Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning Continuous Control Policies for Information-Theoretic Active Perception

This paper proposes a method for learning continuous control policies for exploration and active landmark localization. We consider a mobile robot detecting landmarks within a limited sensing range, and tackle the problem of learning a control policy that maximizes the mutual information between the landmark states and the sensor observations. We employ a Kalman filter to convert the partially observable problem in the landmark states to a Markov decision process (MDP), a differentiable field of view to shape the reward function, and an attention-based neural network to represent the control policy. The approach is combined with active volumetric mapping to promote environment exploration in addition to landmark localization. The performance is demonstrated in several simulated landmark localization tasks in comparison with benchmark methods.
An empirical comparison of seasonal growth models

A model for describing the seasonally oscillating growth of fish is presented and compared with the commonly used model of D. Pauly and G. Gaschutz (PG). The new model produces results similar to those obtained from teh PG model, except for teh parameter t o t =0 when t = t 0 .
Efficient Multi-Prize Lottery Tickets: Enhanced Accuracy, Training, and Inference Speed

Recently, Diffenderfer and Kailkhura proposed a new paradigm for learning compact yet highly accurate binary neural networks simply by pruning and quantizing randomly weighted full precision neural networks. However, the accuracy of these multi-prize tickets (MPTs) is highly sensitive to the optimal prune ratio, which limits their applicability. Furthermore, the original implementation did not attain any training or inference speed benefits. In this report, we discuss several improvements to overcome these limitations. We show the benefit of the proposed techniques by performing experiments on CIFAR-10.
Self-supervised Image Clustering from Multiple Incomplete Views via Constrastive Complementary Generation

Incomplete Multi-View Clustering aims to enhance clustering performance by using data from multiple modalities. Despite the fact that several approaches for studying this issue have been proposed, the following drawbacks still persist: 1) It's difficult to learn latent representations that account for complementarity yet consistency without using label information; 2) and thus fails to take full advantage of the hidden information in incomplete data results in suboptimal clustering performance when complete data is scarce. In this paper, we propose Contrastive Incomplete Multi-View Image Clustering with Generative Adversarial Networks (CIMIC-GAN), which uses GAN to fill in incomplete data and uses double contrastive learning to learn consistency on complete and incomplete data. More specifically, considering diversity and complementary information among multiple modalities, we incorporate autoencoding representation of complete and incomplete data into double contrastive learning to achieve learning consistency. Integrating GANs into the autoencoding process can not only take full advantage of new features of incomplete data, but also better generalize the model in the presence of high data missing rates. Experiments conducted on \textcolor{black}{four} extensively-used datasets show that CIMIC-GAN outperforms state-of-the-art incomplete multi-View clustering methods.
Polynomial Preconditioners for Regularized Linear Inverse Problems

This work aims to accelerate the convergence of proximal gradient methods used to solve regularized linear inverse problems. This is achieved by designing a polynomial-based preconditioner that targets the eigenvalue spectrum of the normal operator derived from the linear operator. The preconditioner does not assume any explicit structure on the linear function and thus can be deployed in diverse applications of interest. The efficacy of the preconditioner is validated on three different Magnetic Resonance Imaging applications, where it is seen to achieve faster iterative convergence while achieving similar reconstruction quality.
Matching methods for truncation by death problems

Even in a carefully designed randomised trial, outcomes for some study participants can be missing, or more precisely, ill defined, because participants had died prior to outcome collection. This problem, known as truncation by death, means that the treated and untreated are no longer balanced with respect to covariates determining survival. Therefore, researchers often utilise principal stratification and focus on the Survivor Average Causal Effect (SACE). We present matching-based methods for SACE identification and estimation. We provide identification results motivating the use of matching and discuss practical issues, including the choice of distance measures, matching with replacement, and post-matching estimators. Because the assumptions needed for SACE identification can be too strong, we also present sensitivity analysis techniques and illustrate their use in real data analysis.
Antiprotons from Dark Matter: Effects of a Position-Dependent Diffusion Coefficient

Energetic antiprotons in cosmic rays can serve as an important indirect signature of dark matter. Conventionally, the antiproton flux from dark matter decays or annihilations is calculated by solving the transport equation with a space-independent diffusion coefficient within the diffusion zone of the galaxy, and assuming free propagation outside this zone. Antiproton sources outside of the diffusion zone are ignored. In reality, it is far more likely that the diffusion coefficient increases smoothly with distance from the disk, and the outlying part of the dark matter halo ignored in the conventional approach can be significant, containing as much as 90% of the galactic dark matter by mass in some models. We extend the conventional approach to address these issues. We obtain analytic approximations and numerical solutions for antiproton flux assuming that the diffusion coefficient increases exponentially with the distance from the disk, and including contributions from dark matter annihilations/decays in essentially the full dark matter halo. We find that the antiproton flux predicted in this model deviates from the conventional calculation for the same dark matter parameters by up to about 25%.
Machine learning technique for morphological classification of galaxies from the SDSS. III. The CNN image-based inference of detailed features

This paper follows a series of our works on the applicability of various machine learning methods to morphological galaxy classification (Vavilova et al., 2021, 2022). We exploited the sample of ~315800 low-redshift SDSS DR9 galaxies with absolute stellar magnitudes of −24m < Mr < −19.4m at 0.003 < z < 0.1 redshifts as a target data set for the CNN classifier. Because it is tightly overlapped with the Galaxy Zoo 2 (GZ2) sample, we use these annotated data as the training data set to classify galaxies into 34 detailed features. In the presence of a pronounced difference in visual parameters between galaxies from the GZ2 training data set and galaxies without known morphological parameters, we applied novel procedures, which allowed us for the first time to get rid of this difference for smaller and fainter SDSS galaxies with mr < 17.7. We describe in detail the adversarial validation technique as well as how we managed the optimal train-test split of galaxies from the training data set to verify our CNN model based on the DenseNet-201 realistically. We have also found optimal galaxy image transformations, which help increase the classifier’s generalization ability. We demonstrate for the first time that implication of the CNN model with a train-test split of data sets and size-changing function simulating a decrease in magnitude and size (data augmentation) significantly improves the classification of smaller and fainter SDSS galaxies. It can be considered as another way to improve the human bias for those galaxy images that had a poor vote classification in the GZ project. Such an approach, like autoimmunization, when the CNN classifier, trained on very good galaxy images, is able to retrain bad images from the same homogeneous sample, can be considered co-planar to other methods of combating such a human bias. The most promising result is related to the CNN prediction probability in the classification of detailed features. The accuracy of the CNN classifier is in the range of 83.3—99.4 % depending on 32 features (exception is for “disturbed” (68.55 %) and “arms winding medium” (77.39 %) features). As a result, for the first time, we assigned the detailed morphological classification for more than 140000 low-redshift galaxies, especially at the fainter end. A visual inspection of the samples of galaxies with certain morphological features allowed us to reveal typical problem points of galaxy image classification by shape and features from the astronomical point of view. The morphological catalogs of low-redshift SDSS galaxies with the most interesting features are available through the UkrVO website (http://ukr-vo.org/galaxies/) and VizieR.
The Luminosity Problem: Testing Theories of Star Formation

Abstract Low-mass protostars are less luminous than expected. This luminosity problem is important because the observations appear to be inconsistent with some of the basic premises of star formation theory. Two possible solutions are that stars form slowly, which is supported by recent data, and/or that protostellar accretion is episodic; current data suggest that the latter accounts for less than half the missing luminosity. The solution to the luminosity problem bears directly on the fundamental problem of the time required to form a low-mass star. The protostellar mass and luminosity functions provide powerful tools both for addressing the luminosity problem and for testing theories of star formation. Results are presented for the collapse of singular isothermal spheres, for the collapse of turbulent cores, and for competitive accretion.
Mass spectrum and thermodynamics of quasiconformal gauge theories from gauge/gravity duality

Department of Physics, University of JyvaskylaWe use gauge/gravity duality to study simultaneously the mass spectrum and the thermo-dynamics of a generic quasi-conformal gauge theory, speci ed by its beta function. The betafunction of a quasi-conformal theory almost vanishes, and the coupling is almost constantbetween two widely separated energy scales. Depending on whether the gravity dual hasa black hole or not, the mass spectrum is either a spectrum of quasinormal oscillations ora normal T = 0 mass spectrum. The mass spectrum is quantitatively correlated with thethermal properties of the system. As the theory approaches conformality, the masses have tovanish. We show that in this limit, the masses calculated via gauge/gravity duality satisfyexpected scaling properties.
Electro-osmotic flows under nanoconfinement: A self-consistent approach

We introduce a theoretical and numerical method to investigate the properties of electro-osmotic flows under conditions of extreme confinement. The present approach, aiming to provide a simple modeling of electrolyte solutions described as ternary mixtures, which comprises two ionic species and a third uncharged component, is an extension of our recent work on binary neutral mixtures. The approach, which combines elements of kinetic theory, density functional theory with Lattice-Boltzmann algorithms, is microscopic and self-consistent and does not require the use of constitutive equations to determine the fluxes. Numerical solutions are obtained by solving the resulting coupled equations for the one-particle phase-space distributions of the species by means of a Lattice-Boltzmann discretization procedure. Results are given for the microscopic density and velocity profiles and for the volumetric and charge flow.
Detectability of high-redshift superluminous supernovae with upcoming optical and near-infrared surveys – II. Beyond z = 6

Observational identification of the first stars is one of the great challenges in the modern astronomy. Although a single first star is too faint to be detected, supernova explosions of the first stars can be bright enough. An important question is whether such supernovae can be detected in the limited observational area with realistic observational resources. We perform detailed simulations to study the detectability of superluminous supernovae (SLSNe) at high redshifts, using the observationally-calibrated star formation rate density and supernova occurrence rate. We show that a 100 deg 2 survey with the limiting magnitude of 26 mag in near-infrared wavelengths will be able to discover about 10 SLSNe at z > 10. If the survey is extended to 200 deg 2 with 27 mag depth, about 10 SLSNe can be discovered at z > 15. We emphasize that the observations at � 3 �m are crucial to detect and select SLSNe at z > 10. Our simulations are also applied to the planned survey with Euclid, WFIRST, and WISH. These surveys will be able to detect about 1000, 400, and 3000 SLSNe up to z � 5, 7, and 12, respectively. We conclude that detection of SLSNe at z > 10 is in fact achievable in the near future.
Cold atoms in cavity-generated dynamical optical potentials

We review state-of-the-art theory and experiment of the motion of cold and ultracold atoms coupled to the radiation field within a high-finesse optical resonator in the dispersive regime of the atom-field interaction with small internal excitation. The optical dipole force on the atoms together with the back-action of atomic motion onto the light field gives rise to a complex nonlinear coupled dynamics. As the resonator constitutes an open driven and damped system, the dynamics is non-conservative and in general enables cooling and confining the motion of polarizable particles. In addition, the emitted cavity field allows for real-time monitoring of the particle's position with minimal perturbation up to sub-wavelength accuracy. For many-body systems, the resonator field mediates controllable long-range atom-atom interactions, which set the stage for collective phenomena. Besides correlated motion of distant particles, one finds critical behavior and non-equilibrium phase transitions between states of different atomic order in conjunction with superradiant light scattering. Quantum degenerate gases inside optical resonators can be used to emulate opto-mechanics as well as novel quantum phases like supersolids and spin glasses. Non-equilibrium quantum phase transitions, as predicted by e.g. the Dicke Hamiltonian, can be controlled and explored in real-time via monitoring the cavity field. In combination with optical lattices, the cavity field can be utilized for non-destructive probing Hubbard physics and tailoring long-range interactions for ultracold quantum systems.
Requirements Engineering Processes in the Context of IoT and Requirements Validation Techniques

The Internet of Things made possible an increase in the possibilities of automation and facilitation of people’s daily lives. From home automation to smart buildings, the rise in IoT’s popularity brings a challenge to software development and requirements engineering. Developers and companies are not familiar with the requirements validation processes and techniques that exist in the context of an IoT system. Therefore, possible project failures and rework during software development are issues to be considered by development teams. This work aims at investigating the requirements engineering process and the requirements validation techniques in the IoT context described in the literature. Also, present a guide to support software development teams to have easy access to the processes and techniques proposed in the literature for this context. We conducted a survey of industry practitioners to investigate whether they use and know the processes and techniques identified in the literature. Our findings reveal that the technique most used by practitioners to perform requirements specification are stakeholders meeting and brainstorming and to validate requirements are prototypes and use cases.
In Vitro Mechanisms of Resistance Development to Imipenem-Relebactam in KPC-Producing Klebsiella pneumoniae

Carbapenem-resistant Enterobacterales, such as KPC-producing Klebsiella pneumoniae, represent a major threat to public health. Novel drug combinations including imipenem-relebactam (IPM-REL) have recently been introduced and have been shown to exhibit excellent activity toward such strains. ABSTRACT Carbapenem-resistant Enterobacterales, such as KPC-producing Klebsiella pneumoniae, represent a major threat to public health. Novel drug combinations including imipenem-relebactam (IPM-REL) have recently been introduced and have been shown to exhibit excellent activity toward such strains. However, there has recently been reports of the in vivo emergence of IPM-REL resistance in KPC-producing K. pneumoniae. Here, we evaluated, in vitro, the nature of the mutations that lead to IPM-REL resistance in 5 KPC-producing K. pneumoniae strains, including 2 that produce KPC enzymes conferring ceftazidime-avibactam resistance. An in vitro multi-step selection assay was performed and corresponding mutants obtained. Mutations were identified in OmpK36 as well as 2 different mutant derivatives of KPC. Mutant strains exhibited decreased susceptibility to β-lactams, including the carbapenems, and meropenem-vaborbactam (MEM-VAB). Expression of blaKPC gene variants in an Escherichia coli recombinant strain resulted in a concomitant increased susceptibility to carbapenems and decreased susceptibility to CAZ-AVI, and enzymatic assays showed that the inhibitory activity of both AVI and REL was significantly lowered for both KPC mutants compared to parental enzymes. Complementation assays showed that OmpK36 plays a major role in IPM-REL resistance as well resistance to other ß-lactams and β-lactam/ß-lactamase inhibitor combinations. Overall, this study showed that (i) IPM-REL resistant strains can be obtained from CAZ-AVI-susceptible or -resistant KPC producers, (ii) selection of IPM-REL resistance has a collateral effect on MEM-VAB susceptibility – indicative of shared resistance mechanisms, (iii) and mutations in the KPC sequence may be obtained using IPM-REL selection leading to the possibility of vertical and horizontal transfer of this resistance trait.
Draft Genome Sequences of Chromobacterium Strains Isolated from Water Systems in Central Western Brazil

We report the draft genome sequences of four Chromobacterium strains. This report includes the draft genome sequences of four environmental strains, isolated from surface waters in Brazil. ABSTRACT We report the draft genome sequences of four Chromobacterium strains. This report includes the draft genome sequences of four environmental strains, isolated from surface waters in Brazil.
Upregulation of PD-L1 in Senescence and Aging

Cellular senescence is a stable form of cell cycle arrest associated with proinflammatory responses. Senescent cells can be cleared by the immune system as a part of normal tissue homeostasis. ABSTRACT Cellular senescence is a stable form of cell cycle arrest associated with proinflammatory responses. Senescent cells can be cleared by the immune system as a part of normal tissue homeostasis. However, senescent cells can also accumulate in aged and diseased tissues, contributing to inflammation and disease progression. The mechanisms mediating the impaired immune-mediated clearance of senescent cells are poorly understood. Here, we report that senescent cells upregulate the immune checkpoint molecule PD-L1, the ligand for PD-1 on immune cells, which drives immune cell inactivation. The induction of PD-L1 in senescence is dependent on the proinflammatory program. Furthermore, the secreted factors released by senescent cells are sufficient to upregulate PD-L1 in nonsenescent control cells, mediated by the JAK-STAT pathway. In addition, we show that prolongevity intervention rapamycin downregulates PD-L1 in senescent cells. Last, we found that PD-L1 is upregulated in several tissues in naturally aged mice and in the lungs of idiopathic pulmonary fibrosis patients. Together, our results report that senescence and aging are associated with upregulation of a major immune checkpoint molecule, PD-L1. Targeting PD-L1 may offer new therapeutic opportunities in treating senescence and age-associated diseases.
NUMERICAL SIMULATIONS OF RADIATIVE MAGNETIZED HERBIG–HARO JETS: THE INFLUENCE OF PRE-IONIZATION FROM X-RAYS ON EMISSION LINES

We investigate supersonic, axisymmetric magnetohydrodynamic jets with a time-dependent injection velocity by numerical simulations with the PLUTO code. Using a comprehensive set of parameters, we explore different jet configurations in the attempt to construct models that can be directly compared to observational data of microjets. In particular, we focus our attention on the emitting properties of traveling knots and construct, at the same time, accurate line intensity ratios and surface brightness maps. Direct comparison of the resulting brightness and line intensity ratios distributions with observational data of microjets shows that a closer match can be obtained only when the jet material is pre-ionized to some degree. A very likely source for a pre-ionized medium is photoionization by X-ray flux coming from the central object.