Miracle-dz/UniFAR-FTUs huggingface dataset:
Adversarial Attacks on Adversarial Bandits

We study a security threat to adversarial multi-armed bandits, in which an attacker perturbs the loss or reward signal to control the behavior of the victim bandit player. We show that the attacker is able to mislead any no-regret adversarial bandit algorithm into selecting a suboptimal target arm in every but sublinear (T-o(T)) number of rounds, while incurring only sublinear (o(T)) cumulative attack cost. This result implies critical security concern in real-world bandit-based systems, e.g., in online recommendation, an attacker might be able to hijack the recommender system and promote a desired product. Our proposed attack algorithms require knowledge of only the regret rate, thus are agnostic to the concrete bandit algorithm employed by the victim player. We also derived a theoretical lower bound on the cumulative attack cost that any victim-agnostic attack algorithm must incur. The lower bound matches the upper bound achieved by our attack, which shows that our attack is asymptotically optimal.
On the Lipschitz Constant of Deep Networks and Double Descent

Existing bounds on the generalization error of deep networks assume some form of smooth or bounded dependence on the input variable, falling short of investigating the mechanisms controlling such factors in practice. In this work, we present an extensive experimental study of the empirical Lipschitz constant of deep networks undergoing double descent, and highlight non-monotonic trends strongly correlating with the test error. Building a connection between parameter-space and input-space gradients for SGD around a critical point, we isolate two important factors -- namely loss landscape curvature and distance of parameters from initialization -- respectively controlling optimization dynamics around a critical point and bounding model function complexity, even beyond the training data. Our study presents novels insights on implicit regularization via overparameterization, and effective model complexity for networks trained in practice.
Variational Latent Branching Model for Off-Policy Evaluation

Model-based methods have recently shown great potential for off-policy evaluation (OPE); offline trajectories induced by behavioral policies are fitted to transitions of Markov decision processes (MDPs), which are used to rollout simulated trajectories and estimate the performance of policies. Model-based OPE methods face two key challenges. First, as offline trajectories are usually fixed, they tend to cover limited state and action space. Second, the performance of model-based methods can be sensitive to the initialization of their parameters. In this work, we propose the variational latent branching model (VLBM) to learn the transition function of MDPs by formulating the environmental dynamics as a compact latent space, from which the next states and rewards are then sampled. Specifically, VLBM leverages and extends the variational inference framework with the recurrent state alignment (RSA), which is designed to capture as much information underlying the limited training data, by smoothing out the information flow between the variational (encoding) and generative (decoding) part of VLBM. Moreover, we also introduce the branching architecture to improve the model's robustness against randomly initialized model weights. The effectiveness of the VLBM is evaluated on the deep OPE (DOPE) benchmark, from which the training trajectories are designed to result in varied coverage of the state-action space. We show that the VLBM outperforms existing state-of-the-art OPE methods in general.
Multidimensional dynamic factor models

This paper generalises dynamic factor models for multidimensional dependent data. In doing so, it develops an interpretable technique to study complex information sources ranging from repeated surveys with a varying number of respondents to panels of satellite images. We specialise our results to model microeconomic data on US households jointly with macroeconomic aggregates. This results in a powerful tool able to generate localised predictions, counterfactuals and impulse response functions for individual households, accounting for traditional time-series complexities depicted in the state-space literature. The model is also compatible with the growing focus of policymakers for real-time economic analysis as it is able to process observations online, while handling missing values and asynchronous data releases.
Evolved cataclysmic variables as progenitors of AM CVn stars

We model cataclysmic variables (CVs) with solar metallicity donors (X = 0.7, Z = 0.02) that evolve to form AM CVn stars through the Evolved CV formation channel using various angular momentum loss mechanisms by magnetic braking (AMLMB). We find that the time-scale for AMLMB in our double-dynamo (DD) model is shorter than that of previously used empirical formulae. Owing to the shorter time-scales, a larger parameter space of initial conditions evolves to form AM CVn stars with the DD model than with other models. We perform an analysis of the expected number of AM CVn stars formed through the Evolved CV channel and find about 3 times as many AM CVn stars as reported before. We evolve these systems in detail with the Cambridge stellar evolution code (stars) and show that evolved CVs populate a region with orbital period Porb ≥ 5.5 hr. We evolve our donors beyond their orbital period minimum and find that a significant number become extremely H-exhausted systems. This makes them indistinguishable from systems evolved from the He-star and the White Dwarf (WD) channels in terms of the absence of H in their spectra. We also compare the masses, mass-transfer rates of the donor, and the orbital period with observations. We find that the state of the donor and the absence of H in systems such as YZ LMi and V396 Hya match with our modelled trajectories, while systems such as CR Boo and HP Lib match with our modelled tracks if their actual donor mass lies on the lower-end of the observed mass range.
Coarse-to-Fine Hybrid 3D Mapping System With Co-Calibrated Omnidirectional Camera and Non-Repetitive LiDAR

This letter presents a novel 3D mapping robot with an omnidirectional field-of-view (FoV) sensor suite composed of a non-repetitive LiDAR and an omnidirectional camera. Thanks to the non-repetitive scanning nature of the LiDAR, an automatic targetless co-calibration method is proposed to simultaneously calibrate the intrinsic parameters for the omnidirectional camera and the extrinsic parameters for the camera and LiDAR, which is crucial for the required step in bringing color and texture information to the point clouds in surveying and mapping tasks. Comparisons and analyses are made to target-based intrinsic calibration and mutual information (MI)-based extrinsic calibration, respectively. With this co-calibrated sensor suite, the hybrid mapping robot integrates both the odometry-based mapping mode and stationary mapping mode. Meanwhile, we proposed a new workflow to achieve coarse-to-fine mapping, including efficient and coarse mapping in a global environment with odometry-based mapping mode; planning for viewpoints in the region-of-interest (ROI) based on the coarse map (relies on the previous work [1]); navigating to each viewpoint and performing finer and more precise stationary scanning and mapping of the ROI. The fine map is stitched with the global coarse map, which provides a more efficient and precise result than the conventional stationary approaches and the emerging odometry-based approaches, respectively.
How does HCI Understand Human Agency and Autonomy?

Human agency and autonomy have always been fundamental concepts in HCI. New developments, including ubiquitous AI and the growing integration of technologies into our lives, make these issues ever pressing, as technologies increase their ability to influence our behaviours and values. However, in HCI understandings of autonomy and agency remain ambiguous. Both concepts are used to describe a wide range of phenomena pertaining to sense-of-control, material independence, and identity. It is unclear to what degree these understandings are compatible, and how they support the development of research programs and practical interventions. We address this by reviewing 30 years of HCI research on autonomy and agency to identify current understandings, open issues, and future directions. From this analysis, we identify ethical issues, and outline key themes to guide future work. We also articulate avenues for advancing clarity and specificity around these concepts, and for coordinating integrative work across different HCI communities.
Assertion Inferring Mutants

—Speciﬁcation inference techniques aim at (automat- ically) inferring a set of assertions that capture the exhibited software behaviour by generating and ﬁltering assertions through dynamic test executions and mutation testing. Although powerful, such techniques are computationally expensive due to a large number of assertions, test cases and mutated versions that need to be executed. To overcome this issue, we demonstrate that a small subset, i.e., 12.95% of the mutants used by mutation testing tools is sufﬁcient for assertion inference, this subset is signiﬁcantly different, i.e., 71.59% different from the subsuming mutant set that is frequently cited by mutation testing literature, and can be statically approximated through a learning based method. In particular, we propose AIMS , an approach that selects Assertion Inferring Mutants , i.e., a set of mutants that are well-suited for assertion inference, with 0.58 MCC, 0.79 Precision, and 0.49 Recall. We evaluate AIMS on 46 programs and demonstrate that it has comparable inference capabilities with full mutation analysis (misses 12.49% of assertions) while signiﬁcantly limiting execution cost (runs 46.29 times faster). A comparison with randomly selected sets of mutants, shows the superiority of AIMS by inferring 36% more assertions while requiring approximately equal amount of execution time. We also show that AIMS ’s inferring capabilities are almost complete as it infers
Accurate and efficient multiscale simulation of a heterogeneous elastic beam via computation on small sparse patches

Modern `smart' materials have complex microscale structure, often with unknown macroscale closure. The Equation-Free Patch Scheme empowers us to non-intrusively, efficiently, and accurately simulate over large scales through computations on only small well-separated patches of the microscale system. Here the microscale system is a solid beam of random heterogeneous elasticity. The continuing challenge is to compute the given physics on just the microscale patches, and couple the patches across un-simulated macroscale space, in order to establish efficiency, accuracy, consistency, and stability on the macroscale. Dynamical systems theory supports the scheme. This research program is to develop a systematic non-intrusive approach, both computationally and analytically proven, to model and compute accurately macroscale system levels of general complex physical and engineering systems.
Global 3D Radiation Magnetohydrodynamic Simulations of Accretion onto a Stellar-mass Black Hole at Sub- and Near-critical Accretion Rates

We present global 3D radiation magnetohydrodynamic simulations of accretion onto a 6.62 solar-mass black hole, with quasi-steady-state accretion rates reaching 0.016–0.9 times the critical accretion rate, which is defined as the accretion rate for powering the Eddington luminosity, assuming a 10% radiative efficiency, in three different runs. The simulations show no sign of thermal instability over hundreds of thermal timescales at 10 r g. The energy dissipation occurs close to the mid-plane in the near-critical runs and near the disk surface in the low–accretion rate run. The total radiative luminosity inside ∼20 r g is about 1%–30% of the Eddington limit, with radiative efficiencies of about 6% and 3%, respectively, in the sub- and near-critical accretion regimes. In both cases, self-consistent turbulence generated by the magnetorotational instability leads to angular momentum transfer, and the disk is supported by magnetic pressure. Outflows from the central low-density funnel, with a terminal velocity of ∼0.1c, are seen only in the near-critical runs. We conclude that these magnetic pressure–dominated disks are thermally stable and thicker than the α disk, and that the effective temperature profiles are much flatter than those in the α disks. The magnetic pressures of these disks are comparable within an order of magnitude to the previous analytical magnetic pressure–dominated disk model.
Robust empirical risk minimization via Newton's method

A new variant of Newton's method for empirical risk minimization is studied, where at each iteration of the optimization algorithm, the gradient and Hessian of the objective function are replaced by robust estimators taken from existing literature on robust mean estimation for multivariate data. After proving a general theorem about the convergence of successive iterates to a small ball around the population-level minimizer, consequences of the theory in generalized linear models are studied when data are generated from Huber's epsilon-contamination model and/or heavytailed distributions. An algorithm for obtaining robust Newton directions based on the conjugate gradient method is also proposed, which may be more appropriate for high-dimensional settings, and conjectures about the convergence of the resulting algorithm are offered. Compared to robust gradient descent, the proposed algorithm enjoys the faster rates of convergence for successive iterates often achieved by second-order algorithms for convex problems, i.e., quadratic convergence in a neighborhood of the optimum, with a stepsize that may be chosen adaptively via backtracking linesearch.
STEEL: Singularity-aware Reinforcement Learning

Batch reinforcement learning (RL) aims at leveraging pre-collected data to find an optimal policy that maximizes the expected total rewards in a dynamic environment. Nearly all existing algorithms rely on the absolutely continuous assumption on the distribution induced by target policies with respect to the data distribution, so that the batch data can be used to calibrate target policies via the change of measure. However, the absolute continuity assumption could be violated in practice (e.g., no-overlap support), especially when the state-action space is large or continuous. In this paper, we propose a new batch RL algorithm without requiring absolute continuity in the setting of an infinite-horizon Markov decision process with continuous states and actions. We call our algorithm STEEL: SingulariTy-awarE rEinforcement Learning. Our algorithm is motivated by a new error analysis on off-policy evaluation, where we use maximum mean discrepancy, together with distributionally robust optimization, to characterize the error of off-policy evaluation caused by the possible singularity and to enable model extrapolation. By leveraging the idea of pessimism and under some mild conditions, we derive a finite-sample regret guarantee for our proposed algorithm without imposing absolute continuity. Compared with existing algorithms, by requiring only minimal data-coverage assumption, STEEL significantly improves the applicability and robustness of batch RL. Extensive simulation studies and one real experiment on personalized pricing demonstrate the superior performance of our method in dealing with possible singularity in batch RL.
Single-crystal Pb(ZrxTi1−x)O3 thin films prepared by metal-organic chemical vapor deposition: Systematic compositional variation of electronic and optical properties

Single-crystal thin films of Pb(ZrxTi1−x)O3 (PZT) covering the full compositional range (0⩽x⩽1) were deposited by metal-organic chemical vapor deposition. Epitaxial SrRuO3(001) thin films grown on SrTiO3(001) substrates by rf-magnetron sputter deposition served as template electrode layers to promote the epitaxial growth of PZT. X-ray diffraction, energy-dispersive x-ray spectroscopy, atomic force microscopy, transmission electron microscopy, and optical waveguiding were used to characterize the crystalline structure, composition, surface morphology, microstructure, refractive index, and film thickness of the deposited films. The PZT films were single crystalline for all compositions exhibiting cube-on-cube growth epitaxy with the substrate and showed very high degrees of crystallinity and orientation. The films exhibited typical root mean square surface roughness of ∼1.0–2.5 nm. For tetragonal films, the surface morphology was dominated by grain tilting resulting from ferroelectric domain formation. We r.
Improving the Accuracy-Robustness Trade-off of Classifiers via Adaptive Smoothing

While prior research has proposed a plethora of methods that build neural classifiers robust against adversarial robustness, practitioners are still reluctant to adopt them due to their unacceptably severe clean accuracy penalties. This paper significantly alleviates this accuracy-robustness trade-off by mixing the output probabilities of a standard classifier and a robust classifier, where the standard network is optimized for clean accuracy and is not robust in general. We show that the robust base classifier's confidence difference for correct and incorrect examples is the key to this improvement. In addition to providing intuitions and empirical evidence, we theoretically certify the robustness of the mixed classifier under realistic assumptions. Furthermore, we adapt an adversarial input detector into a mixing network that adaptively adjusts the mixture of the two base models, further reducing the accuracy penalty of achieving robustness. The proposed flexible method, termed"adaptive smoothing", can work in conjunction with existing or even future methods that improve clean accuracy, robustness, or adversary detection. Our empirical evaluation considers strong attack methods, including AutoAttack and adaptive attack. On the CIFAR-100 dataset, our method achieves an 85.21% clean accuracy while maintaining a 38.72% $\ell_\infty$-AutoAttacked ($\epsilon = 8/255$) accuracy, becoming the second most robust method on the RobustBench CIFAR-100 benchmark as of submission, while improving the clean accuracy by ten percentage points compared with all listed models. The code that implements our method is available at https://github.com/Bai-YT/AdaptiveSmoothing.
SeaFormer: Squeeze-enhanced Axial Transformer for Mobile Semantic Segmentation

Since the introduction of Vision Transformers, the landscape of many computer vision tasks (e.g., semantic segmentation), which has been overwhelmingly dominated by CNNs, recently has significantly revolutionized. However, the computational cost and memory requirement render these methods unsuitable on the mobile device, especially for the high-resolution per-pixel semantic segmentation task. In this paper, we introduce a new method squeeze-enhanced Axial TransFormer (SeaFormer) for mobile semantic segmentation. Specifically, we design a generic attention block characterized by the formulation of squeeze Axial and detail enhancement. It can be further used to create a family of backbone architectures with superior cost-effectiveness. Coupled with a light segmentation head, we achieve the best trade-off between segmentation accuracy and latency on the ARM-based mobile devices on the ADE20K and Cityscapes datasets. Critically, we beat both the mobile-friendly rivals and Transformer-based counterparts with better performance and lower latency without bells and whistles. Beyond semantic segmentation, we further apply the proposed SeaFormer architecture to image classification problem, demonstrating the potentials of serving as a versatile mobile-friendly backbone.
Cause-Effect Inference in Location-Scale Noise Models: Maximum Likelihood vs. Independence Testing

A fundamental problem of causal discovery is cause-effect inference, learning the correct causal direction between two random variables. Significant progress has been made through modelling the effect as a function of its cause and a noise term, which allows us to leverage assumptions about the generating function class. The recently introduced heteroscedastic location-scale noise functional models (LSNMs) combine expressive power with identifiability guarantees. LSNM model selection based on maximizing likelihood achieves state-of-the-art accuracy, when the noise distributions are correctly specified. However, through an extensive empirical evaluation, we demonstrate that the accuracy deteriorates sharply when the form of the noise distribution is misspecified by the user. Our analysis shows that the failure occurs mainly when the conditional variance in the anti-causal direction is smaller than that in the causal direction. As an alternative, we find that causal model selection through residual independence testing is much more robust to noise misspecification and misleading conditional variance.
First observations of HF heater‐produced airglow at the High Frequency Active Auroral Research Program facility: Thermal excitation and spatial structuring

HF heater‐produced artificial airglow emissions at 630.0 nm were detected at the High Frequency Active Auroral Research Program (HAARP) ionospheric research facility near Gakona, Alaska (62.39° N 145.15° W), for the first time in March 1999. HF excitation was identified by virtue of two measurements: A region of emissions enhanced 50‐60 R above the background of ∼100 R was observed in the approximate region illuminated by the HF heater beam; and the rise and decay of the airglow followed the HF transmitter on/off cycle with time constants of approximately 80 s and 50 s, respectively. The observations were made in close proximity to the natural aurora, which gradually moved southward over the site during the experiment and eventually overwhelmed the much weaker artificial airglow. Significant structure in the airglow region was observed, including an overall equatorward displacement relative to the nominal transmitter beam, preferential occurrence of intensity maxima along the overhead field line up to and including magnetic zenith, and periods of elongation of the airglow region along the magnetic meridian and later parallel to the encroaching auroral zone. We discuss potential sources of this structuring of the emission shape including auroral plasma transport and neutral winds. Transmitter frequencies of 3.1–3.3 MHz matched the ionospheric plasma frequency near the F region peak at high altitudes of 300–350 km. We identify the primary mechanism behind excitation of the 630.0 nm airglow as thermal electron excitation on the basis of this evidence, along with thermal balance arguments, further substantiated by the observed asymmetry in rise and decay times and a lack of detectable emissions at 557.7 nm.
Generalized Subspace Subcodes in the Rank Metric

Rank-metric codes were studied by E. Gabidulin in 1985 after a brief introduction by Delsarte in 1978 as an equivalent of Reed-Solomon codes, but based on linearized polynomials. They have found applications in many areas, including linear network coding and space-time coding. They are also used in cryptography to reduce the size of the keys compared to Hamming metric codes at the same level of security. However, some families of rank-metric codes suffer from structural attacks due to the strong algebraic structure from which they are defined. It therefore becomes interesting to find new code families in order to address these questions in the landscape of rank-metric codes. \par In this paper, we provide a generalization of Subspace Subcodes in Rank metric introduced by Gabidulin and Loidreau. We also characterize this family by giving an algorithm which allows to have its generator and parity-check matrices based on the associated extended codes. We have also studied the specific case of Gabidulin codes whose underlying decoding algorithms are known. Bounds for the cardinalities of these codes, both in the general case and in the case of Gabidulin codes, are also provided.