Miracle-dz/UniFAR-FTUs huggingface dataset:
RU486 Mitigates Hippocampal Pathology Following Status Epilepticus

Status epilepticus (SE) induces rapid hyper-activation of the hypothalamo–pituitary–adrenocortical (HPA) axis. HPA axis hyperactivity results in excess exposure to high levels of circulating glucocorticoids, which are associated with neurotoxicity and depression-like behavior. These observations have led to the hypothesis that HPA axis dysfunction may exacerbate SE-induced brain injury. To test this hypothesis, we used the mouse pilocarpine model of epilepsy to determine whether use of the glucocorticoid receptor antagonist RU486 can attenuate hippocampal pathology following SE. Excess glucocorticoid secretion was evident 1 day after SE in the mice, preceding the development of spontaneous seizures (which can take weeks to develop). RU486 treatment blocked the SE-associated elevation of glucocorticoid levels in pilocarpine-treated mice. RU486 treatment also mitigated the development of hippocampal pathologies induced by SE, reducing loss of hilar mossy cells and limiting pathological cell proliferation in the dentate hilus. Mossy cell loss and accumulation of ectopic hilar cells are positively correlated with epilepsy severity, suggesting that early treatment with glucocorticoid antagonists could have anti-epileptogenic effects.
Reasoning about Deterministic Action Sequences with Probabilistic Priors

We present a novel algorithm and a new understanding of reasoning about a sequence of deterministic actions with a probabilistic prior. When the initial state of a dynamic system is unknown, a probability distribution can be still specified over the initial states. Estimating the posterior distribution over states (filtering) after some deterministic actions occurred is a problem relevant to AI planning, natural language processing (NLP) and robotics among others. Current approaches to filtering deterministic actions are not tractable even if the distribution over the initial system state is represented compactly. The reason is that state variables become correlated after a few steps. The main innovation in this paper is a method for sidestepping this problem by redefining state variables dynamically at each time step such that the posterior for time t is represented in a factored form. This update is done using a progression algorithm as a subroutine, and our algorithm’s tractability follows when that subroutine is tractable. In particular, our algorithm is tractable for 1:1 and STRIPS actions. We apply our reasoning algorithm about deterministic actions to reasoning about sequences of probabilistic actions and improve the efficiency of the current probabilistic reasoning approaches. We demonstrate the efficiency of the new algorithm experimentally over AI-Planning data sets.
Gaussian interference networks: Lattice alignment

This paper analyzes Gaussian interference channels with more than two users and integer channel gains, and finds a new achievable set of rates using lattice codebooks. It combines lattices with alignment schemes inspired by the ones developed for degrees of freedom (DoF) analysis and shows that similar rate gains can be achieved for finite SNR interference networks as well. In essence, this work can be seen as a generalization of the degrees of freedom alignment results in to finite SNR.
Reconstructing the Three-Dimensional GABAergic Microcircuit of the Striatum

A system's wiring constrains its dynamics, yet modelling of neural structures often overlooks the specific networks formed by their neurons. We developed an approach for constructing anatomically realistic networks and reconstructed the GABAergic microcircuit formed by the medium spiny neurons (MSNs) and fast-spiking interneurons (FSIs) of the adult rat striatum. We grew dendrite and axon models for these neurons and extracted probabilities for the presence of these neurites as a function of distance from the soma. From these, we found the probabilities of intersection between the neurites of two neurons given their inter-somatic distance, and used these to construct three-dimensional striatal networks. The MSN dendrite models predicted that half of all dendritic spines are within 100µm of the soma. The constructed networks predict distributions of gap junctions between FSI dendrites, synaptic contacts between MSNs, and synaptic inputs from FSIs to MSNs that are consistent with current estimates. The models predict that to achieve this, FSIs should be at most 1% of the striatal population. They also show that the striatum is sparsely connected: FSI-MSN and MSN-MSN contacts respectively form 7% and 1.7% of all possible connections. The models predict two striking network properties: the dominant GABAergic input to a MSN arises from neurons with somas at the edge of its dendritic field; and FSIs are inter-connected on two different spatial scales: locally by gap junctions and distally by synapses. We show that both properties influence striatal dynamics: the most potent inhibition of a MSN arises from a region of striatum at the edge of its dendritic field; and the combination of local gap junction and distal synaptic networks between FSIs sets a robust input-output regime for the MSN population. Our models thus intimately link striatal micro-anatomy to its dynamics, providing a biologically grounded platform for further study.
Soft-Computing-Based Embedded Design of an Intelligent Wall/Lane-Following Vehicle

Soft computing techniques are generally well suited for vehicular control systems that are usually modeled by highly nonlinear differential equations and working in unstructured environments. To demonstrate their applicability in real-world applications, two intelligent controllers based on fuzzy logic and artificial neural network are designed for performing a wall-following task. Based on performance and flexibility considerations, the two controllers are implemented onto a reconfigurable hardware platform, namely a field-programmable gate array. As comparative studies of these two embedded hardware controllers designed for the same vehicular application are limited in literature, this research also presents an evaluation of the two controllers, comparing them in terms of hardware resource requirements, operational speeds, and trajectory tracking errors in following different predefined trajectories.
Comparison of s-gram Proximity Measures in Out-of-Vocabulary Word Translation

Classi ed s-grams have been successfully used in cross-language information retrieval (CLIR) as an approximate string matching technique for translating out-of-vocabulary (OOV) words. For example, sgrams have consistently outperformed other approximate string matching techniques, like edit distance or n-grams. The Jaccard coe cient has traditionally been used as an s-gram based string proximity measure. However, other proximity measures for s-gram matching have not been tested. In the current study the performances of seven proximity measures for classi ed s-grams in CLIR context was evaluated using eleven language pairs. The binary proximity measures (e.g. Jaccard coe cient) performed generally better than their non-binary counterparts, but the di erence depended mainly on the padding used with s-grams. When no padding was used, the binary and non-binary proximity measures were nearly equal, though the performance at large deteriorated.
Effects of Internationalization on Ownership Structure: Evidence from Latin American Firms

We analyze the direct and simultaneous effects of internationalization on the ownership structure of Latin American companies based on agency theory. Using a sample of 425 Latin American firms between 2007 and 2011, which corresponds to 1,776 observations, we use random effects and three-stage least squares panel data regression to test these effects. We find that the hypothesized positive effect of internationalization on ownership concentration is rejected. Our results support the negative relationship that is predicted by principal-agent theory when analyzing the effect of ownership on the degree of internationalization. Greater internationalization via the equity entry mode is associated with lower levels of ownership concentration. Finally, there is simultaneity in the determination of the relationship between the degree of internationalization and ownership concentration.
A Flexible Bio-Affective Gaming Interface

Affective bio-feedback can be an important instrument to enhance the game experience. Several studies have provided evidence of the usefulness of physiological signals for affective gaming. However, due to discrepancies in the findings of psychology studies, the pattern matching models employed are limited in the number of emotions they are able to classify. This paper presents a bio-affective gaming interface (BAGI) that can be used to customize a game experience according to the player’s emotional response. Its architecture offers important characteristics for gaming such as: flexibility, scalability and multi-category discrimination in real time. These features are important not only because they allow the classification with pattern matching and machine learning models but also because they make possible the reusability of previous findings and the inclusion of new models to the system. In order to prove the effectiveness of BAGI two different types of neural networks have been trained to recognize emotions. After that, they were incorporated into the system to customize in real-time the computer wallpaper, according to the emotion experienced by the user. The best results were obtained with a probabilistic neural network with accuracy results of 84.46% on the training data and 78.38% on the validation for new independent data sets.
Transcriptional Networks in S. cerevisiae Linked to an Accumulation of Base Excision Repair Intermediates

Upon exposure to DNA damaging agents, Saccharomyces cerevisiae respond by activating a massive transcriptional program that reflects the fact that “DNA damaging” agents also damage other cellular macromolecules. To identify the transcriptional response that is specific to DNA damage, we have modulated the first two enzymes in the base excision repair (BER) pathway generating yeast strains with varied levels of the repair intermediates, abasic sites or strand breaks. We show that the number of abasic sites is significantly increased when the 3-methyladenine DNA glycosylase (Mag): AP endonuclease (Apn1) ratio is increased and that spontaneous frame shift mutation is considerably elevated when either Mag, or Mag plus Apn1, expression is elevated. Expression profiling identified 633 ORFs with differential expression associated with BER modulation. Analysis of transcriptional networks associated with the accumulation of DNA repair intermediates identifies an enrichment for numerous biological processes. Moreover, most of the BER-activated transcriptional response was independent of the classical yeast environmental stress response (ESR). This study highlights that DNA damage in the form of abasic sites or strand breaks resulting from BER modulation is a trigger for substantial genome-wide change and that this response is largely ESR-independent. Taken together, these results suggest that a branch point exists in the current model for DNA damage-signaled transcription in S. cerevisiae.
A XiRisc-based SoC for embedded DSP applications

Reconfigurable computing can face many of the current embedded systems design issues, providing a high degree of flexibility and increasing energy efficiency of computation. This paper introduces the architecture of a system on chip for signal processing applications, including an XiRisc reconfigurable processor as the main computational core. This RISC processor features an extensible instruction set, obtained through dynamic reconfiguration of a programmable gate-array embedded as a processor datapath function unit. A prototype chip has been implemented in 0.13 /spl mu/m CMOS technology. The SoC operates at 166 MHz clock speed and the test of several DSP algorithms showed speed-ups ranging from 5/spl times/ to 80/spl times/ with 65%-95% energy savings. As proof of the architectural improvement, energy and area computational efficiency has grown by a factor ranging from 3/spl times/ to 35/spl times/.
Effective Connectivity within the Default Mode Network: Dynamic Causal Modeling of Resting-State fMRI Data

The Default Mode Network (DMN) is a brain system that mediates internal modes of cognitive activity, showing higher neural activation when one is at rest. Nowadays, there is a lot of interest in assessing functional interactions between its key regions, but in the majority of studies only association of Blood-oxygen-level dependent (BOLD) activation patterns is measured, so it is impossible to identify causal influences. There are some studies of causal interactions (i.e., effective connectivity), however often with inconsistent results. The aim of the current work is to find a stable pattern of connectivity between four DMN key regions: the medial prefrontal cortex (mPFC), the posterior cingulate cortex (PCC), left and right intraparietal cortex (LIPC and RIPC). For this purpose functional magnetic resonance imaging (fMRI) data from 30 healthy subjects (1000 time points from each one) was acquired and spectral dynamic causal modeling (DCM) on a resting-state fMRI data was performed. The endogenous brain fluctuations were explicitly modeled by Discrete Cosine Set at the low frequency band of 0.0078–0.1 Hz. The best model at the group level is the one where connections from both bilateral IPC to mPFC and PCC are significant and symmetrical in strength (p < 0.05). Connections between mPFC and PCC are bidirectional, significant in the group and weaker than connections originating from bilateral IPC. In general, all connections from LIPC/RIPC to other DMN regions are much stronger. One can assume that these regions have a driving role within the DMN. Our results replicate some data from earlier works on effective connectivity within the DMN as well as provide new insights on internal DMN relationships and brain’s functioning at resting state.
Learning Open Loop Control of Complex Motor Tasks

Table lookup with interpolation is used for many learning and adaptation tasks. Redundant mappings capture the important concept of "motor skill," which is important in real, behaving systems. Few, if any, robot skill implementations have dealt with redundant mappings, in which the space to be searched to create the table has much higher dimensionality than the table itself. A practical method for inverting redundant mappings is important in physical systems with limited time for trials. We present the "Guided Table Fill In" algorithm, which uses data already stored in the table to guide search through the space of potential table entries. The algorithm is illustrated and tested on a robot skill learning task both in simulation and on a robot with a flexible link. Our experiments show that the ability to search high dimensional action spaces efficiently allows skill learners to find new behaviors that are qualitatively different from what they were presented or what the system designer may have expected. Thus the use of this technique can allow researchers to seek higher dimensional action spaces for their systems rather than constraining their search space at the risk of excluding the best actions.
Overactive bladder symptom severity is associated with falls in community-dwelling adults: LOHAS study

Objectives To examine the association between overactive bladder (OAB) symptom severity and falls and the contribution of OAB symptoms to falls in a community-dwelling population. Design Cross-sectional study. Setting 2 Japanese municipalities. Participants A total of 2505 residents aged over 40 years, who participated in health check-ups conducted in 2010. OAB symptom assessed via overactive bladder symptom score (OABSS) was divided into six categories based on distribution and Japanese clinical guidelines. Mobility problems and depressive symptoms were assessed via the Timed Up and Go test and the short form of the Center for Epidemiologic Studies Depression Scale, respectively. Primary outcome measures Self-reported any fall and frequent fall (≥2) over the 1-month period. Independent contributions to any fall and frequent falls were assessed via logistic regression to generate population-attributable fractions (PAFs), assuming separate causal relationships between OAB symptoms, mobility problems and depressive symptoms and any or frequent falls. Results Among the total 1350 participants (mean age: 68.3 years) analysed, any fall and frequent falls were reported by 12.7% and 4.4%, respectively. Compared with no OABSS score, moderate-to-severe OAB and mild OAB were associated with any fall (adjusted ORs 2.37 (95% CI 1.12 to 4.98) and 2.51 (95% CI 1.14 to 5.52), respectively). Moderate-to-severe OAB was also strongly associated with frequent falls (adjusted OR 6.90 (95% CI 1.50 to 31.6)). Adjusted PAFs of OAB symptoms were 40.7% (95% CI 0.7% to 64.6%) for any fall and 67.7% (95% CI −23.1% to 91.5%) for frequent falls. Further, these point estimates were similar to or larger than those of mobility problems and depressive symptoms. Conclusions An association does indeed exist between OAB symptom severity and falls, and OAB symptoms might be important contributors to falls among community-dwelling adults. Further longitudinal studies are warranted to examine whether or not OAB symptoms predict risk of future falls and fall-related injuries.
Affect-based adaptive presentation of home videos

In recent times, the proliferation of multimedia devices and reduced costs of data storage have enabled people to easily record and collect a large number of home videos; furthermore, this collection is growing with time. With the popularity of participatory media such as YouTube and facebook, problems are encountered when people intend to share their home videos with others. The first problem is that different people might be interested in different video content. Given the numbers of home videos, it is a time-consuming and hard task to manually select proper content for people with different interests. Secondly, as short videos are becoming more and more popular in media sharing applications, people need to manually cut and edit home videos which is again a tedious task. In this paper, we propose a method that employs affective analysis to automatically create video presentations from home videos. Our novel method adaptively creates presentations based on three properties: emotional tone, local main character and global main character. A novel sparsity-based affective labeling method is proposed to identify the emotional content of the videos. The local and global main characters are determined by applying face recognition in each shot. To demonstrate the proposed method, three kinds of presentations are created for family, acquaintance and outsider. Experimental results show that our method is very effective in video sharing and the users are satisfied with the videos generated by our method.
Multi-domain adaptation for sentiment classification: Using multiple classifier combining methods

Sentiment classification is very domain-specific and good domain adaptation methods, when the training and testing data are drawn from different domains, are sorely needed. In this paper, we address a new approach to domain adaptation for sentiment classification in which classifiers are adapted for a specific domain with training data from multiple source domains. We call this new approach dasiamulti-domain adaptationpsila and present a multiple classifier system (MCS) framework to describe and understand it. Under this framework, we propose a new combining method, called Multi-label Consensus Training (MCT), to combine the base classifiers for selecting dasiaautomatically-labeledpsila samples from unlabeled data in the target domain. The experimental results for sentiment classification show that multi-domain adaptation using this method improves adaptation performance.
Navigating the Gray Area: A School District's Documentation of the Relationship between Disability and Misconduct

Background/Context The administration of student discipline is one of many responsibilities under the purview of teachers and educational leaders across the country. Maintaining a safe environment with minimal disruptions is not an easy task. From existing research on student discipline, we have learned that critical examination of data and continuous reflection are important, particularly in light of documented disparities in discipline practices. Under the Individuals with Disabilities Education Act (IDEA), before a school district may discipline a student with a disability for greater than 10 days, it must first conduct what is referred to as a manifestation determination review (MDR) (20 U.S.C. §1415(k)(1)(E)). During this review process, the current version of the law requires school districts to address two questions: (1) Was the student's behavior caused by, or did it have a direct and substantial relationship to, the student's disability? (2) Was the student's misconduct caused by the district's failure to implement the student's individualized education program (IEP), as required by law? This study examines the implementation of this standard. Research Question This study poses the following research question: What factors (explicit and implicit) influence decision makers when deciding whether a student's misconduct was a manifestation of his or her disability? Research Design This project used a case study approach to examine 80 MDR decisions in one large urban school district in order to better understand how decision makers implement this standard. Specifically, this study reviewed 40 randomly selected decisions wherein decision makers determined that the student's actions were not a manifestation of his or her disability and 40 randomly selected decisions wherein decision makers determined that the student's actions were a manifestation of his or her disability. Findings/Results Decision makers cited the academic and behavioral manifestations of students’ disabilities, students’ behavioral histories, students’ ability to control their actions, and connections to the home and community. Despite these consistent factors, the striking similarities between Yes and No decisions illustrates the overall arbitrary nature of the decision-making process. Conclusions/Recommendations This article discusses implications that attend to both the procedure and substance of manifestation determination review.
Globular clusters and the formation of the outer Galactic halo

The radii of globular clusters are used to study the early evolution of the Galactic halo. It is found that such cluster radii increase with Galactocentric distance and depend on orbit shape. It is suggested that metal enrichment in the Galactic halo might have been mainly a local phenomenon, whereas cluster radii may have been determined by more global properties of the protogalaxy.
Impact of the Rotor Yoke Geometry on Rotor Losses in Permanent-Magnet Machines

This paper presents a study on the rotor losses of surface-mounted permanent-magnet (PM) machine with fractional-slot windings. Two rotor losses sources are considered: the losses due to the slot opening (without currents in the stator) and due to the magneto motive force harmonics (mounting a rotor with demagnetized PMs). The rotor losses are measured at no load and under load. It is shown that the rotor losses under load are lower than considering the sum of the two contributions. This phenomenon is investigated using both analytical models and finite-element-based simulations. Then, the impact of the rotor geometry on the rotor losses is considered, and some design advices are reported.