Miracle-dz/UniFAR-FTUs huggingface dataset:
Non-asymptotic Confidence Intervals of Off-policy Evaluation: Primal and Dual Bounds

Off-policy evaluation (OPE) is the task of estimating the expected reward of a given policy based on offline data previously collected under different policies. Therefore, OPE is a key step in applying reinforcement learning to real-world domains such as medical treatment, where interactive data collection is expensive or even unsafe. As the observed data tends to be noisy and limited, it is essential to provide rigorous uncertainty quantification, not just a point estimation, when applying OPE to make high stakes decisions. This work considers the problem of constructing non-asymptotic confidence intervals in infinite-horizon off-policy evaluation, which remains a challenging open question. We develop a practical algorithm through a primal-dual optimization-based approach, which leverages the kernel Bellman loss (KBL) of Feng et al.(2019) and a new martingale concentration inequality of KBL applicable to time-dependent data with unknown mixing conditions. Our algorithm makes minimum assumptions on the data and the function class of the Q-function, and works for the behavior-agnostic settings where the data is collected under a mix of arbitrary unknown behavior policies. We present empirical results that clearly demonstrate the advantages of our approach over existing methods.
Continuous Uniformization of Rational Relations and Synthesis of Computable Functions

A uniformizer of a binary relation is a function whose graph is contained in the relation and which is deﬁned on the same domain as the relation. It is known that any rational relation of inﬁnite words, i.e. a relation given as a transducer, admits a rational uniformizer. Although rational, those uniformizers are not necessarily well-behaved, in the sense that the i th letter of the output word may depend on the whole inﬁnite input word. In other words, those uniformizers might not be continuous (for the Cantor topology). This paper addresses the question of whether rational relations of inﬁnite words can be uniformized by continuous functions. On the negative side, continuous uniformizers might not exist in general and we prove that deciding their existence is algorithmically impossible. On the positive side, we exhibit a large class of rational relations of inﬁnite words, called weakly deterministic rational relations, for which deciding whether a relation in this class admits a continuous uniformizer is an ExpTime-c problem. This class includes the known classes of deterministic rational relations and automatic relations of inﬁnite words. As an application of the previous result, and by exploiting a connection be-tween computability and continuity for rational functions of inﬁnite words, we show a result on the synthesis of computable functions from speciﬁcations given as weakly deterministic rational relations. In particular, we show that deciding the existence of a computable uniformizer is ExpTime-c and if there is one, it is possible to eﬀectively synthesize a deterministic two-way transducer computing it. This generalizes the classical setting of Church synthesis to asynchronous implementations which can arbitrarily delay the production of their output signals.
A Multi-resolution Approach to Expression Recognition in the Wild

Facial expressions play a fundamental role in human communication. Indeed, they typically reveal the real emotional status of people beyond the spoken language. Moreover, the comprehension of human affect based on visual patterns is a key ingredient for any human-machine interaction system and, for such reasons, the task of Facial Expression Recognition (FER) draws both scientific and industrial interest. In the recent years, Deep Learning techniques reached very high performance on FER by exploiting different architectures and learning paradigms. In such a context, we propose a multi-resolution approach to solve the FER task. We ground our intuition on the observation that often faces images are acquired at different resolutions. Thus, directly considering such property while training a model can help achieve higher performance on recognizing facial expressions. To our aim, we use a ResNet-like architecture, equipped with Squeeze-and-Excitation blocks, trained on the Affect-in-the-Wild 2 dataset. Not being available a test set, we conduct tests and models selection by employing the validation set only on which we achieve more than 90\% accuracy on classifying the seven expressions that the dataset comprises.
Generalized Opinion Dynamics Model for Social Trust Networks in Opinion Maximization.

In this paper, we propose a generalized opinion dynamics model (GODM), which can dynamically compute each person's expressed opinion, to solve the internal opinion maximization problem for social trust networks. In the model, we propose a new, reasonable and interpretable confidence index, which is determined by both person's social status and the evaluation around him. By using the theory of diagonally dominant, we obtain the optimal analytic solution of the Nash equilibrium with maximum overall opinion. We design a novel algorithm to maximize the overall with given budget by modifying the internal opinions of people in the social trust network, and prove its optimality both from the algorithm itself and the traditional optimization algorithm-ADMM algorithms with $l_1$-regulations. A series of experiments are conducted, and the experimental results show that our method is superior to the state-of-the-art in four datasets. The average benefit has promoted $67.5\%$, $83.2\%$, $31.5\%$, and $33.7\%$ on four datasets, respectively.
Ionic solution near an uncharged surface with image forces

We studied the ion density in a dilute electrolyte solution which is next to an uncharged surface at which there is a discontinuity in the dielectric constant. In particular, we treated the restricted primitive model of a 1–1 aqueous solution next to a pure phase of unit dielectric constant. This served as a primitive model of the interface between an electrolytic solution and the air. Monte Carlo data are presented: the image forces are included and treated exactly. The model was also treated using the Born–Green–Yvon (BGY) integral equation approach; a modification of BGY theory which ensures electroneutrality is in very good agreement with the Monte Carlo data. The use of a "screened self-image" approximation to the potential energy has been tested using Monte Carlo computation and is found to be inadequate for the present model. Hypernetted-chain equation results using this approximation are also reported.
Nth Order Analytical Time Derivatives of Inverse Dynamics in Recursive and Closed Forms

Derivatives of equations of motion describing the rigid body dynamics are becoming increasingly relevant for the robotics community and find many applications in design and control of robotic systems. Controlling robots, and multibody systems comprising elastic components in particular, not only requires smooth trajectories but also the time derivatives of the control forces/torques, hence of the equations of motion (EOM). This paper presents novel nth order time derivatives of the EOM in both closed and recursive forms. While the former provides a direct insight into the structure of these derivatives, the latter leads to their highly efficient implementation for large degree of freedom robotic system.
Linear Bandits on Uniformly Convex Sets

Linear bandit algorithms yield $\tilde{\mathcal{O}}(n\sqrt{T})$ pseudo-regret bounds on compact convex action sets $\mathcal{K}\subset\mathbb{R}^n$ and two types of structural assumptions lead to better pseudo-regret bounds. When $\mathcal{K}$ is the simplex or an $\ell_p$ ball with $p\in]1,2]$, there exist bandits algorithms with $\tilde{\mathcal{O}}(\sqrt{nT})$ pseudo-regret bounds. Here, we derive bandit algorithms for some strongly convex sets beyond $\ell_p$ balls that enjoy pseudo-regret bounds of $\tilde{\mathcal{O}}(\sqrt{nT})$, which answers an open question from [BCB12, \S 5.5. ]. Interestingly, when the action set is uniformly convex but not necessarily strongly convex, we obtain pseudo-regret bounds with a dimension dependency smaller than $\mathcal{O}(\sqrt{n})$. However, this comes at the expense of asymptotic rates in $T$ varying between $\tilde{\mathcal{O}}(\sqrt{T})$ and $\tilde{\mathcal{O}}(T)$.
State-of-arts in Multiscale Simulation for Process Development

− The state-of-arts of multiscale simulation (MSS) in science and engineering is briefly presented and MSS for process development (PD-MSS) is proposed to effectively apply the MSS to the process development. The four-level PD-MSS is composed of PLS (process-level simulation), FLS (fluid-level simulation), mFLS (microfluid-level simulation) and MLS (molecular-level simulation). Characteristics and methods of each level, as well as connectivity between the four levels are described. For example in PD-MSS, absorption column, fluidized-bed reactor, and adsorption process are introduced. For successful MSS, it is necessary to understand the multiscale nature in chemical engineering problems, to develop models representing physical phenomena at each scale and between scales, to develop softwares implementing mathematical models on computer, and to have strong computing facilities. MSS should be performed within acceptable accuracy of simulation results, available computation capacity, and reasonable efficiency of calculation. Macroscopic and microscopic scale simulations have been developed relatively well but mesoscale simulation shows a bottleneck in MSS. Therefore, advances on mesoscale models and simulation tools are required to accurately and reliably predict physical phenomena. PD-MSS will find its way into a sustainable technology being able to shorten the duration and to reduce the cost for process development.
Distributed Channel Access for Control Over Unknown Memoryless Communication Channels

We consider the distributed channel access problem for a system consisting of multiple control subsystems that close their loop over a shared wireless network. We propose a distributed method for providing deterministic channel access without requiring explicit information exchange between the subsystems. This is achieved by utilizing timers for prioritizing channel access with respect to a local cost which we derive by transforming the control objective cost to a form that allows its local computation. This property is then exploited for developing our distributed deterministic channel access scheme. A framework to verify the stability of the system under the resulting scheme is then proposed. Next, we consider a practical scenario in which the channel statistics are unknown. We propose learning algorithms for learning the parameters of imperfect communication links for estimating the channel quality and, hence, define the local cost as a function of this estimation and control performance. We establish that our learning approach results in collision-free channel access. The behavior of the overall system is exemplified via a proof-of-concept illustrative example, and the efficacy of this mechanism is evaluated for large-scale networks via simulations.
Informational Steady States and Conditional Entropy Production in Continuously Monitored Systems

The act of measuring a system has profound consequences of dynamical and thermodynamic nature. In particular, the degree of irreversibility ensuing from a non-equilibrium process is strongly affected by measurements aimed at acquiring information on the state of a system of interest: the conditional and unconditional entropy production, which quantify the degree of irreversibility of the open system's dynamics, are related to each other by clearly interpreted informational quantities. Building on a recently proposed collisional-model framework [G. T. Landi {\it et al.}, arXiv:2103.06247], we investigate the case of continuous-variable information carriers prepared in Gaussian states and undergoing Gaussian processes. We build up a toolbox that fully characterizes the thermodynamics of continuously measured non-equilibrium Gaussian systems and processes, illustrating how the instruments hereby introduced provide key insight into recent experiments on mesoscopic quantum systems [Phys. Rev. Lett, {\bf 125}, 080601 (2020)].
Fine with “1234”? An Analysis of SMS One-Time Password Randomness in Android Apps

A fundamental premise of SMS One-Time Password (OTP) is that the used pseudo-random numbers (PRNs) are uniquely unpredictable for each login session. Hence, the process of generating PRNs is the most critical step in the OTP authentication. An improper implementation of the pseudo-random number generator (PRNG) will result in predictable or even static OTP values, making them vulnerable to potential attacks. In this paper, we present a vulnerability study against PRNGs implemented for Android apps. A key challenge is that PRNGs are typically implemented on the server-side, and thus the source code is not accessible. To resolve this issue, we build an analysis tool, OTP-Lint, to assess implementations of the PRNGs in an automated manner without the source code requirement. Through reverse engineering, OTP-Lint identifies the apps using SMS OTP and triggers each app's login functionality to retrieve OTP values. It further assesses the randomness of the OTP values to identify vulnerable PRNGs. By analyzing 6,431 commercially used Android apps downloaded from Google Play and Tencent Myapp, OTP-Lint identified 399 vulnerable apps that generate predictable OTP values. Even worse, 194 vulnerable apps use the OTP authentication alone without any additional security mechanisms, leading to insecure authentication against guessing attacks and replay attacks.
Oversampling errors in multimodal medical imaging are due to the Gibbs effect

To analyze multimodal three-dimensional medical images, interpolation is required for resampling which—unavoidably—introduces an interpolation error. In this work we describe the interpolation method used for imaging and neuroimaging and we characterize the Gibbs effect occurring when using such methods. In the experimental section we consider three segmented three-dimensional images resampled with three different neuroimaging software tools for comparing undersampling and oversampling strategies and to identify where the oversampling error lies. The experimental results indicate that undersampling to the lowest image size is advantageous in terms of mean value per segment errors and that the oversampling error is larger where the gradient is steeper, showing a Gibbs effect.
Bounded Invariant Checking for Stateflow Programs

Stateflow models are complex software models, often used as part of safety-critical software solutions designed with Matlab Simulink. They incorporate design principles that are typically very hard to verify formally. In particular, the standard exhaustive formal verification techniques are unlikely to scale well for the complex designs that are developed in industry. Furthermore, the Stateflow language lacks a formal semantics, which additionally hinders the formal analysis. To address these challenges, we lay here the foundations of a scalable technique for provably correct formal analysis of Stateflow models, with respect to invariant properties, based on bounded model checking (BMC) over symbolic executions. The crux of our technique is: i) a representation of the state space of Stateflow models as a symbolic transition system (STS) over the symbolic configurations of the model, as the basis for BMC, and ii) application of incremental BMC, to generate verification results after each unrolling of the next-state relation of the transition system. To this end, we develop a symbolic structural operational semantics (SSOS) for Stateflow, starting from an existing structural operational semantics (SOS), and show the preservation of invariant properties between the two. Next, we define bounded invariant checking for STS over symbolic configurations as a satisfiability problem. We develop an automated procedure for generating the initial and next-state predicates of the STS, and propose an encoding scheme of the bounded invariant checking problem as a set of constraints, ready for automated analysis with standard, off-the-shelf satisfiability solvers. Finally, we present preliminary performance results by applying our tool on an illustrative example.
The variable absorption in the X-ray spectrum of GRB 190114C

Gamma-ray burst (GRB) 190114C was a bright burst that occurred in the local Universe (z = 0.425). It was the first GRB ever detected at teraelectronvolt (TeV) energies, and this was thanks to MAGIC. We characterize the ambient medium properties of the host galaxy through the study of the absorbing X-ray column density. Using a combination of Swift, XMM-Newton, and NuSTAR observations, we find that the GRB X-ray spectrum is characterized by a high column density that is well in excess of the expected Milky Way value and decreases, by a factor of ∼2, around ∼105 s. Such a variability is not common in GRBs. The most straightforward interpretation of the variability in terms of the photoionization of the ambient medium is not able to account for the decrease at such late times, when the source flux is less intense. Instead, we interpret the decrease as due to a clumped absorber, denser along the line of sight and surrounded by lower-density gas. After the detection at TeV energies of GRB 190114C, two other GRBs were promptly detected. These two also have high intrinsic column density values, and there are hints for a decrease in their column densities as well. We speculate that a high local column density might be a common ingredient of TeV-detected GRBs.
SRG/ART-XC and NuSTAR Observations of the X-Ray pulsar GRO J1008–57 in the Lowest Luminosity State

We report results of the first broadband observation of the transient X-ray pulsar GRO J1008−57 performed in the quiescent state. Observations were conducted quasi-simultaneously with NuSTAR and the Mikhail Pavlinsky ART-XC telescope on board SRG right before the beginning of a Type I outburst. GRO J1008−57 was detected in the state with the lowest observed luminosity around several ×1034 erg s−1 and consequently accreting from the cold disk. Timing analysis allowed us to detect pulsations during this state for the first time. The observed pulsed fraction of about 20% is, however, almost three times lower than in brighter states when the accretion proceeds through the standard disk. We traced the evolution of the broadband spectrum of the source on a scale of three orders of magnitude in luminosity and found that at the lowest luminosities the spectrum transforms into the double-hump structure similarly to other X-ray pulsars accreting at low luminosities (X Persei, GX 304–1, and A 0535+262) reinforcing the conclusion that this spectral shape is typical for these objects.
Fairness On The Ground: Applying Algorithmic Fairness Approaches to Production Systems

Many technical approaches have been proposed for ensuring that decisions made by machine learning systems are fair, but few of these proposals have been stress-tested in real-world systems. This paper presents an example of one team's approach to the challenge of applying algorithmic fairness approaches to complex production systems within the context of a large technology company. We discuss how we disentangle normative questions of product and policy design (like,"how should the system trade off between different stakeholders' interests and needs? ") from empirical questions of system implementation (like,"is the system achieving the desired tradeoff in practice? "). We also present an approach for answering questions of the latter sort, which allows us to measure how machine learning systems and human labelers are making these tradeoffs across different relevant groups. We hope our experience integrating fairness tools and approaches into large-scale and complex production systems will be useful to other practitioners facing similar challenges, and illuminating to academics and researchers looking to better address the needs of practitioners.
Asymmetry Revisited: The Effect of Dust Attenuation and Galaxy Inclination

Dust attenuation of an inclined galaxy can cause additional asymmetries in observations, even if the galaxy has a perfectly symmetric structure. Taking advantage of the integral field spectroscopic data observed by the Sloan Digital Sky Survey-IV Mapping Nearby Galaxies at the Apache Point Observatory survey, we investigate the asymmetries of the emission-line and continuum maps of star-forming disk galaxies. We define new parameters, A a and A b , to estimate the asymmetries of a galaxy about its major and minor axes, respectively. Comparing A a and A b in different inclination bins, we attempt to detect the asymmetries caused by dust. For the continuum images, we find that A a increases with the inclination, while A b is a constant as inclination changes. Similar trends are found for g − r, g − i, and r − i color images. The dependence of the asymmetry on inclination suggests a thin dust layer with a scale height smaller than the stellar populations. For the Hα and Hβ images, neither A a nor A b shows a significant correlation with inclination. Also, we do not find any significant dependence of the asymmetry of E(B − V) g on inclination, implying that the dust in the thick disk component is not significant. Compared to the SKIRT simulation, the results suggest that the thin dust disk has an optical depth of τ V ∼ 0.2. This is the first time that the asymmetries caused by the dust attenuation and the inclination are probed statistically with a large sample. Our results indicate that the combination of the dust attenuation and the inclination effects is a potential indicator of the 3D disk orientation.
Copper Dimer With Acetate-2-Ethylimidazole as Ligands

By aging a mixture of ammonia solution of copper acetate and of 2-ethylimidazole in ethanol, crystals of the copper acetate-2-ethylimidazole complex were obtained. The solid crystallizes in the monoclinic P21/n space group with two formula units per unit cell. In the structure the copper atom is found in distorted square-planar coordination to three oxygen atoms from acetate groups and to the N atom of a 2-ethylimidazole molecule. To the solid stability and the complex configuration, a network of hydrogen bonding interactions is contributing. Both the ethyl and NH groups of 2-ethylimidazole were found forming hydrogen bridges with the oxygen atoms of the acetate groups. The structural information derived from X-ray single-crystal analysis was complemented with infrared and thermogravimetric data.
Instantaneous Frequency Representation Used for CPA Laser Simulation

In the current study, we present a novel intuitive graphical method for the simulation of nonlinear effects on stretched pulses, characterized by a large time-bandwidth product. By way of example, this method allows precise determination of effects occurring in CPA (chirped pulse amplification) laser chains, such as the pre-pulse generation by the nonlinear Kerr effect. This method is not limited to first-order dispersion and can handle all resulting distortions of the generated pre-pulse.
Machine Learning the period finding algorithm

We use differentiable programming and gradient descent to find unitary matrices that can be used in the period finding algorithm to extract period information from the state of a quantum computer post application of the oracle. The standard procedure is to use the inverse quantum Fourier transform. Our findings suggest that that this is not the only unitary matrix appropriate for the period finding algorithm, There exist several unitary matrices that can affect out the same transformation and they are significantly different from each other as well. These unitary matrices can be learned by an algorithm. Neural networks can be applied to differentiate such unitary matrices from randomly generated ones indicating that these unitaries do have characteristic features that cannot otherwise be discerned easily.