Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Applicability of ML Fairness Notions

ML-based predictive systems are increasingly used to support decisions with a critical impact on individuals' lives such as college admission, job hiring, child custody, criminal risk assessment, etc. As a result, fairness emerged as an important requirement to guarantee that predictive systems do not discriminate against specific individuals or entire sub-populations, in particular, minorities. Given the inherent subjectivity of viewing the concept of fairness, several notions of fairness have been introduced in the literature. This paper is a survey of fairness notions that, unlike other surveys in the literature, addresses the question of "which notion of fairness is most suited to a given real-world scenario and why? ". Our attempt to answer this question consists in (1) identifying the set of fairness-related characteristics of the real-world scenario at hand, (2) analyzing the behavior of each fairness notion, and then (3) fitting these two elements to recommend the most suitable fairness notion in every specific setup. The results are summarized in a decision diagram that can be used by practitioners and policy makers to navigate the relatively large catalogue of fairness notions.
FIVE NEW MESOCENTRUS SZÉPLIGETI SPECIES FROM THE AUSTRALIAN REGION (HYMENOPTERA: BRACONIDAE: BETYLOBRACONINAE)

A short review is presented on our knowledge of the betylobraconine species since 1900, the year of the description of the first betylobraconine species by SZEPLIGETI under the name Mesocentrus crassipes. The subfamily was erected by TOBIAS in 1979 with the description of the new genus and new species Betylobracon waterhousi. The systematics of the subfamily taxa are expounded. Five new species are described as follows: Mesocentrus baloghi, M. gilvus, M. reptus, M. rutilus and M. variicolor. New combination was established: Mesocentrus tenuis (PAPP, 1993) (originally described in Yelicones). Hitherto 21 betylobraconine species are known. Additional features are introduced in the redescription of M. crassipes SZEPLIGETI. A key is presented to the seven known Mesocentrus species. With 55 original figures.
When and where: Estimating the date and location of introduction for exotic pests and pathogens

A fundamental question during the outbreak of a novel disease or invasion of an exotic pest is: At what location and date was it first introduced? With this information, future introductions can be anticipated and perhaps avoided. Point process models are commonly used for mapping species distribution and disease occurrence. If the time and location of introductions were known, then point process models could be used to map and understand the factors that influence introductions; however, rarely is the process of introduction directly observed. We propose embedding a point process within hierarchical Bayesian models commonly used to understand the spatio-temporal dynamics of invasion. Including a point process within a hierarchical Bayesian model enables inference regarding the location and date of introduction from indirect observation of the process such as species or disease occurrence records. We illustrate our approach using disease surveillance data collected to monitor white-nose syndrome, which is a fungal disease that threatens many North American species of bats. We use our model and surveillance data to estimate the location and date that the pathogen was introduced into the United States. Finally, we compare forecasts from our model to forecasts obtained from state-of-the-art regression-based statistical and machine learning methods. Our results show that the pathogen causing white-nose syndrome was most likely introduced into the United States 4 years prior to the first detection, but there is a moderate level of uncertainty in this estimate. The location of introduction could be up to 510 km east of the location of first discovery, but our results indicate that there is a relatively high probability the location of first detection could be the location of introduction.
Synthesizing Tasks for Block-based Programming

Block-based visual programming environments play a critical role in introducing computing concepts to K-12 students. One of the key pedagogical challenges in these environments is in designing new practice tasks for a student that match a desired level of difficulty and exercise specific programming concepts. In this paper, we formalize the problem of synthesizing visual programming tasks. In particular, given a reference visual task $\rm T^{in}$ and its solution code $\rm C^{in}$, we propose a novel methodology to automatically generate a set $\{(\rm T^{out}, \rm C^{out})\}$ of new tasks along with solution codes such that tasks $\rm T^{in}$ and $\rm T^{out}$ are conceptually similar but visually dissimilar. Our methodology is based on the realization that the mapping from the space of visual tasks to their solution codes is highly discontinuous; hence, directly mutating reference task $\rm T^{in}$ to generate new tasks is futile. Our task synthesis algorithm operates by first mutating code $\rm C^{in}$ to obtain a set of codes $\{\rm C^{out}\}$. Then, the algorithm performs symbolic execution over a code $\rm C^{out}$ to obtain a visual task $\rm T^{out}$; this step uses the Monte Carlo Tree Search (MCTS) procedure to guide the search in the symbolic tree. We demonstrate the effectiveness of our algorithm through an extensive empirical evaluation and user study on reference tasks taken from the \emph{Hour of the Code: Classic Maze} challenge by \emph{this http URL} and the \emph{Intro to Programming with Karel} course by \emph{this http URL}.
Interval fragmentations with choice: Equidistribution and the evolution of tagged fragments

We consider a Markovian evolution on point processes, the $\Psi$--process, on the unit interval in which points are added according to a rule that depends only on the spacings of the existing point configuration. Having chosen a spacing, a new point is added uniformly within it. Building on previous work of the authors and of Junge, we show that the empirical distribution of points in such a process is always equidistributed under mild assumptions on the rule, generalizing work of Junge. A major portion of this article is devoted to the study of a particular growth--fragmentation process, or cell process, which is a type of piecewise--deterministic Markov process (PDMP). This process represents a linearized version of a size--biased sampling from the $\Psi$--process. We show that this PDMP is ergodic and develop the semigroup theory of it, to show that it describes a linearized version of the $\Psi$--process. This PDMP has appeared in other contexts, and in some sense we develop its theory under minimal assumptions.
Understanding Diversity based Pruning of Neural Networks - Statistical Mechanical Analysis

Deep learning architectures with a huge number of parameters are often compressed using pruning techniques to ensure computational efficiency of inference during deployment. Despite multitude of empirical advances, there is no theoretical understanding of the effectiveness of different pruning methods. We address this issue by setting up the problem in the statistical mechanics formulation of a teacher-student framework and deriving generalization error (GE) bounds of specific pruning methods. This theoretical premise allows comparison between pruning methods and we use it to investigate compression of neural networks via diversity-based pruning methods. A recent work showed that Determinantal Point Process (DPP) based node pruning method is notably superior to competing approaches when tested on real datasets. Using GE bounds in the aforementioned setup we provide theoretical guarantees for their empirical observations. Another consistent finding in literature is that sparse neural networks (edge pruned) generalize better than dense neural networks (node pruned) for a fixed number of parameters. We use our theoretical setup to prove that baseline random edge pruning method performs better than DPP node pruning method. Finally, we draw motivation from our theoretical results to propose a DPP edge pruning technique for neural networks which empirically outperforms other competing pruning methods on real datasets.
Cohomological properties and Arens regularity of Banach algebras

In this paper, we study some cohomlogical properties of Banach algebras. For a Banach algebra $A$ and a Banach $A$-bimodule $B$, we investigate the vanishing of the first Hochschild cohomology groups $H^1(A^n,B^m)$ and $H_{w^*}^1(A^n,B^m)$, where $0\leq m,n\leq 3$. For amenable Banach algebra $A$, we show that there are Banach $A$-bimodules $C$, $D$ and elements $\mathfrak{a}, \mathfrak{b}\in A^{**}$ such that $$Z^1(A,C^*)=\{R_{D^{\prime\prime}(\mathfrak{a})}:~D\in Z^1(A,C^*)\}=\{L_{D^{\prime\prime}(\mathfrak{b})}:~D\in Z^1(A,D^*)\}. $$ where, for every $b\in B$, $L_{b}(a)=ba$ and $R_{b}(a)=a b,$ for every $a\in A$. Moreover, under a condition, we show that if the second transpose of a continuous derivation from the Banach algebra $A$ into $A^*$ i.e., a continuous linear map from $A^{**}$ into $A^{***}$, is a derivation, then $A$ is Arens regular. Finally, we show that if $A$ is a dual left strongly irregular Banach algebra such that its second dual is amenable, then $A$ is reflexive.
Predicting Sample Collision with Neural Networks

Many state-of-art robotics applications require fast and efficient motion planning algorithms. Existing motion planning methods become less effective as the dimensionality of the robot and its workspace increases, especially the computational cost of collision detection routines. In this work, we present a framework to address the cost of expensive primitive operations in sampling-based motion planning. This framework determines the validity of a sample robot configuration through a novel combination of a Contractive AutoEncoder (CAE), which captures a occupancy grids representation of the robot's workspace, and a Multilayer Perceptron, which efficiently predicts the collision state of the robot from the CAE and the robot's configuration. We evaluate our framework on multiple planning problems with a variety of robots in 2D and 3D workspaces. The results show that (1) the framework is computationally efficient in all investigated problems, and (2) the framework generalizes well to new workspaces.
Etude du complexe de réparation par excision de nucléotides

Mon travail de these s’est axe sur deux projets, le premier a porte sur l’etude fonctionnelle de la sous unite TTDA de TFIIH, un facteur general de transcription implique dans la reparation NER, afin d’identifier de nouveaux partenaires de la sous unite TTDA nous avons realise un crible double hybride et ainsi selectionne ZBTB38, une proteine implique dans la repression de genes portant des methylations CpG, nous avons confirme son interaction avec TTDA, et son implication dans la reparation NER. La deuxieme partie a porte sur l’etude du recrutement des facteurs NER sur la chromatine en absence de lesions de l’ADN. En utilisant le systeme rapporteur LacO/LacR nous avons observe que l’immobilisation de l’un des facteurs NER sur la chromatine non endommagee permet l’assemblage du PInC de facon sequentielle et ordonnee. Nous avons aussi revele que TTDA, connue pour etre impliquee dans la trichothiodystrophie, joue un role cle dans la completion du PInC.
TXS 0128+554: A Young Gamma-Ray-emitting Active Galactic Nucleus with Episodic Jet Activity

We have carried out a Chandra X-ray and multifrequency radio Very Long Baseline Array study of the active galactic nucleus (AGN) TXS 0128+554, which is associated with the Fermi γ-ray source 4FGL J0131.2+5547. The AGN is unresolved in a target 19.3 ks Chandra image, and its spectrum is well fit by a simple absorbed power-law model, with no distinguishable spectral features. Its relatively soft X-ray spectrum compared to other compact symmetric objects (CSOs) may be indicative of a thermal emission component, for which we were able to obtain an upper temperature limit of kT = 0.08 keV. The compact radio morphology and measured advance speed of 0.32 c ± 0.07 c indicate a kinematic age of only 82 yr ± 17 yr, placing TXS 0128+554 among the youngest members of the CSO class. The lack of compact, inverted spectrum hotspots and an emission gap between the bright inner jet and outer radio lobe structure indicate that the jets have undergone episodic activity, and were relaunched a decade ago. The predicted γ-ray emission from the lobes, based on an inverse Compton-emitting cocoon model, is three orders of magnitude below the observed Fermi-LAT flux. A comparison to other Fermi-detected and non-Fermi-detected CSOs with redshift z < 0.1 indicates that the γ-ray emission likely originates in the inner jet/core region, and that nearby, recently launched AGN jets are primary candidates for detection by the Fermi-LAT instrument.
Expectation and Price in Incomplete Markets

Risk-neutral pricing dictates that the discounted derivative price is a martingale in a measure equivalent to the economic measure. The residual ambiguity for incomplete markets is here resolved by minimising the entropy of the price measure from the economic measure, subject to mark-to-market constraints, following arguments based on the optimisation of portfolio risk. The approach accounts for market and funding convexities and incorporates available price information, interpolating between methodologies based on expectation and replication.
Online Multi-Facility Location

Facility Location problems ask to place facilities in a way that optimizes a given objective function so as to provide a service to all clients. These are one of the most well-studied optimization problems spanning many research areas such as operations research, computer science, and management science. Traditionally, these problems are solved with the assumption that clients need to be served by one facility each. In many real-world scenarios, it is very likely that clients need a robust service that requires more than one facility for each client. In this paper, we capture this robustness by exploring a generalization of Facility Location problems, called Multi-Facility Location problems, in the online setting. An additional parameter k, which represents the number of facilities required to serve a client, is given. We propose the first online algorithms for the metric and non-metric variants of Multi-Facility Location and measure their performance with competitive analysis, the standard to measure online algorithms, in the worst case, in which the cost of the online algorithm is compared to that of the optimal offline algorithm that knows the entire input sequence in advance.
Black holes seeding cosmological phase transitions

We consider a generic first-order phase transition at finite temperature and investigate to what extent a population of primordial black holes, of variable masses, can affect the rate of bubble nucleation. Using a thin-wall approximation, we construct the Euclidean configurations that describe transition at finite temperature. After the transition, the remnant black hole mass is dictated dynamically by the equations of motion. The transition exponent is computed and displays an explicit dependence on temperature. We find the configuration with the lowest Euclidean action to be static and $O(3)$ symmetric; therefore, the transition takes place via thermal excitation. The transition exponent exhibits a strong dependence on the seed mass black hole, $M_+$, being almost directly proportional. A new nucleation condition in the presence of black holes is derived and the nucleation temperature is compared to the familiar flat-space result, i.e. $S_3/T$. For an electroweak-like phase transition it is possible to enhance the nucleation rate if $M_+ \lesssim 10^{15} M_{\rm P}$. Finally, we outline the possible transition scenarios and the consequences for the power spectrum of stochastic gravitational waves produced due to the first-order phase transition.
Scaling Symbolic Methods using Gradients for Neural Model Explanation

Symbolic techniques based on Satisfiability Modulo Theory (SMT) solvers have been proposed for analyzing and verifying neural network properties, but their usage has been fairly limited owing to their poor scalability with larger networks. In this work, we propose a technique for combining gradient-based methods with symbolic techniques to scale such analyses and demonstrate its application for model explanation. In particular, we apply this technique to identify minimal regions in an input that are most relevant for a neural network's prediction. Our approach uses gradient information (based on Integrated Gradients) to focus on a subset of neurons in the first layer, which allows our technique to scale to large networks. The corresponding SMT constraints encode the minimal input mask discovery problem such that after masking the input, the activations of the selected neurons are still above a threshold. After solving for the minimal masks, our approach scores the mask regions to generate a relative ordering of the features within the mask. This produces a saliency map which explains "where a model is looking" when making a prediction. We evaluate our technique on three datasets - MNIST, ImageNet, and Beer Reviews, and demonstrate both quantitatively and qualitatively that the regions generated by our approach are sparser and achieve higher saliency scores compared to the gradient-based methods alone.
Interior-boundary conditions for the Dirac equation at point sources in three dimensions

A recently proposed approach for avoiding the ultraviolet divergence of Hamiltonians with particle creation is based on interior-boundary conditions (IBCs). The approach works well in the non-relativistic case, i.e., for the Laplacian operator. Here, we study how the approach can be applied to Dirac operators. While this has successfully been done already in one space dimension, and more generally for codimension-1 boundaries, the situation of point sources in three dimensions corresponds to a codimension-3 boundary. One would expect that, for such a boundary, Dirac operators do not allow for boundary conditions because they are known not to allow for point interactions in 3D, which also correspond to a boundary condition. Indeed, we confirm this expectation here by proving that there is no self-adjoint operator on a (truncated) Fock space that would correspond to a Dirac operator with an IBC at configurations with a particle at the origin. However, we also present a positive result showing that there are self-adjoint operators with an IBC (on the boundary consisting of configurations with a particle at the origin) that are away from those configurations, given by a Dirac operator plus a sufficiently strong Coulomb potential.
Entropically-controlled self-assembly of polymer membranes at immiscible liquid interfaces

Self-assembly of polymers at liquid interfaces is an emerging technique to produce all-liquid printable and self-healing devices and membranes. It is crucial to control the assembly process but the mechanisms at play remain unclear. Using two different reflectometric methods, we investigate the spontaneous growth of H-bonded PPO-PMAA membranes at a flat liquid-liquid interface. We find that the membrane thickness h grows with time t as h~t^(1/2), which is reminiscent of a diffusion-limited process. However, counter-intuitively, we observe that this process is faster as the PPO molar mass increases. We are able to rationalize these results with a model which considers the diffusion of the PPO chains within the growing membrane. The architecture of the latter is described as a gel-like porous network, with a pore size much smaller than the radius of the diffusing PPO chains, thus inducing entropic barriers that hinder the diffusion process. From the comparison between the experimental data and the result of the model, we extract some key piece of information about the microscopic structure of the membrane. This study opens the route toward the rational design of self-assembled membranes and capsules with optimal properties.
Outer crust of a cold, nonaccreting neutron star within the quark-meson-coupling model

The outer crust properties of cold non-accreting neutron stars are studied within the framework of the quark-meson coupling (QMC) model, which includes the effects of modifications of the quark structure inside individual nucleons when they are within a high-density nuclear medium. With a unique set of five well-constrained adjustable parameters, which have a clear physical basis, the QMC model gives predictions for the ground state observables of even-even nuclei which agree with experiment as well as traditional models. Furthermore, it gives improved theoretical values for nuclei thought to play a role in the outer crusts of neutron stars but for which experimental data is not available. Using the latest experimental data tables wherever possible but otherwise the predictions from the QMC model, we construct an equation of state for the outer crust which is then used within stellar model calculations to obtain an equilibrium sequence of crustal layers, each characterized by a particular neutron rich nuclei. Various properties of the layers are calculated for a range of neutron-star masses, and comparisons are made with alternative equations of state from the literature. This leads to the conclusion that the QMC model successfully predicts the outer crust properties and is fully comparable with the more traditional mass models, which all depend on a larger number of parameters.
Desempenho de frangos de corte criados em diferentes densidades populacionais, no inverno, no Sul do Brasil

The objective of this research was to determine the influence of the bird density: 10, 12, 14 and 16 birds/square meters on broilers performance, during winter time. After 21 days, the feed consumption was affected by different densities. The highest feed consumption was found on the lowest bird density (10 birds/square meters). The body weight was influenced by the density after 35 days. The lowest body weight was obtained on the highest density (16 birds/square meters). However, the carcass yield was not affected by density. The feed conversion was not affected significantil, When the body weight was reduced, the food intake was decreased too. The viability was not influencied by the densities. The economic analysis concluded that a linear benefit was found in increasing the bird density by 10 to 16 birds/square meters.