Miracle-dz/UniFAR-FTUs huggingface dataset:
Editorial: Preventing Developmental Brain Injury—From Animal Models to Clinical Trials

Department of Food and Nutrition, Kyoto Women’s University, Kyoto, Japan, Division of Development and Growth, Department of Pediatrics, Gynecology and Obstetrics, School of Medicine, University of Geneva, Geneva, Switzerland, Division of Neonatology, Department of Pediatrics Gynecology and Obstetrics, School of Medicine, University of Geneva, Geneva, Switzerland, 4 Robert Debré Hospital, INSERM U1141, Paris-Diderot University, Paris, France
A PARTICLE DARK MATTER FOOTPRINT ON THE FIRST GENERATION OF STARS

Dark matter particles with properties identical to those of dark matter candidates hinted at by several international collaborations dedicated to the experimental detection of dark matter (DAMA, COGENT, CRESST, and CDMS-II, although not, most notably, by LUX), which also have a dark matter asymmetry that is identical to the observed baryon asymmetry (Planck and Wilkinson Microwave Anisotropy Probe), may produce a significant impact on the evolution of the first generation of low-metallicity stars. The lifetimes of these stars in different phases of stellar evolution are significantly extended, namely, in the pre-main sequence, main sequence, and red giant phases. In particular, intermediate-mass stars in the red giant phase experience significant changes in their luminosity and chemical composition. The annihilations of dark matter particles affect the interior of the star in such a way that the 3α reaction becomes less efficient in the production of carbon and oxygen. This dark matter effect contradicts the excess of carbon and other metals observed today in stars of low mass and low metallicity. Hence, we can impose an upper limit on the dark matter halo density, and therefore on the redshift, at which the first generation of low-metallicity stars formed.
Stable Intronic Sequences and Exon Skipping Events in the Human RPE65 Gene: Analysis of Expression in Retinal Pigment Epithelium Cells and Cell Culture Models

Currently, there is much interest in intronic sequence-containing long non-coding RNAs and the role of intronic transcription in regulation of cellular metabolism and fate. Several stable intronic sequence RNAs (sisRNAs) were recently implicated in regulation of parental genes. To investigate transcription from introns of the RPE65 gene, we analyzed RNA-seq and Nanopore sequencing data from different cell models of human retinal pigment epithelium (RPE) and native bovine RPE. We discovered putative stable poly-adenylated transcripts with sequences corresponding to intronic regions of the RPE65 gene in the cytoplasm of RPE cells. These stable intronic sequences could be important for RPE65 transcription, splicing or translation. We also analyzed alternative splicing events in RPE65. Frequent exon skipping events involving exons 2, 3, and 7 were detected. The rate of these events was much higher in human RPE cell cultures compared with native RPE , consistent with lack of translation of RPE65 mRNA in cell cultures.
Novel Microbial-Based Immunotherapy Approach for Crohn's Disease

Background: Current Crohn's disease (CD) therapies focus on suppressing immune function and come with consequent risk, such as infection and cancer. Notwithstanding, most CD patients still experience disease progression. There is a need for new CD treatment strategies that offer better health outcomes for patients. Aims: To assess safety, efficacy, and tolerability of a novel microbial-derived immunotherapy, QBECO, that aims to restore rather than suppress immune function in CD. Methods: A randomized, double-blind, placebo-controlled trial was conducted in 68 patients with moderate-to-severe CD. Primary endpoints: safety and Week 8 clinical improvement. Secondary endpoints: Week 8 clinical response and remission. Week 8 responders continued blinded treatment through Week 16; non-responders received open-label QBECO from Weeks 9–16. Exploratory analyses included immune biomarker and genotype assessments. Results: QBECO was well-tolerated. Mean reduction in Crohn's Disease Activity Index (CDAI) score was −68 for QBECO vs. −31 for placebo at Week 8. Improvement with QBECO continued through Week 16 (-130 CDAI reduction). Week 8 QBECO clinical response, improvement and remission rates were 41.2%, 32.4%, 29.4% vs. 26.5%, 23.5%, 23.5% for placebo. TNFα inhibitor-naïve subjects achieved higher response rates at Week 8 with QBECO (64%) vs. placebo (26%). Specific immune biomarkers were identified that linked to QBECO response. Conclusion: This proof-of-concept study supports further investigation for the use of QBECO as a novel immunotherapy approach for CD. Biomarker analyses suggests it may be feasible to personalize CD treatment with QBECO. Larger trials are now needed to confirm clinical improvement and the unique biological findings. Clinical Trial Number: NCT01809275 (https://clinicaltrials.gov/ct2/show/NCT01809275)
System-Based Resilience Assessment of Networked Transportation Systems in Metropolitan Areas: Case of Greater Los Angeles

One essential aspect of urban resilience is the ability of the transportation networks to maintain mobility and accessibility during the immediate aftermath, as well as in the long-term recovery, of an extreme event. Despite the critical nature of transportation services, the infrastructure inventory in many developed countries are in poor condition. For example, transportation infrastructure in the United States has repeatedly been graded poorly by American Society of Civil Engineers as it is aged and in need of a major retrofit or replacement effort. It is well established that topology-based approaches in transportation resilience abstract out network supply and travel demand relationships as well as fundamental traffic assignment concepts such as flow under capacity constraints. On the other hand, system-based approaches that are more promising in generating actionable policy insights lack the granularity enabled now by the recent advances in data and modeling, omit the scale of analysis required in metropolitan areas, and ignore formal considerations of the hazard itself which makes studies prone to simplistic what-if assumptions. To address such shortcomings, a preliminary framework intended to couple seismic hazard analysis with transportation network analysis is designed and deployed in a Greater Los Angeles Area case study where the impacts of a 7.3M scenario earthquake are investigated.
Design and performance analysis of a fully distributed source detection algorithm for WSNs

In this article, we consider the detection of a localized source emitting a signal using a wireless sensor network (WSN). We consider that geographically distributed sensor nodes obtain energy measurements and compute cooperatively and in a distributed manner a statistic to decide if the source is present or absent without the need of a central node or fusion center (FC). We first start from the continuous-time signal sensed by the nodes and obtain an equivalent discrete-time hypothesis testing problem. Secondly, we propose a fully distributed scheme, based on the well-known generalized likelihood ratio (GLR) test, which is suitable for a WSN, where resources such as energy and communication bandwidth are typically scarce. In third place, we consider the asymptotic performance of the proposed GLR test. The derived results provide an excellent matching with the scenario in which only a finite amount of measurements are available at each sensor node. We finally show that the proposed distributed algorithm performs as well as the global GLR test in the considered scenarios, requiring only a small number of communication exchanges between nodes and a limited knowledge about the network structure and its connectivity.
Text-Based Information Extraction Using Lexico-Semantic Patterns

In order to cope with the ever increasing amount of data available on the Web, information extraction patterns are frequently employed to gather relevant information. Currently, most patterns use lexical and syntactic elements, but fail to exploit domain semantics. We propose a lexico-semantic pattern-based rule language, i.e., the Hermes Information Extraction Language (HIEL), which exploits a domain ontology for pattern creation. Experiments on financial news show that HIEL rules outperform lexico-syntactic rules and state-of-the-art lexico-semantic JAPE rules in terms of rule creation times and F1 scores.
Active Policy Learning for Robot Planning and Exploration under Uncertainty

This paper proposes a simulation-based active policy learning algorithm for finite-horizon, partially-observed sequential decision processes. The algorithm is tested in the domain of robot navigation and exploration under uncertainty. In such a setting, the expected cost, that must be minimized, is a function of the belief state (filtering distribution). This filtering distribution is in turn nonlinear and depends on an observation model with discontinuities. These discontinuities arise because the robot has a finite field of view and the environment may contain occluding obstacles. As a result, the expected cost is nondifferentiable and very expensive to simulate. The new algorithm overcomes the first difficulty and reduces the number of required simulations as follows. First, it assumes that we have carried out previous simulations which returned values of the expected cost for different corresponding policy parameters. Second, it fits a Gaussian process (GP) regression model to these values, so as to approximate the expected cost as a function of the policy parameters. Third, it uses the GP predicted mean and variance to construct a statistical measure that determines which policy parameters should be used in the next simulation. The process is then repeated using the new parameters and the newly gathered expected cost observation. Since the objective is to find the policy parameters that minimize the expected cost, this iterative active learning approach effectively trades-off between exploration (in regions where the GP variance is large) and exploitation (where the GP mean is low). In our experiments, a robot uses the proposed algorithm to plan an optimal path for accomplishing a series of tasks, while maximizing the information about its pose and map estimates. These estimates are obtained with a standard filter for simultaneous localization and mapping. Upon gathering new observations, the robot updates the state estimates and is able to replan a new path in the spirit of open-loop feedback control.
Joint attention in apes and humans

Joint attention is foundational to the acquisition of language in humans. Numerous theorists have concluded that joint attention is therefore a human species-specific biological adaptation for establishing co-reference. However, the well-documented emergence of humanlike joint attentional skills in our nearest living relatives, the great apes, without any explicit training, poses a challenge for this theoretical perspective. One reaction to these emerging findings from great apes has been the claim that although there is surface similarity in joint attention, there are, nevertheless, deep psychological differences between humans and apes in the display of joint attention. An alternative account emphasises psychol ogical continuity between humans and apes. I will argue for the latter view, in a review of the empirical data on joint attention in humans and great apes
Can imprecise internal motor models explain the ataxic hand trajectories during reaching in young infants?

The first reaching movements of human infants lack limb coordination leading to ataxiclike hand trajectories. Kinematically, these early trajectories are characterized by multiple peaks in the hand velocity profile which gradually decrease in frequency during development. In this paper we explore the hypothesis that the jerky hand trajectories seen in early infancy can be the result of imprecise internal motor models. Results from our simulation suggest that imprecise estimations of multi-joint inter-segmental torques (e.g., Coriolis forces) by the controller may induce multi-peak hand velocity profiles. When the system was allowed to use delayed peripheral feedback (300 ms after reaching onset), the resulting kinematics began to resemble those seen in early infancy. This suggests that the output of an imprecise internal model of limb dynamics coupled with delayed feedback may be sufficient to explain early human hand trajectories. Our data provide an alternative to previous hypotheses theorising jerky trajectories as the result of concatenated mini ballistic movements.
Knowledge-Based Target Selection in a Building Navigation System

This paper is concerned with indoor navigation systems integrated with knowledge bases containing rich semantic data about buildings and their constituent spaces. Specifically, it is concerned with a system which utilizes graph models with labels, attributes and hierarchies storing semantic knowledge. That information can be used to describe navigation targets, as was shown in (Palacz et al., 2016). This paper continues that work and proposes a graphical user interface which allows for specifying complex target selection criteria.
Uncovering the Transcriptional Correlates of Hub Connectivity in Neural Networks

Connections in nervous systems are disproportionately concentrated on a small subset of neural elements that act as network hubs. Hubs have been found across different species and scales ranging from C. elegans to mouse, rat, cat, macaque, and human, suggesting a role for genetic influences. The recent availability of brain-wide gene expression atlases provides new opportunities for mapping the transcriptional correlates of large-scale network-level phenotypes. Here we review studies that use these atlases to investigate gene expression patterns associated with hub connectivity in neural networks and present evidence that some of these patterns are conserved across species and scales.
Simulation of Polyelectrolytes in Solution Using Dissipative Particle Dynamics in the Grand Canonical Ensemble: Interaction Strength and Salt Effects

We have studied a bulk electrolyte, and polyelectrolyte solutions with surfactants or multivalent salt with the explicit presence of counterions and solvent molecules by means of the mesoscopic dissipative particle dynamics (DPD) method in the Grand Canonical ensemble. The electrostatic interactions are calculated using the Ewald sum method and the structure of the fluid is analyzed through the radial distribution function between charged particles. The results are in very good agreement with those reported in the literature using a different method for the calculation of the electrostatic forces, and with those obtained using DPD in the canonical ensemble. We also studied the salt dependent conformation of polyelectrolyte solutions as a function of the solvent quality, and analyzed the electrostatic interaction strength dependence of dilute flexible polyelectrolytes in solution. For the complex systems mentioned above, the electrostatic interactions and the solvent quality play a key role in understanding phenomena that do not occur in noncharged systems.
Bayesian Noisy Word Clustering through Sampling Prototypical Words

This paper describes a new algorithm for sampling prototypical words from a set of noisy words and proposes a noisy word clustering method. In a lexical acquisition task, phoneme sequences recognized by a developmental robot using a phoneme recognizer have many errors. A letter or phoneme sequence involving errors is called a noisy word. To develop a mixture model for noisy words and develop a clustering method, a procedure needs to be developed for the sampling of a prototypical word, i.e., “mean” string, in a cluster of noisy words. Despite a long history regarding methods for treating noisy words, e.g., a stochastic deformation model, the edit distance and their variants, and an efficient sampling procedure for prototypical words have not been developed. In this paper, the mixture of stochastic deformation models, namely a generative model for noisy words, is proposed, and efficient blocked Gibbs samplers for the model are proposed. To develop this procedure, a forward filtering backward sampling procedure is proposed for jointly decoding noisy words and sampling their “mean” string. We applied the proposed clustering method to a set of noisy synthetic words and obtained better results than a baseline method. In particular, a sampling procedure using tied backward sampling demonstrated the best performance in reconstructing original words from noisy words through a clustering process.
Analogies of Information Security

Today, computers are used in virtually all aspects of peoples life, and data is collected of you almost everywhere that you go. At the same time, people have started getting more and more concerned about what parts of their lives have been collected and who has access to that data. In a world where we only seem to get more and more dependent on the computers around us and the valuable information they hold, the necessity for understanding the security behind these systems, and how they can be broken, also increases. Everyone needs a basic understanding of security to be able to stay safe online, not only security experts and computer scientists. Concepts of cyber security and hacking can, however, seem quite intimidating for ordinary people and are usually only for the enthusiasts. How can these subject be introduced to the public in a manner that most people with no background in computer science would be able to understand? In this thesis it will be tested wither analogies and metaphors would make it easier to teach the fundamental subjects of information security and hacking to people with no previous background in computer science and only basic computer skills. This will be done by conducting interview on people with no background in computer science to see what analogies work the best for different topics in information security. From the analogy getting the best response, a small game will be designed with the purpose of using this analogy to teach beginners in the topic. From the interviews conducted, it was found that the topics considered complicated and difficult by the interviewees the analogies had the best effect. The most popular analogy was explaining the basics of public key encryption and this was then made into a small game taking inspiration from several gamification elements. The interviews also reveled that analogies often had little effect for topics that were considered straightforward as they usually became redundant and inferior to ordinary explanations of the topic.
Neues Konzept zur Planung, Ausführung und Überwachung von Roboteraufgaben mit hierarchischen Petri-Netzen

Es wird gezeigt, wie die aufgabenausfuhrungsrelevanten Komponenten einer hybriden Steuerungsarchitektur mit Hilfe von hierarchischen Petri-Netzen umgesetzt, integriert und mit Uberwachungsmodulen verknupft werden konnen. Hierzu wird zunachst ein Konzept zur Generierung von Aufgabenwissen vorgeschlagen, das es erlaubt Bausteine komplexer Handlungen systematisiert zu entwerfen. Im Anschluss wird ein neues Konzept zur online Uberwachung von Bewegungsvorgangen bei humanoiden Robotern vorgestellt.
From social interaction to ethical AI: a developmental roadmap

AI and robot ethics have recently gained a lot of attention because adaptive machines are increasingly involved in ethically sensitive scenarios and cause incidents of public outcry. Much of the debate has been focused on achieving highest moral standards in handling ethical dilemmas on which not even humans can agree, which indicates that the wrong questions are being asked. We suggest to address this ethics debate strictly through the lens of what behavior seems socially acceptable, rather than idealistically ethical. Learning such behavior puts the debate into the very heart of developmental robotics. This paper poses a roadmap of computational and experimental questions to address the development of socially acceptable machines. We emphasize the need for social reward mechanisms and learning architectures that integrate these while reaching beyond limitations of plain reinforcement-learning agents. We suggest to use the metaphor of “needs” to bridge rewards and higher level abstractions such as goals for both communication and action generation in a social context. We then suggest a series of experimental questions and possible platforms and paradigms to guide future research in the area.
MAKER: A Kilobot Swarm

A Kilobot is a small, inexpensive robot designed by the Self-Organizing Systems Research Group at Harvard University. These robots have features that enable researchers to test collective algorithms on hundreds of robots (called a swarm) without the logistical problems that are faced when dealing with a large number of units1. This paper presents a tested procedure on how to construct a Kilobot Swarm. The procedure was created from a combination of the original Kilobot documents made available by Harvard University2, other materials later released by Harvard3, as well as some additional insight and modifications that result in a single document with all of the relevant information to get a Kilobot Swarm operational.
Mapping prior information onto LMI eigenvalue-regions for discrete-time subspace identification

In subspace identification, prior information can be used to constrain the eigenvalues of the estimated state-space model by defining corresponding LMI regions. In this paper, first we argue on what kind of practical information can be extracted from historical data or step-response experiments to possibly improve the dynamical properties of the corresponding model and, also, on how to mitigate the effect of the uncertainty on such information. For instance, prior knowledge regarding the overshoot, the period between damped oscillations and settling time may be useful to constraint the possible locations of the eigenvalues of the discrete-time model. Then, we show how to map the prior information onto LMI regions and, when the obtaining regions are non-convex, to obtain convex approximations.
Winter habitat use by juvenile greater sage-grouse on Parker Mountain, Utah: implications for sagebrush management

Greater sage-grouse (Centrocercus urophasianus; hereafter, sage-grouse) are entirely dependent on sagebrush (Artemisia spp.) for food and cover during winter. Loss or fragmentation of important wintering areas could have a disproportionate affect on population size. We radio-marked and monitored 91 juvenile sage-grouse in south-central Utah from 2008 to 2010. Thirty-four individuals survived to winter (January to March) and were used to evaluate winter habitat use. Resource use was calculated using kernel density estimation of radio-marked individuals and compared to available habitat using a G-test. We found that juvenile sage-grouse used winter habitats characterized by 0 to 5% slopes regardless of aspect and slopes 5 to 15% with south-to-west facing aspects. The importance of high slope (5 to 15%) wintering habitats has not been previously documented in sage-grouse. Most winter use was on a small proportion (3%; 2,910 ha) of available habitat. Important wintering habitats may not be readily identifiable in typical years, and consequently, due to their elevation, may be more susceptible to land management treatments focused on increasing early season livestock or big game winter forage, rendering them unsuitable for winter use by sage-grouse. Prior to implementing land management treatments in lower elevation sagebrush sites with slopes ≤5% regardless of aspect and slopes 5 to 15% south to west in aspect, managers should consider the potential effects of such treatments on the availability of suitable winter habitat to mitigate against winters with above-normal snowfall.