nils-herrmann/clean_scirepeval huggingface dataset:
Information theory in molecular biology

This article introduces the physics of information in the context of molecular biology and genomics. Entropy and information, the two central concepts of Shannon’s theory of information and communication, are often confused with each other but play transparent roles when applied to statistical ensembles (i.e., identically prepared sets) of symbolic sequences. Such an approach can distinguish between entropy and information in genes, predict the secondary structure of ribozymes, and detect the covariation between residues in folded proteins. We also review applications to molecular sequence and structure analysis, and introduce new tools in the characterization of resistance mutations, and in drug design. In a curious twist of history, the dawn of the age of genomics has both seen the rise of the science of bioinformatics as a tool to cope with the enormous amounts of data being generated daily, and the decline of the theory of information as applied to molecular biology. Hailed as a harbinger of a “new movement” (Quastler 1953) along with Cybernetics, the principles of information theory were thought to be applicable to the higher functions of living organisms, and able to analyze such functions as metabolism, growth, and differentiation (Quastler 1953). Today, the metaphors and the jargon of information theory are still widely used (Maynard Smith 1999a, 1999b), as opposed to the mathematical formalism, which is too often considered to be inapplicable to biological information. Clearly, looking back it appears that too much hope was laid upon this theory’s relevance for biology. However, there was well-founded optimism that information theory ought to be able to address the complex issues associated with the storage of information in the genetic code, only to be repeatedly questioned and rebuked (see, e.g., Vincent 1994, Sarkar 1996). In this article, I outline the concepts of entropy and information (as defined by Shannon) in the context of molecular biology. We shall see that not only are these terms well-defined and useful, they also coincide precisely with what we intuitively mean when we speak about information stored in genes, for example. I then present examples of applications of the theory to measuring the information content of biomolecules, the identification of polymorphisms, RNA and protein secondary structure prediction, the prediction and analysis of molecular interactions, and drug design. 1 Entropy and Information Entropy and information are often used in conflicting manners in the literature. A precise understanding, both mathematical and intuitive, of the notion of information (and its relationship to entropy) is crucial for applications in molecular biology. Therefore, let us begin by outlining Shannon’s original entropy concept (Shannon, 1948). 1.1 Shannon’s Uncertainty Measure Entropy in Shannon’s theory (defined mathematically below) is a measure of uncertainty about the identity of objects in an ensemble. Thus, while “en-
Topology and Geometry of Half-Rectified Network Optimization

The loss surface of deep neural networks has recently attracted interest in the optimization and machine learning communities as a prime example of highdimensional non-convex problem. Some insights were recently gained using spin glass models and mean-field approximations, but at the expense of strongly simplifying the nonlinear nature of the model. In this work, we do not make any such assumption and study conditions on the data distribution and model architecture that prevent the existence of bad local minima. Our theoretical work quantifies and formalizes two important folklore facts: (i) the landscape of deep linear networks has a radically different topology from that of deep half-rectified ones, and (ii) that the energy landscape in the non-linear case is fundamentally controlled by the interplay between the smoothness of the data distribution and model over-parametrization. Our main theoretical contribution is to prove that half-rectified single layer networks are asymptotically connected, and we provide explicit bounds that reveal the aforementioned interplay. The conditioning of gradient descent is the next challenge we address. We study this question through the geometry of the level sets, and we introduce an algorithm to efficiently estimate the regularity of such sets on large-scale networks. Our empirical results show that these level sets remain connected throughout all the learning phase, suggesting a near convex behavior, but they become exponentially more curvy as the energy level decays, in accordance to what is observed in practice with very low curvature attractors.
Evaluating Students Performance by Artificial Neural Network using WEKA

Data mining is the process of extracting hidden patterns and useful information from large set of data is now becoming part of current inventions. Data mining now can be applied to different fields like marketing, education; health etc. Data mining in field of education is named as educational data mining. Educational data mining can help institutions to predict the performance of their students so as to improve their academic results. In this paper artificial neural network is used to predict the performance of student. Multilayer Perceptron Neural Network is used for the implementation of prediction strategy. Experiment is conducted using weka and real time dataset available.
The ecosystem of SpatialHadoop

There is a recent outbreak in the amounts of spatial data generated by different sources, e.g., smart phones, space telescopes, and medical devices, which urged researchers to exploit the existing distributed systems to process such amounts of spatial data. However, as these systems are not designed for spatial data, they cannot fully utilize its spatial properties to achieve high performance. In this paper, we describe SpatialHadoop, a full-fledged MapReduce framework which extends Hadoop to support spatial data efficiently. SpatialHadoop consists of four main layers, namely, language, indexing, query processing, and visualization. The language layer provides a high level language with standard spatial data types and operations to make the system accessible to non-technical users. The indexing layer supports standard spatial indexes, such as grid, R-tree and R+-tree, inside Hadoop file system in order to speed up spatial operations. The query processing layer encapsulates the spatial operations supported by SpatialHadoop such as range query, k nearest neighbor, spatial join and computational geometry operations. Finally, the visualization layer allows users to produce images that describe very large datasets to make it easier to explore and understand big spatial data. SpatialHadoop is already used as a main component in several real systems such as MNTG, TAREEG, TAGHREED, and SHAHED.
Hall versus conventional stainless steel crown techniques: in vitro investigation of marginal fit and microleakage using three different luting agents.

This study's purpose was to investigate microleakage and marginal discrepancies in stainless steel crowns (SSCs) placed using conventional and Hall techniques and cemented with three different luting agents.



Seventy-eight human primary maxillary second molars were randomly assigned to two groups (N=39), and SSCs were applied either with the Hall or conventional technique. These two groups were further subgrouped according to the material used for crown cementation (N=13 per group). Two specimens in each group were processed for scanning electron microscopy investigation. The extent of microleakage and marginal fit was quantified in millimeters on digitally photographed sections using image analysis software. The data were compared with a two-way independent and a two-way mixed analysis of variance (P=.05).



The scores in the Hall group were significantly worse than those in the conventional technique group (P<.05). In both groups, resin cement displayed the lowest extent of microleakage, followed by glass ionomer and polycarboxylate cements (P<.05).



Stainless steel crowns applied using the Hall technique displayed higher microleakage scores than those applied using the conventional technique, regardless of the cementation material. When the interaction of the material and technique was assessed, resin cement presented as the best choice for minimizing microleakage in both techniques.
Automarking: Automatic Assessment of Open Questions

A number of Learning Management Systems (LMSs) exist on the market today. A subset of a LMS is the component in which student assessment is managed. In some forms of assessment, such as open questions, the LMS is incapable of evaluating the students’ responses and therefore human intervention is necessary. In order to assess at higher levels of Bloom’s (1956) taxonomy, it is necessary to include open-style questions in which the student is given the task as well as the freedom to arrive at a response without the comfort of recall words and/or phrases. Automating the assessment process of open questions is an area of research that has been ongoing since the 1960s. Earlier work focused on statistical or probabilistic approaches based primarily on conceptual understanding. Recent gains in Natural Language Processing have resulted in a shift in the way in which free text can be evaluated. This has allowed for a more linguistic approach which focuses heavily on factual understanding. This study will leverage the research conducted in recent studies in the area of Natural Language Processing, Information Extraction and Information Retrieval in order to provide a fair, timely and accurate assessment of student responses to open questions based on the semantic meaning of those responses.
Mindfulness meditation-based pain relief: a mechanistic account.

Pain is a multidimensional experience that involves interacting sensory, cognitive, and affective factors, rendering the treatment of chronic pain challenging and financially burdensome. Further, the widespread use of opioids to treat chronic pain has led to an opioid epidemic characterized by exponential growth in opioid misuse and addiction. The staggering statistics related to opioid use highlight the importance of developing, testing, and validating fast-acting nonpharmacological approaches to treat pain. Mindfulness meditation is a technique that has been found to significantly reduce pain in experimental and clinical settings. The present review delineates findings from recent studies demonstrating that mindfulness meditation significantly attenuates pain through multiple, unique mechanisms-an important consideration for the millions of chronic pain patients seeking narcotic-free, self-facilitated pain therapy.
A critical review of interfaces with the peripheral nervous system for the control of neuroprostheses and hybrid bionic systems.

Considerable scientific and technological efforts have been devoted to develop neuroprostheses and hybrid bionic systems that link the human nervous system with electronic or robotic prostheses, with the main aim of restoring motor and sensory functions in disabled patients. A number of neuroprostheses use interfaces with peripheral nerves or muscles for neuromuscular stimulation and signal recording. Herein, we provide a critical overview of the peripheral interfaces available and trace their use from research to clinical application in controlling artificial and robotic prostheses. The first section reviews the different types of non-invasive and invasive electrodes, which include surface and muscular electrodes that can record EMG signals from and stimulate the underlying or implanted muscles. Extraneural electrodes, such as cuff and epineurial electrodes, provide simultaneous interface with many axons in the nerve, whereas intrafascicular, penetrating, and regenerative electrodes may contact small groups of axons within a nerve fascicle. Biological, technological, and material science issues are also reviewed relative to the problems of electrode design and tissue injury. The last section reviews different strategies for the use of information recorded from peripheral interfaces and the current state of control neuroprostheses and hybrid bionic systems.
Evaluation of Physicochemical Properties of Iranian Mango Seed Kernel Oil

Mango (Mangifera indica Linn.) is one of the most important tropical fruits in the world. During processing of mango, by-products such as peel and kernel are generated. The oil of mango seed kernel was extracted using Soxhlet apparatus and fatty acid composition shows that mango seed kernel oil consist of about 44–48% saturated fatty acids and 52–56% unsaturated. Stearic acid (37.73%) was the main saturated fatty acid,while oleic acid(46.22%) was the major unsaturated fatty acid in mango seed kernel oil. The specific gravity(0.9 at 40 , refractive index(1.443 at 40 , peroxide value(1.2 meq/kg), unsaponifitable matter (2.9%),free fatty acid(1.5%),saponification number(195) ,iodine number(55),melting point(30 , and total lavibond colour(25) for mango seed kernel oil was determined. Result shows that mango seed kernel oil is more stable than many other vegetable oils rich in unsaturated fatty acids. Such oils seem to be suitable for blending with vegetable oils, stearin manufacturing, confectionery industry or/and in the soap industry.
A systematic review on cloud computing

Cloud computing is an ascending technology that has introduced a new paradigm by rendering a rational computational model possible. It has changed the dynamics of IT consumption by means of a model that provides on-demand services over the Internet. Unlike the traditional hosting service, cloud computing services are paid for per usage and may expand or shrink based on demand. Such services are, in general, fully managed by cloud providers that require users nothing but a personal computer and an Internet access. In recent years, this model has attracted the attention of researchers, investors and practitioners, many of whom have proposed a number of applications, structures and fundamentals of cloud computing, resulting in various definitions, requirements and models. Despite the interest and advances in the field, issues such as security and privacy, service layer agreement, resource sharing, and billing have opened up new questions about the real gains of the model. Although cloud computing is based on a 50-year-old business model, evidence from this study indicates that cloud computing still needs to expand and overcome present limitations that prevent the full use of its potential. In this study, we critically review the state of the art in cloud computing with the aim of identifying advances, gaps and new challenges.
Vascular, glial, and lymphatic immune gateways of the central nervous system

Immune privilege of the central nervous system (CNS) has been ascribed to the presence of a blood–brain barrier and the lack of lymphatic vessels within the CNS parenchyma. However, immune reactions occur within the CNS and it is clear that the CNS has a unique relationship with the immune system. Recent developments in high-resolution imaging techniques have prompted a reassessment of the relationships between the CNS and the immune system. This review will take these developments into account in describing our present understanding of the anatomical connections of the CNS fluid drainage pathways towards regional lymph nodes and our current concept of immune cell trafficking into the CNS during immunosurveillance and neuroinflammation. Cerebrospinal fluid (CSF) and interstitial fluid are the two major components that drain from the CNS to regional lymph nodes. CSF drains via lymphatic vessels and appears to carry antigen-presenting cells. Interstitial fluid from the CNS parenchyma, on the other hand, drains to lymph nodes via narrow and restricted basement membrane pathways within the walls of cerebral capillaries and arteries that do not allow traffic of antigen-presenting cells. Lymphocytes targeting the CNS enter by a two-step process entailing receptor-mediated crossing of vascular endothelium and enzyme-mediated penetration of the glia limitans that covers the CNS. The contribution of the pathways into and out of the CNS as initiators or contributors to neurological disorders, such as multiple sclerosis and Alzheimer’s disease, will be discussed. Furthermore, we propose a clear nomenclature allowing improved precision when describing the CNS-specific communication pathways with the immune system.
Non-Abelian statistics and topological quantum information processing in 1 D wire networks

Jason Alicea,1, 2 Yuval Oreg,3 Gil Refael,1 Felix von Oppen,4 and Matthew P. A. Fisher1 Department of Physics, California Institute of Technology, Pasadena, California 91125 Department of Physics and Astronomy, University of California, Irvine, California 92697 Department of Condensed Matter Physics, Weizmann Institute of Science, Rehovot, 76100, Israel Dahlem Center for Complex Quantum Systems and Fachbereich Physik, Freie Universität Berlin, 14195 Berlin, Germany (Dated: September 21, 2010)
Evaluation of tone mapping operators using a High Dynamic Range display

Tone mapping operators are designed to reproduce visibility and the overall impression of brightness, contrast and color of the real world onto limited dynamic range displays and printers. Although many tone mapping operators have been published in recent years, no thorough psychophysical experiments have yet been undertaken to compare such operators against the real scenes they are purporting to depict. In this paper, we present the results of a series of psychophysical experiments to validate six frequently used tone mapping operators against linearly mapped High Dynamic Range (HDR) scenes displayed on a novel HDR device. Individual operators address the tone mapping issue using a variety of approaches and the goals of these techniques are often quite different from one another. Therefore, the purpose of this investigation was not simply to determine which is the "best" algorithm, but more generally to propose an experimental methodology to validate such operators and to determine the participants' impressions of the images produced compared to what is visible on a high contrast ratio display.
An extensible approach for taming the challenges of JavaScript dead code elimination

JavaScript is becoming the de-facto programming language of the Web. Large-scale web applications (web apps) written in Javascript are commonplace nowadays, with big technology players (e.g., Google, Facebook) using it in their core flagship products. Today, it is common practice to reuse existing JavaScript code, usually in the form of third-party libraries and frameworks. If on one side this practice helps in speeding up development time, on the other side it comes with the risk of bringing dead code, i.e., JavaScript code which is never executed, but still downloaded from the network and parsed in the browser. This overhead can negatively impact the overall performance and energy consumption of the web app. In this paper we present Lacuna, an approach for JavaScript dead code elimination, where existing JavaScript analysis techniques are applied in combination. The proposed approach supports both static and dynamic analyses, it is extensible, and independent of the specificities of the used JavaScript analysis techniques. Lacuna can be applied to any JavaScript code base, without imposing any constraints to the developer, e.g., on her coding style or on the use of some specific JavaScript feature (e.g., modules). Lacuna has been evaluated on a suite of 29 publicly-available web apps, composed of 15,946 JavaScript functions, and built with different JavaScript frameworks (e.g., Angular, Vue.js, jQuery). Despite being a prototype, Lacuna obtained promising results in terms of analysis execution time and precision.
Beneficial metabolic effects of the Malaysian herb Labisia pumila var. alata in a rat model of polycystic ovary syndrome.

New options are needed to prevent and treat metabolic disorders associated with polycystic ovary syndrome (PCOS). Labisia pumila var. alata (LPva)-a Malaysian herb thought to have phytoestrogenic effects-has shown promise in reducing body weight gain in ovariectomized rats. In this study, we investigated the effect of LPva on body composition and metabolic features in female rats treated continuously with dihydrotestosterone, starting before puberty, to induce PCOS.



At 9 weeks of age, the PCOS rats were randomly subdivided into two groups; PCOS LPva and PCOS control. PCOS LPva rats received a daily oral dose of LPva (50mg/kg body weight), dissolved in 1 ml of deionised water, for 4-5 weeks. PCOS controls received 1 ml of deionised water on the same schedule.



LPva increased uterine weight (27%) and insulin sensitivity (36%) measured by euglycemic hyperinsulinemic clamp. Plasma resistin levels were increased and lipid profile was improved in LPva rats. In adipose tissue, LPva decreased leptin mRNA expression but did not affect expression of resistin and adiponectin. No effects on body composition, adipocyte size, or plasma leptin levels were observed.



LPva increases uterine weight, indicating estrogenic effects, and improves insulin sensitivity and lipid profile in PCOS rats without affecting body composition.
A two-stage deinterleaving technique for clustering of radar pulses

Pulse deinterleaving is the process of identifying which received pulses belong to which emitter sources. Pulse deinterleaving techniques generally consist of multi-parameter algorithms that utilize pulse parameters such as angle of arrival, frequency, pulse width and pulse amplitude and interval-only algorithms. In conventional multi-parameter deinterleaving pulse width and pulse amplitude parameters are generally assumed to be unreliable parameters due to distortions caused by the multipath phenomenon in the channel. In this study, a two-stage deinterleaving technique is proposed for such deinterleaving problem. In the first stage radar pulses are deinterleaved according to their frequency and angle of arrival parameters. In the second stage antenna scan rates are firstly estimated by the Levenberg-Marquardt optimization algorithm and then this information is fed back to the first stage to decrease clustering errors. Simulation results show that this goal is accomplished.
Signal Processing Techniques Restructure The Big Data Era

Big data science has been developed into a topic that attracts attention from industry, academia and governments. The main objective in Big Data science is to recognize and extract meaningful information from huge amounts of heterogeneous data and unstructured data (which constitute 95% of big data). Signal Processing (SP) techniques and related statistical learning (SL) tools such as Principal Component Analysis (PCA), R-PCA (Robust PCA), Compressive Sampling (CS), convex optimization (CO), stochastic approximation (SA), kernel based learning (KBL) tasks are used for robustness, compression and dimensionality reduction in Big Data arising challenges. This review paper introduces Big Data related SP techniques and presents applications of this emerging field.