Miracle-dz/UniFAR-FTUs huggingface dataset:
DeepRM: Deep Recurrent Matching for 6D Pose Refinement

Precise 6D pose estimation of rigid objects from RGB images is a critical but challenging task in robotics, augmented reality and human-computer interaction. To address this problem, we propose DeepRM, a novel recurrent network architecture for 6D pose refinement. DeepRM leverages initial coarse pose estimates to render synthetic images of target objects. The rendered images are then matched with the observed images to predict a rigid transform for updating the previous pose estimate. This process is repeated to incrementally refine the estimate at each iteration. The DeepRM architecture incorporates LSTM units to propagate information through each refinement step, significantly improving overall performance. In contrast to current 2-stage Perspective-n-Point based solutions, DeepRM is trained end-to-end, and uses a scalable backbone that can be tuned via a single parameter for accuracy and efficiency. During training, a multi-scale optical flow head is added to predict the optical flow between the observed and synthetic images. Optical flow prediction stabilizes the training process, and enforces the learning of features that are relevant to the task of pose estimation. Our results demonstrate that DeepRM achieves state-of-the-art performance on two widely accepted challenging datasets.
Backpropagation through Combinatorial Algorithms: Identity with Projection Works

Embedding discrete solvers as differentiable layers has given modern deep learning architectures combinatorial expressivity and discrete reasoning capabilities. The derivative of these solvers is zero or undefined, therefore a meaningful replacement is crucial for effective gradient-based learning. Prior works rely on smoothing the solver with input perturbations, relaxing the solver to continuous problems, or interpolating the loss landscape with techniques that typically require additional solver calls, introduce extra hyper-parameters, or compromise performance. We propose a principled approach to exploit the geometry of the discrete solution space to treat the solver as a negative identity on the backward pass and further provide a theoretical justification. Our experiments demonstrate that such a straightforward hyper-parameter-free approach is able to compete with previous more complex methods on numerous experiments such as backpropagation through discrete samplers, deep graph matching, and image retrieval. Furthermore, we substitute the previously proposed problem-specific and label-dependent margin with a generic regularization procedure that prevents cost collapse and increases robustness.
GraphWalks: Efficient Shape Agnostic Geodesic Shortest Path Estimation

Geodesic paths and distances are among the most popular intrinsic properties of 3D surfaces. Traditionally, geodesic paths on discrete polygon surfaces were computed using shortest path algorithms, such as Dijkstra. However, such algorithms have two major limitations. They are non-differentiable which limits their direct usage in learnable pipelines and they are considerably time demanding. To address such limitations and alleviate the computational burden, we propose a learnable network to approximate geodesic paths. The proposed method is comprised by three major components: a graph neural network that encodes node positions in a high dimensional space, a path embedding that describes previously visited nodes and a point classifier that selects the next point in the path. The proposed method provides efficient approximations of the shortest paths and geodesic distances estimations. Given that all of the components of our method are fully differentiable, it can be directly plugged into any learnable pipeline as well as customized under any differentiable constraint. We extensively evaluate the proposed method with several qualitative and quantitative experiments.
Learning to Find Proofs and Theorems by Learning to Refine Search Strategies

We propose a new approach to automated theorem proving where an AlphaZero-style agent is self-training to refine a generic high-level expert strategy expressed as a nondeterministic program. An analogous teacher agent is self-training to generate tasks of suitable relevance and difficulty for the learner. This allows leveraging minimal amounts of domain knowledge to tackle problems for which training data is unavailable or hard to synthesize. As a specific illustration, we consider loop invariant synthesis for imperative programs and use neural networks to refine both the teacher and solver strategies.
Theme Aspect Argumentation Model for Handling Fallacies

From daily discussions to marketing ads to political statements, information manipulation is rife. It is increasingly more important that we have the right set of tools to defend ourselves from manipulative rhetoric, or fallacies. Suitable techniques to automatically identify fallacies are being investigated in natural language processing research. However, a fallacy in one context may not be a fallacy in another context, so there is also a need to explain how and why it has come to be judged a fallacy. For the explainable fallacy identification, we present a novel approach to characterising fallacies through formal constraints, as a viable alternative to more traditional fallacy classifications by informal criteria. To achieve this objective, we introduce a novel context-aware argumentation model, the theme aspect argumentation model, which can do both: the modelling of a given argumentation as it is expressed (rhetorical modelling); and a deeper semantic analysis of the rhetorical argumentation model. By identifying fallacies with formal constraints, it becomes possible to tell whether a fallacy lurks in the modelled rhetoric with a formal rigour. We present core formal constraints for the theme aspect argumentation model and then more formal constraints that improve its fallacy identification capability. We show and prove the consequences of these formal constraints. We then analyse the computational complexities of deciding the satisfiability of the constraints.
Chessboard Mushroom-Type Metasurface for Beamforming Applications

A reconfigurable microwave reflectarray metasurface (MS) is investigated for beamforming applications. The reflected beam direction is changed by applying external dc voltages, which create a reflection phase gradient on the structure. The studied MS comprises a chessboard-like array of metallic patches placed over a grounded dielectric slab with metallic vias connecting the patches to the controlling lines. Tunability is achieved with nonlinear capacitive loads (varactors) inserted between the corners of the metallic patches. The MS is studied analytically, numerically and experimentally, from which reshaping of the radiation pattern is observed according to the applied control voltages on the MS elements. It is shown that the proposed MS-based reflectarray with just 3-by-10 elements is already sufficient to redirect the beam in different directions.
On the Robustness of Safe Reinforcement Learning under Observational Perturbations

Safe reinforcement learning (RL) trains a policy to maximize the task reward while satisfying safety constraints. While prior works focus on the performance optimality, we find that the optimal solutions of many safe RL problems are not robust and safe against carefully designed observational perturbations. We formally analyze the unique properties of designing effective observational adversarial attackers in the safe RL setting. We show that baseline adversarial attack techniques for standard RL tasks are not always effective for safe RL and propose two new approaches - one maximizes the cost and the other maximizes the reward. One interesting and counter-intuitive finding is that the maximum reward attack is strong, as it can both induce unsafe behaviors and make the attack stealthy by maintaining the reward. We further propose a robust training framework for safe RL and evaluate it via comprehensive experiments. This paper provides a pioneer work to investigate the safety and robustness of RL under observational attacks for future safe RL studies. Code is available at: \url{https://github.com/liuzuxin/safe-rl-robustness}
How Have Astronomers Cited Other Fields in the Last Decade?

We present a citation pattern analysis between astronomical papers and 13 other disciplines, based on the arXiv database over the past decade (2010–2020). We analyze 12,600 astronomical papers citing over 14,531 unique publications outside astronomy. Two striking patterns are unraveled. First, general relativity recently became the most cited field by astronomers, a trend highly correlated with the discovery of gravitational waves. Second, the fast growth of referenced papers in computer science and statistics, the first with a notable 15 fold increase since 2015. Such findings confirm the critical role of interdisciplinary efforts involving astronomy, statistics, and computer science in recent astronomical research.
Independent and Decentralized Learning in Markov Potential Games

We propose a multi-agent reinforcement learning dynamics, and analyze its convergence in infinite-horizon discounted Markov potential games. We focus on the independent and decentralized setting, where players do not have knowledge of the game model and cannot coordinate. In each stage, players update their estimate of Q-function that evaluates their total contingent payoff based on the realized one-stage reward in an asynchronous manner. Then, players independently update their policies by incorporating an optimal one-stage deviation strategy based on the estimated Q-function. A key feature of the learning dynamics is that the Q-function estimates are updated at a faster timescale than the policies. We prove that the policies induced by our learning dynamics converge to the set of stationary Nash equilibria in Markov potential games with probability 1. Our results highlight the efficacy of simple learning dynamics in reaching to the set of stationary Nash equilibrium even in environments with minimal information available.
Generating string ﬁeld theory solutions with matters from KBc algebra

The KBc algebra is a subalgebra that has been used to construct classical solutions in Witten’s open string ﬁeld theory, such as the tachyon vacuum solution. The main purpose of this paper is to give various operator sets that satisfy the KBc algebra. In addition, since those sets can contain matter operators arbitrarily, we can reproduce the solution of Kiermaier, Okawa and Soler, and that of Erler and Maccaferri. Starting with a single D-brane solution on the tachyon vacuum, we replace the original KBc in it with an appropriate set to generate each of the above solutions. Thus, it is expected that the KBc algebra, combined with the single D-brane solution, leads to a more uniﬁed description of classical solutions.
Classical and quantum reconciliation of electromagnetic radiation: Vector Unruh modes and zero-Rindler-energy photons

A great deal of evidence has been mounting over the years showing a deep connection between acceleration, radiation, and the Unruh eﬀect. Indeed, the fact that the Unruh eﬀect can be codiﬁed in the Larmor radiation emitted by the charge was used to propose an experiment to experimentally conﬁrm the existence of the Unruh thermal bath. However, such connection has two puzzling issues: (1) how the quantum Unruh eﬀect can be codiﬁed in the classical Larmor radiation and (2) the fundamental role played by zero-Rindler-energy modes of the Unruh thermal bath in such a context. Here we generalize a recent analysis made for the scalar case to the more realistic case of Maxwell electrodynamics and settle these two puzzling issues.
Risk of Stochastic Systems for Temporal Logic Specifications

The wide availability of data coupled with the computational advances in artificial intelligence and machine learning promise to enable many future technologies such as autonomous driving. While there has been a variety of successful demonstrations of these technologies, critical system failures have repeatedly been reported. Even if rare, such system failures pose a serious barrier to adoption without a rigorous risk assessment. This article presents a framework for the systematic and rigorous risk verification of systems. We consider a wide range of system specifications formulated in signal temporal logic (STL) and model the system as a stochastic process, permitting discrete-time and continuous-time stochastic processes. We then define the STL robustness risk as the risk of lacking robustness against failure. This definition is motivated as system failures are often caused by missing robustness to modeling errors, system disturbances, and distribution shifts in the underlying data generating process. Within the definition, we permit general classes of risk measures and focus on tail risk measures such as the value-at-risk and the conditional value-at-risk. While the STL robustness risk is in general hard to compute, we propose the approximate STL robustness risk as a more tractable notion that upper bounds the STL robustness risk. We show how the approximate STL robustness risk can accurately be estimated from system trajectory data. For discrete-time stochastic processes, we show under which conditions the approximate STL robustness risk can even be computed exactly. We illustrate our verification algorithm in the autonomous driving simulator CARLA and show how a least risky controller can be selected among four neural network lane-keeping controllers for five meaningful system specifications.
Momentum dependence of mixing in the pion vector form factor and its effect on

The inclusion of the mixing effect is essential for a precise description of the pion electromagnetic form factor in the process, which quantifies the two-pion contribution to the anomalous magnetic moment of muon . In this study, we analyze the momentum dependence of mixing by considering loop contributions at the next-to-leading order in expansion within the framework of resonance chiral theory. We revisit a previous study [Y. H. Chen, D. L. Yao, and H. Q. Zheng, Commun. Theor. Phys. 69, 1 (2018)] and consider the contribution arising from the kaon mass splitting in the kaon loops and latest experimental data. We perform two types of fits (with momentum-independent or momentum-dependent mixing amplitude) to describe and data within the energy region of 600 900 MeV and decay width of . Furthermore, we compare their results. Our findings indicate that the momentum-independent and momentum-dependent mixing schemes provide appropriate descriptions of the data. However, the momentum-dependent scheme exhibits greater self-consistency, considering the reasonable imaginary part of the mixing matrix element obtained. Regarding the contribution to the anomalous magnetic moment of the muon, , the results obtained from the fits considering the momentum-dependent mixing amplitude are in good agreement with those obtained without incorporating the momentum dependence of mixing, within the margin of errors. Furthermore, based on the fitted values of the relevant parameters, we observe that the decay width of is predominantly influenced by the mixing effect.
NuSTAR Observations of AGNs with Low Observed X-Ray to [O iii] Luminosity Ratios: Heavily Obscured AGNs or Turned-off AGNs?

Type 2 active galactic nuclei (AGNs) show signatures of accretion onto a supermassive black hole through strong, high-ionization, narrow emission lines extended on scales of hundreds to thousands of parsecs, but they lack the broad emission lines from close in to the black hole that characterize type 1 AGNs. The lack of broad emission could indicate obscuration of the innermost nuclear regions, or could indicate that the black hole is no longer strongly accreting. Since high-energy X-rays can penetrate thick obscuring columns, they have the power to distinguish these two scenarios. We present high-energy NuSTAR observations of nine Seyfert 2 AGNs from the Infrared Astronomical Satellite 12 μm survey, supplemented with low-energy X-ray observations from Chandra, XMM-Newton, and Swift. The galaxies were selected to have anomalously low observed 2–10 keV luminosities compared to their [O iii] optical luminosities, a traditional diagnostic of heavily obscured AGNs, reaching into the Compton-thick regime for the highest hydrogen column densities (N H > 1.5 × 1024 cm−2). Based on updated [O iii] luminosities and intrinsic X-ray luminosities based on physical modeling of the hard X-ray spectra, we find that one galaxy was misclassified as type 2 (NGC 5005) and most of the remaining AGNs are obscured, including three confirmed as Compton thick (IC 3639, NGC 1386, and NGC 3982). One galaxy, NGC 3627, appears to have recently deactivated. Compared to the original sample that the nine AGNs were selected from, this is a rate of approximately 1%. We also find a new X-ray changing-look AGN in NGC 6890.
On The Smoothness of Cross-Validation-Based Estimators Of Classifier Performance

Using the inﬂuence function (IF) to estimate the standard error of the bootstrap (BS)-based estimators, that estimate the classiﬁers’ performance in terms of the error rate and the Area Under the ROC curve (AUC), was very successful. Only the “smooth” and di ﬀ erentiable versions of these BS-based estimators were eligible for IF approach. Since then, this successful approach is not extended to the cross validation (CV)-based estimators, which are more important because of their ubiquitous use in the majority of applications and by the majority of practitioners. The literature includes many versions of CV, and each version has di ﬀ erent variants. The smoothness of these versions and variants needs to be studied before adopting the IF for any of them, which is the motivation behind the present article. First, we start by a mathematical formalization of many of these di ﬀ erent versions and variants, that estimate the error rate and the AUC of a classiﬁcation rule, to show the di ﬀ erence among them and the connection to the smooth version of the BS-based estimators. Second, we prove some of their properties and conclude that many of these variants are redundant and “not smooth”, and therefore are not eligible for the IF approach, except the repeated K -fold CV (CVKR) and the Monte-Carlo CV (CVKM). For pedagogy and deeper understanding, we provide a novel experimental example that illustrate the smoothness property. Third, we discuss these versions and variants in terms of some known results in the literature, including e ﬀ ective training sample size, weak correlation, and mean squared estimation error; then, we conclude that there is still no ﬁnal solid advice for practitioners, on the preference of a particular estimator, until a robust IF method is developed and a comprehensive comparative study is conducted. Smoothness.
Learning Locality and Isotropy in Dialogue Modeling

Existing dialogue modeling methods have achieved promising performance on various dialogue tasks with the aid of Transformer and the large-scale pre-trained language models. However, some recent studies revealed that the context representations produced by these methods suffer the problem of anisotropy. In this paper, we find that the generated representations are also not conversational, losing the conversation structure information during the context modeling stage. To this end, we identify two properties in dialogue modeling, i.e., locality and isotropy, and present a simple method for dialogue representation calibration, namely SimDRC, to build isotropic and conversational feature spaces. Experimental results show that our approach significantly outperforms the current state-of-the-art models on three dialogue tasks across the automatic and human evaluation metrics. More in-depth analyses further confirm the effectiveness of our proposed approach.
Align then Fusion: Generalized Large-scale Multi-view Clustering with Anchor Matching Correspondences

Multi-view anchor graph clustering selects representative anchors to avoid full pair-wise similarities and therefore reduce the complexity of graph methods. Although widely applied in large-scale applications, existing approaches do not pay sufficient attention to establishing correct correspondences between the anchor sets across views. To be specific, anchor graphs obtained from different views are not aligned column-wisely. Such an \textbf{A}nchor-\textbf{U}naligned \textbf{P}roblem (AUP) would cause inaccurate graph fusion and degrade the clustering performance. Under multi-view scenarios, generating correct correspondences could be extremely difficult since anchors are not consistent in feature dimensions. To solve this challenging issue, we propose the first study of the generalized and flexible anchor graph fusion framework termed \textbf{F}ast \textbf{M}ulti-\textbf{V}iew \textbf{A}nchor-\textbf{C}orrespondence \textbf{C}lustering (FMVACC). Specifically, we show how to find anchor correspondence with both feature and structure information, after which anchor graph fusion is performed column-wisely. Moreover, we theoretically show the connection between FMVACC and existing multi-view late fusion \cite{liu2018late} and partial view-aligned clustering \cite{huang2020partially}, which further demonstrates our generality. Extensive experiments on seven benchmark datasets demonstrate the effectiveness and efficiency of our proposed method. Moreover, the proposed alignment module also shows significant performance improvement applying to existing multi-view anchor graph competitors indicating the importance of anchor alignment. Our code is available at \url{https://github.com/wangsiwei2010/NeurIPS22-FMVACC}.
Detecting Unknown DGAs without Context Information

New malware emerges at a rapid pace and often incorporates Domain Generation Algorithms (DGAs) to avoid blocking the malware’s connection to the command and control (C2) server. Current state-of-the-art classifiers are able to separate benign from malicious domains (binary classification) and attribute them with high probability to the DGAs that generated them (multiclass classification). While binary classifiers can label domains of yet unknown DGAs as malicious, multiclass classifiers can only assign domains to DGAs that are known at the time of training, limiting the ability to uncover new malware families. In this work, we perform a comprehensive study on the detection of new DGAs, which includes an evaluation of 59,690 classifiers. We examine four different approaches in 15 different configurations and propose a simple yet effective approach based on the combination of a softmax classifier and regular expressions (regexes) to detect multiple unknown DGAs with high probability. At the same time, our approach retains state-of-the-art classification performance for known DGAs. Our evaluation is based on a leave-one-group-out cross-validation with a total of 94 DGA families. By using the maximum number of known DGAs, our evaluation scenario is particularly difficult and close to the real world. All of the approaches examined are privacy-preserving, since they operate without context and exclusively on a single domain to be classified. We round up our study with a thorough discussion of class-incremental learning strategies that can adapt an existing classifier to newly discovered classes.