Miracle-dz/UniFAR-FTUs huggingface dataset:
Restore from Restored: Single-image Inpainting

Recent image inpainting methods show promising results due to the power of deep learning, which can explore external information available from a large training dataset. However, many state-of-the-art inpainting networks are still limited in exploiting internal information available in the given input image at test time. To mitigate this problem, we present a novel and efficient self-supervised fine-tuning algorithm that can adapt the parameters of fully pretrained inpainting networks without using ground-truth clean image in this work. We upgrade the parameters of the pretrained networks by utilizing existing self-similar patches within the given input image without changing network architectures. Qualitative and quantitative experimental results demonstrate the superiority of the proposed algorithm and we achieve state-of-the-art inpainting results on publicly available numerous benchmark datasets.
EDITH : ECG Biometrics Aided by Deep Learning for Reliable Individual Authentication

In recent years, physiological signal-based authentication has shown great promises, for its inherent robustness against forgery. Electrocardiogram (ECG) signal, being the most widely studied biosignal, has also received the highest level of attention in this regard. It has been proven with numerous studies that by analyzing ECG signals from different persons, it is possible to identify them, with acceptable accuracy. In this work, we present, EDITH, a deep learning-based framework for ECG biometrics authentication system. Moreover, we hypothesize and demonstrate that Siamese architectures can be used over typical distance metrics for improved performance. We have evaluated EDITH using 4 commonly used datasets and outperformed the prior works using a fewer number of beats. EDITH performs competitively using just a single heartbeat (96$\sim$99.75% accuracy) and can be further enhanced by fusing multiple beats (100% accuracy from 3 to 6 beats). Furthermore, the proposed Siamese architecture manages to reduce the identity verification Equal Error Rate (EER) to 1.29 %. A limited case study of EDITH with real-world experimental data also suggests its potential as a practical authentication system.
Improper Learning with Gradient-based Policy Optimization

We consider an improper reinforcement learning setting where the learner is given M base controllers for an unknown Markov Decision Process, and wishes to combine them optimally to produce a potentially new controller that can outperform each of the base ones. We propose a gradient-based approach that operates over a class of improper mixtures of the controllers. The value function of the mixture and its gradient may not be available in closed-form; however, we show that we can employ rollouts and simultaneous perturbation stochastic approximation (SPSA) for explicit gradient descent optimization. We derive convergence and convergence rate guarantees for the approach assuming access to a gradient oracle. Numerical results on a challenging constrained queueing task show that our improper policy optimization algorithm can stabilize the system even when each constituent policy at its disposal is unstable 1 .
Plasma sodium and potassium changes in sickle cell patients

The aim of this study was to measure the body electrolytes (in plasma) such as sodium (Na) and potassium (k) in adult sickle cell patients with genotype (HbSS) genotype and compare with controls normal with (HbAA) genotype. The study involved a total of 38 individuals, both males and females in the age range of 16 40 years. There were 3 study groups; steady state group, crisis state group and control group. Flame photometry was used to analyze sodium and potassium. In the males there was a statistically significant reduction in the concentration of sodium in both the steady state (129.40 ± 1.462) and crisis state (121.60 ± 0.678) when compared with control group (134.40 ± 2.040). Also, there was a statistically significant increase (P < 0.05) in the concentration of potassium in both the steady state (4.58 ± 0.171 mmol/l) and crisis state (4.66 ± 0.154 mmol/l) when compared with normal group (3.50 ± 0.172 mmol/l). The regular measurement of plasma sodium and potassium is, therefore, necessary in the management of the sickle cell disease patients.
Capturing the learning curves of generic features maps for realistic data sets with a teacher-student model

Teacher-student models provide a powerful framework in which the typical case performance of highdimensional supervised learning tasks can be studied in closed form. In this setting, labels are assigned to data often taken to be Gaussian i.i. d. by a teacher model, and the goal is to characterise the typical performance of the student model in recovering the parameters that generated the labels. In this manuscript we discuss a generalisation of this setting where the teacher and student can act on different spaces, generated with fixed, but generic feature maps. This is achieved via the rigorous study of a high-dimensional Gaussian covariate model. Our contribution is two-fold: First, we prove a rigorous formula for the asymptotic training loss and generalisation error achieved by empirical risk minimization for this model. Second, we present a number of situations where the learning curve of the model captures the one of a realistic data set learned with kernel regression and classification, with out-of-the-box feature maps such as random projections or scattering transforms, or with pre-learned ones such as the features learned by training multi-layer neural networks. We discuss both the power and the limitations of the Gaussian teacher-student framework as a typical case analysis capturing learning curves as encountered in practice on real data sets. *bruno.loureiro@epfl.ch cedric.gerbelot@ens.fr 1 ar X iv :2 10 2. 08 12 7v 1 [ st at . M L ] 1 6 Fe b 20 21
Nebular Emission from Lanthanide-rich Ejecta of Neutron Star Merger

The nebular phase of lanthanide-rich ejecta of a neutron star merger (NSM) is studied by using a one-zone model, in which the atomic properties are represented by a single species, neodymium (Nd). Under the assumption that β-decay of r-process nuclei is the heat and ionization source, we solve the ionization and thermal balance of the ejecta under non-local thermodynamic equilibrium. The atomic data including energy levels, radiative transition rates, collision strengths, and recombination rate coefficients, are obtained by using atomic structure codes, GRASP2K and HULLAC. We find that both permitted and forbidden lines roughly equally contribute to the cooling rate of Nd II and Nd III at the nebular temperatures. We show that the kinetic temperature and ionization degree increase with time in the early stage of the nebular phase while these quantities become approximately independent of time after the thermalization break of the heating rate because the processes relevant to the ionization and thermalization balance are attributed to two-body collision between electrons and ions at later times. As a result, in spite of the rapid decline of the luminosity, the shape of the emergent spectrum does not change significantly with time after the break. We show that the emission-line nebular spectrum of the pure Nd ejecta consists of a broad structure from 0.5 μm to 20 μm with two distinct peaks around 1 μm and 10 μm.
ATVHunter: Reliable Version Detection of Third-Party Libraries for Vulnerability Identification in Android Applications

Third-party libraries (TPLs) as essential parts in the mobile ecosystem have become one of the most significant contributors to the huge success of Android, which facilitate the fast development of Android applications. Detecting TPLs in Android apps is also important for downstream tasks, such as malware and repackaged apps identification. To identify in-app TPLs, we need to solve several challenges, such as TPL dependency, code obfuscation, precise version representation. Unfortunately, existing TPL detection tools have been proved that they have not solved these challenges very well, let alone specify the exact TPL versions. To this end, we propose a system, named ATVHunter, which can pinpoint the precise vulnerable in-app TPL versions and provide detailed information about the vulnerabilities and TPLs. We propose a two-phase detection approach to identify specific TPL versions. Specifically, we extract the Control Flow Graphs as the coarse-grained feature to match potential TPLs in the pre-defined TPL database, and then extract opcode in each basic block of CFG as the fine-grained feature to identify the exact TPL versions. We build a comprehensive TPL database (189,545 unique TPLs with 3,006,676 versions) as the reference database. Meanwhile, to identify the vulnerable in-app TPL versions, we also construct a comprehensive and known vulnerable TPL database containing 1,180 CVEs and 224 security bugs. Experimental results show AtVHunter outperforms state-of-the-art TPL detection tools, achieving 90.55% precision and 88.79% recall with high efficiency, and is also resilient to widely-used obfuscation techniques and scalable for large-scale TPL detection. Furthermore, to investigate the ecosystem of the vulnerable TPLs used by apps, we exploit newtool to conduct a large-scale analysis on 104,446 apps and find that 9,050 apps include vulnerable TPL versions with 53,337 vulnerabilities and 7,480 security bugs, most of which are with high risks and are not recognized by app developers.
Spin-chain physics of BaCuTe$_{2}$O$_{6}$

P. Bag, N. Ahmed, Vikram Singh, M. Sahoo, A. A. Tsirlin, ∗ and R. Nath † School of Physics, Indian Institute of Science Education and Research Thiruvananthapuram-695551, India Department of Physics, University of Kerala, Kariavattom, Thiruvananthapuram-695581, India Experimental Physics VI, Center for Electronic Correlations and Magnetism, Institute of Physics, University of Augsburg, 86135 Augsburg, Germany (Dated: February 17, 2021)
Propagating fronts for a viscous Hamer-type system

<p style='text-indent:20px;'>Motivated by radiation hydrodynamics, we analyse a <inline-formula><tex-math id="M1">\begin{document}$ 2\times2 $\end{document}</tex-math></inline-formula> system consisting of a one-dimensional viscous conservation law with strictly convex flux –the viscous Burgers' equation being a paradigmatic example– coupled with an elliptic equation, named <b>viscous Hamer-type system</b>. In the regime of small viscosity and for large shocks, namely when the profile of the corresponding underlying inviscid model undergoes a discontinuity –usually called <i>sub-shock</i>– it is proved the existence of a smooth propagating front, regularising the jump of the corresponding inviscid equation. The proof is based on <i>Geometric Singular Perturbation Theory</i> (GSPT) as introduced in the pioneering work of Fenichel [<xref ref-type="bibr" rid="b5">5</xref>] and subsequently developed by Szmolyan [<xref ref-type="bibr" rid="b21">21</xref>]. In addition, the case of small shocks and large viscosity is also addressed via a standard bifurcation argument. </p>
Asynchronous Distributed Optimization via Dual Decomposition and Block Coordinate Subgradient Methods

In this article, we study the problem of minimizing the sum of potentially nondifferentiable convex cost functions with partially overlapping dependences in an asynchronous manner, where communication in the network is not coordinated. We study the behavior of an asynchronous algorithm based on dual decomposition and block coordinate subgradient methods under assumptions weaker than those used in the literature. At the same time, we allow different agents to use local stepsizes with no global coordination. Sufficient conditions are provided for almost sure convergence to the solution of the optimization problem. Under additional assumptions, we establish a sublinear convergence rate that, in turn, can be strengthened to the linear convergence rate if the problem is strongly convex and has Lipschitz gradients. We also extend available results in the literature by allowing multiple and potentially overlapping blocks to be updated at the same time with nonuniform and potentially time-varying probabilities assigned to different blocks. A numerical example is provided to illustrate the effectiveness of the algorithm.
Guarded Kleene Algebra with Tests: Coequations, Coinduction, and Completeness

Guarded Kleene Algebra with Tests (GKAT) is an efficient fragment of KAT, as it allows for almost linear decidability of equivalence. In this paper, we study the (co)algebraic properties of GKAT. Our initial focus is on the fragment that can distinguish between unsuccessful programs performing different actions, by omitting the so-called early termination axiom. We develop an operational (coalgebraic) and denotational (algebraic) semantics and show that they coincide. We then characterize the behaviors of GKAT expressions in this semantics, leading to a coequation that captures the covariety of automata corresponding to behaviors of GKAT expressions. Finally, we prove that the axioms of the reduced fragment are sound and complete w.r. t. the semantics, and then build on this result to recover a semantics that is sound and complete w.r. t. the full set of axioms.
Have Attention Heads in BERT Learned Constituency Grammar?

With the success of pre-trained language models in recent years, more and more researchers focus on opening the “black box” of these models. Following this interest, we carry out a qualitative and quantitative analysis of constituency grammar in attention heads of BERT and RoBERTa. We employ the syntactic distance method to extract implicit constituency grammar from the attention weights of each head. Our results show that there exist heads that can induce some grammar types much better than baselines, suggesting that some heads act as a proxy for constituency grammar. We also analyze how attention heads’ constituency grammar inducing (CGI) ability changes after fine-tuning with two kinds of tasks, including sentence meaning similarity (SMS) tasks and natural language inference (NLI) tasks. Our results suggest that SMS tasks decrease the average CGI ability of upper layers, while NLI tasks increase it. Lastly, we investigate the connections between CGI ability and natural language understanding ability on QQP and MNLI tasks.
Reciprocal Distance Transform Maps for Crowd Counting and People Localization in Dense Crowd

In this paper, we propose a novel map for dense crowd counting and people localization. Most crowd counting methods utilize convolution neural networks (CNN) to regress a density map, achieving signiﬁcant progress recently. However, these regression-based methods are often unable to provide a precise location for each people, attributed to two crucial reasons: 1) the density map consists of a series of blurry Gaussian blobs, 2) severe over-laps exist in the dense region of the density map. To tackle this issue, we propose a novel Reciprocal Distance Trans-form (R-DT) map for crowd counting. Compared with the density maps, the R-DT maps accurately describe the people’s location, without overlap between nearby heads in dense regions. We simultaneously implement crowd counting and people localization with a simple network by re-placing density maps with R-DT maps. Extensive experiments demonstrate that the proposed method outperforms state-of-the-art localization-based methods in crowd counting and people localization tasks, achieving very competitive performance compared with the regression-based meth-ods in counting tasks. In addition, the proposed method achieves a good generalization performance under cross dataset validation, which further veriﬁes the e ﬀ ectiveness of the R-DT map. The code and models are available at https://github.com/dk-liang/RDTM .
Distributionally-Constrained Policy Optimization via Unbalanced Optimal Transport

We consider constrained policy optimization in Reinforcement Learning, where the constraints are in form of marginals on state visitations and global action executions. Given these distributions, we formulate policy optimization as unbalanced optimal transport over the space of occupancy measures. We propose a general purpose RL objective based on Bregman divergence and optimize it using Dykstra's algorithm. The approach admits an actor-critic algorithm for when the state or action space is large, and only samples from the marginals are available. We discuss applications of our approach and provide demonstrations to show the effectiveness of our algorithm.
A Cooperative Memory Network for Personalized Task-oriented Dialogue Systems with Incomplete User Profiles

There is increasing interest in developing personalized Task-oriented Dialogue Systems (TDSs). Previous work on personalized TDSs often assumes that complete user profiles are available for most or even all users. This is unrealistic because In this paper, we study personalized TDSs without assuming that user profiles are complete. We propose a Cooperative Memory Network (CoMemNN) that has a novel mechanism to gradually enrich user profiles as dialogues progress and to simultaneously improve response selection based on the enriched profiles. Cooperative Memory Network (CoMemNN) consists of two core modules: User Profile Enrichment (UPE) and Dialogue Response Selection (DRS). The former enriches incomplete user profiles by utilizing collaborative information from neighbor users as well as current dialogues. The latter uses the enriched profiles to update the current user query so as to encode more useful information, based on which a personalized response to a user request is selected. We conduct extensive experiments on the personalized bAbI dialogue benchmark datasets. We find that CoMemNN is able to enrich user profiles effectively, which results in an improvement of 3.06% in terms of response selection accuracy compared to state-of-the-art methods. We also test the robustness of CoMemNN against incompleteness of user profiles by randomly discarding attribute values from user profiles. Even when discarding 50% of the attribute values, CoMemNN is able to match the performance of the best performing baseline without discarding user profiles, showing the robustness of CoMemNN.
Joint self-supervised blind denoising and noise estimation

We propose a novel self-supervised image blind denoising approach in which two neural networks jointly predict the clean signal and infer the noise distribution. Assuming that the noisy observations are independent conditionally to the signal, the networks can be jointly trained without clean training data. Therefore, our approach is particularly relevant for biomedical image denoising where the noise is difficult to model precisely and clean training data are usually unavailable. Our method significantly outperforms current state-of-the-art self-supervised blind denoising algorithms, on six publicly available biomedical image datasets. We also show empirically with synthetic noisy data that our model captures the noise distribution efficiently. Finally, the described framework is simple, lightweight and computationally efficient, making it useful in practical cases.
Electron beam transverse phase space tomography using nanofabricated wire scanners with submicrometer resolution

Characterization and control of the transverse phase space of high-brightness electron beams is required at free-electron lasers or electron diffraction experiments for emittance measurement and beam optimization as well as at advanced acceleration experiments. Dielectric laser accelerators or plasma accelerators with external injection indeed require beam sizes at the micron level and below. We present a method using nano-fabricated metallic wires oriented at different angles to obtain projections of the transverse phase space by scanning the wires through the beam and detecting the amount of scattered particles. Performing this measurement at several locations along the waist allows assessing the transverse distribution at different phase advances. By applying a novel tomographic algorithm the transverse phase space density can be reconstructed. Measurements at the ACHIP chamber at SwissFEL confirm that the transverse phase space of micrometer-sized electron beams can be reliably characterized using this method.
Analysis of the Mechanism of Retention on a Modified β‐Cyclodextrin/Silica Chiral Stationary Phase using a Computational Chemical Method

Abstract Quantitative analysis of the retention of various optically active solutes on a modified β‐cyclodextrin/silica chiral stationary phase was achieved using a molecular mechanics calculation of the CAChe program. Using computational chemical calculations, the various interactions between each enantiomer and the modified β‐cyclodextrin chiral selector were calculated as energy contributions. These interaction energy values were then compared to the experimental values measured in normal phase liquid chromatography. Among the obtained predicted values, the best correlation was observed between the molecular interaction energy and the selectivity factor, α, calculated for the studied racemates.
Dynamic neighbourhood optimisation for task allocation using multi-agent

In large-scale systems there are fundamental challenges when centralised techniques are used for task allocation. The number of interactions is limited by resource constraints such as on computation, storage, and network communication. We can increase scalability by implementing the system as a distributed task-allocation system, sharing tasks across many agents. However, this also increases the resource cost of communications and synchronisation, and is difficult to scale. In this paper we present four algorithms to solve these problems. The combination of these algorithms enable each agent to improve their task allocation strategy through reinforcement learning, while changing how much they explore the system in response to how optimal they believe their current strategy is, given their past experience. We focus on distributed agent systems where the agents’ behaviours are constrained by resource usage limits, limiting agents to local, rather than system-wide knowledge. We evaluate these algorithms in a simulated environment where agents are given a task composed of multiple subtasks that must be allocated to other agents with differing capabilities, to then carry out those tasks. We also simulate real-life system effects such as networking instability. Our solution is shown to solve the task allocation problem to 6 . 7% of the theoretical optimal within the system configurations considered. It provides 5 × better performance recovery over no-knowledge retention approaches when system connectivity is impacted, and is tested against systems up to 100 agents with less than a 9% impact on the algorithms’ performance.
Online hyperparameter optimization by real-time recurrent learning

Conventional hyperparameter optimization methods are computationally intensive and hard to generalize to scenarios that require dynamically adapting hyperparameters, such as life-long learning. Here, we propose an online hyperparameter optimization algorithm that is asymptotically exact and computationally tractable, both theoretically and practically. Our framework takes advantage of the analogy between hyperparameter optimization and parameter learning in recurrent neural networks (RNNs). It adapts a well-studied family of online learning algorithms for RNNs to tune hyperparameters and network parameters simultaneously, without repeatedly rolling out iterative optimization. This procedure yields systematically better generalization performance compared to standard methods, at a fraction of wallclock time.