Miracle-dz/UniFAR-FTUs huggingface dataset:
Self-Supervised Graph Learning With Hyperbolic Embedding for Temporal Health Event Prediction

Electronic health records (EHRs) have been heavily used in modern healthcare systems for recording patients’ admission information to health facilities. Many data-driven approaches employ temporal features in EHR for predicting specific diseases, readmission times, and diagnoses of patients. However, most existing predictive models cannot fully utilize EHR data, due to an inherent lack of labels in supervised training for some temporal events. Moreover, it is hard for the existing methods to simultaneously provide generic and personalized interpretability. To address these challenges, we propose Sherbet, a self-supervised graph learning framework with hyperbolic embeddings for temporal health event prediction. We first propose a hyperbolic embedding method with information flow to pretrain medical code representations in a hierarchical structure. We incorporate these pretrained representations into a graph neural network (GNN) to detect disease complications and design a multilevel attention method to compute the contributions of particular diseases and admissions, thus enhancing personalized interpretability. We present a new hierarchy-enhanced historical prediction proxy task in our self-supervised learning framework to fully utilize EHR data and exploit medical domain knowledge. We conduct a comprehensive set of experiments on widely used publicly available EHR datasets to verify the effectiveness of our model. Our results demonstrate the proposed model’s strengths in both predictive tasks and interpretable abilities.
Disk Masses and Dust Evolution of Protoplanetary Disks around Brown Dwarfs

We present the largest sample of brown dwarf (BD) protoplanetary disk spectral energy distributions modeled to date. We compile 49 objects with ALMA observations from four star-forming regions: ρ Ophiuchus, Taurus, Lupus, and Upper Scorpius. Studying multiple regions with various ages enables us to probe disk evolution over time. Specifically, from our models, we obtain values for dust grain sizes, dust settling, and disk mass; we compare how each of these parameters vary between the regions. We find that disk mass decreases with age. We also find evidence of disk evolution (i.e., grain growth and significant dust settling) in all four regions, indicating that planet formation and disk evolution may begin to occur at earlier stages. We generally find that these disks contain too little mass to form planetary companions, though we cannot rule out that planet formation may have already occurred. Finally, we examine the disk mass–host mass relationship and find that BD disks are largely consistent with previously determined relationships for disks around T Tauri stars.
Studies of a Quenched Cupola Part I: Overview of Experimental Studies

A bold experiment was carried out in which a cupola was quenched with water and gaseous nitrogen while in full operation. An archeological examination of the cupola’s contents was subsequently carried out which provided a detailed description of the conditions existing from the charge door to the tap hole (18 sections). This paper is the first in a series. It provides an overview of the experimental studies and confirmation that the information obtained from this very small cupola (12.2”, 310mm) was representative of the performance of commercial cupolas.
Deep Tiny Network for Recognition-Oriented Face Image Quality Assessment

Face recognition has made significant progress in recent years due to deep convolutional neural networks (CNN). In many face recognition (FR) scenarios, face images are acquired from a sequence with huge intra-variations. These intra-variations, which are mainly affected by the low-quality face images, cause instability of recognition performance. Previous works have focused on ad-hoc methods to select frames from a video or use face image quality assessment (FIQA) methods, which consider only a particular or combination of several distortions. In this work, we present an efficient non-reference image quality assessment for FR that directly links image quality assessment (IQA) and FR. More specifically, we propose a new measurement to evaluate image quality without any reference. Based on the proposed quality measurement, we propose a deep Tiny Face Quality network (tinyFQnet) to learn a quality prediction function from data. We evaluate the proposed method for different powerful FR models on two classical video-based (or template-based) benchmark: IJB-B and YTF. Extensive experiments show that, although the tinyFQnet is much smaller than the others, the proposed method outperforms state-of-the-art quality assessment methods in terms of effectiveness and efficiency.
Relating dust reference models to conventional systems in manifestly gauge invariant perturbation theory

Models with dust reference fields in relational formalism have proved useful in understanding the construction of gauge-invariant perturbation theory to arbitrary orders in the canonical framework. These reference fields modify the dynamical equations for perturbation equations. However, important questions remain open on the relation with conventional perturbation theories of inflaton coupled to gravity and of multi-fluid systems, and on understanding modifications in terms of physical degrees of freedom. These gaps are filled in this manuscript for Brown-Kuchař and Gaussian dust models, both of which involve three scalar physical degrees of freedom. We establish a relationship of these models with conventional inflationary and multi-fluid system of inflation and ordinary dust by introducing a set of gauge invariant variables on the reduced phase space of the dust reference models. We find the modifications due to dust clocks to Bardeen equation in the longitudinal gauge and Mukhanov-Sasaki equation in the spatially-flat gauge, in terms of physical degrees of freedom. This results in a closed system of equations for all the degrees of freedom needed to explore the evolution of the scalar perturbations. Our analysis shows for the first time that even for two-fluid systems, there is a natural choice of the set of gauge invariant variables for each chosen gauge which not only offers a direct physical interpretation but also results in simplifications to the dynamical equations.
SILVERRUSH. IX. Lyα Intensity Mapping with Star-forming Galaxies at z = 5.7 and 6.6: A Possible Detection of Extended Lyα Emission at ≳100 Comoving Kiloparsecs around and beyond the Virial-radius Scale of Galaxy Dark Matter Halos

We present results of the cross-correlation Lyα intensity mapping with Subaru/Hyper Suprime-Cam (HSC) ultra-deep narrowband images and Lyα emitters (LAEs) at z = 5.7 and 6.6 in a total area of 4 deg2. Although an overwhelming amount of data quality controls have been performed for the narrowband images, we further conduct extensive analyses evaluating systematics of large-scale point-spread function wings, sky subtractions, and unknown errors based on physically uncorrelated signals and sources found in real HSC images and object catalogs, respectively. Removing the systematics, we carefully calculate cross-correlations between Lyα intensity of the narrowband images and the LAEs. We tentatively identify very diffuse Lyα emission with the ≃3σ (≃2σ) significance at ≳100 comoving kiloparsecs (ckpc) far from the LAEs at z = 5.7 (6.6), around and probably even beyond a virial radius of star-forming galaxies with M h ∼ 1011 M ⊙. The diffuse Lyα emission possibly extends up to 1000 ckpc with the surface brightness of 10−20–10−19 erg s−1 cm−2 arcsec−2. We confirm that the small-scale (<150 ckpc) Lyα radial profiles of LAEs are consistent with those obtained by recent Multi-Unit Spectroscopic Explorer observations. Comparisons with numerical simulations suggest that the large-scale (∼150–1000 ckpc) Lyα emission are not explained by unresolved faint neighboring galaxies including satellites, but by a combination of Lyα photons emitted from the central LAE and other unknown sources, such as cold-gas streams and galactic outflow. We find no evolution in the Lyα radial profiles of our LAEs from z = 5.7 to 6.6, where theoretical models predict a flattening of the profile slope made by cosmic reionization, albeit with our moderately large observational errors.
Crowdsourced Labeling for Worker-Task Specialization Model

We consider crowdsourced labeling under a $d$-type worker-task specialization model, where each worker and task is associated with one particular type among a finite set of types and a worker provides a more reliable answer to tasks of the matched type than to tasks of unmatched types. We design an inference algorithm that recovers binary task labels (up to any given recovery accuracy) by using worker clustering, worker skill estimation and weighted majority voting. The designed inference algorithm does not require any information about worker/task types, and achieves any targeted recovery accuracy with the best known performance (minimum number of queries per task). 11This work was supported in part by National Research Foundation of Korea under Grant 2017R1E1A1A01076340; in part by the Ministry of Science and ICT, South Korea, under the ITRC support program under Grant IITP-2021-2018-0-01402; and in part by the Institute of Information and Communications Technology Planning & Evaluation (IITP) grant funded by the Korea Government MSIT under Grant 2020-0-00626.
Learning to Generate Noise for Multi-Attack Robustness

Adversarial learning has emerged as one of the successful techniques to circumvent the susceptibility of existing methods against adversarial perturbations. However, the majority of existing defense methods are tailored to defend against a single category of adversarial perturbation (e.g. $\ell_\infty$-attack). In safety-critical applications, this makes these methods extraneous as the attacker can adopt diverse adversaries to deceive the system. Moreover, training on multiple perturbations simultaneously significantly increases the computational overhead during training. To address these challenges, we propose a novel meta-learning framework that explicitly learns to generate noise to improve the model's robustness against multiple types of attacks. Its key component is Meta Noise Generator (MNG) that outputs optimal noise to stochastically perturb a given sample, such that it helps lower the error on diverse adversarial perturbations. By utilizing samples generated by MNG, we train a model by enforcing the label consistency across multiple perturbations. We validate the robustness of models trained by our scheme on various datasets and against a wide variety of perturbations, demonstrating that it significantly outperforms the baselines across multiple perturbations with a marginal computational cost.
Tackling spam in the era of end-to-end encryption: A case study of WhatsApp

WhatsApp is a popular messaging app used by over a billion users around the globe. Due to this popularity, spam on WhatsApp is an important issue. Despite this, the distribution of spam via WhatsApp remains understudied by researchers, in part because of the end-to-end encryption offered by the platform. This paper addresses this gap by studying spam on a dataset of 2.6 million messages sent to 5,051 public WhatsApp groups in India over 300 days. First, we characterise spam content shared within public groups and find that nearly 1 in 10 messages is spam. We observe a wide selection of topics ranging from job ads to adult content, and find that spammers post both URLs and phone numbers to promote material. Second, we inspect the nature of spammers themselves. We find that spam is often disseminated by groups of phone numbers, and that spam messages are generally shared for longer duration than non-spam messages. Finally, we devise content and activity based detection algorithms that can counter spam.
IoT Solution for Winter Survival of Indoor Plants

Not only does cold climate pose a problem for outdoor plants during winter in the northern hemisphere, but for indoor plants as well: low sunlight, low humidity, and simultaneous cold breezes from windows and heat from radiators all cause problems for indoor plants. People often treat their indoor plants like mere decoration, which can often lead to health issues for the plant or even death of the plant, especially during winter. A plant monitoring system was developed to solve this problem, collecting information on plants' indoor environmental conditions (light, humidity, and temperature) and providing this information in an accessible format for the user. Preliminary functional tests were conducted in similar settings where the system would be used. In addition, the concept was evaluated by interviewing an expert in the field of horticulture. The evaluation results indicate that this kind of system could prove useful; however, the tests indicated that the system requires further development to achieve more practical value and wider usage.
Operationalizing Complex Causes: A Pragmatic View of Mediation

We examine the problem of causal response estimation for complex objects (e.g., text, images, genomics). In this setting, classical \emph{atomic} interventions are often not available (e.g., changes to characters, pixels, DNA base-pairs). Instead, we only have access to indirect or \emph{crude} interventions (e.g., enrolling in a writing program, modifying a scene, applying a gene therapy). In this work, we formalize this problem and provide an initial solution. Given a collection of candidate mediators, we propose (a) a two-step method for predicting the causal responses of crude interventions; and (b) a testing procedure to identify mediators of crude interventions. We demonstrate, on a range of simulated and real-world-inspired examples, that our approach allows us to efficiently estimate the effect of crude interventions with limited data from new treatment regimes.
H2 molecular gas absorption-selected systems trace CO molecular gas-rich galaxy overdensities

Absorption-selected galaxies offer an effective way to study low-mass galaxies at high redshift. However, the physical properties of the underlying galaxy population remain uncertain. In particular, the multiphase circumgalactic medium is thought to hold key information on gas flows into and out of galaxies that are vital for galaxy evolution models. Here, we present ALMA observations of CO molecular gas in host galaxies of H2-bearing absorbers. In our sample of six absorbers, we detect molecular gas-rich galaxies in five absorber fields, although we did not target high-metallicity (>50 per cent solar) systems for which previous studies reported the highest detection rate. Surprisingly, we find that the majority of the absorbers are associated with multiple galaxies rather than single haloes. Together with the large impact parameters, these results suggest that the H2-bearing gas seen in absorption is not part of an extended disc, but resides in dense gas pockets in the circumgalactic and intragroup medium.
Avoiding Traps in Nonconvex Problems

Iterative projection methods may become trapped at non-solutions when the constraint sets are nonconvex. Two kinds of parameters are available to help avoid this behavior and this study gives examples of both. The first kind of parameter, called a hyperparameter, includes any kind of parameter that appears in the definition of the iteration rule itself. The second kind comprises metric parameters in the definition of the constraint sets, a feature that arises when the problem to be solved has two or more kinds of variables. Through examples we show the importance of properly tuning both kinds of parameters and offer heuristic interpretations of the observed behavior.
Copula-frailty models for recurrent event data based on Monte Carlo EM algorithm

Multi-type recurrent events are often encountered in medical applications when two or more different event types could repeatedly occur over an observation period. For example, patients may experience recurrences of multi-type nonmelanoma skin cancers in a clinical trial for skin cancer prevention. The aims in those applications are to characterize features of the marginal processes, evaluate covariate effects, and quantify both the within-subject recurrence dependence and the dependence among different event types. We use copula-frailty models to analyze correlated recurrent events of different types. Parameter estimation and inference are carried out by using a Monte Carlo expectation-maximization (MCEM) algorithm, which can handle a relatively large (i.e. three or more) number of event types. Performances of the proposed methods are evaluated via extensive simulation studies. The developed methods are used to model the recurrences of skin cancer with different types.
Who Is the Strongest Enemy? Towards Optimal and Efficient Evasion Attacks in Deep RL

Evaluating the worst-case performance of a reinforcement learning (RL) agent under the strongest/optimal adversarial perturbations on state observations (within some constraints) is crucial for understanding the robustness of RL agents. However, finding the optimal adversary is challenging, in terms of both whether we can find the optimal attack and how efficiently we can find it. Existing works on adversarial RL either use heuristics-based methods that may not find the strongest adversary, or directly train an RL-based adversary by treating the agent as a part of the environment, which can find the optimal adversary but may become intractable in a large state space. In this paper, we propose a novel attacking algorithm which has an RL-based "director" searching for the optimal policy perturbation, and an "actor" crafting state perturbations following the directions from the director (i.e. the actor executes targeted attacks). Our proposed algorithm, PA-AD, is theoretically optimal against an RL agent and significantly improves the efficiency compared with prior RL-based works in environments with large or pixel state spaces. Empirical results show that our proposed PA-AD universally outperforms state-of-the-art attacking methods in a wide range of environments. Our method can be easily applied to any RL algorithms to evaluate and improve their robustness.
Towards Explainable Abnormal Infant Movements Identification: A Body-part Based Prediction and Visualisation Framework

Providing early diagnosis of cerebral palsy (CP) is key to enhancing the developmental outcomes for those affected. Diagnostic tools such as the General Movements Assessment (GMA), have produced promising results in early diagnosis, however these manual methods can be laborious.In this paper, we propose a new framework for the automated classification of infant body movements, based upon the GMA, which unlike previous methods, also incorporates a visualization framework to aid with interpretability. Our proposed framework segments extracted features to detect the presence of Fidgety Movements (FMs) associated with the GMA spatiotemporally. These features are then used to identify the body-parts with the greatest contribution towards a classification decision and high-light the related body-part segment providing visual feedback to the user.We quantitatively compare the proposed framework’s classification performance with several other methods from the literature and qualitatively evaluate the visualization’s veracity. Our experimental results show that the proposed method performs more robustly than comparable techniques in this setting whilst simultaneously providing relevant visual interpretability.
Constraining the Effect of Convective Inhibition on the Thermal Evolution of Uranus and Neptune

The internal heat flows of both Uranus and Neptune remain major outstanding problems in planetary science. Uranus’s surprisingly cold effective temperature is inconsistent with adiabatic thermal evolution models, while Neptune’s substantial internal heat flow is twice its received insolation. In this work, we constrain the magnitude of influence condensation, including latent heat and inhibition of convection, can have on the thermal evolution of these bodies. We find that while the effect can be significant, it is insufficient to solve the Uranus faintness problem on its own. Self-consistently considering the effects of both latent heat release and stable stratification, methane condensation can speed up the cooldown time of Uranus and Neptune by no more than 15%, assuming 5% molar methane abundance. Water condensation works in the opposite direction; water condensation can slow down the cooldown timescale of Uranus and Neptune by no more than 15%, assuming 12% molar water abundance. We also constrain the meteorological implications of convective inhibition. We demonstrate that sufficiently abundant condensates will relax to a state of radiative–convective equilibrium requiring finite activation energy to disrupt. We also comment on the importance of considering convective inhibition when modeling planetary interiors.
Improving parametric mass modelling of lensing clusters through a perturbative approach

We present a new method to model the mass distribution of galaxy clusters that combines a parametric and a free-form approach to reconstruct cluster cores with strong lensing constraints. It aims at combining the advantages of both approaches, by keeping the robustness of the parametric component with an increased flexibility thanks to a free-form surface of B-spline functions. We demonstrate the capabilities of this new approach on the simulated cluster Hera, which has been used to evaluate lensing codes for the analysis of the Frontier Fields clusters. The method leads to better reproduction of the constraints, with an improvement by a factor ∼ 3− 4 on the root-mean-square error on multiple-image positions, when compared to parametric-only approaches. The resulting models show a better accuracy in the reconstruction of the amplitude of the convergence field while conserving a high fidelity on other lensing observables alreadywell reproduced.Wemake this method publicly available through its implementation in the Lenstool software.
Inelastic dark matter at the Fermilab Short Baseline Neutrino Program

We study the sensitivity of the Fermilab Short-Baseline Neutrino (SBN) experiments, MicroBooNE, ICARUS, and SBND, to MeV- to GeV-scale inelastic dark matter interacting through a dark photon mediator. These models provide interesting scenarios of light thermal dark matter, which, while challenging to probe with direct and indirect detection experiments, are amenable to accelerator-based searches. We consider production of the dark sector states with both the Fermilab Booster 8 GeV and NuMI 120 GeV proton beams and study the signatures of scattering and decay of the heavy excited dark state in the SBN detectors. These distinct signatures probe complementary regions of parameter space. All three experiments will be able to cover new ground, with an excellent near-term opportunity to search for cosmologically motivated targets explaining the observed dark matter abundance.
Real Time Egocentric Object Segmentation: THU-READ Labeling and Benchmarking Results

Egocentric segmentation has attracted recent interest in the computer vision community due to their potential in Mixed Reality (MR) applications. While most previous works have been focused on segmenting egocentric human body parts (mainly hands), little attention has been given to egocentric objects. Due to the lack of datasets of pixel-wise annotations of egocentric objects, in this paper we contribute with a semantic-wise labeling of a subset of 2124 images from the RGB-D THU-READ Dataset. We also report benchmarking results using Thundernet, a real-time semantic segmentation network, that could allow future integration with end-to-end MR applications.