within mid-infrared region.
Eye colour: portals into pigmentation genes and ancestry.

Several recent papers have tried to address the genetic determination of eye colour via microsatellite linkage, testing of pigmentation candidate gene polymorphisms and the genome wide analysis of SNP markers that are informative for ancestry. These studies show that the OCA2 gene on chromosome 15 is the major determinant of brown and/or blue eye colour but also indicate that other loci will be involved in the broad range of hues seen in this trait in Europeans.
Did You Enjoy the Ride? Understanding Passenger Experience via Heterogeneous Network Embedding

Online taxicab platforms like DiDi and Uber have impacted hundreds of millions of users on their choices of traveling, but how do users feel about the ride-sharing services, and how to improve their experience? While current ride-sharing services have collected massive travel data, it remains challenging to develop data-driven techniques for modeling and predicting user ride experience. In this work, we aim to accurately predict passenger satisfaction over their rides and understand the key factors that lead to good/bad experiences. Based on in-depth analysis of large-scale travel data from a popular taxicab platform in China, we develop PHINE (Pattern-aware Heterogeneous Information Network Embedding) for data-driven user experience modeling. Our PHINE framework is novel in that it is composed of spatial-temporal node binding and grouping for addressing the inherent data variation, and pattern preservation based joint training for modeling the interactions among drivers, passengers, locations, and time. Extensive experiments on 12 real-world travel datasets demonstrate the effectiveness of PHINE over strong baseline methods. We have deployed PHINE in the DiDi Big Data Center, delivering high-quality predictions for passenger satisfaction on a daily basis.
Asynchronous Parallel Greedy Coordinate Descent

In this paper, we propose and study an Asynchronous parallel Greedy Coordinate Descent (Asy-GCD) algorithm for minimizing a smooth function with bounded constraints. At each iteration, workers asynchronously conduct greedy coordinate descent updates on a block of variables. In the first part of the paper, we analyze the theoretical behavior of Asy-GCD and prove a linear convergence rate. In the second part, we develop an efficient kernel SVM solver based on Asy-GCD in the shared memory multi-core setting. Since our algorithm is fully asynchronous—each core does not need to idle and wait for the other cores—the resulting algorithm enjoys good speedup and outperforms existing multi-core kernel SVM solvers including asynchronous stochastic coordinate descent and multi-core LIBSVM.
An Exploration of Dropout with LSTMs

Long Short-Term Memory networks (LSTMs) are a component of many state-of-the-art DNN-based speech recognition systems. Dropout is a popular method to improve generalization in DNN training. In this paper we describe extensive experiments in which we investigated the best way to combine dropout with LSTMs– specifically, projected LSTMs (LSTMP). We investigated various locations in the LSTM to place the dropout (and various combinations of locations), and a variety of dropout schedules. Our optimized recipe gives consistent improvements in WER across a range of datasets, including Switchboard, TED-LIUM and AMI.
Rainfall forecasting using neural network: A survey

An accurate rainfall forecasting is very important for agriculture dependent countries like India. For analyzing the crop productivity, use of water resources and pre-planning of water resources, rainfall prediction is important. Statistical techniques for rainfall forecasting cannot perform well for long-term rainfall forecasting due to the dynamic nature of climate phenomena. Artificial Neural Networks (ANNs) have become very popular, and prediction using ANN is one of the most widely used techniques for rainfall forecasting. This paper provides a detailed survey and comparison of different neural network architectures used by researchers for rainfall forecasting. The paper also discusses the issues while applying different neural networks for yearly/monthly/daily rainfall forecasting. Moreover, the paper also presents different accuracy measures used by researchers for evaluating performance of ANN.
Switching linear dynamical systems for speech recognition

This paper describes the application of Rao-Blackwellised Gibbs sampling (RBGS) to speech recognition using switching linear dynamical systems (SLDSs) as the acoustic model. The SLDS is a hybrid of standard hidden Markov models (HMMs) and linear dynamical systems. It is an extension of the stochastic segment model (SSM) where segments are assumed independent. SLDSs explicitly take into account the strong co-articulation present in speech using a Gauss-Markov process in a low dimensional, latent, state space. Unfortunately , inference in SLDS is intractable unless the discrete state sequence is known. RBGS is one approach that may be applied for both improved training and decoding for this form of intractable model. The theory of SLDS and RBGS is described, along with an efficient proposal distribution. The performance of the SLDS and SSM using RBGS for training and inference is evaluated on the ARPA Resource Management task.
User Interface Design for E-Learning Software

User interface (UI) is point of interaction between user and computer software. The success and failure of a software application depends on User Interface Design (UID). Possibility of using a software, easily using and learning are issues influenced by UID. The UI is significant in designing of educational software (e-Learning). Principles and concepts of learning should be considered in addition to UID principles in UID for e-learning. In this regard, to specify the logical relationship between education, learning, UID and multimedia at first we readdress the issues raised in previous studies. It is followed by examining the principle concepts of e-learning and UID. Then, we will see how UID contributes to e-learning through the educational software built by authors. Also we show the way of using UI to improve learning and motivating the learners and to improve the time efficiency of using e-learning software. e-Learning, User Interface Design, Self learning, Educational Multimedia
NK Cells and Trophoblasts: Partners in Pregnancy

In placental mammals, viviparity—the production of living young within the mother’s body—evolved under the auspices of the immune system. Elements of immunity were incorporated, giving pregnancy a mildly inflammatory character. Formation of the placenta, the organ that feeds the fetus, involves a cooperation between maternal natural killer (NK) cells and fetal trophoblast cells that remodels the blood supply. Recent research reveals that this process and human reproductive success are influenced by polymorphic HLA-C ligands and their killer cell immunoglobulin-like receptors (KIR).
A fully automated correction method of EOG artifacts in EEG recordings

A fully automated method for reducing EOG artifacts is presented and validated.



The correction method is based on regression analysis and was applied to 18 recordings with 22 channels and approx. 6 min each. Two independent experts scored the original and corrected EEG in a blinded evaluation.



The expert scorers identified in 5.9% of the raw data some EOG artifacts; 4.7% were corrected. After applying the EOG correction, the expert scorers identified in another 1.9% of the data some EOG artifacts, which were not recognized in the uncorrected data.



The advantage of a fully automated reduction of EOG artifacts justifies the small additional effort of the proposed method and is a viable option for reducing EOG artifacts. The method has been implemented for offline and online analysis and is available through BioSig, an open source software library for biomedical signal processing.



Visual identification and rejection of EOG-contaminated EEG segments can miss many EOG artifacts, and is therefore not sufficient for removing EOG artifacts. The proposed method was able to reduce EOG artifacts by 80%.
Generalised Kernel Machines

The generalised linear model (GLM) is the standard approach in classical statistics for regression tasks where it is appropriate to measure the data misfit using a likelihood drawn from the exponential family of distributions. In this paper, we apply the kernel trick to give a non-linear variant of the GLM, the generalised kernel machine (GKM), in which a regularised GLM is constructed in a fixed feature space implicitly defined by a Mercer kernel. The MATLAB symbolic maths toolbox is used to automatically create a suite of generalised kernel machines, including methods for automated model selection based on approximate leave-one-out cross-validation. In doing so, we provide a common framework encompassing a wide range of existing and novel kernel learning methods, and highlight their connections with earlier techniques from classical statistics. Examples including kernel ridge regression, kernel logistic regression and kernel Poisson regression are given to demonstrate the flexibility and utility of the generalised kernel machine.
Characteristic Analysis and Comparison of IPMSM for HEV According to Pole and Slot Combination

Interior permanent magnet synchronous motor (IPMSM) is usually applied to traction motor in the hybrid electric vehicle (HEV). All motors including IPMSM have different parameters and characteristics with various combinations of the number of poles and slots. The proper combination can improve characteristics of traction system ultimately. This paper deals with analysis of the characteristics of IPMSM for mild type HEV according to the combinations of number of poles and slots. The specific models with 16-pole/18-slot, 16-pole/24-slot and 12-pole/18-slot combinations are introduced. And the advantages and disadvantages of these three models are compared. The characteristics of each model are computed in d-q axis equivalent circuit analysis and finite element analysis. After then, the proper combination of the number of poles and slots for HEV traction motor is presented after comparing these three models.
"Knowing value'' logic as a normal modal logic

Recent years witness a growing interest in nonstandard epistemic logics of “knowing whether”, “knowing what”, “knowing how” and so on. These logics are usually not normal, i.e., the standard axioms and reasoning rules for modal logic may be invalid. In this paper, we show that the conditional “knowing value” logic proposed by Wang and Fan [10] can be viewed as a disguised normal modal logic by treating the negation of Kv operator as a special diamond. Under this perspective, it turns out that the original first-order Kripke semantics can be greatly simplified by introducing a ternary relation R i in standard Kripke models which associates one world with two i-accessible worlds that do not agree on the value of constant c. Under intuitive constraints, the modal logic based on such Kripke models is exactly the one studied by Wang and Fan [10,11]. Moreover, there is a very natural binary generalization of the “knowing value” diamond, which, surprisingly, does not increase the expressive power of the logic. The resulting logic with the binary diamond has a transparent normal modal system which sharpens our understanding of the “knowing value” logic and simplifies some previous hard problems.
Efficient graph computation on hybrid CPU and GPU systems

Graphs are used to model many real objects such as social networks and web graphs. Many real applications in various fields require efficient and effective management of large-scale, graph-structured data. Although distributed graph engines such as GBase and Pregel handle billion-scale graphs, users need to be skilled at managing and tuning a distributed system in a cluster, which is a non-trivial job for ordinary users. Furthermore, these distributed systems need many machines in a cluster in order to provide reasonable performance. Several recent works proposed non-distributed graph processing platforms as complements to distributed platforms. In fact, efficient non-distributed platforms require less hardware resource and can achieve better energy efficiency than distributed ones. GraphChi is a representative non-distributed platform that is disk-based and can process billions of edges on CPUs in a single PC. However, the design drawbacks of GraphChi on I/O and computation model have limited its parallelism and performance. In this paper, we propose a general, disk-based graph engine called gGraph to process billion-scale graphs efficiently by utilizing both CPUs and GPUs in a single PC. GGraph exploits full parallelism and full overlap of computation and I/O processing as much as possible. Experiment results show that gGraph outperforms GraphChi and PowerGraph. In addition, gGraph achieves the best energy efficiency among all evaluated platforms.
Gradient Episodic Memory for Continuum Learning

One major obstacle towards AI is the poor ability of models to solve new problems quicker, and without forgetting previously acquired knowledge. To better understand this issue, we study the problem of continual learning, where the model observes, once and one by one, examples concerning a sequence of tasks. First, we propose a set of metrics to evaluate models learning over a continuum of data. These metrics characterize models not only by their test accuracy, but also in terms of their ability to transfer knowledge across tasks. Second, we propose a model for continual learning, called Gradient Episodic Memory (GEM) that alleviates forgetting, while allowing beneficial transfer of knowledge to previous tasks. Our experiments on variants of the MNIST and CIFAR-100 datasets demonstrate the strong performance of GEM when compared to the state-of-the-art.
Performance Evaluation of 5G Millimeter-Wave Cellular Access Networks Using a Capacity-Based Network Deployment Tool

The next fifth generation (5G) of wireless communication networks comes with a set of new features to satisfy the demand of data-intensive applications: millimeter wave frequencies, massive antenna arrays, beamforming, dense cells, et c. In this paper, we investigate the use of beamforming techniques through various architectures and evaluate the performance of 5G wireless access networks, using a capacity-based network deployment tool. This tool is proposed and applied to a realistic area in Ghent, Belgium, to simulate realistic 5G networks that respond to the instantaneous bit rate required by the active use rs. The results show that, with beamforming, 5G networks require almost 15% more base stations and 4 times less power to provide more capacity to the users and same coverage performances, in comparison with the 4G reference network. Moreover, they are 3 times more energy efficient than the 4G network and the hybrid beamforming architecture appears to be a suitable architecture for beamforming to be considered when designing a 5G cellular network.
Comparing the effect of different spine and leg designs for a small bounding quadruped robot

We present Lynx-robot, a quadruped, modular, compliant machine. It alternately features a directly actuated, single-joint spine design, or an actively supported, passive compliant, multi-joint spine configuration. Both spine configurations bend in the sagittal plane. This study aims at characterizing these two, largely different spine concepts, for a bounding gait of a robot with a three segmented, pantograph leg design. An earlier, similar-sized, bounding, quadruped robot named Bobcat with a two-segment leg design and a directly actuated, single-joint spine design serves as a comparison robot, to study and compare the effect of the leg design on speed, while keeping the spine design fixed. Both proposed spine designs (single rotatory and active and multi-joint compliant) reach moderate, self-stable speeds.