Miracle-dz/UniFAR-FTUs huggingface dataset:
The clustering of critical points in the evolving cosmic web

Focusing on both small separations and baryonic acoustic oscillation scales, the cosmic evolution of the clustering properties of peak, void, wall, and filament-type critical points is measured using two-point correlation functions in ΛCDM dark matter simulations as a function of their relative rarity. A qualitative comparison to the corresponding theory for Gaussian random fields allows us to understand the following observed features: (i) the appearance of an exclusion zone at small separation, whose size depends both on rarity and signature (i.e. the number of negative eigenvalues) of the critical points involved; (ii) the amplification of the baryonic acoustic oscillation bump with rarity and its reversal for cross-correlations involving negatively biased critical points; (iii) the orientation-dependent small-separation divergence of the cross-correlations of peaks and filaments (respectively voids and walls) that reflects the relative loci of such points in the filament’s (respectively wall’s) eigenframe. The (cross-) correlations involving the most non-linear critical points (peaks, voids) display significant variation with redshift, while those involving less non-linear critical points seem mostly insensitive to redshift evolution, which should prove advantageous to model. The ratios of distances to the maxima of the peak-to-wall and peak-to-void over that of the peak-to-filament cross-correlation are ${\sim} \sqrt{2}$ and ${\sim} \sqrt{3}$, respectively, which could be interpreted as the cosmic crystal being on average close to a cubic lattice. The insensitivity to redshift evolution suggests that the absolute and relative clustering of critical points could become a topologically robust alternative to standard clustering techniques when analysing upcoming surveys such as Euclid or Large Synoptic Survey Telescope (LSST).
Multimodal Trajectory Prediction via Topological Invariance for Navigation at Uncontrolled Intersections

We focus on decentralized navigation among multiple non-communicating rational agents at \emph{uncontrolled} intersections, i.e., street intersections without traffic signs or signals. Avoiding collisions in such domains relies on the ability of agents to predict each others' intentions reliably, and react quickly. Multiagent trajectory prediction is NP-hard whereas the sample complexity of existing data-driven approaches limits their applicability. Our key insight is that the geometric structure of the intersection and the incentive of agents to move efficiently and avoid collisions (rationality) reduces the space of likely behaviors, effectively relaxing the problem of trajectory prediction. In this paper, we collapse the space of multiagent trajectories at an intersection into a set of modes representing different classes of multiagent behavior, formalized using a notion of topological invariance. Based on this formalism, we design Multiple Topologies Prediction (MTP), a data-driven trajectory-prediction mechanism that reconstructs trajectory representations of high-likelihood modes in multiagent intersection scenes. We show that MTP outperforms a state-of-the-art multimodal trajectory prediction baseline (MFP) in terms of prediction accuracy by 78.24% on a challenging simulated dataset. Finally, we show that MTP enables our optimization-based planner, MTPnav, to achieve collision-free and time-efficient navigation across a variety of challenging intersection scenarios on the CARLA simulator.
Explainable Automated Fact-Checking: A Survey

A number of exciting advances have been made in automated fact-checking thanks to increasingly larger datasets and more powerful systems, leading to improvements in the complexity of claims which can be accurately fact-checked. However, despite these advances, there are still desirable functionalities missing from the fact-checking pipeline. In this survey, we focus on the explanation functionality – that is fact-checking systems providing reasons for their predictions. We summarize existing methods for explaining the predictions of fact-checking systems and we explore trends in this topic. Further, we consider what makes for good explanations in this specific domain through a comparative analysis of existing fact-checking explanations against some desirable properties. Finally, we propose further research directions for generating fact-checking explanations, and describe how these may lead to improvements in the research area.
Reliable Off-policy Evaluation for Reinforcement Learning

Off-policy evaluation is an important topic in reinforcement learning, which estimates the expected cumulative reward of a target policy using logged trajectory data generated from a different behavior policy, without execution of the target policy. It is imperative to quantify the uncertainty of the off-policy estimate before deployment of the target policy. Here we leverage methodologies from (Wasserstein) distributionally robust optimization to provide robust and optimistic cumulative reward estimates. With proper selection of the size of the distributional uncertainty set, these estimates serve as confidence bounds with nonasymptotic and asymptotic guarantees under stochastic or adversarial environments. We also generalize those results to batch reinforcement learning.
FACEGAN: Facial Attribute Controllable rEenactment GAN

The face reenactment is a popular facial animation method where the person’s identity is taken from the source image and the facial motion from the driving image. Recent works have demonstrated high quality results by combining the facial landmark based motion representations with the generative adversarial networks. These models perform best if the source and driving images depict the same person or if the facial structures are otherwise very similar. However, if the identity differs, the driving facial structures leak to the output distorting the reenactment result. We propose a novel Facial Attribute Controllable rEenactment GAN (FACEGAN), which transfers the facial motion from the driving face via the Action Unit (AU) representation. Unlike facial landmarks, the AUs are independent of the facial structure preventing the identity leak. Moreover, AUs provide a human interpretable way to control the reenactment. FACEGAN processes background and face regions separately for optimized output quality. The extensive quantitative and qualitative comparisons show a clear improvement over the state-of-the-art in a single source reenactment task. The results are best illustrated in the reenactment video provided in the supplementary material. The source code will be made available upon publication of the paper.
Gated Recurrent Fusion With Joint Training Framework for Robust End-to-End Speech Recognition

The joint training framework for speech enhancement and recognition methods have obtained quite good performances for robust end-to-end automatic speech recognition (ASR). However, these methods only utilize the enhanced feature as the input of the speech recognition component, which are affected by the speech distortion problem. In order to address this problem, this paper proposes a gated recurrent fusion (GRF) method with joint training framework for robust end-to-end ASR. The GRF algorithm is used to dynamically combine the noisy and enhanced features. Therefore, the GRF can not only remove the noise signals from the enhanced features, but also learn the raw fine structures from the noisy features so that it can alleviate the speech distortion. The proposed method consists of speech enhancement, GRF and speech recognition. Firstly, the mask based speech enhancement network is applied to enhance the input speech. Secondly, the GRF is applied to address the speech distortion problem. Thirdly, to improve the performance of ASR, the state-of-the-art speech transformer algorithm is used as the speech recognition component. Finally, the joint training framework is utilized to optimize these three components, simultaneously. Our experiments are conducted on an open-source Mandarin speech corpus called AISHELL-1. Experimental results show that the proposed method achieves the relative character error rate (CER) reduction of 10.04% over the conventional joint enhancement and transformer method only using the enhanced features. Especially for the low signal-to-noise ratio (0 dB), our proposed method can achieves better performances with 12.67% CER reduction, which suggests the potential of our proposed method.
AI on the Bog: Monitoring and Evaluating Cranberry Crop Risk

Machine vision for precision agriculture has attracted considerable research interest in recent years. The goal of this paper is to develop an end-to-end cranberry health monitoring system to enable and support real time cranberry over-heating assessment to facilitate informed decisions that may sustain the economic viability of the farm. Toward this goal, we propose two main deep learning-based modules for: 1) cranberry fruit segmentation to delineate the exact fruit regions in the cranberry field image that are exposed to sun, 2) prediction of cloud coverage conditions and sun irradiance to estimate the inner temperature of exposed cranberries. We develop drone-based field data and ground-based sky data collection systems to collect video imagery at multiple time points for use in crop health analysis. Extensive evaluation on the data set shows that it is possible to predict exposed fruit’s inner temperature with high accuracy (0.02% MAPE). The sun irradiance prediction error was found to be 8.41-20.36% MAPE in the 5-20 minutes time horizon. With 62.54% mIoU for segmentation and 13.46 MAE for counting accuracies in exposed fruit identification, this system is capable of giving informed feedback to growers to take precautionary action (e.g., irrigation) in identified crop field regions with higher risk of sunburn in the near future. Though this novel system is applied for cranberry health monitoring, it represents a pioneering step forward for efficient farming and is useful in precision agriculture beyond the problem of cranberry overheating.
The Association Of Serum Cortisol Level With Microalbuminuria In Patients With Type 2 Diabetes And Prediabetes

Whether cortisol secretion is linked with microalbuminuria remains undefined. We aimed to investigate the relationship between serum cortisol levels and the presence of microalbuminuria in patients with type 2 diabetes (T2DM) and prediabetes. A cross-sectional study was conducted with 211 patients with T2DM or prediabetes. Serum cortisol was measured at 8:00 h, 16:00 h, and 0:00 h. The level and circadian rhythm of ACTH were also evaluated. Urine excretion of albumin was measured. Patients were subdivided into microalbuminuria (MAU) group (n= 120) and normoalbuminuria (NAU) group (n = 91) according to the status of microalbuminuria. Levels of serum cortisol (8:00 h: 426.9 ± 155.0 nmol/; 16:00 h: 303.7 ± 144.7 nmol/L) were significantly higher in MAU group than in NAU group (8:00 h: 370.2 ±130.6 nmol/L, P = 0.004; 16:00 h: 234.7 ± 120.2 nmol/L, P = 0.001). After adjustment for multiple factors, the correlation between cortisol levels (both at 8:00 h (P = 0.005) and at 16:00 h (P = 0.001)) and microalbuminuria remained consistent and significant. Higher levels of cortisol (cut-off value: 390.5 nmol/L at 8:00 h, 203.5 nmol/L at 16:00 h) help to detect the development of microalbuminuria. Serum cortisol secretion is associated with the presence of microalbuminuria in patients with T2DM and patients with prediabetes. Higher levels of cortisol, even in the normal range, may be related with the development of microalbuminuria.
Matchings on trees and the adjacency matrix: A determinantal viewpoint

Let G$$ G $$ be a finite tree. For any matching M$$ M $$ of G$$ G $$ , let U(M)$$ U(M) $$ be the set of vertices uncovered by M$$ M $$ . Let ℳG$$ {\mathcal{M}}_G $$ be a uniform random maximum size matching of G$$ G $$ . In this paper, we analyze the structure of U(ℳG)$$ U\left({\mathcal{M}}_G\right) $$ . We first show that U(ℳG)$$ U\left({\mathcal{M}}_G\right) $$ is a determinantal process. We also show that for most vertices of G$$ G $$ , the process U(ℳG)$$ U\left({\mathcal{M}}_G\right) $$ in a small neighborhood of that vertex can be well approximated based on a somewhat larger neighborhood of the same vertex. Then we show that the normalized Shannon entropy of U(ℳG)$$ U\left({\mathcal{M}}_G\right) $$ can be also well approximated using the local structure of G$$ G $$ . In other words, in the realm of trees, the normalized Shannon entropy of U(ℳG)$$ U\left({\mathcal{M}}_G\right) $$ —that is, the normalized logarithm of the number of maximum size matchings of G$$ G $$ —is a Benjamini‐Schramm continuous parameter.
Electrochemical double layer capacitor using a microemulsion electrolyte

The use of aqueous electrolytes in energy storage devices is traditionally limited by the voltage stability window of water at 1.23 V. We present the use of a microemulsion based electrolyte which, although mostly water by mass, has a voltage stability window of up to 5 V. This allows the cost and safety benefits of aqueous electrolytes to finally be realised in high-voltage systems. Supercapacitors constructed using this electrolyte were able to achieve and maintain a capacitance of ~40 F/g and an energy density ~40 Wh/kg of with a Coulombic efficiency of 99\% for over 10,000 cycles on activated carbon.
Test Anxiety and Associated Factors Among First-Year Health Science Students of University of Gondar, Northwest Ethiopia: A Cross-Sectional Study

Background The experience of extreme worry and self-doubt before taking a test can drastically hinder an individual’s ability to perform well and may lead to miserable. Studies about test anxiety and associated factors among first-year health science students in Ethiopia are limited. Therefore, this study is meant to provide essential data for future interventions. Objective To assess test anxiety and associated factors among first-year regular undergraduate health science students of the University of Gondar, northwest Ethiopia, 2019. Methods An institutional-based cross-sectional study with a stratified random sampling technique. Test anxiety was assessed using the Test Anxiety Questionnaire. Data were analyzed using Statistical Package for Social Science (SPSS) version 20. Bivariate and multivariate logistic regression analysis was performed. A P-value of less than 0.05 was considered statistically significant in the multivariate analysis, and the strength of association was measured using adjusted odds ratio at a 95% confidence interval. Results In this study, the prevalence of test anxiety was 54.7% (95% CI=49.40–60.20). The result revealed that fathers’ education of grade 9–12 (AOR=0.31, 95% CI=0.14–0.69), mothers’ education of grade 9–12 (AOR=2.43, 95% CI=1.07–5.47), psychological distress (AOR=8.37, 95% CI=4.29–16.39), the field of studies; midwifery (AOR=3.56, 95% CI=1.07–11.76), and medicine (AOR=6.79, 95% CI=1.64–28.22) were significantly associated with test anxiety at a P-value<0.05. Conclusion The study found that test anxiety is a major problem of first-year undergraduate health science students. Mothers’ education of grades 9–12, psychological distress, midwifery, and medical field of studies were risk factors of test anxiety, whereas fathers’ education of grade 9–12 was protective for test anxiety. This showed that an increment in the education of father and mother has a controversial effect on test anxiety. It can be reduced by providing suitable trainings for first-year health science students in dealing with factors causing test anxiety.
PRVNet: Variational Autoencoders for Massive MIMO CSI Feedback

In a frequency division duplexing multiple-input multiple-output (FDD-MIMO) system, the user equipment (UE) send the downlink channel state information (CSI) to the base station for performance improvement. However, with the growing complexity of MIMO systems, this feedback becomes expensive and has a negative impact on the bandwidth. Although this problem has been largely studied in the literature, the noisy nature of the feedback channel is less considered. In this paper, we introduce PRVNet, a neural architecture based on variational autoencoders (VAE). VAE gained large attention in many fields (e.g., image processing, language models, or recommendation system). However, it received less attention in the communication domain generally and in CSI feedback problem specifically. We also introduce a different regularization parameter for the learning objective, which proved to be crucial for achieving competitive performance. In addition, we provide an efficient way to tune this parameter using KL-annealing. Empirically, we show that the proposed model significantly outperforms state-of-the-art, including two neural network approaches. The proposed model is also proved to be more robust against different levels of noise.
Inverting the Pose Forecasting Pipeline with SPF2: Sequential Pointcloud Forecasting for Sequential Pose Forecasting

Many autonomous systems forecast aspects of the future in order to aid decision-making. For example, self-driving vehicles and robotic manipulation systems often forecast future object poses by first detecting and tracking objects. However, this detect-then-forecast pipeline is expensive to scale, as pose forecasting algorithms typically require labeled sequences of object poses, which are costly to obtain in 3D space. Can we scale performance without requiring additional labels? We hypothesize yes, and propose inverting the detect-then-forecast pipeline. Instead of detecting, tracking and then forecasting the objects, we propose to first forecast 3D sensor data (e.g., point clouds with $100$k points) and then detect/track objects on the predicted point cloud sequences to obtain future poses, i.e., a forecast-then-detect pipeline. This inversion makes it less expensive to scale pose forecasting, as the sensor data forecasting task requires no labels. Part of this work's focus is on the challenging first step -- Sequential Pointcloud Forecasting (SPF), for which we also propose an effective approach, SPFNet. To compare our forecast-then-detect pipeline relative to the detect-then-forecast pipeline, we propose an evaluation procedure and two metrics. Through experiments on a robotic manipulation dataset and two driving datasets, we show that SPFNet is effective for the SPF task, our forecast-then-detect pipeline outperforms the detect-then-forecast approaches to which we compared, and that pose forecasting performance improves with the addition of unlabeled data.
An Empirical Evaluation of Bluetooth-based Decentralized Contact Tracing in Crowds

Digital contact tracing is being used by many countries to help contain COVID-19's spread in a post-lockdown world. Among the various available techniques, decentralized contact tracing that uses Bluetooth received signal strength indication (RSSI) to detect proximity is considered less of a privacy risk than approaches that rely on collecting absolute locations via GPS, cellular-tower history, or QR-code scanning. As of October 2020, there have been millions of downloads of such Bluetooth-based contract-tracing apps, as more and more countries officially adopt them. However, the effectiveness of these apps in the real world remains unclear due to a lack of empirical research that includes realistic crowd sizes and densities. This study aims to fill that gap, by empirically investigating the effectiveness of Bluetooth-based contact tracing in crowd environments with a total of 80 participants, emulating classrooms, moving lines, and other types of real-world gatherings. The results confirm that Bluetooth RSSI is unreliable for detecting proximity, and that this inaccuracy worsens in environments that are especially crowded. In other words, this technique may be least useful when it is most in need, and that it is fragile when confronted by low-cost jamming. Moreover, technical problems such as high energy consumption and phone overheating caused by the contact-tracing app were found to negatively influence users' willingness to adopt it. On the bright side, however, Bluetooth RSSI may still be useful for detecting coarse-grained contact events, for example, proximity of up to 20m lasting for an hour. Based on our findings, we recommend that existing contact-tracing apps can be re-purposed to focus on coarse-grained proximity detection, and that future ones calibrate distance estimates and adjust broadcast frequencies based on auxiliary information.
Screening and Information-Sharing Externalities

In many settings, multiple uninformed agents bargain simultaneously with a single informed agent in each of multiple periods. For example, workers and firms negotiate each year over salaries, and the firm has private information about the value of workers' output. I study the effects of transparency in these settings; uninformed agents may observe others' past bargaining outcomes, e.g. wages. I show that in equilibrium, each uninformed agent will choose in each period whether to try to separate the informed agent's types (screen) or receive the same outcome regardless of type (pool). In other words, the agents engage in a form of experimentation via their bargaining strategies. There are two main theoretical insights. First, there is a complementary screening effect: the more agents screen in equilibrium, the lower the information rents that each will have to pay. Second, the payoff of the informed agent will have a certain supermodularity property, which implies that equilibria with screening are "fragile" to deviations by uninformed agents. I apply the results to study pay-secrecy regulations and anti-discrimination policy. I show that, surprisingly, penalties for pay discrimination have no impact on bargaining outcomes. I discuss how this result depends on the legal framework for discrimination cases, and suggest changes to enhance the efficacy of anti-discrimination regulations. In particular, anti-discrimination law should preclude the so-called "salary negotiation defense".
Facile 
ab initio
 approach for self-localized polarons from canonical transformations

Electronic states in a crystal can localize due to strong electron-phonon (e-ph) interactions, forming so-called small polarons. Methods to predict the formation and energetics of small polarons are either computationally costly or not geared toward quantitative predictions. Here we show a formalism based on canonical transformations to compute the polaron formation energy and wavefunction using ab initio e-ph interactions. Comparison of the calculated polaron and band edge energies allows us to determine whether charge carriers in a material favor a localized small polaron over a delocalized Bloch state. Due to its low computational cost, our approach enables efficient studies of the formation and energetics of small polarons, as we demonstrate by investigating electron and hole polaron formation in alkali halides and metal oxides and peroxides. We outline refinements of our scheme and extensions to compute transport in the polaron hopping regime.
Nonadiabatic, Relativistic, and Leading-Order QED Corrections for Rovibrational Intervals of ^{4}He_{2}^{+} (X ^{2}Σ_{u}^{+}).

The rovibrational intervals of the ^{4}He_{2}^{+} molecular ion in its X ^{2}Σ_{u}^{+} ground electronic state are computed by including the nonadiabatic, relativistic, and leading-order quantum-electrodynamics corrections. Good agreement of theory and experiment is observed for the rotational excitation series of the vibrational ground state and the fundamental vibration. The lowest-energy rotational interval is computed to be 70.937 69(10) cm^{-1} in agreement with the most recently reported experimental value, 70.937 589(23)(60)_{sys} cm^{-1} [L. Semeria et al., Phys. Rev. Lett. 124, 213001 (2020)PRLTAO0031-900710.1103/PhysRevLett.124. 213001].