Miracle-dz/UniFAR-FTUs huggingface dataset:
Sex differences in social synchronization of conditioned fear

Socially coordinated threat responses support the survival of social groups, and the distinct social roles of males and females predict sex differences in such coordination. However, this study area remains unexplored in mice, the most commonly used laboratory species. Here, we examined two behaviors: a recently reported ‘fear synchrony,’ where paired mice synchronize auditory-conditioned freezing more strongly in males, and a newly identified ‘CS-induced affiliation,’ where mice increase proximity upon a conditioned stimulus. These behaviors necessitate the integration of social cues with emotional CS. To understand how sex influences that process, we manipulated social cues through partner familiarity and emotional states via prior stress. Unfamiliarity moderately reduced synchrony in male dyads but not in females. Whereas stress disrupted male synchrony and contrarily enhanced female synchrony. Unfamiliarity eliminated CS-induced affiliation in both sexes, while stress caused males to distance each other but had no effects in females. Interestingly, heterosexual dyads showed resilience in both coordinated behaviors unaffected by stress or unfamiliarity. These findings reveal sex-specific adaptations in socio-emotional integration when orchestrating socially coordinated behaviors and suggest that the sex-recognition circuits confer stress- and unfamiliarity-resilience, in particular, in heterosexual dyads.
High Nucleotide Diversity Accompanies Differential DNA Methylation in Naturally Diverging Populations

Epigenetic mechanisms such as DNA methylation (DNAme) are thought to comprise an invaluable adaptive toolkit in the early stages of local adaptation, especially when genetic diversity is constrained. However, the link between genetic diversity and DNAme has been scarcely examined in natural populations, despite its potential to shed light on the evolutionary forces acting on methylation state. Here, we analysed reduced-representation bisulfite sequencing and whole genome pool-seq data from marine and freshwater stickleback populations to examine the relationship between DNAme variation (between- and within-population), and nucleotide diversity in the context of freshwater adaptation. We find that sites that are differentially methylated between populations have higher underlying standing genetic variation, with diversity higher among sites that gained methylation in freshwater than those that lost it. Strikingly, while nucleotide diversity is generally lower in the freshwater population as expected from a population bottleneck, this is not the case for sites which lost methylation which instead have elevated nucleotide diversity in freshwater compared to marine. Subsequently, we show that nucleotide diversity is higher among sites with ancestrally variable methylation and also positively correlates with the sensitivity to environmentally induced methylation change. Both suggest that as selection on the control of methylation state becomes relaxed, so too does selection against mutations at the sites themselves. Increased epigenetic variance in a population is therefore likely to precede genetic diversification.
Barcoded overexpression screens in gut Bacteroidales identify genes with new roles in carbon utilization and stress resistance

A mechanistic understanding of host-microbe interactions in the gut microbiome is hindered by poorly annotated bacterial genomes. While functional genomics can generate large gene-to- phenotype datasets to accelerate functional discovery, their applications to study gut anaerobes have been limited. For instance, most gain-of-function screens of gut-derived genes have been performed in Escherichia coli and assayed in a small number of conditions. To address these challenges, we developed Barcoded Overexpression BActerial shotgun library sequencing (Boba-seq). We demonstrate the power of this approach by assaying genes from diverse gut Bacteroidales overexpressed in Bacteroides thetaiotaomicron. From hundreds of experiments, we identified new functions and novel phenotypes for 29 genes involved in carbohydrate metabolism or tolerance to antibiotics or bile salts. Highlights include the discovery of a D- glucosamine kinase, a raffinose transporter, and several routes that increase tolerance to bile salts through lipid biosynthesis. This approach can be readily applied to develop screens in other strains and additional phenotypic assay types.
A description of the genus Denitromonas nom. rev.: Denitromonas iodatirespirans sp. nov. a novel iodate-reducing bacterium and two novel perchlorate-reducing bacteria Denitromonas halophila and Denitromonas ohlonensis isolated from San Francisco Bay intertidal mudflats

The genus Denitromonas is currently a non-validated taxon that has been identified in several recent publications as members of microbial communities arising from marine environments. Very little is known about the biology of Denitromonas spp. , and no pure cultures are presently found in any culture collections. The current epitaph of Denitromonas was given to the organism under the assumption that all members of this genus are denitrifying bacteria. This study performs phenotypic and genomic analyses on three new Denitromonas spp. isolated from tidal mudflats in the San Francisco Bay. We demonstrate that Denitromonas spp. are indeed all facultative denitrifying bacteria that utilize a variety of carbon sources such as acetate, lactate, and succinate. In addition, individual strains also use the esoteric electron acceptors perchlorate, chlorate, and iodate. Both 16S and Rps/Rpl phylogenetic analyses place Denitromonas spp. as a deep branching clade in the family Zoogloeaceae, separate from either Thauera spp. , Azoarcus spp. , or Aromatoleum spp. Genome sequencing reveals a G+C content ranging from 63.72% to 66.54%, and genome sizes range between 4.39-5.18 Mb. Genes for salt tolerance and denitrification are distinguishing features that separate Denitromonas spp. from the closely related Azoarcus and Aromatoleum genera.
Simplicite de groupes d'automorphismes d'espaces a courbure negative

We prove that numerous negatively curved simply connected locally compact polyhedral complexes, admitting a discrete cocompact group of automorphisms, have automorphism groups which are locally compact, uncountable, non linear and virtually simple. Examples include hyperbolic buildings, Cayley graphs of word hyperbolic Coxeter systems, and generalizations of cubical complexes, that we call even polyhedral complexes. We use tools introduced by Tits in the case of automorphism groups of trees, and Davis-Moussong's geometric realisation of Coxeter systems.
Transient thermo-elastic disturbances in a visco-elastic semi-space due to a moving internal heat source

Purpose – This paper aims to investigate the transient disturbances created by an internal line heat source that suddenly starts moving uniformly inside a visco‐elastic half‐space.Design/methodology/approach – Generalised theory of thermo‐elasticity with relaxation time proposed by Lord‐Shulman is applied. The material of the semi‐infinite medium is an isotropic visco‐elastic solid of Kelvin‐Voight type. Fourier and Laplace transform techniques are used.Findings – Applying the Fourier and Laplace transform techniques, expressions for displacement components in the transformed domain are found. These expressions prove the existence of three waves – a modified thermal wave, a visco‐elastic wave of defused nature and a transverse visco‐elastic wave.Research limitations/implications – Surface displacement components were evaluated on the boundary for only a short time.Originality/value – The paper provides numerical results that are illustrated graphically to highlight the variations of surface displacement c.
Symmetry and the macroscopic dynamics of antiferromagnetic materials in the presence of spin-polarized current

Antiferromagnetic (AFM) materials with zero or vanishingly small macroscopic magnetization are nowadays the constituent elements of spintronic devices. However, possibility to use them as active elements that show nontrivial controllable magnetic dynamics is still discussible. In the present paper we extend the theory [A.F. Andreev, V.I. Marchenko, Sov. Phys. --- Uspekhi, 23 (1980), 21] of macroscopic dynamics in AFMs for the cases typical for spin-valve devices. In particular, we consider the solid-like magnetic dynamics of AFMs with strong exchange coupling in the presence of spin-polarized current and give an expression for the current-induced Rayleigh dissipation function in terms of the rotation vector for different types %generalized potential of AFMs. Basing on the analysis of linearized equations of motion we predict the current-induced reorientation and AFM resonance, and found the values of critical currents in terms of AFMR frequencies and damping constants. We show the possibility of current-induced spin-diode effect and second-harmonic generation in AF layer. The proposed approach is generalized for the description of current-related phenomena in inhomogeneous AFMs.
Solucions Paral·leles del TSP utilitzant Oracle Coherence

[CATALA] L'objectiu d'aquest treball es resoldre de diferents maneres el famos problema combinatori del viatjant de comerc. Les millors alternatives trobades seran implementades i paral·lelitzades amb el framework d'Oracle Coherence per mostrar els resultats i poder extreure conclusions.
An Augmented Reality Serious Game for Learning Intelligent Wheelchair Control: Comparing Configuration and Tracking Methods

This work proposes an augmented reality serious game (ARSG) for supporting individuals with motor disabilities while controlling robotic wheelchairs. A racing track was used as the game narrative; this included restriction areas, static and dynamic virtual objects, as well as obstacles and signs. To experience the game, a prior configuration of the environment, made through a smartphone or a computer, was required. Furthermore, a visualization tool was developed to exhibit user performance while using the ARSG. Two user studies were conducted with 10 and 20 participants, respectively, to compare (1) how different devices enable configuring the ARSG, and (2) different tracking capabilities, i.e., methods used to place virtual content on the real-world environment while the user interacts with the game and controls the wheelchair in the physical space: C1—motion tracking using cloud anchors; C2—offline motion tracking. Results suggest that configuring the environment with the computer is more efficient and accurate, in contrast to the smartphone, which is characterized as more engaging. In addition, condition C1 stood out as more accurate and robust, while condition C2 appeared to be easier to use.
Master Computer Science

Current approaches to centralising data gathering and creating larger data silos have emerged as a prominent approach to building robust, state-of-the-art models. However, centralised data gathering is not always possible due to data regulations on sensitive information like electronic health records. Federated learning can help us overcome such challenges by introducing a decentralised learning paradigm without transferring sensitive data. Under the federated setting, multiple organisations can jointly train a shared model by keeping the data local and only communicating an instance of the shared model. In this work, we will look at the fundamentals of federated learning and provide a literature study of this emerging ﬁeld. We also designed a benchmarking application to study federated setting hyperparameters and their eﬀect on the model accuracy and convergence.
Role of Micronutrients and Gut Microbiota-Derived Metabolites in COVID-19 Recovery

A balanced and varied diet provides diverse beneficial effects on health, such as adequate micronutrient availability and a gut microbiome in homeostasis. Besides their participation in biochemical processes as cofactors and coenzymes, vitamins and minerals have an immunoregulatory function; meanwhile, gut microbiota and its metabolites coordinate directly and indirectly the cell response through the interaction with the host receptors. Malnourishment is a crucial risk factor for several pathologies, and its involvement during the Coronavirus Disease 2019 pandemic has been reported. This pandemic has caused a significant decline in the worldwide population, especially those with chronic diseases, reduced physical activity, and elder age. Diet and gut microbiota composition are probable causes for this susceptibility, and its supplementation can play a role in reestablishing microbial homeostasis and improving immunity response against Coronavirus Disease 2019 infection and recovery. This study reviews the role of micronutrients and microbiomes in the risk of infection, the severity of disease, and the Coronavirus Disease 2019 sequelae.
Co-Comment Network: A Novel Approach for Construction of Social Networks within Reddit

This work analyzes the online Reddit comments and proposes a novel approach to construct a social network automatically within Reddit that connects users to it. The investigation reveals that users tend to form links to others who have a mutual interest in interacting with some topics or posts. This mutual interest among users can make connections among them in the network. This study shows that Reddit contains a large, strongly connected core of high-degree nodes, surrounded by many small clusters of low-degree nodes which the authors deﬁne as the Co-Comment network. One of the important research focuses is to examine whether the networks built from the Reddit comments show the desirable social network properties, e.g., power-law distribution, small-world effect, and clustering coefﬁcient. Results obtained from the experiment conﬁrm that the Co-Comment network conforms to social network properties. The authors also discover communities within the Co-Comment network and verify them to validate the constructed network.
A DRL-Driven Intelligent Optimization Strategy for Resource Allocation in Cloud-Edge-End Cooperation Environments

Complex dynamic services and heterogeneous network environments make the asymmetrical control a curial issue to handle on the Internet. With the advent of the Internet of Things (IoT) and the fifth generation (5G), the emerging network applications lead to the explosive growth of mobile traffic while bringing forward more challenging service requirements to future radio access networks. Therefore, how to effectively allocate limited heterogeneous network resources to improve content delivery for massive application services to ensure network quality of service (QoS) becomes particularly urgent in heterogeneous network environments. To cope with the explosive mobile traffic caused by emerging Internet services, this paper designs an intelligent optimization strategy based on deep reinforcement learning (DRL) for resource allocation in heterogeneous cloud-edge-end collaboration environments. Meanwhile, the asymmetrical control problem caused by complex dynamic services and heterogeneous network environments is discussed and overcome by distributed cooperation among cloud-edge-end nodes in the system. Specifically, the multi-layer heterogeneous resource allocation problem is formulated as a maximal traffic offloading model, where content caching and request aggregation mechanisms are utilized. A novel DRL policy is proposed to improve content distribution by making cache replacement and task scheduling for arriving content requests in accordance with the information about users’ history requests, in-network cache capacity, available link bandwidth and topology structure. The performance of our proposed solution and its similar counterparts are analyzed in different network conditions.
Influence of Key Parameters on Torque to Mass Ratio in Surface-Mounted PM Machines with Non-Overlapping Windings

This paper analytically derives the torque density expression from the perspective of active material mass for surface-mounted permanent-magnet machines with non-overlapping windings. Based on this, the influence of key parameters, including the split ratio, aspect ratio and PM thickness to air-gap length ratio (hm/g), on the torque to mass ratio (TMR) is investigated under fixed copper loss and rated torque conditions. Guidance of selecting these parameters is given. Moreover, further analysis is carried out when the volume and rated torque are flexible in order to verify applicability of conclusions. Results show that TMR is a concave function of the split ratio, which means the split ratio at ends within the adequate scale can achieve the lightest machine. In addition, TMR increases and then may level off as the machine becomes flatter. Besides, a negative correlation is found between TMR and hm/g. The analytical results are validated by finite element analysis.
A Digital-Twin Based Architecture for Software Longevity in Smart Homes

Smart homes usually consist of smart objects (SOs) with limited resources and capabilities, and therefore constrain the complexity of applications that can be performed on them. In particular, updating smart objects within a smart home is a challenging undertaking, as seemingly insignificant updates affect the longevity of the deployment if they cause previously established dependencies to break. In this paper, we propose an architecture that we call Longevity Digital Twins (LDTs) as a strategic counterpart of SOs, aimed at running at the edge, as local to the smart home as possible. With this architecture, the capabilities of a SO can be virtually enhanced to support the software update process in the smart home. In this context, foresighted software management requires both a local capability to describe involved functionalities together with awareness about existing dependencies in this distributed system. Using a simulated smart home environment, we first measure the impact of conventional update strategies and then present the noticeable improvement that LDTs offer to this problem. Going further, we present the analysis of a real-world use case that showcases the potential of LDTs on how it could not only prevent the installation of breaking updates but also extend a SOs capabilities and its overall longevity.
Business Decision making using Data Science

Several organizations started to adopt the strategy to have data scientist professionals to help them identify the true value of the cost vs benefits and since data science is on fire for a quite long time, adopting the same comes in handy for several factors. i.e., investment, net profit, etc. At the same time, the clarity is missing around what data science is and this tends to introduce the concept of unambiguous theory or assumptions. In this research, we are aiming to pin down the fact of what data science is and how it can lead to crucial decision-making using a data-driven approach. We firmly believe that the accurate limitations of data science scope cannot be defined due to its vast possibilities. If the business wants to align themselves using the data science by understanding the true potential, the following needs to be underlined: 1) understand the importance of related concepts using the relationship approach, 2) identify how fundamental principles of data science can be useful for the different business use case, 3) Identify what the data science can offer depending on our requirement. In this research, we offer a decision-making solution for businesses using the underlying data science principles.
Innovative SMC Insulation Technique Applied to Axial Flux Machine Prototypes

The paper describes in detail the realization of an axial flux machine prototype adopting an innovative Soft Magnetic Composite (SMC) material. The novel technique here presented regards a Layer-by-Layer deposition adopted to insulate pure iron powder grains previously selected. The obtained material is then used to prepare the machine's stator parts. The activity steps are detailed: from the powder preparation to the molding phase, the consequent milling for the final shape, and the consequent magnetic, energetic and mechanical characterization. The prototype design and assembly imply the realization of the stator with the adopted innovative material, and the Authors also realized the preparation of the rotor equipped with bonded magnets. The preliminary experimental results are presented at the end, and considering the machine to be the first trial with the presented material, the efficiency of 77% should be viewed as a promising result for the future development of the activity.
Biodiversity Knowledge Retrieval Application Using Natural Language Processing Technique

Biodiversity knowledge can be considered as complex information that involves several levels including genes, species, and ecology. As the data is complex, it may exist in form of unstructured and unorganized. The ways researchers collect their data according to their interest needs can worsen this situation because there are no standard ways of doing it. Also, the imperfect collaboration between the biological scientist and the information technology community can result in inconsistent data retrieval. These problems can be overcome by the development of retrieval application as it assists in managing and retrieving biodiversity information. The collection of biodiversity data is stored in the database. By doing this way, it will be easier to be managed as it is kept in the most structured way. For retrieving the information, this application received the user’s query as an input. The query will go through several natural language processing techniques including tokenization of a query, removing the stop words, and lemmatizing the generated tokens to produce better output for the user. The process will be repeated until the user is satisfied with the output he or she is looking for. Several test cases have been done in the testing phase and this application has proven to work accordingly based on the conducted analysis. Recall and precision metrics were also being conducted and the result is quite promising. Further research is recommended for producing a better result by improving the query processing techniques and adding more data to the database.
On sumsets in ${\Bbb F}_2^n$

Let ${\Bbb F}_2$ be the finite field of two elements, ${\Bbb F}_2^n$ be the vector space of dimension $n$ over ${\Bbb F}_2$. For sets $A,\,B\subseteq{\Bbb F}_2^n$, their sumset is defined as the set of all pairwise sums $a+b$ with $a\in A,\,b\in B$. Ben Green and Terence Tao proved that, let $K\geq 1$, if$A,\,B\subseteq{\Bbb F}_2^n$ and $|A+B|\leq K|A|^{1\over 2}|B|^{1\over 2}$, then there exists a subspace $H\subseteq{\Bbb F}_2^n$ with $$ |H|\gg\exp(-O(\sqrt{K}\log K))|A| $$ and $x,\,y\in{\Bbb F}_2^n$ such that $$ |A\cap(x+H)|^{1\over 2}|B\cap(y+H)|^{1\over 2}\geq{1\over 2K}|H|. $$ In this note, we shall use the method of Green and Tao with some modification to prove that if $$ |H|\gg\exp(-O(\sqrt{K}))|A|, $$ then the above conclusion still holds true.
Model Modulated Predictive Control (M2PC) of Induction Motors including Magnetic Saturation and Iron Losses

This paper proposes a Model Modulated Predictive Control (M2PC) for rotating induction motors (RIM) taking into consideration both the magnetic saturation and the iron losses. The proposed M2PC is based on a previously developed dynamic model of the RIM considering both self and cross saturation and iron losses; the proposed algorithm uses the discrete-time version of the dynamic model to compute the current prediction and the resulting predicted current error. The built-in PWM modulator chooses the optimal pair of voltage space-vector to be applied by the inverter to minimize the current error. The magnetic model permits obtaining good dynamic performance in every working condition. The results obtained in numerical simulation have shown that the control can maintain good dynamic performances in both loaded and unloaded working conditions.