Miracle-dz/UniFAR-FTUs huggingface dataset:
Changes in Cortical Plasticity in Relation to a History of Concussion during Adolescence

Adolescence and early adulthood is a critical period for neurophysiological development potentially characterized by an increased susceptibility to the long-term effects of traumatic brain injury. The current study investigated differences in motor cortical physiology and neuroplastic potential across a cohort of young adults with adolescent concussion history and those without. Transcranial magnetic stimulation (TMS) was used to assess motor evoked potential (MEP) amplitude, short-interval cortical inhibition (SICI) and intracortical facilitation (ICF) before and after intermittent theta burst stimulation (iTBS). Pre-iTBS, MEP amplitude, but not SICI or ICF, was greater in the concussion history group. Post-iTBS, the expected increase in MEP amplitude and ICF was tempered in the concussion history group. Change in SICI was variable within the concussion history group. Post hoc assessment revealed that SICI was significantly lower in individuals whose concussion was not diagnosed at the time of injury compared to both those without a concussion history or whose concussion was medically diagnosed. Concussive impacts during adolescence appear to result in a persistent reduction of the ability to modulate facilitatory motor networks. Failure to report/identify concussive impacts close to injury during adolescence also appears to produce persistent change in inhibitory networks. These findings highlight the potential long-term impact of adolescent concussion upon motor cortical physiology.
Eligibility-based Round Robin for fair and efficient packet scheduling in wormhole switching networks

Interconnection networks of parallel systems are used for servicing traffic generated by different applications, often belonging to different users. When multiple users contend for channel bandwidth, fairness in bandwidth sharing becomes a key requirement. In fact, enforcing a fair sharing of channel bandwidth improves flow isolation, thus preventing misbehaving flows from affecting the performance of other flows. We present a novel packet scheduling algorithm, called eligibility-based round robin (EBRR), devised to provide fair queueing in interconnection networks. In fact, EBRR meets the constraints imposed by wormhole switching, which is the most popular switching technique in interconnection networks of parallel systems. It can also be applied to packet switching wide area networks (WANs), such as IP and ATM. We show that EBRR has O(1) complexity and better delay and fairness properties than existing algorithms of comparable complexity. Here, we also investigate the means for assessing the fairness of a scheduler: we show that using the relative fairness bound as a fairness measure may lead to erroneous results. We then propose an alternative measure, called the generalized relative fairness bound, that allows fairness to be assessed more precisely.
Real-Time Virtual Audio Reality

A real-time virtual audio reality model has been cr eated. The system includes model-based sound synthesizers, geometrical room acoustics modeling, binaural auralization for headphone or loudspeaker listening, and hiqh-quality animation. This paper discusses the following subsystems of the designed environment: The implementation of the aud io processing softand hardware, and the design of a dedicated multiprocessor DSP hardware pla tform. The design goal of the overall project has been to create a virtual musical event that is uthentic both in terms of audio and visual quality . Novelties of this system include a real-time imagesource algorithm for rooms of arbitrary shape, shorter HRTF filter approximations for more efficie nt auralization, and a network-based distributed implementation of the audio processing softand hardware.
The Magnitudes of Economic and Non-Economic Factors on the Demand for U.S. Domestic Air Travel

The Transportation Research Forum, founded in 1958, is an independent, nonprofit organization of transportation professionals who conduct, use, and benefit from research. Its purpose is to provide an impartial meeting ground for carriers, shippers, government officials, consultants, university researchers, suppliers, and others seeking exchange of information and ideas related to both passenger and freight transportation. More information on the Transportation Research Forum can be found on the Web at www.trforum. org.
Pleasure-arousal-dominance driven facial expression simulation

Expressing and recognizing affective states with respect to facial expressions is an important aspect in perceiving virtual humans as more natural and believable. Based on the results of an empirical study a system for simulating emotional facial expressions for a virtual human has been evolved. This system consists of two parts: (1) a control architecture for simulating emotional facial expressions with respect to Pleasure, Arousal, and Dominance (PAD) values, (2) an expressive output component for animating the virtual human's facial muscle actions called Action Units (AUs), modeled following the Facial Action Coding System (FACS). A large face repertoire of about 6000 faces arranged in PAD-space with respect to two dominance values (dominant vs. submissive) is obtained as a result of the empirical study. Using the face repertoire an approach towards realizing facial mimicry for a virtual human based on backward mapping AUs displaying an emotional facial expression on PAD-values is outlined. A preliminary evaluation of this first approach is realized with AUs corresponding to the basic emotions Happy and Angry.
Data Uncertainty in Markov Chains: Application to Cost-Effectiveness Analyses of Medical Innovations

Cost-effectiveness studies of medical innovations often suffer from data inadequacy. When Markov chains are used as a modeling framework for such studies, this data inadequacy can manifest itself as imprecision in the elements of the transition matrix. In this paper, we study how to compute maximal and minimal values for the discounted value of the chain with respect to a vector of state-wise costs or rewards as these uncertain transition parameters jointly vary within a given uncertainty set. We show that these problems are computationally tractable if the uncertainty set has a row-wise structure. Conversely, we prove that the row-wise structure is necessary for tractability. Without it, the problems become computationally intractable strongly NP-hard. We apply our model to assess the cost-effectiveness of fecal immunochemical testing FIT, a new screening method for colorectal cancer. Our results show that despite the large uncertainty in FIT's performance, it could be cost-effective relative to the prevailing screening method of colonoscopy. The online appendix is available at https://doi.org/10.1287/opre.2017. 1685 .
An analysis on Dual Diagnosis Revealed a Possible deviation of Schizophrenia Polygenic Risk Score by Mexican Amerindian Ancestry

In order to summarized the polygenic background of psychiatric diseases, polygenic risk scores (PRS) have been developed. Recently, PRS have been use to predict patients with higher comorbidities in psychiatric diseases, like dual diagnosis. PRS are principally derived in analysis of Caucasian and Asian populations, we are not aware of how this PRS could be applied in populations with high admixture. In order to explored this, the present work has the aim to analyzed if previous calculated PRS for psychiatric diseases could predict dual diagnosis in Mexican population, and also, if PRS calculation could be influenced by Mexican Amerindian (MA) global ancestry. We performed PRS calculation, using PRSice, with summary genome-wide association statistics previously published for psychiatric diseases, and also, performed Nagelkerke correlation test in order to established if PRS are correlated with dual diagnosis. We found that dual diagnosis could be predicted by major depressive disorder polygenic risk score. Nevertheless, schizophrenia polygenic risk score is highly correlated with global MA ancestry, independently of the schizophrenia diagnosis. Our results reinforced the notion that PRS calculation could be deviated by the MA global ancestry, nevertheless analysis on larger sample sizes are required in order to clarified this issue.
An Empirical Study of Mobile Ad Targeting

Advertising, long the financial mainstay of the web ecosystem, has become nearly ubiquitous in the world of mobile apps. While ad targeting on the web is fairly well understood, mobile ad targeting is much less studied. In this paper, we use empirical methods to collect a database of over 225,000 ads on 32 simulated devices hosting one of three distinct user profiles. We then analyze how the ads are targeted by correlating ads to potential targeting profiles using Bayes' rule and Pearson's chi squared test. This enables us to measure the prevalence of different forms of targeting. We find that nearly all ads show the effects of application- and time-based targeting, while we are able to identify location-based targeting in 43% of the ads and user-based targeting in 39%.
MULTI-DATA APPROACH ( MDA ) FOR ENHANCED LAND USE / LAND COVER MAPPING

For spatial decision support and regional (agro-)ecosystem modeling, land use data are of central importance and have to be available in a spatial data infrastructure for regional modeling. Usually, land use data are available, but they lack the desired information detail for many purposes. For example, in official land use maps, agricultural land use is generally differentiated between arable land, grassland, orchards and some special land use classes like paddy fields. For detailed agro-ecosystem modeling, this information resolution is rather poor. Here, disaggregated land use data which provide information about the major crops and crop rotations as well as management data like date of sowing, fertilization, irrigation, harvest etc. are needed. The analysis of multispectral, hyperspectral and/or radar data from satellite or airborne sensors is a standard method to retrieve such kind of information with remote sensing methodologies. By using a Multi-Data Approach (MDA), the retrieved information from multitemporal and multisensoral remote sensing analysis can be integrated into official land use data to enhance both the information level (e. g. crop rotations) of existing land use data and the quality of the land use classification. The workflow of the MDA to generate enhanced land use and land cover data consists basically of two components: (i) the methods and data of the remote sensing analysis and (ii) the methods and data of the GIS analysis.
Carbon Balance and Management

Background: The amount of carbon dioxide in the atmosphere steadily increases as a consequence of anthropogenic emissions but with large interannual variability caused by the terrestrial biosphere. These variations in the CO2 growth rate are caused by large-scale climate anomalies but the relative contributions of vegetation growth and soil decomposition is uncertain. We use a biogeochemical model of the terrestrial biosphere to differentiate the effects of temperature and precipitation on net primary production (NPP) and heterotrophic respiration (Rh) during the two largest anomalies in atmospheric CO2 increase during the last 25 years. One of these, the smallest atmospheric year-to-year increase (largest land carbon uptake) in that period, was caused by global cooling in 1992/93 after the Pinatubo volcanic eruption. The other, the largest atmospheric increase on record (largest land carbon release), was caused by the strong El Niño event of 1997/98. Results: We find that the LPJ model correctly simulates the magnitude of terrestrial modulation of atmospheric carbon anomalies for these two extreme disturbances. The response of soil respiration to changes in temperature and precipitation explains most of the modelled anomalous CO2 flux. Conclusion: Observed and modelled NEE anomalies are in good agreement, therefore we suggest that the temporal variability of heterotrophic respiration produced by our model is reasonably realistic. We therefore conclude that during the last 25 years the two largest disturbances of the global carbon cycle were strongly controlled by soil processes rather then the response of vegetation to these large-scale climatic events. Background Anthropogenic emissions continuously add about 7000– 8000 million metric tons of carbon to the atmosphere per annum [1]. Atmospheric carbon dioxide measurements show that the rate of increase of atmospheric CO2 varies substantially from year to year [2]. It is widely accepted that these variations are caused by the terrestrial biosphere through the processes of carbon uptake during photosynthesis and carbon release during soil respiration [3]. Additionally, strong disturbances such as large-scale fires can significantly alter the exchange of carbon between terrestrial ecosystems and the atmosphere. For example, up to 65 % of the observed CO2 growth rate in 1998 was attributed to burnt biomass in tropical and boreal regions [4]. Published: 27 July 2006 Carbon Balance and Management 2006, 1:7 doi:10.1186/1750-0680-1-7 Received: 11 July 2006 Accepted: 27 July 2006 This article is available from: http://www.cbmjournal. com/content/1/1/7 © 2006 Erbrecht and Lucht; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Construction of correlation functions in two and three dimensions

This article focuses on the construction, directly in physical space, of simply parametrized covariance functions for data‐assimilation applications. A self‐contained, rigorous mathematical summary of relevant topics from correlation theory is provided as a foundation for this construction. Covariance and correlation functions are defined, and common notions of homogeneity and isotropy are clarified. Classical results are stated, and proven where instructive. Included are smoothness properties relevant to multivariate statistical‐analysis algorithms where wind/wind and wind/mass correlation models are obtained by differentiating the correlation model of a mass variable. the Convolution Theorem is introduced as the primary tool used to construct classes of covariance and cross‐covariance functions on three‐dimensional Euclidean space R3. Among these are classes of compactly supported functions that restrict to covariance and cross‐covariance functions on the unit sphere S2, and that vanish identically on subsets of positive measure on S2. It is shown that these covariance and cross‐covariance functions on S2, referred to as being space‐limited, cannot be obtained using truncated spectral expansions. Compactly supported and space‐limited covariance functions determine sparse covariance matrices when evaluated on a grid, thereby easing computational burdens in atmospheric data‐analysis algorithms.
Mitochondrial Encephalomyopathy in Drosophila

Mitochondrial encephalomyopathies are common and devastating multisystem genetic disorders characterized by neuromuscular dysfunction and tissue degeneration. Point mutations in the human mitochondrial ATP6 gene are known to cause several related mitochondrial disorders: NARP (neuropathy, ataxia, and retinitis pigmentosa), MILS (maternally inherited Leigh's syndrome), and FBSN (familial bilateral striatal necrosis). We identified a pathogenic mutation in the Drosophila mitochondrial ATP6 gene that causes progressive, adult-onset neuromuscular dysfunction and myodegeneration. Our results demonstrate ultrastructural defects in the mitochondrial innermembrane, neural dysfunction, and a marked reduction in mitochondrial ATP synthase activity associated with this mutation. This Drosophila mutant recapitulates key features of the human neuromuscular disorders enabling detailed in vivo studies of these enigmatic diseases.
An algorithm for learning real-time automata

We describe an algorithm for learning simple timed automata, known as real-time automata. The transitions of real-time automata can have a temporal constraint on the time of occurrence of the current symbol relative to the previous symbol. The learning algorithm is similar to the redblue fringe state-merging algorithm for the problem of learning deterministic finite automata. In addition to state merges, our algorithm can perform state splits by making use of the time values in the input data. We tested our learning algorithm on randomly generated problems. The results are promising and show that learning a real-time automaton directly from timed data outperforms a method that uses sampling in order to deal with the timed data.
Three-Dimensional Imaging and Scattering Mechanism Estimation Over Urban Scenes Using Dual-Baseline Polarimetric InSAR Observations at L-Band

This paper introduces new polarimetric algorithms for generating 3-D images and estimating scattering mechanisms from polarimetric multibaseline (MB) interferometric synthetic aperture radar (SAR) measurements. First, an MB interferometric SAR signal model is generalized to the fully polarimetric configuration, establishing the notion of polarimetric reflectivity. Subsequently, polarimetric beamforming, Capon, and MUSIC methods that determine optimal polarization combinations for height estimation are developed. These new techniques allow for extracting the height of reflectors, the associated scattering mechanisms, and the polarimetric (pseudo)reflectivities. By means of polarimetric dual-baseline interferometric SAR observations of an urban environment, the performance of the conceived algorithms is examined in detail. Producing 3-D images of a building layover, the quality of the approaches is compared in terms of refined resolution and lowered side lobes. Furthermore, the scattering processes occurring in urban scenes are investigated thoroughly by analyzing the optimal reflection types. The algorithms are validated using dual-baseline polarimetric SAR interferometric data at L-band acquired by German Aerospace Center's experimental SAR system over Dresden city.
From Motive Dispositions to States to Outcomes: An Intensive Experience Sampling Study on Communal Motivational Dynamics in Couples

Embedded in a theoretically founded process model (termed Dynamics of Motive Satisfaction, ‘DynaMoS’), the present study examined the links between the implicit dispositional communion motive, everyday motivational dynamics, and relationship outcomes in couples. Within–subject processes are proposed to explain between–subject associations of dispositional motives and relationship satisfaction. For an empirical test of the model, data on the dispositional partner–related need for communion and global relationship satisfaction were obtained from 152 individuals in heterosexual relationships. In an extensive experience sampling spanning 2 weeks, a subsample of 130 individuals answered questions about their current motivational states, mood, state relationship satisfaction, and experiences with their partner five times a day. The results were largely consistent with the DynaMoS model: (1) individuals with a strong dispositional implicit communion motive reported more often to be in a communal motivational state; (2) communally motivated individuals were more likely to engage in subsequent instrumental behaviour; and (3) relationship experiences that potentially satisfy communion motivation led to more positive relationship outcomes when individuals were motivated before compared with when they were not. It is discussed how these results and the experience sampling method can foster our understanding of how dispositional characteristics translate into everyday processes and shape relationship outcomes. Copyright © 2018 European Association of Personality Psychology
Transcranial Direct Current Stimulation during Sleep Improves Declarative Memory

In humans, weak transcranial direct current stimulation (tDCS) modulates excitability in the motor, visual, and prefrontal cortex. Periods rich in slow-wave sleep (SWS) not only facilitate the consolidation of declarative memories, but in humans, SWS is also accompanied by a pronounced endogenous transcortical DC potential shift of negative polarity over frontocortical areas. To experimentally induce widespread extracellular negative DC potentials, we applied anodal tDCS (0.26 mA/cm2) repeatedly (over 30 min) bilaterally at frontocortical electrode sites during a retention period rich in SWS. Retention of declarative memories (word pairs) and also nondeclarative memories (mirror tracing skills) learned previously was tested after this period and compared with retention performance after placebo stimulation as well as after retention intervals of wakefulness. Compared with placebo stimulation, anodal tDCS during SWS-rich sleep distinctly increased the retention of word pairs (p < 0.005). When applied during the wake retention interval, tDCS did not affect declarative memory. Procedural memory was also not affected by tDCS. Mood was improved both after tDCS during sleep and during wake intervals. tDCS increased sleep depth toward the end of the stimulation period, whereas the average power in the faster frequency bands (θ,α, andβ) was reduced. Acutely, anodal tDCS increased slow oscillatory activity <3 Hz. We conclude that effects of tDCS involve enhanced generation of slow oscillatory EEG activity considered to facilitate processes of neuronal plasticity. Shifts in extracellular ionic concentration in frontocortical tissue (expressed as negative DC potentials during SWS) may facilitate sleep-dependent consolidation of declarative memories.
Efficient classification of Parkinson's disease using extreme learning machine and hybrid particle swarm optimization

One of the most well-known problems in machine learning framework is classification of Parkinson's Disease (PD) to patient people and healthy people. Due to the importance of that problem, utilization of a novel learning method is necessary. For this purpose, this paper proposes the utilization of Extreme Learning Machine (ELM) as a type of feed-forward neural network with a single hidden layer to classify the PD patients. However ELM is known as the one of the fast and accurate learning methods, selection of relevant feature elements of PD dataset can be effective on improving the classification performance of ELM. To this end, this paper proposes Hybrid Particle Swarm Optimization (PSO) as the second innovation to efficiently select the relevant feature elements. The main advantage of Hybrid PSO is locally improving of particles in order to jump over the local optimum solution and quickly converging to the global optimal solution. Evaluation of the proposed method on PD dataset proves the superiority of the propose method on the problem of PD classification, in comparison to the other learning methods.