Miracle-dz/UniFAR-FTUs huggingface dataset:
Slow-wave brain connectivity predicts executive functioning and group belonging in socially vulnerable individuals

Important efforts have been made to describe the neural and cognitive features of healthy and clinical populations. However, the neural and cognitive features of socially vulnerable individuals remain largely unexplored, despite their proneness to developing neurocognitive disorders. Socially vulnerable individuals can be characterised as socially deprived, having a low socioeconomic status, suffering from chronic social stress, and exhibiting poor social adaptation. While it is known that such individuals are likely to perform worse than their peers on executive function tasks, studies on healthy but socially vulnerable groups are lacking. In the current study, we explore whether neural power and connectivity signatures can characterise executive function performance in healthy but socially vulnerable individuals, shedding light on the impairing effects that chronic stress and social disadvantages have on cognition. We measured resting-state electroencephalography and executive functioning in 38 socially vulnerable participants and 38 matched control participants. Our findings indicate that while neural power was uninformative, lower delta and theta phase synchrony are associated with worse executive function performance in all participants, whereas delta phase synchrony is higher in the socially vulnerable group compared to the control group. Finally, we found that delta phase synchrony and years of schooling are the best predictors for belonging to the socially vulnerable group. Overall, these findings suggest that exposure to chronic stress due to socioeconomic factors and a lack of education are associated with changes in slow-wave neural connectivity and executive functioning.
Probing the physiological role of the plastid outer-envelope membrane using the oemiR plasmid collection

Plastids are the site of complex biochemical pathways, most prominently photosynthesis. The organelle evolved through endosymbiosis with a cyanobacterium, which is exemplified by the outer envelope (OE) membrane that harbors more than 40 proteins in Arabidopsis. Their evolutionary conservation indicates high significance for plant cell function. While a few proteins are well-studied as part of the protein translocon complex the majority of OE protein (OEP) functions is unclear. Gaining a deeper functional understanding has been complicated by the lack of observable loss-of-function mutant phenotypes, which is often rooted in functional genetic redundancy. Therefore, we designed OE-specific artificial micro RNAs (oemiRs) capable of downregulating transcripts from several loci simultaneously. We successfully tested oemiR function by performing a proof-of-concept screen for pale and cold-sensitive mutants. An in-depth analysis of pale mutant alleles deficient in the translocon component TOC75 using proteomics provided new insights into putative compensatory import pathways. The cold stress screen not only recapitulated three previously known phenotypes of cold-sensitive mutants, but also identified four mutants of additional oemiR OE loci. Altogether our study revealed a role of the OE to tolerate cold conditions and showcasts the power of the oemiR collection to research the significance of OEPs.
AUTOMATIC RELEVANCE DETERMINATION IN NONNEGATIVE MATRIX FACTORIZATION BASED ON A ZERO-INFLATED COMPOUND POISSON-GAMMA DISTRIBUTION

In this paper, we consider the determination of the number of factors in nonnega-tive matrix factorization (NMF) for a zero-inﬂated data matrix. This zero-inﬂated case leads to poor approximation to the nonnegative data matrix. To address this problem, we use the zero-inﬂated compound Poisson-gamma distribution as the error distribution in NMF. In addition, we consider automatic relevance determination (ARD) for model order selection. Our simulation study shows that our method is better than the basic ARD method for zero-inﬂated data. We apply our proposed method to real-world purchasing data to determine the number of buying patterns.
Quantum Mechanics

Das Buch ist in einer dritten, v ̈ ollig überarbeiteten Auflage erschienen. Es ist als Begleitung eines wenigstens einj ̈ ahrigen Kurses gedacht und wendet sich an Studenten, die Vordiplomskenntnisse in Mechanik, Elektrodynamik sowie Atomphysik besitzen. Die Quantenmechnik wird in ihrer ganzen Breite behandelt. Das Buch beginnt mit der Wellenmechanik, der ein sehr großer Platz einger ̈ aumt und auf die immer wieder zurückgegriffen wird. Freie Bewegung, harmonischer Oszillator, st ̈ uckweise konstante Potentiale werden ausf ̈ uhrlich behandelt. Ebenso detailreich werden N ̈ aherungsverfahren wie WKB und Variationsrechnung diskutiert. Mathematische Grundlagen ̈ ub r Vektorräume, Operatoren und deren Eigenschaften schließen sich an. Der Drehimpuls wird im Zusammenhang mit sph ̈ arisch symmetrischen Potentialen und Streutheorie eingeführt. Spin, Zweiniveausysteme, Symmetrien und St ̈ orungstheorie werden ebenfalls gründlich besprochen. In h ̈ oheren Kapiteln behandelt das Buch einf ̈ uhrend komplexere und moderne Themen der Quantenmechanik wie Vielteilchensysteme, Quantenoptik, relativistische Quantenmechanik und Quantenstatistik/Informationstheorie. Der Stoff wird detailreich besprochen, viele Beispiele werden durchgerechnet. Behandelter Stoff wird immer wieder aufgegriffen und so vertieft. Die Aufgaben sind in den Text eingeflochten, um durch sofortiges L ̈ osen ein tieferes Verst ̈ andnis zu erm ̈ oglichen. Musterlösungen werden allerdings nicht angegeben. Der Autor behandelt auch Themen, die in anderen Quantenmechanikb ̈ uchern eher zu kurz kommen, so die koh ̈ arenten Zust ände, den getriebenen harmonischen Oszillator und nichtorthogonale Basen.
NAPE-PLD in the ventral tegmental area regulates reward events, feeding and energy homeostasis

The N-acyl phosphatidylethanolamine-specific phospholipase D (NAPE-PLD) catalyzes the production of N-acylethanolamines (NAEs), a family of endogenous bioactive lipids, which are involved in various biological processes ranging from neuronal functions to energy homeostasis and feeding behaviors. Reward-dependent behaviors depend on the dopamine (DA) transmission between the ventral tegmental area (VTA) and the nucleus accumbens (NAc) which conveys reward-values and scales reinforced behaviors. However, whether and how NAPE-PLD may contribute to the regulation of feeding and reward-dependent behaviors has not been investigated yet. This biological question is of paramount importance since NAEs are altered in obesity and metabolic disorders. Here, we show that transcriptomic meta-analysis highlights a potential role for NAPE-PLD within the VTA→NAc circuit. Using brain-specific invalidation approaches, we report that the integrity of NAPE-PLD is required for the proper homeostasis of NAEs within the midbrain VTA and it affects food-reward behaviors. Moreover, region-specific knock-down of NAPE-PLD in the VTA resulted in enhanced food-reward seeking and reinforced behaviors which were associated with increased in vivo DA release dynamics in response to both food and non-food-related rewards together with heightened tropism towards food consumption. Furthermore, midbrain knock-down of NAPE-PLD, which led to increased energy expenditure and adapted nutrients partitioning, elicited a relative protection against high-fat diet-mediated body fat gain and obesity-associated metabolic features. In conclusion, these findings unravel a new key role of VTA NAPE-PLD in shaping DA-dependent events, feeding behaviors and energy homeostasis, thus providing new insights on the regulation of body metabolism. Highlights - NAPE-PLD and NAEs are enriched in the VTA and regulate food-reinforced behaviors and reward processes. - NAPE-PLD scales in vivo VTA→NAc dopamine dynamics. - NAPE-PLD in the VTA contributes to whole-body energy balance and metabolic efficiency. - Downregulation of VTA NAPE-PLD ameliorates obesity-associated metabolic features.
Effect of Mn substitution on structural and dielectric properties of bismuth ferrite

ABSTRACT Bismuth ferrite (BiFeO3 or BFO) and manganese doped bismuth ferrite (BiFe1-xMnxO3, × = 0.1, × = 0.2) ceramic samples were fabricated using sol-gel processing technique. Pure phase BFO powder and its composites were obtained after annealing the as-prepared powder at 500°C. Ceramic samples were then fabricated by pressing in the form of circular discs followed by sintering at 550°C for 2 hours. The influence of Mn doping on structural, morphological and dielectric properties of BiFeO3 were studied and analysed. The frequency responses of dielectric constant (κ) and loss tangent (tanδ) have been investigated over a wide range of frequencies at room temperature.
Loss of REP1 impacts choroidal melanogenesis in choroideremia

Choroideremia (CHM) is a rare X-linked chorioretinal dystrophy affecting the photoreceptors, retinal pigment epithelium (RPE) and choroid, however, the involvement of the choroid in disease progression is not fully understood. CHM is caused by mutations in the CHM gene, encoding the ubiquitously expressed Rab escort protein 1 (REP1). REP1 plays an important role in intracellular trafficking of vesicles, including melanosomes. In this study, we examined ultrastructure of the choroid in chmru848 fish and Chmnull/WT mouse models using transmission electron microscopy. Significant pigmentary disruptions were observed, with a lack of melanosomes in the choroid of chmru848fish from 4 days post fertilisation (4dpf). Total melanin and expression of melanogenesis genes tyr, tryp1a, mitf, dct and pmel were also reduced from 4dpf. In Chmnull/WT mice, choroidal melanosomes were significantly smaller at 1 month and at 1 year, eumelanin was reduced, and the choroid was thicker. The choroid in CHM patients was also examined using optical coherence tomography (OCT) and OCT- angiography (OCT-A) and the area of preserved choriocapillaris (CC) was found to be smaller than that of overlying photoreceptors, indicating that the choroid is degenerating at a faster rate. Histopathology of an enucleated eye from a 74-year-old CHM male patient revealed isolated areas of RPE but no associated underlying CC. Significant degenerative changes in the choroid of CHM patients and animal models are seen, highlighting the importance of administrative routes involving the choroid, such as suprachoroidal delivery. Pigmentary disruptions in CHM animal models reveal an important role for REP1 in melanogenesis, and drugs that improve melanin production represent a potential novel therapeutic avenue.
Autophagy restricts fungal accommodation in the roots of Arabidopsis thaliana

Endophytic root colonisation of the host plant Arabidopsis thaliana by the beneficial root endophyte Serendipita indica is characterised by an initial biotrophic phase followed by a restricted host cell death-associated phase. During colonisation two secreted S. indica effectors synergistically produce the cell death inducer deoxyadenosine (dAdo). Although the mechanism of regulated cell death is important for fungal accommodation, the host molecular pathways that restrict S. indica-induced cell death and allow the maintenance of a mutualistic relationship are largely unknown. Here we show that autophagy, a major degradation pathway plays a key role in the beneficial interaction between S. indica and Arabidopsis. Autophagy restricts fungal colonisation and exerts a protective function against dAdo-induced cell death. Impairment of dAdo uptake in an autophagy mutant background confers resistance to dAdo-induced cell death and precludes the symbiosis-mediated cell death. Taken together, we propose that autophagy-driven pro-survival responses are required to facilitate a long-lasting mutualistic interaction between S. indica and Arabidopsis.
Cell division angle regulates the tissue mechanics and tunes the amount of cerebellar folding

Modeling has proposed that the amount of neural tissue folding is set by the level of differential-expansion between tissue layers and that the wavelength is set by the thickness of the outer layer. Here we used inbred mouse strains with distinct amounts of cerebellar folding to investigate these predictions. We identified a critical period where the folding amount diverges between the strains. In this period, regional changes in the level of differential-expansion between the external granule layer (EGL) and underlying core correlate with the folding amount in each strain. Additionally, the thickness of the EGL is regionally adjusted during the critical period alongside corresponding changes in wavelength. While the number of SHH-expressing Purkinje cells predicts the folding amount, the proliferation rate in the EGL is the same between the strains. However, regional changes in the cell division angle within the EGL predicts both the tangential-expansion and thickness of the EGL. Cell division angle is likely a tunable mechanism whereby both the level of differential-expansion and thickness of the EGL are regionally tuned to set the amount and wavelength of folding.
Processus stochastiques matriciels, systèmes de racines et probabilités non commutatives

On etudie quelques aspects de certaines diffusions matricielles pour lesquelles on utilise des outils d'analyse harmonique pour repondre a des questions de nature probabiliste : on commence par le processus de Laguerre, puis on s'interesse au processus de Dunkl radial qui generalise le processus des valeurs propres de ces diffusions. On regarde ensuite le processus de Jacobi dans le cas ou la taille de la matrice tend vers l'infini, ceci nous plonge dans le monde des probabilites libres. Le dernier chapitre est consacre a la resolution d'un probleme de grandes deviations pour des statistiques de processus de Jacobi univaries.
Hypersonic Orbiter Separation From A Carrier

The separation between an orbiter and a carrier at hypersonic speed is a very critical process. For a detailed 3-D-Orbiter configuration and a carrier stage idealized by a flat plate the aerodynamic characteristics – in particular the shock interaction effects – are thoroughly investigated and analyzed. Using finite volume methods, in the numerical simulation in viscid, laminar and turbulent flows are studied. The computational results are verified with available experimental data. For a secure aerodynamic dominated separation process, it is recommended that the initial angle of attack should be greater than 2°.
Two-point Crossover Operator in Genetic Algorithm for Deep Learning Compiler

The wide usage of tensor computation in deep neural networks (DNNs) has boosted the high demand for high-performance and flexible library implementation on different hardware platforms, which is time-cost and inefficient. Deep learning compilers (DLCs) are therefore proposed, such as Ansor, to search the optimization computation combinations automatically. Ansor can generate high-performance tensor programs by employing Genetic Algorithm (GA) in its auto-tuning process. However, the structure information of an individual is easily destroyed by the uniform crossover operator, which leads to low search efficiency. In this paper, we propose a two-point crossover operator applied in Ansor called Ansor-TPC, which can optimize tensor computation with higher efficiency. The tensor expression can be computed with random schedules regarded as individuals. When performing the crossover operator, Ansor-TPC exchanges parent genes at two points instead of every point, which can preserve the structure information of programs and find the optimal schedule combination in the large search space. A high-performance program is generated for targeted hardware based on the optimized schedule configuration. Ansor-TPC is compared with the benchmarks at different levels. In terms of average performance, Ansor-TPC achieves 1.07--23.2× performance speedup. In terms of the best performance, Ansor-TPC outperforms by up to 1.06--23.0×.
What You Always Wanted to Know About Evolution Strategies, But Never Dared to Ask

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Evaluation of the impact of various modifications to CMA-ES that facilitate its theoretical analysis

In this paper we introduce modified versions of CMA-ES with the objective to help to prove convergence of CMA-ES. In order to ensure that the modifications do not alter the performances of the algorithm too much, we benchmark variants of the algorithm derived from them on problems of the bbob test suite. We observe that the main performances losses are observed on ill-conditioned problems, which is probably due to the absence of cumulation in the adaptation of the covariance matrix. However, the versions of CMA-ES presented in this paper have globally similar performances to the original CMA-ES.
Using Evolutionary Computation to Find Parameters that Promote Egalitarian Major Evolutionary Transitions

Evolutionary transitions, where replicating units combine to form more complex units, are a major source of the complexity found in nature. In this paper we aim to find conditions that promote egalitarian major transitions in a digital artificial ecology. We identify major transitions in this context by observing changes in fitness across different levels of organization. Fitness increases primarily at the community level suggest the occurrence of major transitions. We employ a genetic algorithm using lexicase selection to find regions of parameter space that promote community-level fitness increases. This approach successfully finds multiple ecological community structures that appear to support major transitions. These results illustrate the power of evolutionary computation for exploring the parameter space of complex simulations and push us closer to an understanding of the factors that lead to egalitarian major transitions.
Surrogate-Assisted NSGA-II Algorithm for Expensive Multiobjective Optimization

Most real world problems are multiobjective by nature and expensive to evaluate. We propose in this work a surrogate assisted approach for multiobjective evolutionary algorithms by building a surrogate model on each objective. However, integrating a surrogate model within an optimization process generates complexity with additional hyper-parameters to tune. Empirical validation on standards MOO benchmark problems of a use case based on NSGA-II and surrogates using SVM regression shows a significant improvement of the optimization cost in terms of true objectives evaluations, especially for low budget. We also discuss the behavior of the proposed algorithm using different values of the parameter calibrating the use of the surrogate model.
Component-based thinking in designing interactive multiobjective evolutionary methods

Multiobjective optimization problems have multiple conflicting objective functions to be optimized simultaneously. They have many Pareto optimal solutions representing different trade-offs, and a decision-maker needs to find the most preferred one. Although most multiobjective evolutionary algorithms approximate the Pareto optimal set, their variants incorporate preference information to focus on a subset of solutions that interest the decision-maker. Interactive methods allow decision-makers to provide preference information iteratively during the solution process, enabling them to learn about available solutions and their preferences' feasibility. Nevertheless, most interactive evolutionary methods do not sufficiently support the decision-maker in finding the most preferred solution and may be cognitively too demanding. We propose a framework for designing and implementing interactive evolutionary methods. It contains algorithmic components based on similarities in the structure of existing preference-based evolutionary algorithms and decision-makers' needs during interaction. The components can be combined in different ways to create new interactive methods or to instantiate the existing ones. We show an example of the implementation of the proposed framework composed of three elements: a graphical user interface, a database, and a set of algorithmic components. The resulting software can be utilized to develop new methods and increase their usability in real-world applications.
A Relative Approach to Comparative Performance Analysis for Quantum Optimization

We discuss a small study on how to compare the performance of various solving techniques for quadratic unconstrained binary optimization (QUBO). Since well-known metrics are seldomly applicable, we suggest comparing the relative performance, i.e., how much the quality of solution (compared to other solutions of the same solver) for a QUBO shifts between different solving techniques. We propose looking for big shifts systematically for an empirical complexity analysis. Code is available at github.com/thomasgabor/gecco-relative.
Benchmarking the Borg algorithm on the Biobjective bbob-biobj Testbed

The Borg MOEA [8] is an optimization algorithm, designed to handle real-world problems of a multi objective and multimodal nature. In this report, we examine the effectiveness of Borg for solving optimization problems with only two objectives. To this end, we benchmark the performance of the algorithm on the bbob-biobj test suite via the COCO platform, comparing it to current state-of-the-art algorithms. The study uses standard values for all the parameters but one, as retrieved from http://borgmoea.org/. The only parameter that varies between different problem instances is the ε parameter, a crucial scale tuning parameter. To adapt this parameter, we devised and applied a heuristic. We find that the algorithm performs respectably, although it does not surpass the current state-of-the-art algorithms for any of the problem instances examined, and particularly loses performance on problems with a high-dimensional search space. Additionally, we observed that our heuristic for tuning the ε-parameter results in significant performance improvements compared to using a fixed value for ε.