Miracle-dz/UniFAR-FTUs huggingface dataset:
HIV_A_372204 355..363

Purpose: Fall is one of the geriatric syndromes and a significant public health concern, which causes potentially severe consequences among the elderly. Geriatric syndromes are common among PLHIV and affect younger age than the general population. This study attempted to identify the risk of falls in PLHIV on antiretroviral therapy (ART) and its related factors among older adults with HIV infection. Methods: This cross-sectional study was conducted from December 2019 to May 2020 among PLHIV aged ≥40 years who received ART for at least 6 months in HIV Integrated Clinic, Cipto Mangunkusumo Hospital, Jakarta. Bivariate and multivariate analyses were performed using Poisson regression with robust estimator using STATA version 12.0. Results: A total of 102 participants mainly consisted of males (83.3%) with a median age of 45 (IQR 5) years. The risk of fall was detected in 52% of these participants. PLHIV who have history of falls, current CD4 below 200 cells/mm, and pre-frail–frail status were associated with an increased risk of falls in the future. An LPV/r-based regimen was found to be a protective factors of risk of falls among PLHIV. Conclusion: History of falls, current CD4 below 200 cells/mm, and pre-frail and frail status were identified as factors associated with a greater risk of fall among PLHIV.
ACTIVE GALACTIC NUCLEUS FEEDBACK AND ENTROPY INJECTION IN GALAXY CLUSTER CORES

We make the first estimate of non-gravitational energy profiles in galaxy cluster cores (and beyond) based on observational data. Comparing the observed entropy profiles within r500, from the Representative XMM-Newton Cluster Structure Survey to simulated base entropy profiles without feedback from both adaptive mesh refinement (AMR) and smoothed particle hydrodynamic (SPH) non-radiative simulations, we estimate the amount of additional non-gravitational energy, EICM, contained in the intracluster medium (ICM), as well as the total energy feedback, EFeedback, from active galactic nuclei (AGNs; the central AGNs in most cases) into the clusters. The total feedback energy scales with the mean spectroscopic temperature as and for the SPH and AMR baseline profiles. The mean non-gravitational energy per particle within r500 remaining in the ICM after energy lost during cooling is ϵICM = 2.8 ± 0.8 keV for the SPH theoretical relation and ϵICM = 1.7 ± 0.9 keV for the AMR theoretical relation. We use the NRAO/VLA Sky Survey source catalog to determine the radio luminosity, LR, at 1.4 GHz of the central source(s) of our sample. For Tsp > 3 keV, the EFeedback correlates with LR, although with different normalization for cool-core and non-cool-core clusters. We show that AGNs could provide a significant portion of the feedback.
Discretionary Latitude and Relational Contracting

We use economic experiments to examine the nature of relational trading under a menu of incomplete contracts ranging from the repeat purchase mechanism of Klein and Leffler (1981) to highly incomplete contracts that are completely unenforceable by third-parties. Our results suggest that, with barriers to complete contracting, increasing the degree of contractual incompleteness can enhance efficiency. Intuitively, more incomplete contracts provide parties with greater discretionary latitude to reward and punish unenforceable performance factors. Moreover, trading under moderately incomplete contracts is characterized by efficiency wages, rent sharing and reputation based trading, whereas fully incomplete contracts that permit maximum discretion yield trading patterns that are closer what is observed under a perfectly complete contract. Our results are consistent with the theory of strategic ambiguity of Bernheim and Whinston (1998) and can be rationalized by a simple model of relational contracting that embeds different degrees of discretionary latitude. that the two are equivalent when parties are risk neutral and sellers hold the correct expectations the buyer’s willingness to pay the bonus. We show, however, that
A Categorical setting for Determinants and Traces

The aim of this paper is to investigate some applications of a certain universal problem. The universal problem deals with categories C which for every object A have some ‘‘structure” on the set C(A, A) of endomorphisms of A and calls for a universal solution, relative to this structure, that is associated with C(A, A) and centralizes the set of automorphisms of A. The commutative version of this universal problem asks for a universal solution, relative to the said structure, that abelianizes the canonical monoid structure of C(A, A).
Booster-SHOT: Boosting Stacked Homography Transformations for Multiview Pedestrian Detection with Attention

Improving multi-view aggregation is integral for multi-view pedestrian detection, which aims to obtain a bird's-eye-view pedestrian occupancy map from images captured through a set of calibrated cameras. Inspired by the success of attention modules for deep neural networks, we first propose a Homography Attention Module (HAM) which is shown to boost the performance of existing end-to-end multiview detection approaches by utilizing a novel channel gate and spatial gate. Additionally, we propose Booster-SHOT, an end-to-end convolutional approach to multiview pedestrian detection incorporating our proposed HAM as well as elements from previous approaches such as view-coherent augmentation or stacked homography transformations. Booster-SHOT achieves 92.9% and 94.2% for MODA on Wildtrack and MultiviewX respectively, outperforming the state-of-the-art by 1.4% on Wildtrack and 0.5% on MultiviewX, achieving state-of-the-art performance overall for standard evaluation metrics used in multi-view pedestrian detection.
Rogue Planets and Brown Dwarfs: Predicting the Populations Free-floating Planetary Mass Objects Observable with JWST

Free-floating (or rogue) planets are planets that are liberated (or ejected) from their host systems. Although simulations predict their existence in substantial numbers, direct observational evidence for free-floating planets with masses below ∼5 M Jup is still lacking. Several cycle-1 observing programs with JWST aim to hunt for them in four different star-forming clusters. These surveys are designed to be sensitive to masses of 1–15 M Jup (assuming a hot-start formation), which corresponds to spectral types of early L to late T for the ages of these clusters. If the existing simulations are not wide off the mark, we show here that the planned programs are likely to find up to 10–20 giant rogue planets in moderate density clusters like NGC1333 or IC348, and several dozen to ∼100 in high-density regions like NGC2024 and the Orion Nebula Cluster. These numbers correspond to 1%–5% of the total cluster population; they could be substantially higher if stars form multiple giant planets at birth. In contrast, the number of free-floating brown dwarfs, formed from core collapse (like stars) is expected to be significantly lower, only about 0.25% of the number of stars, or 1–7 for the clusters considered here. Below 10 M Jup that number drops further by an order of magnitude. We also show that the planned surveys are not at risk of being significantly contaminated by field brown dwarfs in the foreground or background, after spectroscopic confirmation. Taken together, our results imply that if a population of L and T dwarfs were to be found in these JWST surveys, it is expected to be predominantly made up of rogue planets.
Vacancy-related color centers in twodimensional silicon carbide monolayers

Basic vacancy defects in twodimensional silicon carbide (2D-SiC) are examined by means of density functional theory calculations to explore their magneto-optical properties as well as their potential in quantum technologies. In particular, the characteristic hyperfine tensors and optical excited states of carbon-vacancy, silicon-vacancy, and carbon antisite-vacancy pair defects in 2D-SiC are determined that are the key fingerprints of these defects that may be observed in electron paramagnetic resonance and photoluminescence experiments, respectively. Besides the fundamental characterization of the most basic native defects, we show that the negatively charged carbon antisite-vacancy defect is a promising candidate for realizing a near-infrared single-photon quantum emitter with spin doublet ground state, where the negative charge state may be provided by nitrogen doping of 2D-SiC. We find that the neutral carbon-vacancy with spin triplet ground state might be used for quantum sensing with a broad emission in the visible.
SN2019wxt: An Ultrastripped Supernova Candidate Discovered in the Electromagnetic Follow-up of a Gravitational Wave Trigger

We present optical, radio, and X-ray observations of a rapidly evolving transient SN2019wxt (PS19hgw), discovered during the search for an electromagnetic counterpart to the gravitational-wave (GW) trigger S191213g. Although S191213g was not confirmed as a significant GW event in the off-line analysis of LIGO-Virgo data, SN2019wxt remained an interesting transient due to its peculiar nature. The optical/near-infrared (NIR) light curve of SN2019wxt displayed a double-peaked structure evolving rapidly in a manner analogous to currently known ultrastripped supernovae (USSNe) candidates. This double-peaked structure suggests the presence of an extended envelope around the progenitor, best modeled with two components: (i) early-time shock-cooling emission and (ii) late-time radioactive 56Ni decay. We constrain the ejecta mass of SN2019wxt at M ej ≈ 0.20M ⊙, which indicates a significantly stripped progenitor that was possibly in a binary system. We also followed up SN2019wxt with long-term Chandra and Jansky Very Large Array observations spanning ∼260 days. We detected no definitive counterparts at the location of SN2019wxt in these long-term X-ray and radio observational campaigns. We establish the X-ray upper limit at 9.93 × 10−17 erg cm−2 s−1 and detect an excess radio emission from the region of SN2019wxt. However, there is little evidence for SN1993J- or GW170817-like variability of the radio flux over the course of our observations. A substantial host-galaxy contribution to the measured radio flux is likely. The discovery and early-time peak capture of SN2019wxt in optical/NIR observations during EMGW follow-up observations highlight the need for dedicated early, multiband photometric observations to identify USSNe.
Feature selection enhancement and feature space visualization for speech-based emotion recognition

Robust speech emotion recognition relies on the quality of the speech features. We present speech features enhancement strategy that improves speech emotion recognition. We used the INTERSPEECH 2010 challenge feature-set. We identified subsets from the features set and applied principle component analysis to the subsets. Finally, the features are fused horizontally. The resulting feature set is analyzed using t-distributed neighbour embeddings (t-SNE) before the application of features for emotion recognition. The method is compared with the state-of-the-art methods used in the literature. The empirical evidence is drawn using two well-known datasets: Berlin Emotional Speech Dataset (EMO-DB) and Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) for two languages, German and English, respectively. Our method achieved an average recognition gain of 11.5% for six out of seven emotions for the EMO-DB dataset, and 13.8% for seven out of eight emotions for the RAVDESS dataset as compared to the baseline study.
A Novel Plug-and-Play Approach for Adversarially Robust Generalization

In this work, we propose a robust framework that employs adversarially robust training to safeguard the machine learning models against perturbed testing data. We achieve this by incorporating the worst-case additive adversarial error within a fixed budget for each sample during model estimation. Our main focus is to provide a plug-and-play solution that can be incorporated in the existing machine learning algorithms with minimal changes. To that end, we derive the ready-to-use solution for several widely used loss functions with a variety of norm constraints on adversarial perturbation for various supervised and unsupervised ML problems, including regression, classification, two-layer neural networks, graphical models, and matrix completion. The solutions are either in closed-form, 1-D optimization, semidefinite programming, difference of convex programming or a sorting-based algorithm. Finally, we validate our approach by showing significant performance improvement on real-world datasets for supervised problems such as regression and classification, as well as for unsupervised problems such as matrix completion and learning graphical models, with very little computational overhead.
Feature Selection for Fault Detection and Prediction based on Event Log Analysis

Event logs are widely used for anomaly detection and prediction in complex systems. Existing log-based anomaly detection methods usually consist of four main steps: log collection, log parsing, feature extraction, and anomaly detection, wherein the feature extraction step extracts useful features for anomaly detection by counting log events. For a complex system, such as a lithography machine consisting of a large number of subsystems, its log may contain thousands of different events, resulting in abounding extracted features. However, when anomaly detection is performed at the subsystem level, analyzing all features becomes expensive and unnecessary. To mitigate this problem, we develop a feature selection method for log-based anomaly detection and prediction. Specifically, our method consists of three main modules: the Log Event Vectorization module that converts semi-structured log texts into time series; the Selection of Relevant Features module that leverages Kendall rank correlation and Granger causality test to select log events for fault detection and prediction; and the Removal of Redundant Features module that utilises Kendall rank correlation to reduce redundant log events. Results on 25 real-world datasets show that our method can detect and predict faults more accurately by selecting a small proportion of log events, thereby improving the effectiveness and efficiency.
Vine Copula Based Portfolio Level Conditional Risk Measure Forecasting

Accurately estimating risk measures for financial portfolios is critical for both financial institutions and regulators. However, many existing models operate at the aggregate portfolio level and thus fail to capture the complex cross-dependencies between portfolio components. To address this, a new approach is presented that uses vine copulas in combination with univariate ARMA-GARCH models for marginal modelling to compute conditional portfolio-level risk measure estimates by simulating portfolio-level forecasts conditioned on a stress factor. A quantile-based approach is then presented to observe the behaviour of risk measures given a particular state of the conditioning asset(s). In a case study of Spanish equities with different stress factors, the results show that the portfolio is quite robust to a sharp downturn in the American market. At the same time, there is no evidence of this behaviour with respect to the European market.
Higher fundamental forms of the conformal boundary of asymptotically de Sitter spacetimes

We provide a partial characterization of the conformal infinity of asymptotically de Sitter spacetimes by deriving constraints that relate the asymptotics of the stress-energy tensor with conformal geometric data. The latter is captured using recently defined objects, called higher conformal fundamental forms. For the boundary hypersurface, these generalize to higher order the trace-free part of the second form.
SoMoFormer: Social-Aware Motion Transformer for Multi-Person Motion Prediction

Multi-person motion prediction remains a challenging problem, especially in the joint representation learning of individual motion and social interactions. Most prior methods only involve learning local pose dynamics for individual motion (without global body trajectory) and also struggle to capture complex interaction dependencies for social interactions. In this paper, we propose a novel Social-Aware Motion Transformer (SoMoFormer) to effectively model individual motion and social interactions in a joint manner. Specifically, SoMoFormer extracts motion features from sub-sequences in displacement trajectory space to effectively learn both local and global pose dynamics for each individual. In addition, we devise a novel social-aware motion attention mechanism in SoMoFormer to further optimize dynamics representations and capture interaction dependencies simultaneously via motion similarity calculation across time and social dimensions. On both short- and long-term horizons, we empirically evaluate our framework on multi-person motion datasets and demonstrate that our method greatly outperforms state-of-the-art methods of single- and multi-person motion prediction. Code will be made publicly available upon acceptance.
Mechanism Learning for Trading Networks

We study the problem of designing mechanisms for trading networks that satisfy four desired properties: dominant-strategy incentive compatibility, efficiency, weak budget balance (WBB), and individual rationality (IR). Although there exist mechanisms that simultaneously satisfy these properties ex post for combinatorial auctions, we prove the impossibility that such mechanisms do not exist for a broad class of trading networks. We thus propose approaches for computing and learning the mechanisms that satisfy the four properties, in a Bayesian setting, where WBB and IR, respectively, are relaxed to ex ante and interim. For computational and sample efficiency, we introduce several techniques, including game theoretical analysis to reduce the input feature space. We empirically demonstrate that the proposed approaches successfully find the mechanisms with the four properties for those trading networks where the impossibility holds ex post.
Dialogue Policies for Confusion Mitigation in Situated HRI

Confusion is a mental state triggered by cognitive disequilibrium that can occur in many types of task-oriented interaction, including Human-Robot Interaction (HRI). People may become confused while interacting with robots due to communicative or even task-centred challenges. To build a smooth and engaging HRI, it is insufficient for an agent to simply detect confusion; instead, the system should aim to mitigate the situation. In light of this, in this paper, we present our approach to a linguistic design of dialogue policies to build a dialogue framework to alleviate interlocutor confusion. We also outline our sketch and discuss challenges with respect to its operationalisation.
The Interactions of Social Norms About Climate Change: Science, Institutions and Economics

We study the evolution of interest about climate change between different actors of the population, and how the interest of those actors affect one another. We first document the evolution individually, and then provide a model of cross influences between them, that we then estimate with a VAR. We find large swings over time of said interest for the general public by creating a Climate Change Index for Europe and the US (CCI) using news media mentions, and little interest among economists (measured by publications in top journals of the discipline). The general interest science journals and policymakers have a more steady interest, although policymakers get interested much later.
The complex interplay of AGN jet-inflated bubbles and the intracluster medium

We use SWIFT, a smoothed particle hydrodynamics code, to simulate the evolution of bubbles inflated by active galactic nuclei (AGN) jets, as well as their interactions with the ambient intracluster medium (ICM). These jets inflate lobes that turn into bubbles after the jets are turned off (at t = 50 Myr). Almost all of the energy injected into the jets is transferred to the ICM very quickly after they are turned off, with roughly 70% of it in thermal form and the rest in kinetic. At late times (t > 500 Myr) we find the following: 1) the bubbles draw out trailing filaments of low-entropy gas, similar to those recently observed, 2) the action of buoyancy and the uplift of the filaments dominates the energetics of both the bubbles and the ICM and 3) almost all of the originally injected energy is in the form of gravitational potential energy, with the bubbles containing 15% of it, and the rest contained in the ICM. These findings indicate that feedback proceeds mainly through the displacement of gas to larger radii. We find that the uplift of these filaments permanently changes the thermodynamic properties of the ICM by reducing the central density and increasing the central temperature (within 30 kpc). We propose that jet feedback proceeds not only through the heating of the ICM (which can delay cooling), but also through the uplift-related reduction of the central gas density. The latter also delays cooling, on top of reducing the amount of gas available to cool.
New analytical derivation of the mean annual water‐energy balance equation

The coupled water‐energy balance on long‐term time and catchment scales can be expressed as a set of partial differential equations, and these are proven to have a general solution as E/P = F(E0/P, c), where c is a parameter. The state‐space of (P, E0, E) is a set of curved faces in P − E0 − E three‐dimensional space, whose projection into E/P − E0/P two‐dimensional space is a Budyko‐type curve. The analytical solution to the partial differential equations has been obtained as E = E0P/(Pn + E0n)1/n (parameter n representing catchment characteristics) using dimensional analysis and mathematic reasoning, which is different from that found in a previous study. This analytical solution is a useful theoretical tool to evaluate the effect of climate and land use changes on the hydrologic cycle. Mathematical comparisons between the two analytical equations showed that they were approximately equivalent, and their parameters had a perfectly significant linear correlation relationship, while the small difference may be a result of the assumption about derivatives in the previous study.
Dynamical transitions and aging in the superdiffusive Pomeau-Manneville map

The Pomeau-Manneville map is a paradigmatic intermittent dynamical system exhibiting weak chaos and anomalous dynamics. In this paper we analyse the parameter dependence of superdiffusion for the map lifted periodically onto the real line. From numerical simulations we compute the generalised diffusion coefficient (GDC) of this model as a function of the map's nonlinearity parameter. We identify two singular dynamical transitions in the GDC, one where it diverges to infinity, and a second one where it is fully suppressed. Using the continuous-time random walk theory of L\'evy walks we calculate an analytic expression for the GDC and show that it qualitatively reproduces these two transitions. Quantitatively it systematically deviates from the deterministic dynamics for small parameter values, which we explain by slow decay of velocity correlations. Interestingly, imposing aging onto the dynamics in simulations eliminates the dynamical transition that led to suppression of the GDC, thus yielding a non-trivial change in the parameter dependence of superdiffusion. This also applies to a respective intermittent model of subdiffusive dynamics displaying a related transition.