Miracle-dz/UniFAR-FTUs huggingface dataset:
Imitating Opponent to Win: Adversarial Policy Imitation Learning in Two-player Competitive Games

Recent research on vulnerabilities of deep reinforcement learning (RL) has shown that adversarial policies adopted by an adversary agent can influence a target RL agent (victim agent) to perform poorly in a multi-agent environment. In existing studies, adversarial policies are directly trained based on experiences of interacting with the victim agent. There is a key shortcoming of this approach; knowledge derived from historical interactions may not be properly generalized to unexplored policy regions of the victim agent, making the trained adversarial policy significantly less effective. In this work, we design a new effective adversarial policy learning algorithm that overcomes this shortcoming. The core idea of our new algorithm is to create a new imitator to imitate the victim agent's policy while the adversarial policy will be trained not only based on interactions with the victim agent but also based on feedback from the imitator to forecast victim's intention. By doing so, we can leverage the capability of imitation learning in well capturing underlying characteristics of the victim policy only based on sample trajectories of the victim. Our victim imitation learning model differs from prior models as the environment's dynamics are driven by adversary's policy and will keep changing during the adversarial policy training. We provide a provable bound to guarantee a desired imitating policy when the adversary's policy becomes stable. We further strengthen our adversarial policy learning by making our imitator a stronger version of the victim. Finally, our extensive experiments using four competitive MuJoCo game environments show that our proposed adversarial policy learning algorithm outperforms state-of-the-art algorithms.
Joint Optimization of Sensing and Computation for Status Update in Mobile Edge Computing Systems

IoT devices have been widely utilized to detect state transition in the surrounding environment and transmit status updates to the base station for system operations. To guarantee the accuracy of system control, age of information (AoI) is introduced to quantify the freshness of the sensory data and meet the stringent timeliness requirement. Due to the limited computing resources, the status update can be offloaded to the mobile edge computing (MEC) server for execution. Since status updates generated by insufficient sensing operations may be invalid and lead to additional processing time, a joint data sensing and processing optimization problem needs to be considered. Therefore, this work formulates an NP-hard problem that considers the freshness of the status updates and energy consumption of the IoT devices. Subsequently, the problem is decomposed into sampling, sensing, and computation offloading optimization problems. To optimize the system overhead, a multi-variable iterative system cost minimization algorithm is proposed. Simulation results illustrate the efficacy of our method in decreasing the system cost, and indicate the influence of sensing and processing under different scenarios.
Hybrid Reusable Computational Analytics Workflow Management with Cloudmesh

In this paper, we summarize our effort to create and utilize a simple framework to coordinate computational analytics tasks with the help of a workflow system. Our design is based on a minimalistic approach while at the same time allowing to access computational resources offered through the owner’s computer, HPC computing centers, cloud resources, and distributed systems in general. The access to this framework includes a simple GUI for monitoring and managing the workflow, a REST service, a command line interface, as well as a Python interface. The resulting framework was developed for several examples targeting benchmarks of AI applications on hybrid compute resources and as an educational tool for teaching scientists and students sophisticated concepts to execute computations on resources ranging from a single computer to many thousands of computers as part of on-premise and cloud infrastructure. We demonstrate the usefulness of the tool on a number of examples. The code is available as an open-source project in GitHub and is based on an easy-to-enhance tool called cloudmesh.
Robust and Tuning-Free Sparse Linear Regression via Square-Root Slope

We consider the high-dimensional linear regression model and assume that a fraction of the measurements are altered by an adversary with complete knowledge of the data and the underlying distribution. We are interested in a scenario where dense additive noise is heavy-tailed while the measurement vectors follow a sub-Gaussian distribution. Within this framework, we establish minimax lower bounds for the performance of an arbitrary estimator that depend on the the fraction of corrupted observations as well as the tail behavior of the additive noise. Moreover, we design a modification of the so-called Square-Root Slope estimator with several desirable features: (a) it is provably robust to adversarial contamination, and satisfies performance guarantees in the form of sub-Gaussian deviation inequalities that match the lower error bounds, up to logarithmic factors; (b) it is fully adaptive with respect to the unknown sparsity level and the variance of the additive noise, and (c) it is computationally tractable as a solution of a convex optimization problem. To analyze performance of the proposed estimator, we prove several properties of matrices with sub-Gaussian rows that may be of independent interest.
Cross-correlation of the kinematic Sunyaev-Zel’dovich effect and 21 cm intensity mapping with tidal reconstruction

We discuss the possibility of studying diffuse baryon distributions with kinematic Sunyaev-Zel'dovich (kSZ) effect by correlating cosmic microwave background (CMB) temperature fluctuations with density fluctuations from 21\,cm intensity mapping (IM). The biggest challenge for the cross-correlation is the loss of large-scale information in IM, due to foregrounds and the zero spacing problem of interferometers. We apply the tidal reconstruction algorithm to restore the lost large-scale modes, which increases the correlation by more than a factor of three. With the predicted foreground level, we expect a $\sim20\,\sigma$ detection of kSZ signal for $0.8\lesssim z\lesssim2.5$ with CHIME and Planck, and a $\sim40\, \sigma$ detection with HIRAX and Planck. The significance can be greatly increased with next-generation facilities of higher spatial resolutions.
XMM-NEWTON AND CHANDRA OBSERVATIONS OF THE EJECTA-DOMINATED MIXED-MORPHOLOGY GALACTIC SUPERNOVA REMNANT G352.7−0.1

We present a spatial and spectral X-ray analysis of the Galactic supernova remnant (SNR) G352.7−0.1 using archival data from observations made with the XMM-Newton X-ray Observatory and the Chandra X-ray Observatory. Prior X-ray observations of this SNR had revealed a thermal center-filled morphology that contrasts with a shell-like radio morphology, thus establishing G352.7−0.1 as a member of the class of Galactic SNRs known as mixed-morphology SNRs (MMSNRs). Our study confirms that the X-ray emission comes from the SNR interior and must be ejecta dominated. Spectra obtained with XMM-Newton may be fit satisfactorily with a single thermal component (namely a non-equilibrium ionization component with enhanced abundances of silicon and sulfur). In contrast, spectra extracted by Chandra from certain regions of the SNR cannot always be fit by a single thermal component. For those regions, a second thermal component with solar abundances or two thermal components with different temperatures and thawed silicon and sulfur abundances (respectively) can generate a statistically acceptable fit. We argue that the former scenario is more physically plausible: on the basis of parameters of our spectral fits, we calculate physical parameters including X-ray emitting mass (∼45 M☉ for solar abundances). We find no evidence for overionization in the X-ray emitting plasma associated with the SNR: this phenomenon has been seen in other MMSNRs. We have conducted a search for a neutron star within the SNR by using a hard (2–10 keV) Chandra image but could not identify a firm candidate. We also present (for the first time) the detection of infrared emission from this SNR as detected at 24 μm by the MIPS on board Spitzer. Finally, we discuss the properties of G352.7−0.1 in the context of other ejecta-dominated MMSNRs.
Evaluation of large-scale synthetic data for Grammar Error Correction

Grammar Error Correction(GEC) mainly re-lies on the availability of high quality of large amount of synthetic parallel data of grammatically correct and erroneous sentence pairs. The quality of the synthetic data is evaluated on how well the GEC system performs when pre-trained using it. But this does not provide much insight into what are the necessary fac-tors which deﬁne the quality of these data. So this work aims to introduce 3 metrics - reliability, diversity and distribution match to provide more insight into the quality of large-scale synthetic data generated for the GEC task, as well as automatically evaluate them. Evaluating these three metrics automatically can also help in providing feedback to the data generation systems and thereby improve the quality of the synthetic data generated dynamically
Scoring Black-Box Models for Adversarial Robustness

Deep neural networks are susceptible to adversarial inputs and various methods have been proposed to defend these models against adversarial attacks under different perturbation models. The robustness of models to adversarial attacks has been analyzed by first constructing adversarial inputs for the model, and then testing the model performance on the constructed adversarial inputs. Most of these attacks require the model to be white-box, need access to data labels, and finding adversarial inputs can be computationally expensive. We propose a simple scoring method for black-box models which indicates their robustness to adversarial input. We show that adversarially more robust models have a smaller $l_1$-norm of LIME weights and sharper explanations.
There is more than one kind of robustness: Fooling Whisper with adversarial examples

Whisper is a recent Automatic Speech Recognition (ASR) model displaying impressive robustness to both out-of-distribution inputs and random noise. In this work, we show that this robustness does not carry over to adversarial noise. We show that we can degrade Whisper performance dramatically, or even transcribe a target sentence of our choice, by generating very small input perturbations with Signal Noise Ratio of 35-45dB. We also show that by fooling the Whisper language detector we can very easily degrade the performance of multilingual models. These vulnerabilities of a widely popular open-source model have practical security implications and emphasize the need for adversarially robust ASR.
DISCOVERY OF A POSSIBLY SINGLE BLUE SUPERGIANT STAR IN THE INTRA-CLUSTER REGION OF VIRGO CLUSTER OF GALAXIES

IC 3418 is a dwarf irregular galaxy falling into the Virgo cluster, and a 17 kpc long trail is seen behind the galaxy, which is considered to have formed due to ram pressure stripping. The trail contains compact knots and diffuse blobs of ultraviolet and blue optical emission and, thus, it is a clear site of recent star formation but in an unusual environment, surrounded by a million degree intra-cluster medium. We report on our optical spectroscopy of a compact source in the trail, SDSS J122952.66+112227.8, and show that the optical spectrum is dominated by emission from a massive blue supergiant star. If confirmed, our report would mark the farthest star with spectroscopic observation. We interpret that a massive O-type star formed in situ in the trail has evolved recently out of the main sequence into this blue supergiant phase, and now lacks any detectable spectral sign of its associated H ii region. We argue that turbulence within the ram pressure striped gaseous trail may play a dominant role for the star formation within such trails.
The least-control principle for local learning at equilibrium

Equilibrium systems are a powerful way to express neural computations. As special cases, they include models of great current interest in both neuroscience and machine learning, such as deep neural networks, equilibrium recurrent neural networks, deep equilibrium models, or meta-learning. Here, we present a new principle for learning such systems with a temporally- and spatially-local rule. Our principle casts learning as a least-control problem, where we first introduce an optimal controller to lead the system towards a solution state, and then define learning as reducing the amount of control needed to reach such a state. We show that incorporating learning signals within a dynamics as an optimal control enables transmitting activity-dependent credit assignment information, avoids storing intermediate states in memory, and does not rely on infinitesimal learning signals. In practice, our principle leads to strong performance matching that of leading gradient-based learning methods when applied to an array of problems involving recurrent neural networks and meta-learning. Our results shed light on how the brain might learn and offer new ways of approaching a broad class of machine learning problems.
Enabling the discovery of fast transients. A kilonova science module for the Fink broker

We describe the fast transient classification algorithm in the center of the kilonova (KN) science module currently implemented in the Fink broker and report classification results based on simulated catalogs and real data from the ZTF alert stream. We used noiseless, homogeneously sampled simulations to construct a basis of principal components (PCs). All light curves from a more realistic ZTF simulation were written as a linear combination of this basis. The corresponding coefficients were used as features in training a random forest classifier. The same method was applied to long (>30 days) and medium (<30 days) light curves. The latter aimed to simulate the data situation found within the ZTF alert stream. Classification based on long light curves achieved 73.87% precision and 82.19% recall. Medium baseline analysis resulted in 69.30% precision and 69.74% recall, thus confirming the robustness of precision results when limited to 30 days of observations. In both cases, dwarf flares and point Type Ia supernovae were the most frequent contaminants. The final trained model was integrated into the Fink broker and has been distributing fast transients, tagged as KN_candidates, to the astronomical community, especially through the GRANDMA collaboration. We showed that features specifically designed to grasp different light curve behaviors provide enough information to separate fast (KN-like) from slow (non-KN-like) evolving events. This module represents one crucial link in an intricate chain of infrastructure elements for multi-messenger astronomy which is currently being put in place by the Fink broker team in preparation for the arrival of data from the Vera Rubin Observatory Legacy Survey of Space and Time.
Neural network quantum state with proximal optimization: a ground-state searching scheme based on variational Monte Carlo

Neural network quantum states (NQS), incorporating with variational Monte Carlo (VMC) method, are shown to be a promising way to investigate quantum many-body physics. Whereas vanilla VMC methods perform one gradient update per sample, we introduce a novel objective function with proximal optimization (PO) that enables multiple updates via reusing the mismatched samples. Our VMC-PO method keeps the advantage of the previous importance sampling gradient optimization algorithm [L. Yang, et al , Phys. Rev. Research 2 , 012039(R)(2020)] that eﬃciently uses sampled states. PO mitigates the numerical instabilities during network updates, which is similar to stochastic reconﬁguration (SR) methods, but achieves an alternative and simpler implement with lower computational complexity. We investigate the performance of our VMC-PO algorithm for ground-state searching with a 1-dimensional transverse-ﬁeld Ising model and 2-dimensional Heisenberg antiferromagnet on a square lattice, and demonstrate that the reached ground-state energies are comparable to state-of-the-art results.
BIMRL: Brain Inspired Meta Reinforcement Learning

Sample efficiency has been a key issue in reinforcement learning (RL). An efficient agent must be able to leverage its prior experiences to quickly adapt to similar, but new tasks and situations. Meta-RL is one attempt at formalizing and ad-dressing this issue. Inspired by recent progress in meta-RL, we introduce BIMRL, a novel multi-layer architecture along with a novel brain-inspired memory module that will help agents quickly adapt to new tasks within a few episodes. We also utilize this memory module to design a novel intrinsic reward that will guide the agent's exploration. Our architecture is inspired by findings in cognitive neuroscience and is compatible with the knowledge on connectivity and functionality of different regions in the brain. We empirically validate the effectiveness of our proposed method by competing with or surpassing the performance of some strong baselines on multiple MiniGrid environments.
Robust telescope scheduling

This paper presents a technique for building robust telescope schedules that tend not to break. The technique is called Just-In-Case (JIC) scheduling and it implements the common sense idea of being prepared for likely errors, just in case they should occur. The JIC algorithm analyzes a given schedule, determines where it is likely to break, reinvokes a scheduler to generate a contingent schedule for each highly probable break case, and produces a 'multiply contingent' schedule. The technique was developed for an automatic telescope scheduling problem, and the paper presents empirical results showing that Just-In-Case scheduling performs extremely well for this problem.
Electron Density Variations in the Interstellar Medium and the Average Frequency Profile of a Scintle from Pulsar Scintillation Spectra

We observed the scintillation pattern of nine bright pulsars at 324 MHz and three at 1.68 GHz and analyzed the wavenumber spectrum, which is related to electron density variations of the plasma turbulence of the interstellar medium (ISM). For all pulsars the frequency section of the autocorrelation function (ACF) of the dynamic spectra to at least 45% of the maximum corresponds to predictions of scattering theories with a range of power-law exponents of the wavenumber spectrum of 3.56 ≤ α ≤ 3.97 with errors ≤0.05 and a mean with standard deviation of 3.76 ± 0.13. The range includes α = 3.67 for the Kolmogorov spectrum. Similar results, although with larger errors, were found from the Fourier transform of the ACFs down to ∼10−3 of the maximum. No clear case of a distinction between thin-screen and extended-medium scattering models was found. The average frequency profile of the scintles can be characterized for steep wavenumber spectra with α ≲ 4 by a cusp with a somewhat rounded peak. For flatter spectra, down to at least α ∼ 3.56 the cusp with its peak becomes more pronounced and its decay steepens. We discuss our findings in the context of the scattering characteristics of the ISM.
Pulsation Theory Models for Cepheids in Eclipsing Binary Systems

Physical parameters were recently measured for several Cepheids in eclipsing binary systems in the LMC. It is a good opportunity to compare these results with models obtained from pulsation theory. Having well determined physical parameters of the stars, we can calculate the corresponding pulsation periods for a given mode and check for instability. For stars that lack data, unknown or poorly defined parameters can be further constrained. We present the comparison for six Cepheids, showing that, in general, the results are in very good agreement.
Magnetic stars from a FEROS cool Ap star survey

New magnetic Ap stars with split Zeeman components are presented. These stars were discovered from observations with the Fibre-fed Extended Range Optical Spectrograph (FEROS) spectrograph at the European Southern Observatory (ESO) 2.2-m telescope. 15 new magnetic stars are analysed here. Several stars with very strong magnetic fields were found, including HD 70702 with a 15-kG magnetic field strength, and HD 168767 with a 16.5-kG magnetic field strength measured using split Zeeman components of spectral lines and by comparison with synthetic calculations. The physical parameters of the stars were estimated from photometric and spectroscopic data. Together with previously published results for stars with strong magnetic fields, the relationship between magnetic field strength and rotation period is discussed.
Representations of certain normed algebras

We show that for a normal locally-${\mathscr P}$ space $X$ (where ${\mathscr P}$ is a topological property subject to some mild requirements) the subset $C_{\mathscr P}(X)$ of $C_b(X)$ consisting of those elements whose support has a neighborhood with ${\mathscr P}$, is a subalgebra of $C_b(X)$ isometrically isomorphic to $C_c(Y)$ for some unique (up to homeomorphism) locally compact Hausdorff space $Y$. The space $Y$ is explicitly constructed as a subspace of the Stone--\v{C}ech compactification $\beta X$ of $X$ and contains $X$ as a dense subspace. Under certain conditions, $C_{\mathscr P}(X)$ coincides with the set of those elements of $C_b(X)$ whose support has ${\mathscr P}$, it moreover becomes a Banach algebra, and simultaneously, $Y$ satisfies $C_c(Y)=C_0(Y)$. This includes the cases when ${\mathscr P}$ is the Lindel\"{o}f property and $X$ is either a locally compact paracompact space or a locally-${\mathscr P}$ metrizable space. In either of the latter cases, if $X$ is non-${\mathscr P}$, $Y$ is non-normal, and $C_{\mathscr P}(X)$ fits properly between $C_0(X)$ and $C_b(X)$; even more, we can fit a chain of ideals of certain length between $C_0(X)$ and $C_b(X)$. The known construction of $Y$ enables us to derive a few further properties of either $C_{\mathscr P}(X)$ or $Y$. Specifically, when ${\mathscr P}$ is the Lindel\"{o}f property and $X$ is a locally-${\mathscr P}$ metrizable space, we show that \[\dim C_{\mathscr P}(X)=\ell(X)^{\aleph_0},\] where $\ell(X)$ is the Lindel\"{o}f number of $X$, and when ${\mathscr P}$ is countable compactness and $X$ is a normal space, we show that \[Y=\mathrm{int}_{\beta X}\upsilon X\] where $\upsilon X$ is the Hewitt realcompactification of $X$.
WFC3 IR Ground P-Flats

IR external flat field exposures from WFC3 thermal-vacuum campaign 3 have been used to construct and deliver an initial set of IR pixel-to-pixel flat field reference files for the 15 imaging filters in the WFC3 IR channel. The flats have a mean signal-to-noise ratio of ~500, which yields corrections down to a level of ~0.2%. The flats have some artifacts related to their ground-based origin, which will need to be addressed in order to properly calibrate on-orbit images. These data show that the IR4 flight detector easily meets and exceeds the applicable IR flat field-related CEI requirements.
Relations Learning between BMI and Injuries by Bayesian Matrix Completion

Concerning basketball injuries, trainers should carry out specific training programs for athletes based on their characteristics to reduce the risk of injury. In basketball games, BMI is a main index for players. Therefore, this paper proposes a Bayesian matrix completion algorithm to learn the correlation probability between BMI and various injuries of players, which can assist trainers to make customized training plans for various players. And this algorithm will be applied to the existing injury cases in NBA statistical database. The high rank assumption is adopted in the process of matrix completion, and the matrix is completed according to some special attributes of Bayesian matrix. Experimental results show that the algorithm is practical and effective.