Miracle-dz/UniFAR-FTUs huggingface dataset:
Normalized gradient flow optimization in the training of ReLU artificial neural networks

The training of artiﬁcial neural networks (ANNs) is nowadays a highly relevant algorithmic procedure with many applications in science and industry. Roughly speaking, ANNs can be regarded as iterated compositions between aﬃne linear functions and certain ﬁxed nonlinear functions, which are usually multidimensional versions of a one-dimensional so-called activation function. The most popular choice of such a one-dimensional activation function is the rectiﬁed linear unit (ReLU) activation function which maps a real number to its positive part R ∋ x 7→ max { x, 0 } ∈ R . In this article we propose and analyze a modiﬁed variant of the standard training procedure of such ReLU ANNs in the sense that we propose to restrict the negative gradient ﬂow dynamics to a large submanifold of the ANN parameter space, which is a strict C ∞ -submanifold of the entire ANN parameter space that seems to enjoy better regularity properties than the entire ANN parameter space but which is also suﬃciently large and suﬃciently high dimensional so that it can represent all ANN realization functions that can be represented through the entire ANN parameter space. In the special situation of shallow ANNs with just one-dimensional
A S TATISTICAL M ANIFOLD F RAMEWORK FOR P OINT

A large class of problems in machine learning involve data sets in which each data point is a point cloud in R. Many applications involving point cloud data sets require a means of measuring not only distances, but also angles, volumes, derivatives, and other more advanced geometric notions. Existing approaches for the most part tend to be ad hoc, and lack coordinate-invariance. In this paper we develop a Riemannian geometric structure for point cloud data. By interpreting a point cloud as samples from some underlying probability distribution, the space of point cloud data can be given the structure of a statistical manifold, with the Fisher information metric acting as a natural Riemannian metric. The only requirement on the part of the user is the choice of a meaningful underlying probability distribution, which is more intuitive and natural to make than what is required in existing ad hoc formulations. Two autoencoder case studies involving point cloud data are presented to demonstrate the advantages of our statistical manifold framework: (i) interpolating between two 3D point cloud data sets to smoothly deform one object into another; (ii) finding an optimal set of latent space coordinates that minimizes distortion.
High Dose-Rate Ionizing Radiation Source from Tight Focusing in Air of a mJ-class Femtosecond Laser

: Ultrashort electron beams with femtosecond to picosecond bunch durations oﬀer unique opportunities to explore active research areas ranging from ultrafast structural dynamics to ultra-high dose-rate radiobiological studies. We present a straightforward method to generate MeV-ranged electron beams in ambient air through the tight focusing of a few-cycle, mJ-class femtosecond IR laser. At one meter from the source, the highest measured dose rate of 0.15 Gy/s exceeds the yearly dose limit in less than one second and warrants the implementation of radiation protection. Two-dimensional Particle-In-Cell simulations conﬁrm that the acceleration mechanism is based on the relativistic ponderomotive force and show theoretical agreement with the measured electron energy. Furthermore, we discuss the scalability of this method with the continuing development of mJ-class high average power lasers, moreover providing a promising approach for FLASH radiation therapy.
Exploration of 3D wavelet scattering transform coefficients for line-intensity mapping measurements

The wavelet scattering transform (WST) has recently gained attention in the context of large-scale structure studies, being a possible generator of summary statistics encapsulating non-Gaussianities beyond the reach of the conventional power spectrum. This work examines the three-dimensional solid harmonic WST in the context of a three-dimensional line-intensity mapping measurement to be undertaken by current and proposed phases of the CO Mapping Array Project (COMAP). The WST coefficients demonstrate interpretable behaviour in the context of noiseless CO line-intensity simulations. The contribution of the cosmological z ∼ 3 signal to these coefficients is also detectable in principle even in the Pathfinder phase of COMAP. Using the peak-patch method to generate large numbers of simulations and incorporating observational noise, we numerically estimate covariance matrices and show that careful choices of WST hyperparameters and rescaled or reduced coefficient sets are both necessary to keep covariances well-conditioned. Fisher forecasts show that even a reduced ‘shapeless’ set of ℓ-averaged WST coefficients show constraining power that can exceed that of the power spectrum alone even with similar detection significance. The full WST could improve parameter constraints even over the combination of the power spectrum and the voxel intensity distribution, showing that it uniquely encapsulates shape information about the line-intensity field. However, practical applications urgently require further understanding of the WST in key contexts like covariances and cross-correlations.
Developing an NLP-based Recommender System for the Ethical, Legal, and Social Implications of Synthetic Biology

Introduction Synthetic biology is an emerging field that involves the engineering and re-design of organisms for purposes such as food security, health, and environmental protection. As such, it poses numerous ethical, legal, and social implications (ELSI) for researchers and policy makers. Various efforts to ensure socially responsible synthetic biology are underway. Policy making is one regulatory avenue, and other initiatives have sought to embed social scientists and ethicists on synthetic biology projects (Balmer et al., 2015). However, given the nascency of synthetic biology, the number of heterogeneous domains it spans (Raimbault et al., 2016), and the open nature of many ethical questions, it has proven challenging to establish widespread concrete policies, and including social scientists and ethicists on synthetic biology teams has met with mixed success.
Effects of Varying Land Coverage, Rotation Period, and Water Vapor on Equatorial Climates that Bridge the Gap between Earth-like and Titan-like

Saturn’s largest moon, Titan, has an Earth-like volatile cycle, but with methane playing the role of water and surface liquid reservoirs geographically isolated at high latitudes. We recreate Titan’s characteristic dry hydroclimate at the equator of an Earth-like climate model without seasons and with water as the condensable by varying a small set of planetary parameters. We use three observationally motivated criteria for Titan-like conditions at the equator: 1) the peak in surface specific humidity is not at the equator, despite it having the warmest annual-mean temperatures; 2) the vertical profile of specific humidity in the equatorial column is nearly constant through the lower troposphere; and 3) the relative humidity near the surface at the equator is significantly lower than saturation (lower than 60%). We find that simply reducing the available water at the equator does not fully reproduce Titan-like conditions. We additionally vary the rotation period and volatility of water to mimic Titan’s slower rotation and more abundant methane vapor. Longer rotation periods coupled with a dry equatorial surface meet fewer of the Titan-like criteria than equivalent experiments with shorter rotation periods. Experiments with higher volatility of water meet more criteria than those with lower volatility, with some of those with the highest volatility meeting all three, demonstrating that an Earth-like planet can display Titan-like climatology by changing only a few physical parameters.
A note on holographic renormalization for non-conformal branes by recursion

We review holographic renormalization for non-conformal branes using the Hamilton-Jacobi formalism. We provide the tools required for the computation of the holographic dictionary, while we present some marginally new technical results regarding holographic renormalization for $Dp$-branes, $p<5$, for Einstein-Maxwell theory coupled to a dilaton. The computations are based on a recursive algorithm by Papadimitriou, that solves the radial Hamilton-Jacobi equation in the asymptotic boundary of the bulk. This paper is a short version of the Master thesis of the author written in 2014 at the Instituto de F\'isica Te\'orica, UAM, Madrid.
Workers of the Internet Unite? Online Freelancer Organisation Among Remote Gig Economy Workers in Six Asian and African Countries

This article presents findings regarding collective organisation among online freelancers in middle-income countries. Drawing on research in Southeast Asia and Sub-Saharan Africa, we find that the specific nature of the online freelancing labour process gives rise to a distinctive form of organisation, in which social media groups play a central role in structuring communication and unions are absent. Previous research is limited to either conventional freelancers or ‘microworkers’ who do relatively low-skilled tasks via online labour platforms. This study uses 107 interviews and a survey of 658 freelancers who obtain work via a variety of online platforms to highlight that Internet-based communities play a vital role in their work experiences. Internet-based communities enable workers to support each other and share information. This, in turn, increases their security and protection. However, these communities are fragmented by nationality, occupation and platform.
Building a Relation Extraction Baseline for Gene-Disease Associations: A Reproducibility Study

Reproducibility is an important task in scientific research. It is crucial for researchers to compare newly developed systems with the state-of-the-art to assess whether they made a breakthrough. However previous works may not be immediately reproducible, for example due to the lack of source code. In this work we reproduce DEXTER, a system to automatically extract Gene-Disease Associations (GDAs) from biomedical abstracts [1]. The goal is to provide a benchmark for future works regarding Relation Extraction (RE), enabling researchers to test and compare their results.
Rediscovering a little known fact about the t-test: algebraic, geometric, distributional and graphical considerations

We discuss the role that the null hypothesis should play in the construction of a test statistic used to make a decision about that hypothesis. To construct the test statistic for a point null hypothesis about a binomial proportion, a common recommendation is to act as if the null hypothesis is true. We argue that, on the surface, the one-sample t-test of a point null hypothesis about a Gaussian population mean does not appear to follow the recommendation. We show how simple algebraic manipulations of the usual t-statistic lead to an equivalent test procedure consistent with the recommendation, we provide geometric intuition regarding this equivalence, and we consider extensions to testing nested hypotheses in Gaussian linear models. We discuss an application to graphical residual diagnostics where the form of the test statistic makes a practical difference. We argue that these issues should be discussed in advanced undergraduate and graduate courses.
Competition between Rayleigh–Bénard and horizontal convection

Abstract We investigate the dynamics of a fluid layer subject to a bottom heat flux and a top monotonically increasing temperature profile driving horizontal convection (HC). We use direct numerical simulations and consider a large range of flux-based Rayleigh numbers $10^6 \leq Ra_F \leq 10^9$ and imposed top horizontal to bottom vertical heat flux ratios $0 \leq \varLambda \leq 1$. The fluid domain is a closed two-dimensional box with aspect ratio $4\leq \varGamma \leq 16$ and we consider no-slip boundaries and adiabatic side walls. We demonstrate a regime transition from Rayleigh–Bénard (RB) convection to HC at $\varLambda \approx 10^{-2}$, which is independent of $Ra_F$ and $\varGamma$. At small $\varLambda$, the flow is organised in multiple overturning cells with approximately unit aspect ratio, whereas at large $\varLambda$ a single cell is obtained. The RB-relevant Nusselt number scaling with $Ra_F$ and the HC-relevant Nusselt number scaling with the horizontal Rayleigh number $Ra_L=Ra_F\varLambda \varGamma ^4$ are in good agreement with previous results from classical RB convection and HC studies in the limit $\varLambda \ll 10^{-2}$ and $\varLambda \gg 10^{-2}$, respectively. We demonstrate that the system is multi-stable near the transition $\varLambda \approx 10^{-2}$, i.e. the exact number of cells not only depends on $\varLambda$ but also on the system's history. Our results suggest that subglacial lakes, which motivated this study, are likely to be dominated by RB convection, unless the slope of the ice–water interface, which controls the horizontal temperature gradient via the pressure-dependence of the freezing point, is greater than unity.
Smooth Anonymity for Sparse Binary Matrices

When working with user data providing well-deﬁned privacy guarantees is paramount. In this work we aim to manipulate and share an entire sparse dataset with a third party privately. In fact, diﬀerential privacy has emerged as the gold standard of privacy, however, when it comes to sharing sparse datasets, as one of our main results, we prove that any diﬀerentially private mechanism that maintains a reasonable similarity with the initial dataset is doomed to have a very weak privacy guarantee. Hence we need to opt for other privacy notions such as k -anonymity are better at preserving utility in this context. In this work we present a variation of k -anonymity, which we call smooth k -anonymity and design simple algorithms that eﬃciently provide smooth k -anonymity. We further perform an empirical evaluation to back our theoretical guarantees, and show that our algorithm improves the performance in downstream machine learning tasks on anonymized data.
Design and characterization of a recoil ion momentum spectrometer for investigating molecular fragmentation dynamics upon MeV energy ion impact ionization.

We present the development and performance of a newly built recoil ion momentum spectrometer to study the fragmentation dynamics of ionized molecules. The spectrometer is based on the two-stage Wiley-McLaren geometry and satisfies both time and velocity focusing conditions. An electrostatic lens has been introduced in the drift region to achieve velocity imaging and higher angular collection. The spectrometer is equipped with a 2D position-sensitive detector with multi-hit coincidence electronics. Ionic fragments with kinetic energy ∼8 eV can be detected with 4π collection. The overall performance of the spectrometer has been tested by carrying out three-dimensional ion imaging measurements for diatomic (N2) and polyatomic (CH2Cl2) molecules under the impact of 1 MeV protons. Three-dimensional momentum and kinetic energy release distributions were derived from the measured position and time-of-flight spectra. The observed features of the various fragmentation channels as well as the measured kinetic energy release distributions are in complete agreement with the available data.
A Simple Novel Global Optimization Algorithm and Its Performance on Some Benchmark Functions

This paper propose a new frame work for finding global minima which we call optimization by cut. In each iteration, it takes some samples from the feasible region and evaluates the objective function at these points. Based on the observations it cuts off from the feasible region a subregion that is unlikely to contain a global minimum. The procedure is then repeated with the feasible region replaced by the remaining region until the remaining region is ``small'' enough. If a global minimum is kept in the remaining region of each iteration, then it can be located with an arbitrary precision. The frame work is surprisingly efficient in view of its simple form and can be applied to black-box functions since neither special structure nor derivative information is required. The performance of the proposed frame work is evaluated on some benchmark functions and the results show that it can find a global minimum rather quickly.
Flexible Ramping Product Procurement in Day-Ahead Markets

This article puts forward a methodology for procuring flexible ramping products (FRPs) in the day-ahead market (DAM). The proposed methodology comprises two market passes, the first of which employs a stochastic unit commitment (SUC) model that explicitly evaluates the uncertainty and the intra-hourly and inter-hourly variability of net load so as to minimize the expected total operating cost. The second pass clears the DAM while imposing FRP requirements. The cornerstone of our work is to set the FRP requirements at levels that drive the DAM decisions toward the optimal SUC decisions. Our methodology provides an economic underpinning for the stipulated FRP requirements, and it brings forth DAM awards that reduce the costs toward the expected total cost under SUC, while conforming to the chief DAM design principles. By preemptively considering the dispatch costs before awarding FRPs, it can further avert unexpectedly high costs that could result with the deployment of procured FRPs. We conduct numerical studies and lay out the relative merits of the proposed methodology vis-\`a-vis selected benchmarks based on various evaluation metrics.
Multiscale modeling of semimetal contact to two-dimensional transition metal dichalcogenide semiconductor

A multiscale simulation approach is developed to simulate the contact transport properties between semimetal and a monolayer two-dimensional transition metal dichalcogenide (TMDC) semiconductor. The results elucidate the mechanisms for low contact resistance between semimetal and TMDC semiconductor contacts from a quantum transport perspective. The simulation results compare favorably with recent experiments. Furthermore, the results show that the contact resistance of a bismuth-MoS2 contact can be further reduced by engineering the dielectric environment and doping the TMDC material to [Formula: see text]. The quantum transport simulation indicates the possibility to achieve an ultrashort contact transfer length of ∼1 nm, which can allow aggressive scaling of the contact size.
Cartan calculi on the free loop spaces

A typical example of a Cartan calculus consists of the Lie derivative and the contraction with vector fields of a manifold on the derivation ring of the de Rham complex. In this manuscript, a second stage of the Cartan calculus is investigated. In a general setting, the stage is formulated with operators obtained by the Andr\'e-Quillen cohomology of a commutative differential graded algebra $A$ on the Hochschild homology of $A$ in terms of the homotopy Cartan calculus in the sense of Fiorenza and Kowalzig. Moreover, the Cartan calculus is interpreted geometrically with maps from the rational homotopy group of the monoid of self-homotopy equivalences on a space $M$ to the derivation ring on the loop cohomology of $M$. We also give a geometric description to Sullivan's isomorphism, which relates the geometric Cartan calculus to the algebraic one, via the $\Gamma_1$ map due to F\'elix and Thomas.
-1-Phylogenic tree of Escherichia coli isolated from Bat Myotis emarginatus based on 16srRNA Prokaryote Sequences

: Background : Studies on the microbial ecology of bats are limited and such information is necessary to determine the ecological significance of these hosts. The present study aimed at isolation and phylogenetic identification of Escherichia coli ( E. coli ) from bat Myotis emarginatus based on 16srRNA Prokaryote gene Sequences Methodology: We investigated the presence of E. coli in tissue samples (spleen, liver, kidney, lung, stomach, and intestines) from insectivory bat Myotis emarginatus . E. coli identified by cultures media and biochemical test then DNA extracted and subjected to a polymerase chain reaction (PCR) for amplification 16srRNA Prokaryote gene. Nucleotide sequences were searched in GenBank using the advanced Basic Local Alignment Search Tool (BLAST). Results : E. coli isolated from different parts of the bat and mainly appeared in intestines. 16srRNA gene was detected in all isolates and genes. Nucleotide sequences depended on E. coli phylogeny. E. coli phylogenetic tree generated by the NCBI-GenBank database and the accession number codes of these isolates are MK418911 and MK418912. Conclusion: bat Myotis emarginatus is a good reservoir for E. coli and 16S rRNA gene detected in all isolates so have a high capability for identifying E. coli .
A Columnwise Update Algorithm for Sparse Stochastic Matrix Factorization

Nonnegative matrix factorization arises widely in machine learning and data analysis. In this paper, for a given factorization of rank r, we consider the sparse stochastic matrix factorization (SSMF) of decomposing a prescribed m-by-n stochastic matrix V into a product of an m-by-r stochastic matrix W and an r-by-n stochastic matrix H, where both W and H are required to be sparse. With the prescribed sparsity level, we reformulate the SSMF as an unconstrained nonconvex-nonsmooth minimization problem and introduce a column-wise update algorithm for solving the minimization problem. We show that our algorithm converges globally. The main advantage of our algorithm is that the generated sequence converges to a special critical point of the cost function, which is nearly a global minimizer over each column vector of the W-factor and is a global minimizer over the H-factor as a whole if there is no sparsity requirement on H. Numerical experiments on both synthetic and real data sets are given to demonstrate the effectiveness of our proposed algorithm.
Stars That Approach within One Parsec of the Sun: New and More Accurate Encounters Identified in Gaia Data Release 3

Close encounters of stars to the Sun could affect life on Earth through gravitational perturbations of comets in the Oort cloud or exposure to ionizing radiation. By integrating orbits through the Galactic potential, I identify which of 33 million stars in Gaia DR3 with complete phase space information come close to the Sun. 61 stars formally approach within 1 pc, although there is high confidence in only 42 (two thirds) of these, the rest being spurious measurements or (in) binary systems. Most of the stars will encounter within the past or future 6 Myr; earlier/later encounters are less common due to the magnitude limit of the Gaia radial velocities (RVs). Several close encountering stars are identified for the first time, and the encounter times, distances, and velocities of previously known close encounters are determined more precisely on account of the significantly improved precision of Gaia DR3 over earlier releases. The K7 dwarf Gl 710 remains the closest known encounter, with an estimated (median) encounter distance of 0.0636 pc (90% confidence interval 0.0595–0.0678 pc) to take place in 1.3 Myr. The new second closest encounter took place 2.8 Myr ago: this was the G3 dwarf HD 7977, now 76 pc away, which approached within less than 0.05 pc of the Sun with a probability of one third. The apparent close encounter of the white dwarf UPM J0812-3529 is probably spurious due to an incorrect RV in Gaia DR3.