Miracle-dz/UniFAR-FTUs huggingface dataset:
Graphic User Interface for Molecular Dynamics Simulation of Thin Films

In this work, a software for simulating nanoindentation in thin films was implemented. For the software constructions, the Molecular Dynamics technique was used. The model applied for the graphic interface (software) is an approximation that reproduces the characteristic curves tendencies in test of materials hardness; on the other hand, the software was designed and built in a friendly environment, where the user can develop its own thin films, not only in monolayers, but also in multilayers. This interface allows to control the film thickness, the number of layers and other physical properties as the dissociation energy or the energy parameter in the sphere; furthermore, it is possible to control the steps of the dynamical time. For ensuring the proper working of the software, several simulations using the chromium parameters as the reference material were carried out, reproducing experimental tendencies.
Functional and Histological Effects of Chronic Neural Electrode Implantation

Permanent injury to the cranial nerves can often result in a substantial reduction in quality of life. Novel and innovative interventions can help restore form and function in nerve paralysis, with bioelectric interfaces among the more promising of these approaches. The foreign body response is an important consideration for any bioelectric device as it influences the function and effectiveness of the implant. The purpose of this review is to describe tissue and functional effects of chronic neural implantation among the different categories of neural implants and highlight advances in peripheral and cranial nerve stimulation.
The HVC Microcircuit: The Synaptic Basis for Interactions between Song Motor and Vocal Plasticity Pathways

Synaptic interactions between telencephalic neurons innervating descending motor or basal ganglia pathways are essential in the learning, planning, and execution of complex movements. Synaptic interactions within the songbird telencephalic nucleus HVC are implicated in motor and auditory activity associated with learned vocalizations. HVC contains projection neurons (PNs) (HVCRA) that innervate song premotor areas, other PNs (HVCX) that innervate a basal ganglia pathway necessary for vocal plasticity, and interneurons (HVCINT). During singing, HVCRA fire in temporally sparse bursts, possibly because of HVCINT-HVCRA interactions, and a corollary discharge can be detected in the basal ganglia pathway, likely because of synaptic transmission from HVCRA to HVCX cells. During song playback, local interactions, including inhibition onto HVCX cells, shape highly selective responses that distinguish HVC from its auditory afferents. To better understand the synaptic substrate for the motor and auditory properties of HVC, we made intracellular recordings from pairs of HVC neurons in adult male zebra finch brain slices and used spike-triggered averages to assess synaptic connectivity. A major synaptic interaction between the PNs was a disynaptic inhibition from HVCRA to HVCX, which could link song motor signals in the two outputs of HVC and account for some of the song playback-evoked inhibition in HVCX cells. Furthermore, single interneurons made divergent connections onto PNs of both types, and either PN type could form reciprocal connections with interneurons. In these two regards, the synaptic architecture of HVC resembles that described in some pattern-generating networks, underscoring features likely to be important to singing and song learning.
Energy-effective issue logic

The issue logic of a dynamically-scheduled superscalar processor is a complex mechanism devoted to start the execution of multiple instructions every cycle. Due to its complexity, it is responsible for a significant percentage of the energy consumed by a microprocessor. The energy consumption of the issue logic depends on several architectural parameters, the instruction issue queue size being one of the most important. In this paper we present a technique to reduce the energy consumption of the issue logic of a high-performance superscalar processor. The proposed technique is based on the observation that the conventional issue logic wastes a significant amount of energy for useless activity. In particular, the wake-up of empty entries and operands that are ready represents an important source of energy waste. Besides, we propose a mechanism to dynamically reduce the effective size of the instruction queue. We show that on average the effective instruction queue size can be reduced by a factor of 26% with minimal impact on performance. This reduction together with the energy saved for empty and ready entries result in about 90.7% reduction in the energy consumed by the wake-up logic, which represents 14.9% of the total energy of the assumed processor.
Pricing options on realized variance

Abstract.Models which hypothesize that returns are pure jump processes with independent increments have been shown to be capable of capturing the observed variation of market prices of vanilla stock options across strike and maturity. In this paper, these models are employed to derive in closed form the prices of derivatives written on future realized quadratic variation. Alternative work on pricing derivatives on quadratic variation has alternatively assumed that the underlying returns process is continuous over time. We compare the model values of derivatives on quadratic variation for the two types of models and find substantial differences.
LRP1 Assembles Unique Co-receptor Systems to Initiate Cell Signaling in Response to Tissue-type Plasminogen Activator and Myelin-associated Glycoprotein*

Background: LRP1 is a cell signaling receptor in neurons. Results: LRP1, NMDA receptor, and Trk compose a single system, activating cell signaling in response to tPA and α2-macroglobulin. MAG binding to LRP1 recruits p75NTR but not Trk. Conclusion: Ligand-specific co-receptor recruitment explains how LRP1 activates distinct cell signaling pathways in response to different ligands. Significance: Distinct co-receptor assemblies allow LRP1 to regulate the cellular response to its microenvironment. In addition to functioning as an activator of fibrinolysis, tissue-type plasminogen activator (tPA) interacts with neurons and regulates multiple aspects of neuronal cell physiology. In this study, we examined the mechanism by which tPA initiates cell signaling in PC12 and N2a neuron-like cells. We demonstrate that enzymatically active and inactive tPA (EI-tPA) activate ERK1/2 in a biphasic manner. Rapid ERK1/2 activation is dependent on LDL receptor-related protein-1 (LRP1). In the second phase, ERK1/2 is activated by tPA independently of LRP1. The length of the LRP1-dependent phase varied inversely with the tPA concentration. Rapid ERK1/2 activation in response to EI-tPA and activated α2-macroglobulin (α2M*) required the NMDA receptor and Trk receptors, which assemble with LRP1 into a single pathway. Assembly of this signaling system may have been facilitated by the bifunctional adapter protein, PSD-95, which associated with LRP1 selectively in cells treated with EI-tPA or α2M*. Myelin-associated glycoprotein binds to LRP1 with high affinity but failed to induce phosphorylation of TrkA or ERK1/2. Instead, myelin-associated glycoprotein recruited p75 neurotrophin receptor (p75NTR) into a complex with LRP1 and activated RhoA. p75NTR was not recruited by other LRP1 ligands, including EI-tPA and α2M*. Lactoferrin functioned as an LRP1 signaling antagonist, inhibiting Trk receptor phosphorylation and ERK1/2 activation in response to EI-tPA. These results demonstrate that LRP1-initiated cell signaling is ligand-dependent. Proteins that activate cell signaling by binding to LRP1 assemble different co-receptor systems. Ligand-specific co-receptor recruitment provides a mechanism by which one receptor, LRP1, may trigger different signaling responses.
Characteristics Surface Temperature of Solar Cell Polycrystalline Type to Output Power

To meet the needs of electrical energy, there are alternative energy sources such as solar power in a form of solar power plant. An important equipment aim to handle the of converting of solar energy into electrical energy are solar cells. The development of devices used to modify solar energy into electrical energy has been done since the mid-first half of the last century. Gradually the device is named by scientists with a photovoltaic device, or so-called solar cells (solar cell. This research tested polycrystalline solar module in sunny weather, bright cloudy and overcast. The test results show the effect of solar cell surface temperature to the value of its output power [1]. The condition of the polycrystalline solar panels will work optimally at the measured 32° C - 50° C temperature range on the surface of the solar cell.
Learning and Predicting the Evolution of Social Networks

With the increasing availability of large social network data, there is also an increasing interest in analyzing how those networks evolve over time. Traditionally, the analysis of social networks has focused only on a single snapshot of a network. Researchers have already verified that social networks follow power-law degree distribution, have a small diameter, and exhibit small-world structure and community structure. Attempts to explain the properties of social networks have led to dynamic models inspired by the preferential attachment models which assumes that new network nodes have a higher probability of forming links with high-degree nodes, creating a rich-get-richer effect. Although some effort has been devoted to analyzing global properties of social network evolution, not much has been done to study graph evolution at a microscopic level. A first step in this direction investigated a variety of network formation strategies, showing that edge locality plays a critical role in network evolution. We propose a different approach. Following the paradigm of association rules and frequent-pattern mining, our work searches for typical patterns of structural changes in dynamic networks. Mining for such local patterns is a computationally challenging task that can provide further insight into the increasing amount of evolving network data. Beyond the notion of graph evolution rules (GERs), a concept that we introduced in an earlier work, we developed the Graph Evolution Rule Miner (GERM) software to extract such rules and applied these rules to predict future network evolution.
Incorporation of ciprofloxacin/laponite in polycaprolactone electrospun nanofibers: drug release and antibacterial studies

Electrospun nanofibers with sustained drug release are a challenge but it can be improved by using hydrophobic polymer. Polycaprolactone (PCL) is a hydrophobic and biocompatible polymer. In this work, we have proposed a drug release mechanism by preparation of ciprofloxacin (Cip)/Laponite (LAP) complex and then incorporation in PCL nanofibers through electrospinning technique. In addition, drug incorporation was confirmed by FTIR and morphology of electrospun nanofibers was revealed by SEM. Drug loading was measured by using spectrophotometer. PCL/LAP/Cip NFs proved sustained drug release as compared to PCL NFs and PCL/Cip NFs. Furthermore, PCL/LAP/Cip NFs were used as antimicrobial agent and higher effect measured.
Connecting Abstract Description Systems

Combining knowledge representation and reasoning formalisms like description logics (DLs), temporal logics, and logics of space, is worthwhile but difficult. It is worthwhile because usually realistic application domains comprise various aspects of the world, thus requiring suitable combinations of formalisms modeling each of these aspects. It is difficult because the computational behavior of the resulting hybrids is often much worth than the behavior of its components. In this paper we propose a combination method which is robust in the computational sense and still allows for certain interactions between the combined systems. The combination method, called E-connection, will be defined and investigated for so-called abstract description systems (ADS) which include all standard description logics, various logics of time and space, modal logics, and epistemic logics. The main theoretical result is that every E-connection of any finite number of decidable ADSs is decidable as well. Four instances of E-connections of ADSs will be discussed: (1) the Econnection of DLs with the logic MS intended for quantitative reasoning about space, (2) the E-connection of DLs with the logic S4u (containing RCC-8) that can be used for qualitative reasoning about space, (3) the E-connection of two DLs (ALCO and SHIQ), and (4) the E-connection of DLs with propositional temporal logic PTL and S4u. Computer Science Institute, Intelligent Systems Department, University of Leipzig, Augustus-Platz 10–11, 04109 Leipzig, Germany. Email: {kutz,wolter}@informatik.uni-leipzig.de Department of Computer Science, King’s College London, Strand, London WC2R 2LS, U.K. Email: mz@dcs.kcl. ac.uk
Functional MRI: Genesis, State of the art and the Sequel

The last 25 years have seen functional magnetic resonance imaging (fMRI) grow from an interesting experimental imaging technique in the hands of some to a primary investigation of choice in the localization and lateralization of brain function prior to surgery. Developments in the field of computational neurosciences have transformed fMRI analysis from classical subtractive type analysis to dynamic casual modeling, and now to graph theory analysis. This has widened the scope of fMRI, and is therefore finding applications in understanding neural correlates of diseases like autism and Alzheimer’s disease,[1,2] prognostication of diseases like traumatic brain injury,[3] and has the potential to direct therapy. [4,5] It is unfortunately true that this widened ambit has not received the clinical attention it deserves, probably because fMRI is susceptible to artifacts from skull base and blood products and has reduced sensitivity in patients with vascular malformations, or because a change in medical practice usually lags behind the technological and scientific developments that make it possible. This review focuses on the developmental chronology of fMRI image analysis in the last 25 years with highlights on major milestones like developments in the field of paradigms, analysis methods, resting state fMRI, and functional connectivity. To make the statistical images of brain at work more colorful, the article starts with genesis of fMRI and ends with the hope of a promising bright future. Many inputs for this article are obtained from a series of 103 review articles edited by Bandettini et al.,[6] compiling personal experiences of pioneers in this field. Interested readers are encouraged to refer to these for a more complete overview.
Energy-Efficient Joint Sensing Duration, Detection Threshold, and Power Allocation Optimization in Cognitive OFDM Systems

This paper investigates an energy efficiency optimization problem in cognitive orthogonal frequency division multiplexing systems. The goal is to maximize the energy efficiency by adapting the sensing duration, detection threshold, and transmit power to the constraints of the energy consumption of the secondary network and the interference to the primary network in a statistical manner. First, the case of identical detection threshold for all subcarriers is considered. In order to circumvent the intractability of the resulting problem, an alternate iteration framework is proposed to iteratively solve the three decoupled subproblems: sensing duration optimization, detection threshold optimization, and power allocation optimization. By exploiting the characteristics of each subproblem, the proposed framework is proved to be convergent. Then, the case with individual detection threshold for each subcarrier is explored. By proving that the optimal detection threshold is the root of a quadratic equation with one unknown variable, the proposed framework can be applied with minor modification. Simulation results show that the proposed alternating optimization framework can approach rapidly to the optimal solution, with less than 1% gap. Compared with the existing schemes, both the cases with identical and individual detection thresholds can achieve a considerable energy efficiency gain, with the latter further outperforming the former.
CCN2 is required for bleomycin-induced skin fibrosis in mice.

OBJECTIVE No therapy for fibrotic disease is available. The proadhesive matricellular protein connective tissue growth factor CCN2 is a marker of fibrotic cells; however, the specific role of CCN2 in connective tissue biology in general and in fibrogenesis in particular is unclear. The aim of this study was to assess whether adult mice bearing a smooth muscle cell/fibroblast-specific deletion of CCN2 are resistant to bleomycin-induced skin scleroderma. METHODS Cutaneous fibrosis was induced in mice by subcutaneous injection of bleomycin. Untreated control groups were injected with phosphate buffered saline. Mice bearing a fibroblast/smooth muscle cell-specific deletion of CCN2 were investigated for changes in dermal thickness, collagen content, and the number of α-smooth muscle actin (α-SMA)-positive cells. Dermal fibroblasts were isolated to assess whether the induction of collagen and α-SMA messenger RNA in response to transforming growth factor β (TGFβ) was impaired. RESULTS The loss of CCN2 resulted in resistance to bleomycin-induced skin fibrosis. In response to bleomycin, wild-type mice possessed, but CCN2-deficient mice lacked, abundant α-SMA-expressing myofibroblasts within fibrotic lesions. Fibroblast responses to TGFβ, a potent inducer of myofibroblast differentiation, were not affected. Collectively, these results indicate that CCN2 is essential for bleomycin-induced skin fibrosis, likely due to a defect in myofibroblast recruitment. CONCLUSION These data indicate that therapeutic strategies that involve blocking CCN2 in vivo may be of benefit in combating fibrotic skin disease.
Nanoscale simulation of crystal defects with application to mantle minerals

Minor and trace elements can influence the chemical and physical properties of the Earth's mantle, whether by forming separate minerals that can host trace elements at far higher concentrations than major mantle minerals, or by influencing physical properties of major minerals, for instance by enhancing diffusion or pinning topological defects. The present study uses atomistic computational modeling to investigate the properties of calcium phosphate minerals at mantle pressures, and to determine if cation vacancies can lubricate dislocation glide in mantle minerals. With their large, high-coordination cation sites, calcium phosphate minerals can potentially dissolve large quantities of incompatible elements, and may be an important mantle reservoirs for these elements. Quantum mechanical calculations are used to determine the variation of structure and properties of apatite, a common low-pressure phosphate, with the identity of its channel anion. The crystal structure and compressibility are both found to vary substantially with channel anion identity. Tuite, which forms by the breakdown of apatite at high pressure, may extend the phosphorous cycle beyond the stability field of apatite and into the lower mantle. DFT calculations are used to show that the pressure at which apatite decomposes to form tuite is sensitive to the identity of the channel anion. The calculated bulk moduli of the calciumhosting sites in tuite are considerably lower than those of the M site in CaSiO3 perovskite (cpv). Atomistic calculations show that strontium and barium impurities partition strongly from cpv into tuite. The second part of this thesis considers the role that dislocations play in hosting vacancyrelated defects, and influence that these segregated defects may have on the rheology of
Analysis of privacy in online social networks of runet

In recent years, social networking sites (SNSs) gained high popularity among Internet users as they combine the best of both worlds: befriending people outside real life situations and staying in touch with people already known. An important aspect of any SNS are user profiles, which allow users to virtually publish anything about themselves, including highly personal or sensitive information. With the inception of SNSs, the problem of personal information disclosure and privacy implications has turned into a serious issue. While privacy issues in SNSs have been extensively analyzed in the past five years showcasing flagships of "western" SNSs like Facebook and MySpace, SNSs that target mainly Russian speaking audiences are not yet analyzed and demand investigation. The goals of this paper are twofold: (1) to raise the awareness of the public to the problems of information revelation by studying the amount and type of information disclosed by users of Runet (Russian Segment of the Internet) SNSs (2) to compare our findings to the results of previous studies in the context of "western" SNSs. We investigate different aspects of information revelation of more than 30 million user profiles collected from five Runet SNS considered in this paper. In addition, we conducted a survey among a Russian speaking population to assess both the level of awareness of the privacy issues and the level of trust, and compared the results to previous studies. While the results indicate that Runet users tend to disclose less information and are more concerned about privacy implications, there is still a substantial gap between western and Runet SNS providers in understanding of privacy implications and implementation of security measures, which leads to exposure of extensive amounts of personal information.
Towards Temporal Scoping of Relational Facts based on Wikipedia Data

Most previous work in information extraction from text has focused on named-entity recognition, entity linking, and relation extraction. Less attention has been paid given to extracting the temporal scope for relations between named entities; for example, the relation president-Of(John F. Kennedy, USA) is true only in the time-frame (January 20, 1961 - November 22, 1963). In this paper we present a system for temporal scoping of relational facts, which is trained on distant supervision based on the largest semi-structured resource available: Wikipedia. The system employs language models consisting of patterns automatically bootstrapped from Wikipedia sentences that contain the main entity of a page and slot-fillers extracted from the corresponding infoboxes. This proposed system achieves state-of-the-art results on 6 out of 7 relations on the benchmark Text Analysis Conference 2013 dataset for temporal slot filling (TSF), and outperforms the next best system in the TAC 2013 evaluation by more than 10 points.
Literature review.

Reinforcement learning is an attractive method of machine learning. However, as the state space of a given problem increases, reinforcement learning becomes increasingly inefficient. Hierarchical reinforcement learning is one method of increasing the efficiency of reinforcement learning. It involves breaking the overall goal of a problem into a hierarchy subgoals, and then attempting to achieve each subgoal separately. This paper discusses various approaches at hierarchical reinforcement learning, and of automatic subgoal discovery by agents.