Miracle-dz/UniFAR-FTUs huggingface dataset:
Fast Noise Removal in Hyperspectral Images via Representative Coefficient Total Variation

Mining structural priors in data is a widely recognized technique for hyperspectral image (HSI) denoising tasks, whose typical ways include model-based methods and data-based methods. The model-based methods have good generalization ability, while the runtime can hardly meet the fast processing requirements of the practical situations due to the large size of an HSI <inline-formula> <tex-math notation="LaTeX">${\mathbf {X}}\in \mathbb {R}^{\textrm {MN}\times B}$ </tex-math></inline-formula>. For the data-based methods, they perform relatively fast on new test data once they have been trained. However, their generalization ability is always insufficient. In this article, we propose a fast model-based approach via a novel regularizer named the representative coefficient total variation (RCTV) to simultaneously characterize the low-rank and local smooth properties. The RCTV regularizer is proposed based on the observation that the representative coefficient matrix <inline-formula> <tex-math notation="LaTeX">${\mathbf {U}}\in \mathbb {R}^{\textrm {MN}\times R} (R\ll B)$ </tex-math></inline-formula> obtained by orthogonally transforming the original HSI <inline-formula> <tex-math notation="LaTeX">${\mathbf {X}}$ </tex-math></inline-formula> can inherit the strong local-smooth prior of <inline-formula> <tex-math notation="LaTeX">${\mathbf {X}}$ </tex-math></inline-formula>. Since <inline-formula> <tex-math notation="LaTeX">$R/B$ </tex-math></inline-formula> is very small, the model based on the RCTV regularizer has lower time complexity. In addition, we find that the representative coefficient matrix <inline-formula> <tex-math notation="LaTeX">${\mathbf {U}}$ </tex-math></inline-formula> is robust to noise, and thus, the RCTV regularizer can somewhat promote the robustness of the HSI denoising model. Extensive experiments on mixed noise removal demonstrate that the proposed method realizes a perfect compromise between denoising performance and denoising speed compared with other state-of-the-art methods. Remarkably, the denoising speed of our proposed method outperforms all competing model-based techniques and is comparable with the deep learning-based approaches. The code of our algorithm is released at <uri>https://github.com/andrew-pengjj/rctv.git</uri>.
FingerFlex: Inferring Finger Trajectories from ECoG signals

Motor brain-computer interface (BCI) development relies critically on neural time series decoding algorithms. Recent advances in deep learning architectures allow for automatic feature selection to approximate higher-order dependencies in data. This article presents the FingerFlex model - a convolutional encoder-decoder architecture adapted for finger movement regression on electrocorticographic (ECoG) brain data. State-of-the-art performance was achieved on a publicly available BCI competition IV dataset 4 with a correlation coefficient between true and predicted trajectories up to 0.74. The presented method provides the opportunity for developing fully-functional high-precision cortical motor brain-computer interfaces.
Developing Modular Autonomous Capabilities for sVAS Operations

Small teams in the field can benefit from the ca-pabilities provided by small Uncrewed Aerial Systems (sUAS) for missions such as reconnaissance, hostile attribution, remote emplacement, and search and rescue. The mobility, communications, and flexible payload capacity of sUas can offer teams new levels of situational awareness and enable more highly co-ordinated missions than previously possible. However, piloting such aircraft for specific missions draws personnel away from other mission-critical tasks, increasing the load on remaining personnel while also increasing complexity of operations. For wider adoption and use of sUAS for security and humanitarian missions, safe and robust autonomy must be employed to reduce this burden on small teams. In this paper, we present the development of the Collaborative-UAS for Hostile Attribution, Surveillance, Emplacement, and Reconnaissance (CHASER) testbed, for rapidly prototyping capabilities that will reduce strain on small teams through sensor-guided autonomous con-trol. We attempt to address autonomy needs that are not filled by commercial sUAS platforms by creating and testing a series of composable modules that can be rapidly reconfigured to support multiple specific missions. Methods implemented and presented here include radar track correlation, on-board computer vision target detection, target position estimation, closed-loop relative position control, and efficient search of a 3D volume for target acquisition. We configure and test a series of these modules in an example mission, executing a fully autonomous chase of an intruding sUAS in live flight, and demonstrating the success of the modularized autonomy approach. We present performance results from simulation or live flight tests for each module. Lastly, we describe the software architecture that we have developed for flexible controls and comment on how the capabilities presented may enable additional missions.
Pion constituent quark couplings strong form factors: A dynamical approach

Form factors for pions interactions with constituent quarks are investigated as the leading effective couplings obtained from a one loop background field method applied to a global color model. Two pion field definitions are considered and the resulting eleven form factors are expressed in terms of components of the quark and gluon propagators that compose only two momentum dependent functions. A momentum dependent Goldberger Treiman relation is also obtained as one of the ratios between the form factors. The resulting form factors with pion momenta up to 1.5 GeV are exhibited for different quark effective masses and two different non-perturbative gluon propagators and they present similar behavior to fittings of experimental data from nucleons form factors. The corresponding pseudoscalar averaged quadratic radii (a.q.r.) and correction to the axial a.q. r. are presented as functions of the sea quark effective mass, being equal respectively to the scalar and vector ones at the present level of calculation.
Continual Learning of Neural Machine Translation within Low Forgetting Risk Regions

This paper considers continual learning of large-scale pretrained neural machine translation model without accessing the previous training data or introducing model separation. We argue that the widely used regularization-based methods, which perform multi-objective learning with an auxiliary loss, suffer from the misestimate problem and cannot always achieve a good balance between the previous and new tasks. To solve the problem, we propose a two-stage training method based on the local features of the real loss. We first search low forgetting risk regions, where the model can retain the performance on the previous task as the parameters are updated, to avoid the catastrophic forgetting problem. Then we can continually train the model within this region only with the new training data to fit the new task. Specifically, we propose two methods to search the low forgetting risk regions, which are based on the curvature of loss and the impacts of the parameters on the model output, respectively. We conduct experiments on domain adaptation and more challenging language adaptation tasks, and the experimental results show that our method can achieve significant improvements compared with several strong baselines.
Data-free Defense of Black Box Models Against Adversarial Attacks

Several companies often safeguard their trained deep models (i.e., details of architecture, learnt weights, training details etc.) from third-party users by exposing them only as black boxes through APIs. Moreover, they may not even provide access to the training data due to proprietary reasons or sensitivity concerns. In this work, we propose a novel defense mechanism for black box models against adversarial attacks in a data-free set up. We construct synthetic data via generative model and train surrogate network using model stealing techniques. To minimize adversarial contamination on perturbed samples, we propose 'wavelet noise remover' (WNR) that performs discrete wavelet decomposition on input images and carefully select only a few important coefficients determined by our 'wavelet coefficient selection module' (WCSM). To recover the high-frequency content of the image after noise removal via WNR, we further train a 'regenerator' network with an objective to retrieve the coefficients such that the reconstructed image yields similar to original predictions on the surrogate model. At test time, WNR combined with trained regenerator network is prepended to the black box network, resulting in a high boost in adversarial accuracy. Our method improves the adversarial accuracy on CIFAR-10 by 38.98% and 32.01% on state-of-the-art Auto Attack compared to baseline, even when the attacker uses surrogate architecture (Alexnet-half and Alexnet) similar to the black box architecture (Alexnet) with same model stealing strategy as defender. The code is available at https://github.com/vcl-iisc/data-free-black-box-defense
Regression Compatible Listwise Objectives for Calibrated Ranking with Binary Relevance

As Learning-to-Rank (LTR) approaches primarily seek to improve ranking quality, their output scores are not scale-calibrated by design. This fundamentally limits LTR usage in score-sensitive applications. Though a simple multi-objective approach that combines a regression and a ranking objective can effectively learn scale-calibrated scores, we argue that the two objectives are not necessarily compatible, which makes the trade-off less ideal for either of them. In this paper, we propose a practical regression compatible ranking (RCR) approach that achieves a better trade-off, where the two ranking and regression components are proved to be mutually aligned. Although the same idea applies to ranking with both binary and graded relevance, we mainly focus on binary labels in this paper. We evaluate the proposed approach on several public LTR benchmarks and show that it consistently achieves either best or competitive result in terms of both regression and ranking metrics, and significantly improves the Pareto frontiers in the context of multi-objective optimization. Furthermore, we evaluated the proposed approach on YouTube Search and found that it not only improved the ranking quality of the production pCTR model, but also brought gains to the click prediction accuracy. The proposed approach has been successfully deployed in the YouTube production system.
A saturation property for powers of irreducible characters.

Let ̂ be a semisimple finite dimensional complex Lie algebra, and let χ be the character of a (not necessarily irreducible) finite dimensional representation of ^. We say that χ is saturated if for each dominant weight w of χ, the irreducible character xw9 whose highest weight is w, appears in the orthogonal decomposition of χ. The character χ is called eventually saturated if some power of χ is saturated. An irreducible character with highest weight λ is saturated if and only if A is its only dominant weight. When this holds, the highest weight λ is called miniscule or minimal ([Hui], exercise 13.10). For example, the irreducible character of Ad corresponding to the Standard representation is saturated. Although few irreducible characters are saturated, we show that almost all irreducible characters are eventually saturated, and characterize them explicitly.
Physics-inspired machine learning for power grid frequency modelling

The operation of power systems is aﬀected by diverse technical, economic and social factors. Social behaviour determines load patterns, electricity markets regulate the generation and weather-dependent renewables introduce power ﬂuctuations. Thus, power system dynamics must be regarded as a non-autonomous system whose parameters vary strongly with time. However, the external driving factors are usually only available on coarse scales and the actual dependencies of the dynamic system parameters are generally unknown. Here, we propose a physics-inspired machine learning model that bridges the gap between large-scale drivers and short-term dynamics of the power system. Integrating stochastic diﬀerential equations and artiﬁcial neural networks, we construct a probabilistic model of the power grid frequency dynamics in Continental Europe. Its probabilistic prediction outperforms the daily average proﬁle, which is an important benchmark. Using the integrated model, we identify and explain the parameters of the dynamical system from the data, which reveals their strong time-dependence and their relation to external drivers such as wind power feed-in and fast generation ramps. Finally, we generate synthetic time series from the model, which successfully reproduce central characteristics of the grid frequency such as their heavy-tailed distribution. All in all, our work emphasises the importance of modelling power system dynamics as a stochastic non-autonomous system with both intrinsic dynamics and external drivers.
Ceramic industry wastewater treatment by chemical coagulation process: a statistical optimization of operating parameters

This study deals with chemical oxygen demand (COD) removal from ceramic industry wastewater by chemical coagulation using alum and ferric chloride (FeCl 3 ) as coagulants. The study also focuses on the capillary suction time (CST) of sludge samples which is an important sludge dewatering parameter. Response surface methodology (RSM) approach was employed to evaluate the effects and interactions of the operating variables and to optimize the performance of the process. Significant quadratic polynomial models were obtained (R 2 = 96.26% for alum and R 2 =89.15% for FeCl 3 for COD removal; R 2 = 96.6% for alum and R 2 =90.9% for FeCl 3 for CST of sludge, respectively). Alum was more effective coagulant for ceramic industry wastewater treatment as compared with FeCl 3 . Numerical optimization based on desirability function was employed; in a 36 min trial 95.2% of COD removal was achieved at alum dosage of 3.3 g/L and pH 5. The optimization study shows that the minimum CST of sludge was found 17.4 s at alum dosage of 5 g/L and pH 5 in a reaction time of 16 min. The results indicate that the RSM is suitable for the design and optimization of chemical coagulation process using alum as a coagulant fort he treatment of ceramic industry wastewater.
A remark on the Hochschild dimension of liberated quantum groups

Let $A$ be a Hopf algebra equipped with a projection onto the coordinate Hopf algebra $\mathcal{O}(G)$ of a semisimple algebraic group $G$. It is shown that if $A$ admits a suitably non-degenerate comodule $V$ and the induced $G$-module structure of $V$ is non-trivial, then the third Hochschild homology group of $A$ is non-trivial.
Weak boson fusion at 100 TeV

From the LHC runs we know that, with increasing collider energy, weak-boson-fusion Higgs production dominates as an environment for precision measurements. We show how a future hadron collider performs for three challenging benchmark signatures. Because all of these measurements rely on the tagging jet signature, we first give a comprehensive analysis of weak-boson-fusion kinematics and a proposed two-step jet veto at a 100 TeV hadron collider. We then find this machine to be sensitive to invisible Higgs branching ratios of 0.5%, a second-generation muon Yukawa coupling of 2%, and an enhanced total Higgs width of around 5%, the latter with essentially no model dependence. This kind of performance crucially relies on a sufficient detector coverage and a dedicated weak-boson-fusion trigger channel.
P4P: Conflict-Aware Motion Prediction for Planning in Autonomous Driving

Motion prediction is crucial in enabling safe motion planning for autonomous vehicles in interactive scenarios. It allows the planner to identify potential conflicts with other traffic agents and generate safe plans. Existing motion predictors often focus on reducing prediction errors, yet it remains an open question on how well they help identify conflicts for the planner, which are critical to the safety of autonomous vehicles. In this paper, we evaluate state-of-the-art predictors through novel conflict-related metrics, such as the success rate of identifying conflicts. Surprisingly, the predictors suffer from a low success rate and thus lead to a large percentage of collisions when we test the prediction-planning system in an interactive simulator. To fill the gap, we propose a simple but effective alternative that combines a physics-based trajectory generator and a learning-based relation predictor to identify conflicts and infer conflict relations. We demonstrate that our predictor, P4P, achieves superior performance over existing learning-based predictors in realistic interactive driving scenarios from Waymo Open Motion Dataset.
Reexamination of Tolman's law and the Gibbs adsorption equation for curved interfaces

In manuscript arXiv:1703.08719 [cond-mat.soft], it was claimed that the well-known deduction of Tolman's law is not rigorous, since Tolman's argument implies that two different definitions of the surface tension, called $\gamma$ and $\bar\gamma$ in the manuscript, coincide. This claim is retracted as it can be shown by free-energy minimization that $\gamma = \bar\gamma$ indeed holds for the Laplace radius. Joachim Gros, Philipp Rehner, Carlos Vega, Oivind Wilhelmsen, and the anonymous reviewers of The Journal of Chemical Physics contributed to finding the mistake in the manuscript.
Exploiting Qubit Reuse through Mid-circuit Measurement and Reset

Quantum measurement is important to quantum computing as it extracts the outcome of the circuit at the end of the computation. Previously, all measurements have to be done at the end of the circuit. Otherwise, it will incur significant errors. But it is not the case now. Recently IBM started supporting dynamic circuits through hardware (instead of software by simulator). With mid-circuit hardware measurement, we can improve circuit efficacy and fidelity from three aspects: (a) reduced qubit usage, (b) reduced swap insertion, and (c) improved fidelity. We demonstrate this using real-world applications Bernstein Verizani on real hardware and show that circuit resource usage can be improved by 60\%, and circuit fidelity can be improved by 15\%. We design a compiler-assisted tool that can find and exploit the tradeoff between qubit reuse, fidelity, gate count, and circuit duration. We also developed a method for identifying whether qubit reuse will be beneficial for a given application. We evaluated our method on a representative set of essential applications. We can reduce resource usage by up to 80\% and circuit fidelity by up to 20\%.
Modeling Solids in Nuclear Astrophysics with Smoothed Particle Hydrodynamics

Smoothed particle hydrodynamics (SPH) is a frequently applied tool in computational astrophysics to solve the fluid dynamics equations governing the systems under study. For some problems, for example when involving asteroids and asteroid impacts, the additional inclusion of material strength is necessary in order to accurately describe the dynamics. In compact stars, that is white dwarfs and neutron stars, solid components are also present. Neutron stars have a solid crust, which is the strongest material known in nature. However, their dynamical evolution, when modeled via SPH or other computational fluid dynamics codes, is usually described as a purely fluid dynamics problem. Here, we present the first 3D simulations of neutron star crustal toroidal oscillations including material strength with the Los Alamos National Laboratory SPH code FleCSPH. In the first half of the paper, we present the numerical implementation of solid material modeling together with standard tests. The second half is on the simulation of crustal oscillations in the fundamental toroidal mode. Here, we dedicate a large fraction of the paper to approaches that can suppress numerical noise in the solid. If not minimized, the latter can dominate the crustal motion in the simulations.
DriveFuzz: Discovering Autonomous Driving Bugs through Driving Quality-Guided Fuzzing

Autonomous driving has become real; semi-autonomous driving vehicles in an affordable price range are already on the streets, and major automotive vendors are actively developing full self-driving systems to deploy them in this decade. Before rolling the products out to the end-users, it is critical to test and ensure the safety of the autonomous driving systems, consisting of multiple layers intertwined in a complicated way. However, while safety-critical bugs may exist in any layer and even across layers, relatively little attention has been given to testing the entire driving system across all the layers. Prior work mainly focuses on white-box testing of individual layers and preventing attacks on each layer. In this paper, we aim at holistic testing of autonomous driving systems that have a whole stack of layers integrated in their entirety. Instead of looking into the individual layers, we focus on the vehicle states that the system continuously changes in the driving environment. This allows us to design DriveFuzz, a new systematic fuzzing framework that can uncover potential vulnerabilities regardless of their locations. DriveFuzz automatically generates and mutates driving scenarios based on diverse factors leveraging a high-fidelity driving simulator. We build novel driving test oracles based on the real-world traffic rules to detect safety-critical misbehaviors, and guide the fuzzer towards such misbehaviors through driving quality metrics referring to the physical states of the vehicle. DriveFuzz has discovered 30 new bugs in various layers of two autonomous driving systems (Autoware and CARLA Behavior Agent) and three additional bugs in the CARLA simulator. We further analyze the impact of these bugs and how an adversary may exploit them as security vulnerabilities to cause critical accidents in the real world.
Could Giant Pretrained Image Models Extract Universal Representations?

Frozen pretrained models have become a viable alternative to the pretraining-then-finetuning paradigm for transfer learning. However, with frozen models there are relatively few parameters available for adapting to downstream tasks, which is problematic in computer vision where tasks vary significantly in input/output format and the type of information that is of value. In this paper, we present a study of frozen pretrained models when applied to diverse and representative computer vision tasks, including object detection, semantic segmentation and video action recognition. From this empirical analysis, our work answers the questions of what pretraining task fits best with this frozen setting, how to make the frozen setting more flexible to various downstream tasks, and the effect of larger model sizes. We additionally examine the upper bound of performance using a giant frozen pretrained model with 3 billion parameters (SwinV2-G) and find that it reaches competitive performance on a varied set of major benchmarks with only one shared frozen base network: 60.0 box mAP and 52.2 mask mAP on COCO object detection test-dev, 57.6 val mIoU on ADE20K semantic segmentation, and 81.7 top-1 accuracy on Kinetics-400 action recognition. With this work, we hope to bring greater attention to this promising path of freezing pretrained image models.