Miracle-dz/UniFAR-FTUs huggingface dataset:
A robust approach for deep neural networks in presence of label noise: relabelling and filtering instances during training

Deep learning has outperformed other machine learning algorithms in a variety of tasks, and as a result, it is widely used. However, like other machine learning algorithms, deep learning, and convolutional neural networks (CNNs) in particular, perform worse when the data sets present label noise. Therefore, it is important to develop algorithms that help the training of deep networks and their generalization to noise-free test sets. In this paper, we propose a robust training strategy against label noise, called RAFNI, that can be used with any CNN. This algorithm filters and relabels instances of the training set based on the predictions and their probabilities made by the backbone neural network during the training process. That way, this algorithm improves the generalization ability of the CNN on its own. RAFNI consists of three mechanisms: two mechanisms that filter instances and one mechanism that relabels instances. In addition, it does not suppose that the noise rate is known nor does it need to be estimated. We evaluated our algorithm using different data sets of several sizes and characteristics. We also compared it with state-of-the-art models using the CIFAR10 and CIFAR100 benchmarks under different types and rates of label noise and found that RAFNI achieves better results in most cases.
The Planetary Systems Imager adaptive optics system: an initial optical design and performance analysis tool for the PSI-Red AO system

The Planetary Systems Imager (PSI) is a proposed instrument for the Thirty Meter Telescope (TMT) that provides an extreme adaptive optics (AO) correction to a multi-wavelength instrument suite optimized for high contrast science. PSI's broad range of capabilities, spanning imaging, polarimetry, integral field spectroscopy, and high resolution spectroscopy from 0.6–5 μm, with a potential channel at 10 μm, will enable breakthrough science in the areas of exoplanet formation and evolution. Here, we present a preliminary optical design and performance analysis of the 2–5 μm component of the PSI AO system, which must deliver the wavefront quality necessary to support infrared high contrast science cases.
Investigating the role of Word Embeddings in sentiment analysis

During decades, Natural language processing (NLP) expanded its range of tasks, from document classification to automatic text summarization, sentiment analysis, text mining, machine translation, automatic question answering and others. In 2018, T. Young described NLP as a theory-motivated range of computational techniques for the automatic analysis and representation of human language. Outside and before AI, human language has been studied by specialists from various disciplines: linguistics, philosophy, logic, psychology. The aim of this work is to build a neural network to perform a sentiment analysis on Italian reviews from the chatbot customer service. Sentiment analysis is a data mining process which identifies and extracts subjective information from text. It could help to understand the social sentiment of clients, respect a business product or service. It could be a simple classification task that analyses a sentence and tells whether the underlying sentiment is positive or negative. The potentiality of deep learning techniques made this simple classification task evolve, creating new, more complex sentiment analysis, e.g. Intent Analysis and Contextual Semantic Search [1].
Influence of cosmological expansion in local experiments

Whether the cosmological expansion can influence the local dynamics, below the galaxy clusters scale, has been the subject of intense investigations in the past three decades. In this work, we consider McVittie and Kottler spacetimes, embedding a spherical object in a FLRW spacetime. We calculate the influence of the cosmological expansion on the frequency shift of a resonator and estimate its effect on the exchange of light signals between local observers. In passing, we also clarify some of the statements made in the literature.
Matrix functions via linear systems built from continued fractions

A widely used approach to compute the action $f(A)v$ of a matrix function $f(A)$ on a vector $v$ is to use a rational approximation $r$ for $f$ and compute $r(A)v$ instead. If $r$ is not computed adaptively as in rational Krylov methods, this is usually done using the partial fraction expansion of $r$ and solving linear systems with matrices $A- \tau I$ for the various poles $\tau$ of $r$. Here we investigate an alternative approach for the case that a continued fraction representation for the rational function is known rather than a partial fraction expansion. This is typically the case, for example, for Pad\'e approximations. From the continued fraction, we first construct a matrix pencil from which we then obtain what we call the CF-matrix (continued fraction matrix), a block tridiagonal matrix whose blocks consist of polynomials of $A$ with degree bounded by 1 for many continued fractions. We show that one can evaluate $r(A)v$ by solving a single linear system with the CF-matrix and present a number of first theoretical results as a basis for an analysis of future, specific solution methods for the large linear system. While the CF-matrix approach is of principal interest on its own as a new way to compute $f(A)v$, it can in particular be beneficial when a partial fraction expansion is not known beforehand and computing its parameters is ill-conditioned. We report some numerical experiments which show that with standard preconditioners we can achieve fast convergence in the iterative solution of the large linear system.
Towards Fully Automated News Reporting in Brazilian Portuguese

We introduce robot journalists that cover two pressing topics in Brazilian society: COVID-19 spread and Legal Amazon deforestation. Our approach is able to automatically analyze structured domain data, select relevant content, generate news texts and publish them on the Web. We provide a thorough description of our system architecture, report on the results of automatic evaluation, discuss some of the advantages of robot-journalism in society, and point out further steps in our work. Corpus and code are publicly available.
Optimal policy design to mitigate epidemics on networks using an SIS model

Understanding how to effectively control an epidemic spreading on a network is a problem of paramount importance for the scientific community. The ongoing COVID-19 pandemic has highlighted the need for policies that mitigate the spread, without relying on pharmaceutical interventions. These policies typically entail lockdowns and mobility restrictions, having thus nonnegligible socio-economic consequences for the population. We focus on the problem of finding the optimum policies that "flatten the epidemic curve" while limiting the negative consequences for the society, and formulate it as a nonlinear control problem over a finite prediction horizon. We utilize the model predictive control theory to design a strategy to effectively control the disease, balancing safety and normalcy. An explicit formalization of the control scheme is provided for the susceptible–infected–susceptible epidemic model over a network. Its performance and flexibility are demonstrated by means of numerical simulations.
Nachweis aktivierter intrazellulärer Signalwege in humanen glialen Tumoren durch Phosphorylierungs-spezifische Immunhistochemie

Das klinische Verhalten glialer Hirntumore basiert zum grosen Teil auf genetischen Veranderungen, welche die zellulare Signaltransduktion beeinflussen. Durch die Entwicklung von Phosphorylierungs-spezifischen Antikorpern, die an formalinfixiertem und parraffineingebettetem Gewebe (formalin fixed parraffin embedded, FFPE) aktivierte Proteine immunhistochemisch nachweisen konnen, steht eine Methode zur Verfugung, die Signaltransduktion in situ am Tumorgewebe zu untersuchen. In der vorliegenden Arbeit wurde gepruft, inwieweit die Methodik aussagekraftig und reproduzierbar ist und ob durch den Vergleich mit anderen Komponenten der intrazellularen Signaltransduktion Aussagen uber das Verhalten der Tumore getroffen werden konnen. Ferner wurde gepruft, ob mittels Tissue Arrays eine Methode zur Darstellung der Proteine an einem grosen Kollektiv zur Verfugung steht.
Cosmic Evolution of Barred Galaxies up to z ∼ 0.84

We explore the cosmic evolution of the bar length, strength, and light deficit around the bar for 379 barred galaxies at 0.2 < z ≤ 0.835 using F814W images from the COSMOS survey. Our sample covers galaxies with stellar masses 10.0≤log(M*/M⊙)≤11.4 and various Hubble types. The bar length is strongly related to the galaxy mass, the disk scale length (h), R 50, and R 90, where the last two are the radii containing 50% and 90% of total stellar mass, respectively. Bar length remains almost constant, suggesting little or no evolution in bar length over the last 7 Gyr. The normalized bar lengths (R bar/h, R bar/R 50, and R bar/R 90) do not show any clear cosmic evolution. Also, the bar strength (A 2 and Q b ) and the light deficit around the bar reveal little or no cosmic evolution. The constancy of the normalized bar lengths over cosmic time implies that the evolution of bars and of disks is strongly linked over all times. We discuss our results in the framework of predictions from numerical simulations. We conclude there is no strong disagreement between our results and up-to-date simulations.
Joint search of optimal topology and trajectory for planar linkages

We present an algorithm to compute planar linkage topology and geometry, given a user-specified end-effector trajectory. Planar linkage structures convert rotational or prismatic motions of a single actuator into an arbitrarily complex periodic motion, which is an important component when building low-cost, modular robots, mechanical toys, and foldable structures in our daily lives (chairs, bikes, and shelves). The design of such structures requires trial and error even for experienced engineers. Our research provides semi-automatic methods for exploring novel designs given high-level specifications and constraints. We formulate this problem as a non-smooth numerical optimization with quadratic objective functions and non-convex quadratic constraints involving mixed-integer decision variables (MIQCQP). We propose and compare three approximate algorithms to solve this problem: mixed-integer conic-programming (MICP), mixed-integer nonlinear programming (MINLP), and simulated annealing (SA). We evaluated these algorithms searching for planar linkages involving 10 − 14 rigid links. Our results show that the best performance can be achieved by combining MICP and MINLP, leading to a hybrid algorithm capable of finding the planar linkages within a couple of hours on a desktop machine, which significantly outperforms the SA baseline in terms of optimality. We highlight the effectiveness of our optimized planar linkages by using them as legs of a walking robot.
Disentangling the optical AGN and host-galaxy luminosity with a probabilistic flux variation gradient

We present a novel Probabilistic Flux Variation Gradient (PFVG) approach to to separate the contributions of active galactic nuclei (AGN) and host galaxies in the context of photometric reverberation mapping (PRM) of AGN. We explored the ability of recovering the fractional contribution in a model-independent way using the entire set of light curves obtained through different filters and photometric apertures simultaneously. The method is based on the observed bluer when brighter phenomenon that is attributed to the superimposition of a two-component structure; the red host galaxy, which is constant in time, and the varying blue AGN. We describe the PFVG mathematical formalism and demonstrate its performance using simulated light curves and available PRM observations. The new probabilistic approach is able to recover host-galaxy fluxes to within 1% precision as long as the light curves do not show a significant contribution from time delays. This represents a significant improvement with respect to previous applications of the traditional FVG method to PRM data. The proposed PFVG provides an efficient and accurate way to separate the AGN and host-galaxy luminosities in PRM monitoring data. The method will be especially helpful in the case of large upcoming photometric survey telescopes such as the public optical/near-infrared Legacy Survey of Space and Time (LSST) at the Vera C. Rubin Observatory. Finally, we have made the algorithms freely available as part of our Julia PFVG package.
Does Mandated Corporate Social Responsibility Crowd Out Voluntary Corporate Social Responsibility? Evidence from India

We investigate the implementation of a government of India mandate that requires firms to spend at least 2% of their profits on corporate social responsibility (CSR). We find that mandated firms that voluntarily engaged in CSR before the mandate reduce their CSR spending significantly after the mandate. The erstwhile voluntary CSR spenders increase advertising expenditure plausibly to offset the lost signaling value of voluntary CSR. The 2% mandate negatively impacts valuations and operating performance. Our results show that regulatory intervention in CSR diminishes its signaling value and leads to a reduction in voluntary CSR spending.
Sample and Communication-Efficient Decentralized Actor-Critic Algorithms with Finite-Time Analysis

Actor-critic (AC) algorithms have been widely adopted in decentralized multi-agent systems to learn the optimal joint control policy. However, existing decentralized AC algorithms either do not preserve the privacy of agents or are not sample and communication-efficient. In this work, we develop two decentralized AC and natural AC (NAC) algorithms that are private, and sample and communication-efficient. In both algorithms, agents share noisy information to preserve privacy and adopt mini-batch updates to improve sample and communication efficiency. Particularly for decentralized NAC, we develop a decentralized Markovian SGD algorithm with an adaptive mini-batch size to efficiently compute the natural policy gradient. Under Markovian sampling and linear function approximation, we prove the proposed decentralized AC and NAC algorithms achieve the state-of-the-art sample complexities $\mathcal{O}\big(\epsilon^{-2}\ln(\epsilon^{-1})\big)$ and $\mathcal{O}\big(\epsilon^{-3}\ln(\epsilon^{-1})\big)$, respectively, and the same small communication complexity $\mathcal{O}\big(\epsilon^{-1}\ln(\epsilon^{-1})\big)$. Numerical experiments demonstrate that the proposed algorithms achieve lower sample and communication complexities than the existing decentralized AC algorithm.
Lifting Methods in Mass Partition Problems

Many results about mass partitions are proved by lifting $\mathds {R}^d$ to a higher-dimensional space and dividing the higher-dimensional space into pieces. We extend such methods to use lifting arguments to polyhedral surfaces. Among other results, we prove the existence of equipartitions of $d+1$ measures in $\mathds {R}^d$ by parallel hyperplanes and of $d+2$ measures in $\mathds {R}^d$ by concentric spheres. For measures whose supports are sufficiently well separated, we prove results where one can cut a fixed (possibly different) fraction of each measure either by parallel hyperplanes, concentric spheres, convex polyhedral surfaces of few facets, or convex polytopes with few vertices.
A new characterisation of quasi-hereditary Nakayama algebras and applications

We call a finite dimensional algebra A S-connected if the projective dimensions of the simple A-modules form an intervall. We prove that a Nakayama algebra A is S-connected if and only if A is quasi-hereditary. We apply this result to improve an inequality for the global dimension of quasihereditary Nakayama algebras due to Brown. We furthermore classify the Nakayama algebras where equality is attained in Brown’s inequality and show that they are enumerated by the even indexed Fibonacci numbers if algebra is cyclic and by the odd indexed Fibonacci numbers if algebra is linear.
Bounds on fluctuations for ensembles of quantum thermal machines

We study universal aspects of fluctuations in an ensemble of noninteracting continuous quantum thermal machines in the steady-state limit, beyond linear response. Considering an individual machine, such as a refrigerator, in which relative fluctuations (and high order cumulants) of the cooling heat current to the absorbed heat current, η (n), are upper-bounded, η(n)⩽ηCn with n ⩾ 2 and η C the Carnot efficiency, we prove that an ensemble of N distinct machines similarly satisfies this upper bound on the relative fluctuations of the ensemble, ηN(n)⩽ηCn . For an ensemble of distinct quantum refrigerators with components operating in the tight-coupling limit we further prove the existence of a lower bound on ηN(n) in specific cases, exemplified on three-level quantum absorption refrigerators and resonant-energy thermoelectric junctions. Beyond special cases, the existence of a lower bound on ηN(2) for an ensemble of quantum refrigerators is demonstrated by numerical simulations. We further show that the lower bound on ηN(2) may, however, be violated for an ensemble of quantum engines.
RoadAtlas: Intelligent Platform for Automated Road Defect Detection and Asset Management

With the rapid development of intelligent detection algorithms based on deep learning, much progress has been made in automatic road defect recognition and road marking parsing. This can effectively address the issue of an expensive and time-consuming process for professional inspectors to review the street manually. Towards this goal, we present RoadAtlas, a novel end-to-end integrated system that can support 1) road defect detection, 2) road marking parsing, 3) a web-based dashboard for presenting and inputting data by users, and 4) a backend containing a well-structured database and developed APIs.
ArchivalQA: A Large-scale Benchmark Dataset for Open Domain Question Answering over Archival News Collections

In the last few years, open-domain question an-001 swering (ODQA) has advanced rapidly due to 002 the development of deep learning techniques 003 and the availability of large-scale QA datasets. 004 However, the current datasets are essentially 005 designed for synchronic document collections 006 (e.g., Wikipedia). Temporal news collections 007 such as long-term news archives spanning sev-008 eral decades, are rarely used in training the 009 models despite they are quite valuable for our 010 society. To foster the research in the ﬁeld 011 of ODQA on such historical collections, we 012 present ArchivalQA, a large question answer-013 ing dataset consisting of 532,444 question-014 answer pairs which is designed for temporal 015 news QA. We divide our dataset into four sub-016 parts based on the question difﬁculty levels 017 and the containment of temporal expressions, 018 which we believe are useful for training and 019 testing ODQA systems characterized by dif-020 ferent strengths and abilities. The novel QA 021 dataset-constructing framework that we intro-022 duce can be also applied to create datasets over 023 other types of collections 1 . 024
Cross-lingual Offensive Language Identification for Low Resource Languages: The Case of Marathi

The widespread presence of offensive language on social media motivated the development of systems capable of recognizing such content automatically. Apart from a few notable exceptions, most research on automatic offensive language identification has dealt with English. To address this shortcoming, we introduce MOLD, the Marathi Offensive Language Dataset. MOLD is the first dataset of its kind compiled for Marathi, thus opening a new domain for research in low-resource Indo-Aryan languages. We present results from several machine learning experiments on this dataset, including zero-short and other transfer learning experiments on state-of-the-art cross-lingual transformers from existing data in Bengali, English, and Hindi.
Charge transport in germanium doped phosphorene nanoribbons

New two dimensional structures containing phosphorus and germanium atoms are introduced for nanoelectronic applications. Under various bias voltages, electronic transport in the systems has been studied with the non-equilibrium Green’s function formalism. I–V characteristics have been extracted. The density of states (DOS) and transmission spectra, T(E,Vbias), have been investigated and it was shown that charge transport occurs when the bias voltage reaches about 1 V. The negative differential resistance (NDR) appears in zigzag phosphorene nanoribbons (zPNRs) while it is completely suppressed after replacing edge phosphorus atoms with germanium ones. The calculated molecular projected self-consistent Hamiltonian (MPSH) shows that the spatial distribution of orbital levels has been affected by the electrodes. The studied structures have a band-gap of about 0.7 eV which absorbs light in the visible range and thus these structures could be interesting contenders for solar cells applications.
Stochastic Assignment for Deploying Multiple Marsupial Robots

Marsupial robot teams consist of carrier robots that transport and deploy multiple passenger robots, such as a team of ground robots that carry and deploy multiple aerial robots, to rapidly explore complex environments. We specifically address the problem of planning the deployment times and locations of the carrier robots to best meet the objectives of a mission while reasoning over uncertain future observations and rewards. While prior work proposed optimal, polynomial-time solutions to single-carrier robot systems, the multiple-carrier robot deployment problem is fundamentally harder as it requires addressing conflicts and dependencies between deployments of multiple passenger robots. We propose a centralized heuristic search algorithm for the multiple-carrier robot deployment problem that combines Monte Carlo Tree Search with a dynamic programming-based solution to the Sequential Stochastic Assignment Problem as a rollout action-selection policy. Our results with both procedurally-generated data and data drawn from the DARPA Subterranean Challenge Urban Circuit show the viability of our approach and substantial exploration performance improvements over alternative algorithms.