Miracle-dz/UniFAR-FTUs huggingface dataset:
Observational Learning with Ordered States

When does society eventually learn the truth, or the correct action to take, via observational learning? In a general model of sequential learning over social networks, we develop the interplay of preferences satisfying single-crossing differences (SCD) and a new informational condition, directionally unbounded beliefs (DUB). For a wide class of network structures, SCD preferences and DUB information are a minimal pair of sufficient conditions for learning. Unlike “unbounded beliefs”, which is the informational condition that characterizes learning for all preferences, DUB is broadly compatible with the monotone likelihood ratio property. Our analysis establishes a welfare lower bound under arbitrary preferences and information, which yields a general condition for learning. What drives learning is that a single agent must be able to rule out any wrong action, rather than (individually) take the correct action.
GASP XXXIV: Unfolding the Thermal Side of Ram Pressure Stripping in the Jellyfish Galaxy JO201

X-ray studies of jellyfish galaxies play a crucial role in understanding the interactions between the interstellar medium (ISM) and the intracluster medium (ICM). In this paper, we focused on the jellyfish galaxy JO201. By combining archival Chandra observations, Multi Unit Spectroscopic Explorer Hα cubes, and maps of the emission fraction of the diffuse ionized gas, we investigated both its high-energy spectral properties and the spatial correlation between its X-ray and optical emissions. The X-ray emission of JO201 is provided by both the Compton-thick active galactic nucleus (L = 2.7 · 1041 erg s−1, not corrected for intrinsic absorption) and an extended component (L 1.9–4.5 · 1041 erg s−1) produced by a warm plasma (kT keV), whose luminosity is higher than expected from the observed star formation (L 3.8 · 1040erg s−1). The spectral analysis showed that the X-ray emission is consistent with the thermal cooling of hot plasma. These properties are similar to the ones found in other jellyfish galaxies showing extended X-ray emission. A point-to-point analysis revealed that this X-ray emission closely follows the ISM distribution, whereas CLOUDY simulations proved that the ionization triggered by this warm plasma would be able to reproduce the [O i]/Hα excess observed in JO201. We conclude that the galactic X-ray emitting plasma originates on the surface of the ISM as a result of the ICM–ISM interplay. This process would entail the cooling and accretion of the ICM onto the galaxy, which could additionally fuel the star formation, and the emergence of [O i]/Hα excess in the optical spectrum.
Toward Automated Generation of Affective Gestures from Text: A Theory-Driven Approach

Communication in both human-human and human-robot interac-tion (HRI) contexts consists of verbal (speech-based) and non-verbal(facial expressions, eye gaze, gesture, body pose, etc.) components.The verbal component contains semantic and affective information;accordingly, HRI work on the gesture component so far has focusedon rule-based (mapping words to gestures) and data-driven (deep-learning) approaches to generating speech-paired gestures basedon either semantics or the affective state. Consequently, most ges-ture systems are confined to producing either semantically-linkedor affect-based gesticures. This paper introduces an approach forenabling human-robot communication based on a theory-drivenapproach to generate speech-paired robot gestures using both se-mantic and affective information. Our model takes as input textand sentiment analysis, and generates robot gestures in terms oftheir shape, intensity, and speed.
An RL-Based Adaptive Detection Strategy to Secure Cyber-Physical Systems

Increased dependence on networked, software based control has escalated the vulnerabilities of Cyber Physical Systems (CPSs). Detection and monitoring components developed leveraging dynamical systems theory are often employed as lightweight security measures for protecting such safety critical CPSs against false data injection attacks. However, existing approaches do not correlate attack scenarios with parameters of detection systems. In the present work, we propose a Reinforcement Learning (RL) based framework which adaptively sets the parameters of such detectors based on experience learned from attack scenarios, maximizing detection rate and minimizing false alarms in the process while attempting performance preserving control actions.
Prostate Tissue Grading with Deep Quantum Measurement Ordinal Regression

Prostate cancer (PCa) is one of the most common and aggressive cancers worldwide. The Gleason score (GS) system is the standard way of classifying prostate cancer and the most reliable method to determine the severity and treatment to follow. The pathologist looks at the arrangement of cancer cells in the prostate and assigns a score on a scale that ranges from 6 to 10. Automatic analysis of prostate whole-slide images (WSIs) is usually addressed as a binary classification problem, which misses the finer distinction between stages given by the GS. This paper presents a probabilistic deep learning ordinal classification method that can estimate the GS from a prostate WSI. Approaching the problem as an ordinal regression task using a differentiable probabilistic model not only improves the interpretability of the results, but also improves the accuracy of the model when compared to conventional deep classification and regression architectures.
Simulating the Sycamore quantum supremacy circuits

We propose a general tensor network method for simulating quantum circuits. The method is massively more efficient in computing a large number of correlated bitstring amplitudes and probabilities than existing methods. As an application, we study the sampling problem of Google's Sycamore circuits, which are believed to be beyond the reach of classical supercomputers and have been used to demonstrate quantum supremacy. Using our method, employing a small computational cluster containing 60 graphical processing units (GPUs), we have generated one million correlated bitstrings with some entries fixed, from the Sycamore circuit with 53 qubits and 20 cycles, with linear cross-entropy benchmark (XEB) fidelity equals 0.739, which is much higher than those in Google's quantum supremacy experiments.
PET Image Reconstruction with Multiple Kernels and Multiple Kernel Space Regularizers

Kernelized maximum-likelihood (ML) expectation maximization (EM) methods have recently gained prominence in PET image reconstruction, outperforming many previous state-of-the-art methods. But they are not immune to the problems of non-kernelized MLEM methods in potentially large reconstruction error and high sensitivity to iteration number. This paper demonstrates these problems by theoretical reasoning and experiment results, and provides a novel solution to solve these problems. The solution is a regularized kernelized MLEM with multiple kernel matrices and multiple kernel space regularizers that can be tailored for different applications. To reduce the reconstruction error and the sensitivity to iteration number, we present a general class of multi-kernel matrices and two regularizers consisting of kernel image dictionary and kernel image Laplacian quatradic, and use them to derive the single-kernel regularized EM and multi-kernel regularized EM algorithms for PET image reconstruction. These new algorithms are derived using the technical tools of multi-kernel combination in machine learning, image dictionary learning in sparse coding, and graph Laplcian quadratic in graph signal processing. Extensive tests and comparisons on the simulated and in vivo data are presented to validate and evaluate the new algorithms, and demonstrate their superior performance and advantages over the kernelized MLEM and other conventional methods.
GenoML: Automated Machine Learning for Genomics

GenoML is a Python package automating machine learning workflows for genomics (genetics and multi-omics) with an open science philosophy. Genomics data require significant domain expertise to clean, pre-process, harmonize and perform quality control of the data. Furthermore, tuning, validation, and interpretation involve taking into account the biology and possibly the limitations of the underlying data collection, protocols, and technology. GenoML's mission is to bring machine learning for genomics and clinical data to non-experts by developing an easy-to-use tool that automates the full development, evaluation, and deployment process. Emphasis is put on open science to make workflows easily accessible, replicable, and transferable within the scientific community. Source code and documentation is available at https://genoml.com.
Implicit Methods with Reduced Memory for Thermal Radiative Transfer

This paper presents approximation methods for time-dependent thermal radiative transfer problems in high energy density physics. It is based on the multilevel quasidiffusion method defined by the high-order radiative transfer equation (RTE) and the low-order quasidiffusion (aka VEF) equations for the moments of the specific intensity. A large part of data storage in TRT problems between time steps is determined by the dimensionality of grid functions of the radiation intensity. The approximate implicit methods with reduced memory for the time-dependent Boltzmann equation are applied to the high-order RTE, discretized in time with the backward Euler (BE) scheme. The high-dimensional intensity from the previous time level in the BE scheme is approximated by means of the low-rank proper orthogonal decomposition (POD). Another version of the presented method applies the POD to the remainder term of P2 expansion of the intensity. The accuracy of the solution of the approximate implicit methods depends of the rank of the POD. The proposed methods enable one to reduce storage requirements in time dependent problems. Numerical results of a Fleck-Cummings TRT test problem are presented.
SkillVet: Automated Traceability Analysis of Amazon Alexa Skills

Skills, are essential components in Smart Personal Assistants (SPA). The number of skills has grown rapidly, dominated by a changing environment that has no clear business model. Skills can access personal information and this may pose a risk to users. However, there is little information about how this ecosystem works, let alone the tools that can facilitate its study. In this article, we present the largest systematic measurement of the Amazon Alexa skill ecosystem to date. We study developers’ practices in this ecosystem, including how they collect and justify the need for sensitive information, by designing a methodology to identify over-privileged skills with broken privacy policies. We collect 199,295 Alexa skills and uncover that around 43% of the skills (and 50% of the developers) that request these permissions follow bad privacy practices, including (partially) broken data permissions traceability. In order to perform this kind of analysis at scale, we present SkillVet that leverages machine learning and natural language processing techniques, and generates high-accuracy prediction sets. We report several concerning practices, including how developers can bypass Alexa's permission system through account linking and conversational skills, and offer recommendations on how to improve transparency, privacy and security. Resulting from the responsible disclosure we did, 13% of the reported issues no longer pose a threat at submission time.
Mobile Touchless Fingerprint Recognition: Implementation, Performance and Usability Aspects

This work presents an automated touchless fingerprint recognition system for smartphones. We provide a comprehensive description of the entire recognition pipeline and discuss important requirements for a fully automated capturing system. Also, our implementation is made publicly available for research purposes. During a database acquisition, a total number of 1,360 touchless and touch-based samples of 29 subjects are captured in two different environmental situations. Experiments on the acquired database show a comparable performance of our touchless scheme and the touch-based baseline scheme under constrained environmental influences. A comparative usability study on both capturing device types indicates that the majority of subjects prefer the touchless capturing method. Based on our experimental results we analyze the impact of the current COVID-19 pandemic on fingerprint recognition systems. Finally, implementation aspects of touchless fingerprint recognition are summarized.
Convergence Analysis of Dual Decomposition Algorithm in Distributed Optimization: Asynchrony and Inexactness

Dual decomposition is widely utilized in the distributed optimization of multiagent systems. In practice, the dual decomposition algorithm is desired to admit an asynchronous implementation due to imperfect communication, such as time delay and packet drop. In addition, computational errors also exist when the individual agents solve their own subproblems. In this article, we analyze the convergence of the dual decomposition algorithm in the distributed optimization when both the communication asynchrony and the subproblem solution inexactness exist. We find that the interaction between asynchrony and inexactness slows down the convergence rate from <inline-formula><tex-math notation="LaTeX">$\mathcal {O} (1 / k)$</tex-math></inline-formula> to <inline-formula><tex-math notation="LaTeX">$\mathcal {O} (1 / \sqrt{k})$</tex-math></inline-formula>. Specifically, with a constant step size, the value of the objective function converges to a neighborhood of the optimal value, and the solution converges to a neighborhood of the optimal solution. Moreover, the violation of the constraints diminishes in <inline-formula><tex-math notation="LaTeX">$\mathcal {O} (1 / \sqrt{k})$</tex-math></inline-formula>. Our result generalizes and unifies the existing ones that only consider either asynchrony or inexactness. Finally, numerical simulations validate the theoretical results.
New SU UMa-type star ZTF18abdlzhd in the Zwicky Transient Facility data

We carried out a search for unknown dwarf novae in a public data release of the Zwicky Transient Facility survey and suspected that the object ZTF18abdlzhd is a SU UMa-type star. Performed multicolor CCD observations permit us to follow its fading from an outburst in August and an entire superoutburst in October 2020. The duration of the superoutburst is 13 days. We detected superhumps with period P = 0. 06918(3) that are characteristic of UGSU type stars.
Contextual modal types for algebraic effects and handlers

Programming languages with algebraic effects often track the computations’ effects using type-and-effect systems. In this paper, we propose to view an algebraic effect theory of a computation as a variable context; consequently, we propose to track algebraic effects of a computation with contextual modal types. We develop ECMTT, a novel calculus which tracks algebraic effects by a contextualized variant of the modal □ (necessity) operator, that it inherits from Contextual Modal Type Theory (CMTT). Whereas type-and-effect systems add effect annotations on top of a prior programming language, the effect annotations in ECMTT are inherent to the language, as they are managed by programming constructs corresponding to the logical introduction and elimination forms for the □ modality. Thus, the type-and-effect system of ECMTT is actually just a type system. Our design obtains the properties of local soundness and completeness, and determines the operational semantics solely by β-reduction, as customary in other logic-based calculi. In this view, effect handlers arise naturally as a witness that one context (i.e., algebraic theory) can be reached from another, generalizing explicit substitutions from CMTT. To the best of our knowledge, ECMTT is the first system to relate algebraic effects to modal types. We also see it as a step towards providing a correspondence in the style of Curry and Howard that may transfer a number of results from the fields of modal logic and modal type theory to that of algebraic effects.
Continuous Coordination As a Realistic Scenario for Lifelong Learning

Current deep reinforcement learning (RL) algorithms are still highly task-specific and lack the ability to generalize to new environments. Lifelong learning (LLL), however, aims at solving multiple tasks sequentially by efficiently transferring and using knowledge between tasks. Despite a surge of interest in lifelong RL in recent years, the lack of a realistic testbed makes robust evaluation of LLL algorithms difficult. Multi-agent RL (MARL), on the other hand, can be seen as a natural scenario for lifelong RL due to its inherent non-stationarity, since the agents' policies change over time. In this work, we introduce a multi-agent lifelong learning testbed that supports both zero-shot and few-shot settings. Our setup is based on Hanabi -- a partially-observable, fully cooperative multi-agent game that has been shown to be challenging for zero-shot coordination. Its large strategy space makes it a desirable environment for lifelong RL tasks. We evaluate several recent MARL methods, and benchmark state-of-the-art LLL algorithms in limited memory and computation regimes to shed light on their strengths and weaknesses. This continual learning paradigm also provides us with a pragmatic way of going beyond centralized training which is the most commonly used training protocol in MARL. We empirically show that the agents trained in our setup are able to coordinate well with unseen agents, without any additional assumptions made by previous works. The code and all pre-trained models are available at https://github.com/chandar-lab/Lifelong-Hanabi.
Computing Subset Feedback Vertex Set via Leafage

A typical example that behaves computationally different in subclasses of chordal graphs is the \textsc{Subset Feedback Vertex Set} (SFVS) problem: given a vertex-weighted graph $G=(V,E)$ and a set $S\subseteq V$, the \textsc{Subset Feedback Vertex Set} (SFVS) problem asks for a vertex set of minimum weight that intersects all cycles containing a vertex of $S$. SFVS is known to be polynomial-time solvable on interval graphs, whereas SFVS remains \NP-complete on split graphs and, consequently, on chordal graphs. Towards a better understanding of the complexity of SFVS on subclasses of chordal graphs, we exploit structural properties of a tree model in order to cope with the hardness of SFVS. Here we consider variants of the \emph{leafage} that measures the minimum number of leaves in a tree model. We show that SFVS can be solved in polynomial time for every chordal graph with bounded leafage. In particular, given a chordal graph on $n$ vertices with leafage $\ell$, we provide an algorithm for SFVS with running time $n^{O(\ell)}$. We complement our result by showing that SFVS is \W[1]-hard parameterized by $\ell$. Pushing further our positive result, it is natural to consider a slight generalization of leafage, the \emph{vertex leafage}, which measures the smallest number among the maximum number of leaves of all subtrees in a tree model. However, we show that it is unlikely to obtain a similar result, as we prove that SFVS remains \NP-complete on undirected path graphs, i.e., graphs having vertex leafage at most two. Moreover, we strengthen previously-known polynomial-time algorithm for SFVS on rooted path graphs that form a proper subclass of undirected path graphs and graphs of mim-width one.
The initial gas-phase sulfur abundance in the Orion Molecular Cloud from sulfur radio recombination lines

The abundances of chemical elements and their depletion factors are essential parameters for understanding the composition of the gas and dust that are ultimately incorporated into stars and planets. Sulfur is an abundant but peculiar element in the sense that, despite being less volatile than other elements (e.g., carbon), it is not a major constituent of dust grains in diffuse interstellar clouds. Here, we determine the gas-phase carbon-to-sulfur abundance ratio, [C]/[S], and the [S] in a dense star-forming cloud from new radio recombination lines (RRLs) detected with the Yebes 40m telescope – at relatively high frequencies (∼40 GHz ≃ 7 mm) and angular resolutions (down to 36″) – in the Orion Bar, a rim of the Orion Molecular Cloud (OMC). We detect nine Cnα RRLs (with n = 51–59) as well as nine narrow line features separated from the Cnα lines by δv = −8.4 ± 0.3 km s−1. Based on this velocity separation, we assign these features to sulfur RRLs, with little contribution of RRLs from the more condensable elements Mg, Si, or Fe. Sulfur RRLs lines trace the photodissociation region of the OMC. In these neutral gas layers, up to AV ≃ 4, the ions C+ and S+ lock in most of the C and S gas-phase reservoir. We determine a relative abundance of [C]Ori/[S]Ori = 10.4 ± 0.6 and, adopting the same [C]Ori measured in the translucent gas toward star θ1 Ori B, an absolute abundance of [S]Ori = (1.4 ± 0.4)⋅10−5. This value is consistent with emission models of the observed sulfur RRLs if N(S+) ≃ 7⋅1017 cm−2 (beam-averaged). The [S]Ori is the “initial” sulfur abundance in the OMC, before an undetermined fraction of the [S]Ori goes into molecules and ice mantles in the cloud interior. The inferred abundance [S]Ori matches the solar abundance, thus implying that there is little depletion of sulfur onto rocky dust grains, with D(S) = 0.0 ± 0.2 dex.
Gradient-Guided Dynamic Efficient Adversarial Training

Adversarial training is arguably an effective but time-consuming way to train robust deep neural networks that can withstand strong adversarial attacks. As a response to the inefﬁciency, we propose the Dynamic Efﬁcient Adversarial Training (DEAT), which gradually increases the adversarial iteration during training. Moreover, we theoretically reveal that the connection of the lower bound of Lipschitz constant of a given network and the magnitude of its partial derivative towards adversarial examples. Supported by this theoretical ﬁnding, we utilize the gradient’s magnitude to quantify the effectiveness of adversarial training and determine the timing to adjust the training procedure. This magnitude based strategy is computational friendly and easy to implement. It is especially suited for DEAT and can also be transplanted into a wide range of adversarial training methods. Our post-investigation suggests that maintaining the quality of the training adversarial examples at a certain level is essential to achieve efﬁcient adversarial training, which may shed some light on future studies.
Cell-Free DNA Fragments as Biomarkers of Islet β-Cell Death in Obesity and Type 2 Diabetes

Type 2 diabetes (T2D) typically occurs in the setting of obesity and insulin resistance, where hyperglycemia is associated with decreased pancreatic β-cell mass and function. Loss of β-cell mass has variably been attributed to β-cell dedifferentiation and/or death. In recent years, it has been proposed that circulating epigenetically modified DNA fragments arising from β cells might be able to report on the potential occurrence of β-cell death in diabetes. Here, we review published literature of DNA-based β-cell death biomarkers that have been evaluated in human cohorts of islet transplantation, type 1 diabetes, and obesity and type 2 diabetes. In addition, we provide new data on the applicability of one of these biomarkers (cell free unmethylated INS DNA) in adult cohorts across a spectrum from obesity to T2D, in which no significant differences were observed, and compare these findings to those previously published in youth cohorts where differences were observed. Our analysis of the literature and our own data suggest that β-cell death may occur in subsets of individuals with obesity and T2D, however a more sensitive method or refined study designs are needed to provide better alignment of sampling with disease progression events.