Miracle-dz/UniFAR-FTUs huggingface dataset:
Association study of 37 genes related to serotonin and dopamine neurotransmission and neurotrophic factors in cocaine dependence

Cocaine dependence is a neuropsychiatric disorder in which both environmental and genetic factors are involved. Several processes, that include reward and neuroadaptations, mediate the transition from use to dependence. In this regard, dopamine and serotonin neurotransmission systems are clearly involved in reward and other cocaine‐related effects, whereas neurotrophic factors may be responsible for neuroadaptations associated with cocaine dependence. We examined the contribution to cocaine dependence of 37 genes related to the dopaminergic and serotoninergic systems, neurotrophic factors and their receptors through a case–control association study with 319 single nucleotide polymorphisms selected according to genetic coverage criteria in 432 cocaine‐dependent patients and 482 sex‐matched unrelated controls. Single marker analyses provided evidence for association of the serotonin receptor HTR2A with cocaine dependence [rs6561333; nominal P‐value adjusted for age = 1.9e−04, odds ratio = 1.72 (1.29–2.30)]. When patients were subdivided according to the presence or absence of psychotic symptoms, we confirmed the association between cocaine dependence and HTR2A in both subgroups of patients. Our data show additional evidence for the involvement of the serotoninergic system in the genetic susceptibility to cocaine dependence.
Omnigraph: Rich Representation and Graph Kernel Learning

OmniGraph, a novel representation to support a range of NLP classification tasks, integrates lexical items, syntactic dependencies and frame semantic parses into graphs. Feature engineering is folded into the learning through convolution graph kernel learning to explore different extents of the graph. A high-dimensional space of features includes individual nodes as well as complex subgraphs. In experiments on a text-forecasting problem that predicts stock price change from news for company mentions, OmniGraph beats several benchmarks based on bag-of-words, syntactic dependencies, and semantic trees. The highly expressive features OmniGraph discovers provide insights into the semantics across distinct market sectors. To demonstrate the method's generality, we also report its high performance results on a fine-grained sentiment corpus.
AIDA-Social: Entity Linking on the Social Stream

Named Entity Linking (NEL) in microblogs is a challenging task due to the use of cryptic abbreviations, insufficient contextual information, and the time-varying importance of entities. We propose three techniques to target these challenges: Mention Normalization, Contextual Enrichment, and Temporal Entity Importance. By combining these novel techniques, we achieve 13% improvement in precision over a state-of-the-art NEL tool.
Combining time‐varying and dynamic multi‐period optimal hedging models

This paper presents an effective way of combining two distinct approaches used in the hedging literature--dynamic programming (DP) and time-series (GARCH) econometrics. Theoretically consistent yet realistic and tractable models are developed for traders interested in hedging a portfolio. Results from a bootstrapping experiment used to construct confidence bands around the competing portfolios suggest that, whereas DP--GARCH outperforms the GARCH approach, they are statistically equivalent to the OLS approach when the markets are stable. Traders may achieve significant gains, however, by adopting the DP--GARCH model rather than the OLS approach when markets are volatile. Copyright 2002, Oxford University Press.
ScalableBody: A Telepresence Robot Supporting Socially Acceptable Interactions and Human Augmentation through Vertical Actuation

Most telepresence robots have a fixed-size body, and are unable to change the camera or display position. Therefore, although making eye contact is important in human expression, current fixed-size telepresence robots fail to achieve this. We propose a novel telepresence robot called ScalableBody, which enables users to make eye contact during conversations by changing its height. ScalableBody extends its body to modify the position of its camera or display. This approach provides eye contacts in remote conversations, thus creating almost same situation when the remote and local users make conversation like a real meeting. As for the remote users, this approach also enables them to experience having a conversation from different heights, such as being a giant or a dwarf. This technique extends the possibilities of remote communication by telepresence robots.
Cloudlet Softwarization for Pervasive Healthcare

Patient monitoring is emerging as an important application of embedded sensors network. Many wireless sensors are implanted in/on the patient body. These tiny wireless sensors make Wireless Body Area Networks (WBANs). WBANs can observe physiological conditions of patient under supervision, and can provide us real-time feedback. Through WBAN a patient is constantly monitored, and in case of some critical situation an immediate action should be required. In today's world most of the healthcare organizations need to modernize their IT infrastructure so that they can be able to provide safer, faster and more efficient healthcare delivery. This paper presents a new architecture based on cloudlet and software defined networking to smooth out devices and access networks heterogeneities and enable fast access for data collection and analysis through the cloud environments.
Multilingual Summarization with Polytope Model

The problem of extractive text summarization for a collection of documents is defined as the problem of selecting a small subset of sentences so that the contents and meaning of the original document set are preserved in the best possible way. In this paper we describe the linear programming-based global optimization model to rank and extract the most relevant sentences to a summary. We introduce three different objective functions being optimized. These functions define a relevance of a sentence that is being maximized, in different manners, such as: coverage of meaningful words of a document, coverage of its bigrams, or coverage of frequent sequences of words. We supply here an overview of our system’s participation in the MultiLing contest of SIGDial 2015.
A Dynamics Model of Rotor Blades for Real-time Helicopter Simulation

We present a dynamics model of rotor blades for real-time helicopter simulation. Collisions between the air flow and the moving blades make helicopters fly. In aerodynamics, or even in computer simulations, they precisely analyzed the collisions between the fluid(air) and the solid object(blades), and calculated the dierential equations from the collisions. Thus, it was hard for them to generate real-time helicopter motions due to massive computations for calculating the equations. In this paper, we start from a geometric model of rotor blades, which reflects the characteristics of real world blades due to the various factors from helicopter aerodynamics, although some factors should be simplified to show real-time behaviors. Based on this geometric model, we present a dynamics model for calculating the forces due to the rotor blades colliding with air flows. Our dynamics model interprets the collisions between the fluid and the solid objects as the action-reaction forces, as originally Newton did. Finally, we present the force equations suitable for the existing rigid-body simulation systems, instead of fluid-dynamics equations. We implement a prototype system for helicopter motions, and it shows sucient real-time processing behavior with ordinary PC’s.
Computing the Semantic Relatedness of Music Genre using Semantic Web Data

Computing the semantic relatedness between two entities has many applications domains. In this paper, we show a new way to compute the semantic relatedness between two resources using semantic web data. Moreover, we show how this measure can be used to compute the semantic relatedness between music genres which can be used for music recommendation systems. We first describe how to build a vector representations for resources in an ontology. Subsequently we show how these vector representations can be used to compute the semantic relatedness of two resources. Finally, as an application, we show that our measure can be used to compute the semantic relatedness of music genres. CCS Concepts •Information systems → Similarity measures; Language models;
Near-field interaction of twisted split-ring resonators

We present experimental, numerical and analytical results for the study of near-field interaction of twisted split-ring resonators, the basic elements of the so-called stereometamaterials. In contrast to previous results, we observe a crossing point in the dispersion curves where the symmetric and antisymmetric modes become degenerate. We introduce a model to describe the interplay between magnetic and electric near-field interactions and demonstrate how this model describes the crossing of the dispersion curves, initially considering lossless identical resonators. Finally, we apply the theory of Morse critical points to demonstrate the competition between losses and fabrication errors in determining whether or not symmetric and antisymmetric modes cross.
Don't Get Too Confident: Uncertainty in SDρ

Tett, Hundley, and Christiansen (2017) raise an important issue related to meta-analysis and our frequent overinterpretation of point estimates to the diminishment of variability of the estimate. We view this as analogous to the situation in which weather forecasters communicate the likely track of hurricanes. Such predictions involve point estimates of where the center of the storm is likely to be at some future time. These point estimates can be connected to identify the most likely path of the storm. In addition to these point estimates, however, forecasters caution that we should also attend to the “cone of uncertainty. ” That is, we should not focus exclusively on the point estimate to the exclusion of the errors of prediction.
Design and Analysis of Reversible Ripple, Prefix and Prefix-Ripple Hybrid Adders

Reversible computing has emerged as promising technology having its applications in quantum computing, nanotechnology and optical computing. This paper presents design and analysis of reversible ripple, prefix and prefix ripple hybrid adders. Firstly an analysis and comparison of all the existing reversible ripple carry adders is presented. The reversible ripple carry adders are characterized by high quantum depth, low quantum cost and/or low garbage outputs and ancilla inputs bits. Secondly design methodology for reversible prefix adders is presented. The reversible prefix adders are characterized by low quantum depth, high quantum cost and/or high garbage outputs and ancilla inputs bits. Finally design of the proposed reversible prefix-ripple hybrid adders is presented and comparison of the different parameters of reversible ripple, prefix and prefix-ripple hybrid adders is illustrated.
The Formation of Modern Muslim Subjectivities: Research Project and Analytical Strategy

This article introduces research questions, theoretical premises and analytical tools on which the Modern Muslim Subjectivities Project (MMSP) builds. The project takes its point of departure in sociological theories on the formation of modern forms of subjectivity and applies these to the Muslim world. The present article discusses three theories in particular: Theories on multiple modernities, theories on successive modernities and post-structuralist approaches to the formation of modern subjectivities. The hypothesis of the project – and thereby the hypothesis of the present article – is that the mentioned so-called “Western” theories can be used in our analyses and understanding of the modern Muslim world as well. The article presents this argument in three steps and illustrates the theoretical discussions with examples from the different subprojects carried out within the shared frame of the MMSP. Denne artikel praesenterer de forskningssporgsmal, teoretiske praemisser og analytiske redskaber, som The Modern Muslim Subjectivities Project (MMSP) bygger pa. Projektet tager udgangspunkt i sociologiske teorier om dannelsen af moderne former for subjektivitet og anvender dem i forhold til den muslimske verden. Naervaerende artikel diskuterer isaer tre teoretiske kilder: teorier om mangfoldige moderniteter (multiple modernities), teorier om fortlobende moderniteter (successive modernities) og poststrukturalistiske tilgange til dannelse af moderne subjektiviteter. Projektets og dermed ogsa artiklens hypotese er, at disse sakaldte “vestlige” teorier ogsa kan bruges i vores forstaelse af den moderne muslimske verden. Artiklen fremlaegger dette argument i tre trin og illustrerer de teoretiske diskussioner med eksempler fra de forskellige delprojekter, som bliver gennemfort inden for den faelles forskningsramme i MMSP.
Stochastic Differential Portfolio Games Based on Utility with Regime Switching Model

Stochastic dynamic investment games with regime switching model in continuous time between two investors are developed. The market coefficients, such as the bank interest rate, stocks appreciation rates, and volatility rates, are modulated by continuous-time Markov chain. There is a single payoff function which depends on both investors' wealth processes. One player chooses a dynamic portfolio strategy in order to maximize this expected payoff, while his opponent is simultaneously choosing a dynamic portfolio strategy so as to minimize the same quantity. Applying the stochastic control theory, the optimal control and value functions for the problems of utility-based games are derived. Especially for CRRA case, we get the explicitly optimal control and value of the games.
A Secure Framework for Monitoring Operating Systems Using SPEs in Cell/B.E.

Recently, even operating systems are often compromised by the attackers. Since a compromised operating system affects all the applications including security software on top of it, the integrity of the operating system should be guaranteed. However, it is difficult to monitor the operating system securely. In this paper, we propose SPE Observer, which is a framework for securely monitoring operating systems using SPEs in Cell/B.E. SPE Observer guarantees the integrity and confidentiality of monitoring systems by the isolation mode of SPEs. To complement the isolation mode, SPE Observer monitors the running status of monitoring systems from an external security proxy. In addition, it schedules monitoring systems to mitigate the performance degradation of applications due to occupying SPEs. We have implemented SPE Observer in PlayStation 3 and developed the integrity monitor of the operating system. According to our experiments, it was shown that the integrity monitor on an SPE could detect a compromised operating system and that the application performance was dramatically improved by scheduling the integrity monitor.
Parameter estimation in the stochastic Morris-Lecar neuronal model with particle filter methods

Parameter estimation in two-dimensional diffusion models with only one coordinate observed is highly relevant in many biological applications, but a statistically difficult problem. In neuroscience, the membrane potential evolution in single neurons can be measured at high frequency, but biophysical realistic models have to include the unobserved dynamics of ion channels. One such model is the stochastic Morris-Lecar model, where random fluctuations in conductance and synaptic input are specifically accounted for by the diffusion terms. It is defined through a non-linear two-dimensional stochastic differential equation with state dependent noise on the non-observed coordinate. The coordinates are coupled, i.e. the unobserved coordinate is non-autonomous, and we are therefore not in the more well-behaved situation of a hidden Markov model. In this paper, we propose a sequential Monte Carlo particle filter algorithm to impute the unobserved coordinate, and then estimate parameters maximizing a pseudo-likelihood through a stochastic version of the Expectation-Maximization algorithm. The performance is evaluated in a simulation study, and it turns out that even the rate scaling parameter governing the opening and closing of ion channels of the unobserved coordinate can be reasonably estimated. Also an experimental data set of intracellular recordings of the membrane potential of a spinal motoneuron of a red-eared turtle is analyzed.
Using Two-Dimensional Gabor Filters for Handwritten Digit Recognition

A feature extraction method for handwritten digit using Gabor filterbank is proposed. I designed a Gabor filterbank which uniformly covers the frequency domain with each filter captures the energy of a localized frequency. The obtained Gabor feature vectors are subjected to the task of classification and feature selection. A comparison against pixel features is presented. Mixing pixel and Gabor features for feature selection and classification is also evaluated.
Who Affects Government Declarations and Why? Contrasting the Left–Right Scale with the Welfare Dimension

A lively debate among students of parliamentary democracy concerns how coalition governments build their policy proposals. Some scholars maintain that government declarations mirror the position of the median party in parliament; others argue that these proposals better agree with the weighted mean of the coalition parties’ electoral promises. This article sheds light on this puzzle by investigating the role played by several political actors in shaping government declarations on two dimensions: the ideological left–right scale and a genuinely policy-based welfare scale. The results reveal that the agenda setters on the two dimensions do not coincide. On the left–right scale, the prime minister’s party plays a leading role. On the welfare scale, government declarations are affected by the party of the median legislator in parliament and by the parties of the labour and social affairs ministers. Furthermore, government declarations on the welfare dimension tend to drift rightwards with adverse economic conditions.
FEED-FORWARD/FEEDBACK CONTROL FOR ACTIVE VIBRATION SUPPRESSION OF FLEXIBLE SPACECRAFT DURING ATTITUDE MANEUVER

Abstract This paper presents an investigation into development of feed-forward and feedback control strategies for active vibration suppression and attitude control of flexible spacecrafts. The feed-forward loop consists of a computed-torque scheme and a command shaping technique based on component synthesis vibration suppression (CSVS) method which was developed based on linear systems theory. For relaxing the requirements of dynamic linearity in traditional input component command in CSVS method, a new approach is developed for designing the input component command by utilizing a structure in which the modal forces of flexible modes vibration are shaped according to a predefined trajectory, which improves the performance of CSVS method in vibration suppression for the nonlinear attitude dynamics. This proposed control strategy is practical as it does not require direct measurement of flexible appendage vibration, and also has a simple structure with low on-line computational load. Numerical simulations demonstrate the effectiveness of the control strategy.
2-D Exact Analytical Model for Surface-Mounted Permanent-Magnet Motors With Semi-Closed Slots

This paper presents an analytical subdomain model to compute the magnetic field distribution in surface-mounted permanent-magnet (PM) motors with semi-closed slots. The proposed model is sufficiently general to be used with any pole and slot combinations including fractional slot machines with distributed or concentrated windings. The model accurately accounts for armature reaction magnetic field and mutual influence between the slots. The analytical method is based on the resolution of two-dimensional Laplace's and Poisson's equations in polar coordinates (by the separation of variables technique) for each subdomain, i.e., magnet, air gap, slot-opening, and slots. Magnetic field distributions, back-EMF, and electromagnetic torque (including cogging torque) computed with the proposed analytical method are compared with those issued from finite-element analyses.
Adult Neural Precursor Cells from the Subventricular Zone Contribute Significantly to Oligodendrocyte Regeneration and Remyelination

Parenchymal oligodendrocyte progenitor cells (pOPCs) are considered the principal cell type responsible for oligodendrogenesis and remyelinaton in demyelinating diseases. Recent studies have demonstrated that neural precursor cells (NPCs) from the adult subventricular zone (SVZ) can also generate new oligodendrocytes after demyelination. However, the relative contribution of NPCs versus pOPCs to remyelination is unknown. We used in vivo genetic fate mapping to assess the behavior of each progenitor type within the corpus callosi (CCs) of mice subjected to cuprizone-induced demyelination. Nestin-CreERT2 and Pdgfra-CreERT2 transgenic mice were crossed with fluorescent Cre reporter strains to map the fate of NPCs and pOPCs respectively. In cuprizone-challenged mice, substantial numbers of NPCs migrated into the demyelinated CC and contributed to oligodendrogenesis. This capacity was most prominent in rostral regions adjacent to the SVZ where NPC-derived oligodendrocytes significantly outnumbered those generated from pOPCs. Sixty-two percent of all nodes of Ranvier in this region were flanked by at least one paranode generated from an NPC-derived oligodendrocyte. Remarkably, g-ratios (ratio of the axon diameter to the diameter of the axon plus myelin sheath) of myelinated axons in regions subject to significant NPC-derived remyelination were equivalent to those of unchallenged controls, and immunoelectron microscopy revealed that NPC-derived myelin was significantly thicker than that generated by pOPCs, regardless of axonal caliber. We also demonstrate that a reduced efficiency of remyelination in the caudal CC was associated with long-term impairment in the maturation of oligodendrogenic NPCs but only transient delay in pOPC differentiation. Collectively, our data define a major distinct role for NPCs in remyelination, identifying them as a key target for enhancing myelin repair in demyelinating diseases.
Differences in cost and benefit of prefetching in circuit-switched and packet-switched networks

Prefetching of data is candidate technology for migration of negative effects caused by low-bandwidth and high-delay connections, especially common in mobile wireless scenarios. An inevitable side effect of prefetching is increased overall transferred data volume. This side effect is investigated and set in relation to the achieved reduction in user waiting time. Simulations on request/response level have been performed to investigate the distinct influence of prefetching in circuit-switched and packet-switched networks on network costs. Simulation results are presented and discussed. Results show that while prefetching can achieve the desired reduction of waiting time for both circuit-switched and packet-switched networks, these gains are coupled with pronounced increase in network cost for packet-switched networks but with slight decrease in network costs for circuit-switched networks.