Miracle-dz/UniFAR-FTUs huggingface dataset:
The traumatized brain. Gray and white matter morphology in dissociative identity disorder and posttraumatic stress disorder

Ondanks de inclusie van dissociatieve identiteitsstoornis (DIS) in de DSM-III tot en met DSM-5, is er een voortdurende discussie over de diagnostische validiteit, etiologie en de theoretische conceptualisering van de stoornis. Dit kan gerelateerd zijn aan het feit dat empirisch onderzoek naar de neurobiologische correlaten van dissociatieve identiteitsstoornis schaars is. Dit proefschrift is dan ook gericht op het leveren van objectieve neurowetenschappelijke gegevens aan de hand van structurele beeldvorming van de hersenen. Hiertoe werd grijze en witte stof van de hersenen onderzocht bij patienten met dissociatieve identiteitsstoornis ten opzichte van patienten met posttraumatische stressstoornis en gezonde controles. Gevonden werd dat zowel dissociatieve identiteitsstoornis als posttraumatische stress-stoornis geassocieerd zijn met grijze stof afwijkingen van de frontale, temporale en insulaire cortex en de hippocampus. Bovendien werd een lagere witte stof integriteit gevonden bij een aantal belangrijke bundels van vezels die betrokken zijn bij de regulatie van emoties. Interessant is dat de hippocampus afwijkingen gecorreleerd blijken te zijn met jeugdtrauma. We vonden eveneens een kleiner volume in de inferieure parietale cortex en een groter dorsaal striatum bij patienten met dissociatieve identiteitsstoornis in vergelijking met patienten met posttraumatische stressstoornis. Deze afwijkingen bleken te correleren met symptomen van dissociatie en depersonalisatie, een bevinding die op betrokkenheid van deze regio's bij de symptomatologie van DID kan wijzen. De bevindingen in dit proefschrift leveren hoofdzakelijk objectieve empirische steun ten gunste van een trauma-gerelateerde etiologie voor DIS.
Not an Interlingua, But Close: Comparison of English AMRs to Chinese and Czech

Abstract Meaning Representations (AMRs) are rooted, directional and labeled graphs that abstract away from morpho-syntactic idiosyncrasies such as word category (verbs and nouns), word order, and function words (determiners, some prepositions). Because these syntactic idiosyncrasies account for many of the cross-lingual differences, it would be interesting to see if this representation can serve, e.g., as a useful, minimally divergent transfer layer in machine translation. To answer this question, we have translated 100 English sentences that have existing AMRs into Chinese and Czech to create AMRs for them. A cross-linguistic comparison of English to Chinese and Czech AMRs reveals both cases where the AMRs for the language pairs align well structurally and cases of linguistic divergence. We found that the level of compatibility of AMR between English and Chinese is higher than between English and Czech. We believe this kind of comparison is beneficial to further refining the annotation standards for each of the three languages and will lead to more compatible annotation guidelines between the languages.
Wi-Max Physical Layer Simulator Using Different Modulation Schemes

Wi-MAX is a wireless technology which offers high data rate transmission in broadband. The Worldwide interoperability for microwave access (Wi-MAX) based on IEEE 802.16, has been one of the most important technologies in communication networks providing voice, data and video services with different type of QoS (Quality of Service) during last few years. In this paper, the architecture of the Wi-MAX physical layer simulator is presented. The main blocks are implemented with the aid of the Matlab Simulink and the bit error rate (BER) curves are presented with varying SNR under different digital modulations in AWGN channel.
Evaluation of Spatially Targeted Strategies to Control Non-Domiciliated Triatoma dimidiata Vector of Chagas Disease

Background Chagas disease is a major neglected tropical disease with deep socio-economical effects throughout Central and South America. Vector control programs have consistently reduced domestic populations of triatomine vectors, but non-domiciliated vectors still have to be controlled efficiently. Designing control strategies targeting these vectors is challenging, as it requires a quantitative description of the spatio-temporal dynamics of village infestation, which can only be gained from combinations of extensive field studies and spatial population dynamic modelling. Methodology/Principal Findings A spatially explicit population dynamic model was combined with a two-year field study of T. dimidiata infestation dynamics in the village of Teya, Mexico. The parameterized model fitted and predicted accurately both intra-annual variation and the spatial gradient in vector abundance. Five different control strategies were then applied in concentric rings to mimic spatial design targeting the periphery of the village, where vectors were most abundant. Indoor insecticide spraying and insect screens reduced vector abundance by up to 80% (when applied to the whole village), and half of this effect was obtained when control was applied only to the 33% of households closest to the village periphery. Peri-domicile cleaning was able to eliminate up to 60% of the vectors, but at the periphery of the village it has a low effect, as it is ineffective against sylvatic insects. The use of lethal traps and the management of house attractiveness provided similar levels of control. However this required either house attractiveness to be null, or ≥5 lethal traps, at least as attractive as houses, to be installed in each household. Conclusion/Significance Insecticide and insect screens used in houses at the periphery of the village can contribute to reduce house infestation in more central untreated zones. However, this beneficial effect remains insufficient to allow for a unique spatially targeted strategy to offer protection to all households. Most efficiently, control should combine the use of insect screens in outer zones to reduce infestation by both sylvatic and peri-domiciliated vectors, and cleaning of peri-domicile in the centre of the village where sylvatic vectors are absent. The design of such spatially mixed strategies of control offers a promising avenue to reduce the economic cost associated with the control of non-domiciliated vectors.
Differentiation of human neural progenitor cell-derived spiral ganglion-like neurons: a time-lapse video study

Abstract Conclusions: Human neural progenitor cells can differentiate into spiral ganglion-like cells when exposed to inner ear-associated growth factors. The phenotype bears resemblance to human sphere-derived neurons. Objective: To establish an in vitro model for the human auditory nerve to replace and complement in vivo animal experiments and ultimately human in vivo transplantation. Methods: Human neural progenitors were differentiated under conditions developed for in vitro survival of human primary spiral ganglion culture with media containing growth factors associated with inner ear development. Differentiation was documented using time-lapse video microscopy. Time-dependent marker expression was evaluated using immunocytochemistry with fluorescence and laser confocal microscopy. Results: Within 14 days of differentiation, neural progenitors adopted neural phenotype and expressed spiral ganglion-associated markers.
How Much Supervision? Corpus-Based Lexeme Sentiment Estimation

This paper is focused on comparing corpus-based methods for estimating word sentiment. Evaluated algorithms represent varying degrees of supervision and range from regression alike approaches to more heavily supervised classifications. The main idea is to explore the opportunities arising from mining medium sized, balanced corpora - as opposed to web as a corpus paradigm. The comparisons have been carried using sentiment estimator benchmarks designed to take into account classification and regression problems as well as varying granularity of predicted sentiment scores: from simple to complex scales. Overall, the results turn out to be very promising and indicate superiority of supervised algorithms, especially for lower sentiment granularity predictions. However, unsupervised methods can be still considered as an interesting alternative in the case of the most fine-grained, regression like scenarios of sentiment estimation. In these cases heavy supervision and large number of features are less attractive than simple unsupervised methods.
Uncovering the riffled independence structure of ranked data

Representing distributions over permutations can be a daunting task due to the fact that the number of permutations of n objects scales factorially in n. One recent way that has been used to reduce storage complexity has been to exploit probabilistic independence, but as we argue, full independence assumptions impose strong sparsity constraints on distributions and are unsuitable for modeling rankings. We identify a novel class of independence structures, called riffled independence, encompassing a more expressive family of distributions while retaining many of the properties necessary for performing efficient inference and reducing sample complexity. In riffled independence, one draws two permutations independently, then performs the riffle shuffle, common in card games, to combine the two permutations to form a single permutation. Within the context of ranking, riffled independence corresponds to ranking disjoint sets of objects independently, then interleaving those rankings. In this paper, we provide a formal introduction to riffled independence and propose an automated method for discovering sets of items which are riffle independent from a training set of rankings. We show that our clustering-like algorithms can be used to discover meaningful latent coalitions from real preference ranking datasets and to learn the structure of hierarchically decomposable models based on riffled independence. AMS 2000 subject classifications: Primary 68T37, 60C05; secondary 60B15.
Comparative and Quantitative Global Proteomics Approaches: An Overview

Proteomics became a key tool for the study of biological systems. The comparison between two different physiological states allows unravelling the cellular and molecular mechanisms involved in a biological process. Proteomics can confirm the presence of proteins suggested by their mRNA content and provides a direct measure of the quantity present in a cell. Global and targeted proteomics strategies can be applied. Targeted proteomics strategies limit the number of features that will be monitored and then optimise the methods to obtain the highest sensitivity and throughput for a huge amount of samples. The advantage of global proteomics strategies is that no hypothesis is required, other than a measurable difference in one or more protein species between the samples. Global proteomics methods attempt to separate quantify and identify all the proteins from a given sample. This review highlights only the different techniques of separation and quantification of proteins and peptides, in view of a comparative and quantitative global proteomics analysis. The in-gel and off-gel quantification of proteins will be discussed as well as the corresponding mass spectrometry technology. The overview is focused on the widespread techniques while keeping in mind that each approach is modular and often recovers the other.
A Cryptosystem Based on DLP gamma = alphaabetab mod p

This paper introduces mainly the concept of the Public Key Cryptosystem or PKC, whose security is based on the new Discrete Logarithm Problem known as DLP-III and DLP-IV with the two distinct exponentiations, i.e. γ ≡ α a β b mod p in the multiplicative group of the ﬁnite ﬁeld Z ∗ p of the order p − 1. We show that the proposed Public Key Cryptosystem based on DLP-IV provides more security because of double computation comparing with the well known the Discrete Logarithm Problem II or DLP-II with one exponentiation, i.e. β ≡ α a mod p in the multiplicative group of the ﬁnite ﬁeld Z ∗ p of the order p − 1 at the same eﬃciency level.
Potential for Microscale Bacterial Fe Redox Cycling at the Aerobic-Anaerobic Interface

Recent studies of bacterial Fe(II) oxidation at circumneutral pH by a newly-isolated lithotrophic β-Proteobacterium (strain TW2) are reviewed in relation to a conceptual model that accounts for the influence of biogenic Fe(III)-binding ligands on patterns of Fe(II) oxidation and Fe(III) oxide deposition in opposing gradients of Fe(II) and O2. The conceptual model envisions complexation of Fe(III) by biogenic ligands as mechanism which alters the locus of Fe(III) oxide deposition relative to Fe(II) oxidation so as to delay/retard cell encrustation with Fe(III) oxides. Experiments examining the potential for bacterial Fe redox cycling in microcosms containing ferrihydrite-coated sand and a coculture of a lithotrophic Fe(II)-oxidizing bacterium (strain TW2) and a dissimilatory Fe(III)-reducing bacterium (Shewanella algae strain BrY) are described and interpreted in relation to an extended version of the conceptual model in which Fe(III)-binding ligands promote rapid microscale Fe redox cycling. The coculture systems showed minimal Fe(III) oxide accumulation at the sand-water interface, despite intensive O2 input from the atmosphere and measurable dissolved O2 to a depth of 2 mm below the sand-water interface. In contrast, a distinct layer of oxide precipitates formed in systems containing Fe(III)-reducing bacteria alone. Voltammetric microelectrode measurements revealed much lower concentrations of dissolved Fe(II) in the coculture systems. Examination of materials from the cocultures by fluorescence in situ hybridization indicated close physical juxtapositioning of Fe(II)-oxidizing and Fe(III)reducing bacteria in the upper few mm of sand. Together these results indicate that Fe(II)-oxidizing bacteria have the potential to enhance the coupling of Fe(II) oxidation and Fe(III) reduction at redox interfaces, thereby promoting rapid microscale cycling of Fe.
Alinearized model of saturated self-excited synchronous reluctance generator

This paper presents a linearized model of saturated self-excited synchronous reluctance generator considering the effect of saturation in both the direct and quadrature axes. The proposed model has been applied to obtain eigenvalues for different operating conditions considering only d-axis saturation and both d- and q-axis saturation. The importance of the inclusion of the q-axis saturation on the steady-state stability analysis of a saturated self-excited synchronous reluctance generator is manifested using this proposed model. The effects of load power factor, excitation capacitance, and active and reactive power output on the steady-state performance are also demonstrated in this paper
An Approximating Fair Congestion Control Scheme at Core Network

Congestion is a natural phenomenon in core network, and is unavoidable if the queuing system is operated near capacity. Providing fairness among users with different levels of aggressiveness, and different levels of responsiveness is necessary for efficient network flow control. The scalability of current solutions depends on the number of active flows and the number of flows whose rate is greater that the current fair share. In this paper we propose a new fair active queue management scheme, called SDP (Selective Differentiated Penalization), that use the recent buffer resources allocation history to differentially penalize misbehaving flows by dropping more of their packets to make a tradeoff between scalability and fairness. The first principle of SDP is that the resulting throughput of each flow should be bounded by the max-min fair share bandwidth. The performance of SDP is aided by the fact that the recent history of buffer resource allocations can provide accurate rate estimates of active flow and the fair share threshold. Simulated results suggest that SDP provides a reasonable degree of max-min fair bandwidth allocation in a wide variety of operating conditions.
Estimating eddy diffusivities from noisy Lagrangian observations

The problem of estimating the eddy diffusivity from Lagrangian observations in the presence of measurement error is studied in this paper. We consider a class of incompressible velocity fields for which is can be rigorously proved that the small scale dynamics can be parameterised in terms of an eddy diffusivity tensor. We show, by means of analysis and numerical experiments, that subsampling of the data is necessary for the accurate estimation of the eddy diffusivity. The optimal sampling rate depends on the detailed properties of the velocity field. Furthermore, we show that averaging over the data only marginally reduces the bias of the estimator due to the multiscale structure of the problem, but that it does significantly reduce the effect of observation error.
Probabilistic Regression Using Basis Function Models ; CU-CS-975-04

Our goal is to accurately estimate the error in any prediction of a regression model. We propose a probabilistic regression framework for basis function regression models, which includes widely used kernel methods such as support vector machines and nonlinear ridge regression. The framework outputs a point specific estimate of the probability that the true regression surface lies between two user specified values, denoted by y1 and y2. More formally, given anyy2 > y1, we estimate the Pr(y1 ≤ y ≤ y2|x, f̂(x)), wherey is a true regression surface, x is the input, andf̂(x) is the basis function model. Thus the framework encompasses the less general standard error bar approach used in regression. We assume that the training data is independent and identically distributed (iid) from a stationary distribution, and make no specific distribution assumptions (e.g. no Gaussian or other specific distributions are assumed). Theory is presented showing that as the number of training points increases, estimates of Pr(y1 ≤ y ≤ y2|x, f̂(x)) approach the true value. Experimental evidence demonstrates that our framework gives reliable probability estimates, without sacrificing mean squared error regression accuracy.
Application of BCI Technology for Color Prediction Using Brainwaves

In this paper we present the results of the research regarding color recognition using brain-waves read from a consumer grade non-intrusive Brain Computer Interface device. The brainwaves obtained from the device are pre-processed and filtered using different algorithms and then several machine learning techniques are applied for prediction of three colors. At the end, the obtained results are presented and discussed.
Anomalous heat conduction in polyethylene chains: Theory and molecular dynamics simulations

Fermi, Pasta, and Ulam’s 1 FPU “remarkable little discovery” that nonlinear chains of oscillators can be nonergodic led to the discovery of solitons and gave birth to the modern field of computational nonlinear dynamics. 2,3 The FPU problem was originally proposed in order to study the system’s rate of thermalization and approach to equilibrium. This was based on an expected evolution toward an equipartition of mode energy, as a result of the mode-mode interactions induced by the nonlinear particle interactions. The surprising recurrence phenomenon that was observed suggested that even an anharmonic chain of oscillators can have infinite thermal conductivity. 2,3
Communication process and collaborative work in Web 2.0 environment

Because the higher level of media richness improves the performance of collaborative works such as knowledge sharing, efforts to raise media richness are encouraged. The Channel Expansion Theory argues that individuals' perceptions of media richness vary according to each individual's knowledge base built from prior experiences related to the communication situation. This study explored the channel expansion effects in the new CMC environment, Web 2.0. In particular, we considered communication process modes (i.e., conveyance and convergence) as a factor moderating the effects. The research model was verified by an experiment with student subjects.
Functional Varying Coefficient Models for Longitudinal Data

The proposed functional varying coefficient model provides a versatile and flexible analysis tool for relating longitudinal responses to longitudinal predictors. Specifically, this approach provides a novel representation of varying coefficient functions through suitable auto and cross-covariances of the underlying stochastic processes, which is particularly advantageous for sparse and irregular designs, as often encountered in longitudinal studies. Existing methodology for varying coefficient models is not adapted to such data. The proposed approach extends the customary varying coefficient models to a more general setting, in which not only current but also recent past values of the predictor time course may have an impact on the current value of the response time course. The influence of past predictor values is modeled by a smooth history index function, while the effects on the response are described by smooth varying coefficient functions. The resulting estimators for varying coefficient and history index functions are shown to be asymptotically consistent for sparse designs. In addition, prediction of unobserved response trajectories from sparse measurements on a predictor trajectory is obtained, along with asymptotic pointwise confidence bands. The proposed methods perform well in simulations, especially when compared with commonly used local polynomial smoothing methods for varying coefficient models, and are illustrated with longitudinal primary biliary liver cirrhosis data. The data application and detailed assumptions and proofs can be found in online Supplemental Material.
A Technique for Mitigating Thermal Stress and Extending Life Cycle of Power Electronic Converters Used for Wind Turbines

Over the last two decades, various models have been developed to assess and improve the reliability of power electronic conversion systems (PECs) with a focus on those used for wind turbines. However, only few studies have dealt with mitigating the PECs thermo-mechanical effects on their reliability taking into account variations in wind characteristics. This work critically investigates this issue and attempts to offer a mitigating technique by, first, developing realistic full scale (FS) and partial scale (PS) induction generator models combined with two level back-to-back PECs. Subsequently, deriving a driving algorithm, which reduces PEC’s operating temperature by controlling its switching patterns. The developed switching procedure ensures minimum temperature fluctuations by adapting the variable DC link and system’s frequency of operation. It was found for both FS and PS topologies, that the generator side converters have higher mean junction temperatures where the grid side ones have more fluctuations on their thermal profile. The FS and PS cycling temperatures were reduced by 12 °C and 5 °C, respectively. Moreover, this led to a significant improvement in stress; approximately 27 MPa stress reduction for the FS induction generator PEC.
Principal component analysis for spectral indices of stellar populations

We apply the method of principal component analysis to a sample of simple stellar population to select some age sensitive spectral indices. Besides the well-known age sensitive index, H�, we find some other spectral indices have great potential to determine the age of stellar population, such as G4300, Fe4383, C24668, and Mgb. In addition, we find these spectral indices sensitivity to age depends on the metallicity