Miracle-dz/UniFAR-FTUs huggingface dataset:
Time evolution of the electric field in a Hall thruster

In order to characterize the natural oscillations of the electric field, the time-dependent Xe+ ion velocity distribution function is measured in the near-field plume of a low power permanent magnet Hall thruster. The distribution functions are obtained by means of a time-resolved laser-induced fluorescence technique based on a photon-counting method, while time coherence is ensured by applying a sinusoidal potential modulation on a floating electrode. The temporal resolution is 100 ns. The electric field profile is composed of two peaks. This peculiar shape is not yet understood. The electric field is shown to oscillate outside the thruster discharge channel. The results do not reveal the propagation of an ionization front.
Causal Social Explanations for Stochastic Sequential Multi-Agent Decision-Making

We present a novel framework to generate causal explanations for the decisions of agents in stochastic sequential multi-agent environments. Explanations are given via natural language conversations answering a wide range of user queries and requiring associative, interventionist, or counterfactual causal reasoning. Instead of assuming any speciﬁc causal graph, our method relies on a generative model of interactions to simulate counterfactual worlds which are used to identify the salient causes behind decisions. We implement our method for motion planning for autonomous driving and test it in simulated scenarios with coupled interactions. Our method correctly identiﬁes and ranks the relevant causes and delivers concise explanations to the users’ queries.
Climate model-driven seasonal forecasting approach with deep learning

Abstract Understanding seasonal climatic conditions is critical for better management of resources such as water, energy, and agriculture. Recently, there has been a great interest in utilizing the power of Artificial Intelligence (AI) methods in climate studies. This paper presents cutting-edge deep-learning models (UNet++, ResNet, PSPNet, and DeepLabv3) trained by state-of-the-art global CMIP6 models to forecast global temperatures a month ahead using the ERA5 reanalysis dataset. ERA5 dataset was also used for fine-tuning as well performance analysis in the validation dataset. Ten different setups (with CMIP6 and CMIP6 + ERA5 fine-tuning) including six meteorological parameters (i.e., 2 m temperature, 10 m eastward component of wind, 10 m northward component of wind, geopotential height at 500 hPa, mean sea-level pressure, and precipitation flux) and elevation were used with both four different algorithms. For each model 14 different sequential and nonsequential temporal settings were used. The mean absolute error (MAE) analysis revealed that UNet++ with CMIP6 with 2 m temperature + elevation and ERA5 fine-tuning model with “Year 3 Month 2” temporal case provided the best outcome with an MAE of 0.7. Regression analysis over the validation dataset between the ERA5 data values and the corresponding AI model predictions revealed slope and $ {R}^2 $ values close to 1 suggesting a very good agreement. The AI model predicts significantly better than the mean CMIP6 ensemble between 2016 and 2021. Both models predict the summer months more accurately than the winter months.
Self-improving object detection via disagreement reconciliation

Object detectors often experience a drop in performance when new environmental conditions are insufficiently represented in the training data. This paper studies how to automatically fine-tune a pre-existing object detector while exploring and acquiring images in a new environment without relying on human intervention, i.e., in a self-supervised fashion. In our setting, an agent initially explores the environment using a pre-trained off-the-shelf detector to locate objects and associate pseudo-labels. By assuming that pseudo-labels for the same object must be consistent across different views, we devise a novel mechanism for producing refined predictions from the consensus among observations. Our approach improves the off-the-shelf object detector by 2.66% in terms of mAP and outperforms the current state of the art without relying on ground-truth annotations.
Kernel-Based Distributed Q-Learning: A Scalable Reinforcement Learning Approach for Dynamic Treatment Regimes

In recent years, large amounts of electronic health records (EHRs) concerning chronic diseases, such as cancer, diabetes, and mental disease, have been collected to facilitate medical diagnosis. Modeling the dynamic properties of EHRs related to chronic diseases can be efficiently done using dynamic treatment regimes (DTRs), which are a set of sequential decision rules. While Reinforcement learning (RL) is a widely used method for creating DTRs, there is ongoing research in developing RL algorithms that can effectively handle large amounts of data. In this paper, we present a novel approach, a distributed Q-learning algorithm, for generating DTRs. The novelties of our research are as follows: 1) From a methodological perspective, we present a novel and scalable approach for generating DTRs by combining distributed learning with Q-learning. The proposed approach is specifically designed to handle large amounts of data and effectively generate DTRs. 2) From a theoretical standpoint, we provide generalization error bounds for the proposed distributed Q-learning algorithm, which are derived within the framework of statistical learning theory. These bounds quantify the relationships between sample size, prediction accuracy, and computational burden, providing insights into the performance of the algorithm. 3) From an applied perspective, we demonstrate the effectiveness of our proposed distributed Q-learning algorithm for DTRs by applying it to clinical cancer treatments. The results show that our algorithm outperforms both traditional linear Q-learning and commonly used deep Q-learning in terms of both prediction accuracy and computation cost.
Depth Estimation and Image Restoration by Deep Learning From Defocused Images

Monocular depth estimation and image deblurring are two fundamental tasks in computer vision, given their crucial role in understanding 3D scenes. Performing any of them by relying on a single image is an ill-posed problem. The recent advances in the field of Deep Convolutional Neural Networks (DNNs) have revolutionized many tasks in computer vision, including depth estimation and image deblurring. When it comes to using defocused images, the depth estimation and the recovery of the All-in-Focus (Aif) image become related problems due to defocus physics. Despite this, most of the existing models treat them separately. There are, however, recent models that solve these problems simultaneously by concatenating two networks in a sequence to first estimate the depth or defocus map and then reconstruct the focused image based on it. We propose a DNN that solves the depth estimation and image deblurring in parallel. Our Two-headed Depth Estimation and Deblurring Network (2HDED:NET) extends a conventional Depth from Defocus (DFD) networks with a deblurring branch that shares the same encoder as the depth branch. The proposed method has been successfully tested on two benchmarks, one for indoor and the other for outdoor scenes: NYU-v2 and Make3D. Extensive experiments with 2HDED:NET on these benchmarks have demonstrated superior or close performances to those of the state-of-the-art models for depth estimation and image deblurring.
The Impact of Star-formation-rate Surface Density on the Electron Density and Ionization Parameter of High-redshift Galaxies

We use the large spectroscopic data set of the MOSFIRE Deep Evolution Field survey to investigate some of the key factors responsible for the elevated ionization parameters (U) inferred for high-redshift galaxies, focusing in particular on the role of star-formation-rate surface density (ΣSFR). Using a sample of 317 galaxies with spectroscopic redshifts z spec ≃ 1.9–3.7, we construct composite rest-frame optical spectra in bins of ΣSFR and infer electron densities, n e, using the ratio of the [O ii] λ λ3727, 3730 doublet. Our analysis suggests a significant (≃3σ) correlation between n e and ΣSFR. We further find significant correlations between U and ΣSFR for composite spectra of a subsample of 113 galaxies, and for a smaller sample of 25 individual galaxies with inferences of U. The increase in n e—and possibly also the volume filling factor of dense clumps in H ii regions—with ΣSFR appear to be important factors in explaining the relationship between U and ΣSFR. Further, the increase in n e and SFR with redshift at a fixed stellar mass can account for most of the redshift evolution of U. These results suggest that the gas density, which sets n e and the overall level of star formation activity, may play a more important role than metallicity evolution in explaining the elevated ionization parameters of high-redshift galaxies.
Quantifying the Vulnerabilities of the Online Public Square to Adversarial Manipulation Tactics

Social media, seen by some as the modern public square, is vulnerable to manipulation. By controlling inauthentic accounts impersonating humans, malicious actors can amplify disinformation within target communities. The consequences of such operations are difficult to evaluate due to the challenges posed by collecting data and carrying out ethical experiments that would influence online communities. Here we use a social media model that simulates information diffusion in an empirical network to quantify the impacts of several adversarial manipulation tactics on the quality of content. We find that the presence of influential accounts, a hallmark of social media, exacerbates the vulnerabilities of online communities to manipulation. Among the explored tactics that bad actors can employ, infiltrating a community is the most likely to make low-quality content go viral. Such harm can be further compounded by inauthentic agents flooding the network with low-quality, yet appealing content, but is mitigated when bad actors focus on specific targets, such as influential or vulnerable individuals. These insights suggest countermeasures that platforms could employ to increase the resilience of social media users to manipulation.
Lasserre Hierarchy for Graph Isomorphism and Homomorphism Indistinguishability

We show that feasibility of the $t^\text{th}$ level of the Lasserre semidefinite programming hierarchy for graph isomorphism can be expressed as a homomorphism indistinguishability relation. In other words, we define a class $\mathcal{L}_t$ of graphs such that graphs $G$ and $H$ are not distinguished by the $t^\text{th}$ level of the Lasserre hierarchy if and only if they admit the same number of homomorphisms from any graph in $\mathcal{L}_t$. By analysing the treewidth of graphs in $\mathcal{L}_t$ we prove that the $3t^\text{th}$ level of Sherali--Adams linear programming hierarchy is as strong as the $t^\text{th}$ level of Lasserre. Moreover, we show that this is best possible in the sense that $3t$ cannot be lowered to $3t-1$ for any $t$. The same result holds for the Lasserre hierarchy with non-negativity constraints, which we similarly characterise in terms of homomorphism indistinguishability over a family $\mathcal{L}_t^+$ of graphs. Additionally, we give characterisations of level-$t$ Lasserre with non-negativity constraints in terms of logical equivalence and via a graph colouring algorithm akin to the Weisfeiler--Leman algorithm. This provides a polynomial time algorithm for determining if two given graphs are distinguished by the $t^\text{th}$ level of the Lasserre hierarchy with non-negativity constraints.
USR: Unsupervised Separated 3D Garment and Human Reconstruction via Geometry and Semantic Consistency

Dressed people reconstruction from images is a popular task with promising applications in the creative media and game industry. However, most existing methods reconstruct the human body and garments as a whole with the supervision of 3D models, which hinders the downstream interaction tasks and requires hard-to-obtain data. To address these issues, we propose an unsupervised separated 3D garments and human reconstruction model (USR), which reconstructs the human body and authentic textured clothes in layers without 3D models. More specifically, our method proposes a generalized surface-aware neural radiance field to learn the mapping between sparse multi-view images and geometries of the dressed people. Based on the full geometry, we introduce a Semantic and Confidence Guided Separation strategy (SCGS) to detect, segment, and reconstruct the clothes layer, leveraging the consistency between 2D semantic and 3D geometry. Moreover, we propose a Geometry Fine-tune Module to smooth edges. Extensive experiments on our dataset show that comparing with state-of-the-art methods, USR achieves improvements on both geometry and appearance reconstruction while supporting generalizing to unseen people in real time. Besides, we also introduce SMPL-D model to show the benefit of the separated modeling of clothes and the human body that allows swapping clothes and virtual try-on.
First Analysis of In Situ Observation of Surface Alfvén Waves in an ICME Flux Rope

Alfvén waves (AWs) are ubiquitous in space and astrophysical plasma. Their crucial role in various physical processes has triggered intense research in solar–terrestrial physics. Simulation studies have proposed the generation of AWs along the surface of a cylindrical flux rope, referred to as surface AWs (SAWs); however, the observational verification of this distinct wave has been elusive to date. We report the first in situ observation of SAWs in a flux rope of an interplanetary coronal mass ejection. We apply the Walén test to identify them. We have used Elsässer variables to estimate the characteristics of SAWs. They may be excited by the movement of the flux rope’s footpoints or by instabilities along the boundaries of the plasma magnetic cloud. Here, the change in plasma density or field strength in the surface-aligned magnetic field may trigger SAWs.
STUDY OF OUTPUT ENERGY OF BIS-NICKEL PASSIVELY Q-SWITCHED Nd:YAG LASER

A Bis-Nickel passively Q-switched Nd:YAG laser is experimentally studied. According to the obtained results for the laser output energy, we found that: (i) the laser output energy has a step by step behavior. (ii) In any step, the output energy has approximately constant value. (iii) The highest output energy is obtained from lowest initial transmission in this work. (iv) As the initial transmission decreases, the laser output energy increases. We deduced to increase the single pulse energy, a Q-switch with lower initial transmission should be used. Apparently, this behavior is true in the passive Q-switches.
Evaporating Heat Transfer in Horizontal Internal Spiral-grooved Tubes in the Region of Low Flow Rates

This paper presents some experimental data about evaporating heat transfer in horizontal tubes about 4.75mm I.D. with small internal spiral grooves using R efrigerant R12. The groove inclinations of the tested internal spiral-grooved tubes are 4, 7, 15 and 30 degrees. It is found that a spiral-grooved tube can increase the evaporating heat transfer coefficient in tubes from one and a half times to twice as much as that of a smooth tube for annular flow region, and about ten times due to capillary phenomenon in grooves for stratified flow region.
Losses in a high-temperature superconductor exposed to AC and DC transport currents and magnetic fields

Summary form only given, as follows. The development of high-temperature superconductors (HTSs) is for the moment intensive and it is likely that HTSs will be used in different electric devices in a few years. In some of these devices that conductor will be exposed to both AC and DC transport currents and magnetic fields. In the design of electric devices, the power loss is indeed one of the most important parameters. Therefore there is a need of models that predict the power loss in an HTS under application-like conditions. In this paper, the authors present a semi-empirical model including a transport current and an applied magnetic field with both AC and DC components. The semi-empirical model is mainly based on the critical state theory. The results of the model are compared and found to be in good agreement with experimental results obtained with a calorimetric experimental set-up. The power loss was measured on a multi-filamentary Bi-2223 tape as function of transport current and applied magnetic field at a fixed temperature and frequency.
The Archimedean two-wire spiral antenna

A pair of equally excited but oppositely sensed Archimedean two-wire spirals situated close to one another in the same plane-a doublet-is used to generate a linearly polarized field in which the direction of polarization and phase are controlled or varied independently of each other by rotation of the spiral radiators. An array of these doublets can be made to scan by rotation of the several spiral elements; an eight-doublet array which was made to scan over an 83\deg sector with small amplitude variation is discussed. A doublet fed from a ring network can be employed as a polarization diversity circuit. A virtual doublet is achieved by placing a single spiral in a fight angle trough. A preliminary scanning array comprising four spirals in a trough was made to scan \pm36\deg . The possibility of using a parasitic spiral in conjunction with a driven spiral for obtaining linear polarization of variable direction and phase is indicated. Also, a brief simplified analysis of the two-wire Archimedean spiral is presented, which leads to the concept of higher-order modes of radiation.
Can inertia‐gravity waves persistently alter the tropopause inversion layer?

Previous simulations of baroclinic life cycles have shown, among many other features, the evolution of a tropopause inversion layer (TIL) as well as the spontaneous emission of inertia‐gravity waves (IGWs). This study suggests that the latter two are related to each other, i.e., that IGWs may affect the TIL in a persistent manner. The IGWs are emitted along the jet and grow to large amplitudes, leading to the appearance of low‐gradient Richardson numbers that indicate Kelvin‐Helmholtz instability. Ensuing energy dissipation, local heating, and turbulence may persistently alter the thermodynamical structure of the tropopause region and, therefore, contribute to TIL formation or alter an existing TIL. Moreover, the flow in the region of the IGW favors the occurrence of wave capture, which may enhance the effect of wave breaking.
Asymmetry models for contingency tables

Abstract For the quasi-symmetry (QS) model, applicable to square contingency tables with commensurable classification variables, it is proved that under certain conditions, it is the closest model to symmetry in terms of the Kullback-Leibler distance. Replacing the Kullback-Leibler distance by f-divergence we introduce a generalized quasi-symmetry model, the QS[f], and develop interpretational aspects for its parameters. QS is a special case of QS[f], whereas the most characteristic of the newly introduced QS-type models is the Pearsonian QS model. We compute maximum likelihood estimates of the parameters of the Pearsonian QS model and compare it, through examples and simulation studies, to the classical QS model in terms of goodness of fit and of the powers of the tests for marginal homogeneity conditional on the QS and Pearsonian QS models.
Creditors’ Rights, Threat of Liquidation, and the Labor and Capital Choices of Firms

In 2002, India introduced a legal reform that allowed secured creditors to seize and liquidate a defaulter’s assets, thereby strengthening creditors’ rights. We study the impact of the legal change on firms’ real decisions regarding their capital and labor, exploiting variation in their prepolicy proportion of collateralizable assets. We find that firms increased employment and reduced their capital investments. These effects are especially strong for firms in regions with less-efficient courts. Our results are consistent with an increased threat of liquidation for firms following the passage of the law.
The end of the line: competitive exclusion and the extinction of historical entities

Identifying competitive exclusion at the macroevolutionary scale has typically relied on demonstrating a reciprocal, contradictory response by two co-occurring, functionally similar clades. Finding definitive examples of such a response in fossil time series has proven challenging, however, as has controlling for the effects of a changing physical environment. We take a novel approach to this issue by quantifying variation in trait values that capture almost the entirety of function for steam locomotives (SL), a known example of competitive exclusion from material culture, with the goal of identifying patterns suitable for assessing clade replacement in the fossil record. Our analyses find evidence of an immediate, directional response to the first appearance of a direct competitor, with subsequent competitors further reducing the realized niche of SLs, until extinction was the inevitable outcome. These results demonstrate when interspecific competition should lead to extinction and suggest that clade replacement may only occur when niche overlap between an incumbent and its competitors is near absolute and where the incumbent is incapable of transitioning to a new adaptive zone. Our findings provide the basis for a new approach to analyse putative examples of competitive exclusion that is largely free of a priori assumptions.
Temporal change in functional rarity in marine fish assemblages

Recent research has uncovered rapid compositional and structural reorganization of ecological assemblages, with these changes particularly evident in marine ecosystems. However, the extent to which these ongoing changes in taxonomic diversity are a proxy for change in functional diversity is not well understood. Here we focus on trends in rarity to ask how taxonomic rarity and functional rarity covary over time. Our analysis, drawing on 30 years of scientific trawl data, reveals that the direction of temporal shifts in taxonomic rarity in two Scottish marine ecosystems is consistent with a null model of change in assemblage size (i.e. change in numbers of species and/or individuals). In both cases, however, functional rarity increases, as assemblages become larger, rather than showing the expected decrease. These results underline the importance of measuring both taxonomic and functional dimensions of diversity when assessing and interpreting biodiversity change.
STLV-1 Commonly Targets Neurons in the Brain of Asymptomatic Non-Human Primates

The human T-cell leukemia virus (HTLV)-1 is responsible for an aggressive neurodegenerative disease (HAM/TSP) and multiple neurological alterations. The capacity of HTLV-1 to infect central nervous system (CNS) resident cells, together with the neuroimmune-driven response, has not been well-established. ABSTRACT The human T-cell leukemia virus (HTLV)-1 is responsible for an aggressive neurodegenerative disease (HAM/TSP) and multiple neurological alterations. The capacity of HTLV-1 to infect central nervous system (CNS) resident cells, together with the neuroimmune-driven response, has not been well-established. Here, we combined the use of human induced pluripotent stem cells (hiPSC) and of naturally STLV-1-infected nonhuman primates (NHP) as models with which to investigate HTLV-1 neurotropism. Hence, neuronal cells obtained after hiPSC differentiation in neural polycultures were the main cell population infected by HTLV-1. Further, we report the infection of neurons with STLV-1 in spinal cord regions as well as in brain cortical and cerebellar sections of postmortem NHP. Additionally, reactive microglial cells were found in infected areas, suggesting an immune antiviral response. These results emphasize the need to develop new efficient models by which to understand HTLV-1 neuroinfection and suggest an alternative mechanism that leads to HAM/TSP.