Miracle-dz/UniFAR-FTUs huggingface dataset:
Two types of quaking and shear unjamming: state diagram for soft granular particles under shear

Understanding intermittency, an ubiquitous behavior in flows of packed grains, is pivotal for establishing the rheology of granular matter. A straightforward explanation has been missing despite the long development of theories at various levels of abstraction. Here, we propose the use of a Stribeck-Hertz model that starts with the classic Coulomb friction but also takes into account the tribology between particles, i.e. the reduction of friction coefficient with speed as is commonly observed. Our numerical experiments reveal a state diagram covering a wide range of packing fractions, and produce the quaking intermittency in the mid-range of a dimensionless shear rate defined accordingly, in consistence with our recent laboratory observation [Phys. Rev. Lett. 126.128001 (2021)]. Monitoring the discontinuities in the evolution of mean contact number allows us to distinguish two types of quaking event, which represent different pathways of microstructural change underlying the spontaneous jamming-unjamming transitions along with the intermittency. These studies prompt us to reexamine the prevailing paradigm of shear jamming -- once a more realistic interparticle tribology is considered.
Solving math word problems with process- and outcome-based feedback

Recent work has shown that asking language models to generate reasoning steps improves performance on many reasoning tasks. When moving beyond prompting, this raises the question of how we should supervise such models: outcome-based approaches which supervise the final result, or process-based approaches which supervise the reasoning process itself? Differences between these approaches might naturally be expected not just in final-answer errors but also in reasoning errors, which can be difficult to detect and are problematic in many real-world domains such as education. We run the first comprehensive comparison between process- and outcome-based approaches trained on a natural language task, GSM8K. We find that pure outcome-based supervision produces similar final-answer error rates with less label supervision. However, for correct reasoning steps we find it necessary to use process-based supervision or supervision from learned reward models that emulate process-based feedback. In total, we improve the previous best results from 16.8% $\to$ 12.7% final-answer error and 14.0% $\to$ 3.4% reasoning error among final-answer-correct solutions.
Learning to Play Trajectory Games Against Opponents With Unknown Objectives

Many autonomous agents, such as intelligent vehicles, are inherently required to interact with one another. Game theory provides a natural mathematical tool for robot motion planning in such interactive settings. However, tractable algorithms for such problems usually rely on a strong assumption, namely that the objectives of all players in the scene are known. To make such tools applicable for ego-centric planning with only local information, we propose an adaptive model-predictive game solver, which jointly infers other players' objectives online and computes a corresponding generalized Nash equilibrium (GNE) strategy. The adaptivity of our approach is enabled by a differentiable trajectory game solver whose gradient signal is used for maximum likelihood estimation (MLE) of opponents' objectives. This differentiability of our pipeline facilitates direct integration with other differentiable elements, such as neural networks (NNs). Furthermore, in contrast to existing solvers for cost inference in games, our method handles not only partial state observations but also general inequality constraints. In two simulated traffic scenarios, we find superior performance of our approach over both existing game-theoretic methods and non-game-theoretic model-predictive control (MPC) approaches. We also demonstrate our approach's real-time planning capabilities and robustness in two-player hardware experiments.
Energy-efficient tunable-stiffness soft robots using second moment of area actuation

The optimal stiffness for soft swimming robots depends on swimming speed, which means no single stiffness can maximise efficiency in all swimming conditions. Tunable-stiffness would produce an increased range of high-efficiency swimming speeds for robots with flexible propulsors and enable soft control surfaces for steering underwater vehicles. We propose and demonstrate a method for tunable soft robotic stiffness using inflatable rubber tubes to stiffen a silicone foil through pressure and second moment of area change. We achieved double the effective stiffness of the system for an input pressure change from 0 to 0.8 bar and 2 J energy input. We achieved a resonant amplitude gain of 5 to 7 times the input amplitude and tripled the high-gain frequency range compared to a foil with fixed stiffness. These results show that changing second moment of area is an energy effective approach to tunable-stiffness robots.
Rotation of the dislocation grid in multilayer FeSe films and visualization of electronic nematic domains via orbital-selective tunneling

Understanding the interplay of structural and electronic symmetry breaking in Fe-based high temperature superconductors remains of high interest. In this work we grow strain-patterned multilayer FeSe thin films in a range of thicknesses using molecular beam epitaxy. We study the formation of electronic nematic domains and spatially-varying strain using scanning tunneling microscopy and spectroscopy. We directly visualize the formation of edge dislocations that give rise to a two-dimensional edge dislocation network in the films. Interestingly, we observe a 45 degree in-plane rotation of the dislocation network as a function of film thickness, yielding antisymmetric strain along different directions. This results in different coupling ratios between electronic nematic domains and antisymmetric strain. Lastly, we are able to distinguish between different orthogonal nematic domains by revealing a small energy-dependent difference in differential conductance maps between the two regions. This could be explained by orbital-selective tip-sample tunneling. Our observations bring new insights into the dislocation network formation in epitaxial thin films and provide another nanoscale tool to explore electronic nematicity in Fe-based superconductors.
Competency-Aware Neural Machine Translation: Can Machine Translation Know its Own Translation Quality?

Neural machine translation (NMT) is often criticized for failures that happenwithout awareness. The lack of competency awareness makes NMT untrustworthy. This is in sharp contrast to human translators who give feedback or conduct further investigations whenever they are in doubt about predictions. To fill this gap, we propose a novel competency-aware NMT by extending conventional NMT with a self-estimator, offering abilities to translate a source sentence and estimate its competency.The self-estimator encodes the information of the decoding procedure and then examines whether it can reconstruct the original semantics of the source sentence. Experimental results on four translation tasks demonstrate that the proposed method not only carries out translation tasks intact but also delivers outstanding performance on quality estimation.Without depending on any reference or annotated data typically required by state-of-the-art metric and quality estimation methods, our model yields an even higher correlation with human quality judgments than a variety of aforementioned methods, such as BLEURT, COMET, and BERTScore. Quantitative and qualitative analyses show better robustness of competency awareness in our model.
Evaluation of the impact of the indiscernibility relation on the fuzzy-rough nearest neighbours algorithm

Fuzzy rough sets are well-suited for working with vague, imprecise or uncertain information and have been succesfully applied in real-world classiﬁcation problems. One of the prominent representatives of this theory is fuzzy-rough nearest neighbours (FRNN), a classiﬁcation algorithm based on the classical k-nearest neighbours algorithm. The crux of FRNN is the indiscernibility relation, which measures how similar two elements in the data set of interest are. In this paper, we investigate the impact of this indiscernibility relation on the performance of FRNN classiﬁcation. In addition to relations based on distance functions and kernels, we also explore the eﬀect of distance metric learning on FRNN for the ﬁrst time. Furthermore, we also introduce an asymmetric, class-speciﬁc relation based on the Mahalanobis distance which uses the correlation within each class, and which shows a signiﬁcant improvement over the regular Mahalanobis distance, but is still beaten by the Manhattan distance. Overall, the Neighbourhood Components Analysis algorithm is found to be the best performer, trading speed for accuracy.
Randomized gradient-free methods in convex optimization

This review presents modern gradient-free methods to solve convex optimization problems. By gradient-free methods, we mean those that use only (noisy) realizations of the objective value. We are motivated by various applications where gradient information is prohibitively expensive or even unavailable. We mainly focus on three criteria: oracle complexity, iteration complexity, and the maximum permissible noise level.
Isolation Scheme for Virtual Network Embedding Based on Reinforcement Learning for Smart City Vertical Industries

—Modern ICT infrastructure is built on virtualization technologies, which connect a diverse set of dedicated networks to support a variety of smart city vertical industries (SCVI), such as energy, healthcare, manufacturing, entertainment, and intelligent transportation. The wide range of SCVI use cases require services to operate continuously and reliably. The violation of isolation by a speciﬁc SCVI, that is, a SCVI network must operate independently of other SCVI networks, complicates service assurance for infrastructure providers (InPs) signiﬁcantly. As a result, a solution must be considered from the standpoint of isolation, which raises two issues: ﬁrst, these SCVI networks have diverse resource requirements, and second, they necessitate additional functionality requirements such as isolation. Based on the above two problems faced by SCVI use cases, we propose a virtual network embedding (VNE) algorithm with resource and isolation constraints based on deep reinforcement learning (DRL). The proposed DRL VNE algorithm can automatically adapt to changing dynamics and outperforms existing three state-of-the-art solutions by 12.9%, 19.0% and 4% in terms of the acceptance rate, the long-term average revenue, and long-term average revenue to cost ratio.
A hydrogen peroxide electrochemical sensor based on silver nanoparticles decorated three-dimensional graphene

A facile strategy has been developed to synthesize sliver nanoparticles (Ag NPs) decorated three-dimensional graphene (3DG) through hydrothermal process. The AgNPs-3DG composites are directly fabricated into a free standing sensing electrode for electrochemical detection of hydrogen peroxide (H2O2) in phosphate buffered solutions. Various techniques equipments including scanning electron microscopy, X-ray diffraction, and Raman spectroscopy are used to characterize the morphology and structure of the as-prepared composite. The electrochemical experiments reveal the AgNPs-3DG based biosensor exhibits fast amperometric sensing, low detection limitation, wide linear responding range, and perfect selectivity for non-enzyme H2O2 detection, indicating the well synergistic effect of Ag NPs high electrocatalytic activity and 3DG high conductivity and large surface area.
Look Around and Refer: 2D Synthetic Semantics Knowledge Distillation for 3D Visual Grounding

The 3D visual grounding task has been explored with visual and language streams comprehending referential language to identify target objects in 3D scenes. However, most existing methods devote the visual stream to capturing the 3D visual clues using off-the-shelf point clouds encoders. The main question we address in this paper is"can we consolidate the 3D visual stream by 2D clues synthesized from point clouds and efficiently utilize them in training and testing? ". The main idea is to assist the 3D encoder by incorporating rich 2D object representations without requiring extra 2D inputs. To this end, we leverage 2D clues, synthetically generated from 3D point clouds, and empirically show their aptitude to boost the quality of the learned visual representations. We validate our approach through comprehensive experiments on Nr3D, Sr3D, and ScanRefer datasets and show consistent performance gains compared to existing methods. Our proposed module, dubbed as Look Around and Refer (LAR), significantly outperforms the state-of-the-art 3D visual grounding techniques on three benchmarks, i.e., Nr3D, Sr3D, and ScanRefer. The code is available at https://eslambakr.github. io/LAR.github. io/.
Numerical Investigations of the Roles of Radiative and Evaporative Feedbacks in Stratocumulus Entrainment and Breakup

Abstract When the surface buoyancy flux is small and the shear is weak, turbulence circulations within a stratus-topped boundary layer are driven by two buoyancy-generating processes at cloud top: radiative cooling and evaporative cooling. These two processes respond very differently to entrainment, however. When the entrainment rate increases, the effectiveness of radiative cooling in driving circulations decreases (a negative feedback) but the effectiveness of evaporative cooling can increase (a positive feedback). The roles of these two competing feedbacks in determining the entrainment rate, and hence in determining cloud breakup, are examined in this paper through large eddy simulations. Three stratus cases (with a small surface buoyancy flux) are simulated: one is stable with respect to the Lilly–Randall–Deardorff cloud-top entrainment instability criterion, and the other two are unstable. Only one of the two cloud decks in the unstable regime dissipates totally; the other remains nearly solid. A me.
Mask the Correct Tokens: An Embarrassingly Simple Approach for Error Correction

Text error correction aims to correct the errors in text sequences such as those typed by humans or generated by speech recognition models.Previous error correction methods usually take the source (incorrect) sentence as encoder input and generate the target (correct) sentence through the decoder. Since the error rate of the incorrect sentence is usually low (e.g., 10%), the correction model can only learn to correct on limited error tokens but trivially copy on most tokens (correct tokens), which harms the effective training of error correction. In this paper, we argue that the correct tokens should be better utilized to facilitate effective training and then propose a simple yet effective masking strategy to achieve this goal.Specifically, we randomly mask out a part of the correct tokens in the source sentence and let the model learn to not only correct the original error tokens but also predict the masked tokens based on their context information. Our method enjoys several advantages: 1) it alleviates trivial copy; 2) it leverages effective training signals from correct tokens; 3) it is a plug-and-play module and can be applied to different models and tasks. Experiments on spelling error correction and speech recognition error correction on Mandarin datasets and grammar error correction on English datasets with both autoregressive and non-autoregressive generation models show that our method improves the correctionaccuracy consistently.
Detection of a bright burst from the repeating fast radio burst 20201124A at 2 GHz

We present a detection of a bright burst from the fast radio burst (FRB) 20201124A, which is one of the most active repeating FRBs, based on S-band observations with the 64 m radio telescope at the Usuda Deep Space Center/JAXA. This is the first FRB observed by using a Japanese facility. Our detection at 2 GHz in 2022 February is the highest frequency for this FRB and the fluence of >189 Jy ms is one of the brightest bursts from this FRB source. We place an upper limit on the spectral index α = −2.14 from the detection of the S band and non-detection of the X band at the same time. We compare the event rate of the detected burst with those from previous research and suggest that the power law of the luminosity function might be broken at lower fluence and the fluences of bright FRBs are distributed up to over 2 GHz with the power law against frequency. In addition, we show that the energy density of the burst detected in this work is comparable to the bright population of one-off FRBs. We propose that repeating FRBs can be as bright as one-off FRBs and only their brightest bursts might be detected, so some repeating FRBs intrinsically might have been classified as one-off FRBs.
FedGS: Federated Graph-based Sampling with Arbitrary Client Availability

While federated learning has shown strong results in opti- mizing a machine learning model without direct access to the original data, its performance may be hindered by in- termittent client availability which slows down the conver- gence and biases the final learned model. There are significant challenges to achieve both stable and bias-free training un- der arbitrary client availability. To address these challenges, we propose a framework named Federated Graph-based Sam- pling (FEDGS), to stabilize the global model update and mitigate the long-term bias given arbitrary client availabil- ity simultaneously. First, we model the data correlations of clients with a Data-Distribution-Dependency Graph (3DG) that helps keep the sampled clients data apart from each other, which is theoretically shown to improve the approximation to the optimal model update. Second, constrained by the far- distance in data distribution of the sampled clients, we fur- ther minimize the variance of the numbers of times that the clients are sampled, to mitigate long-term bias. To validate the effectiveness of FEDGS, we conduct experiments on three datasets under a comprehensive set of seven client availability modes. Our experimental results confirm FEDGS’s advantage in both enabling a fair client-sampling scheme and improving the model performance under arbitrary client availability. Our code is available at https://github.com/WwZzz/FedGS.
SCOOP: Self-Supervised Correspondence and Optimization-Based Scene Flow

Scene flow estimation is a long-standing problem in computer vision, where the goal is to find the 3D motion of a scene from its consecutive observations. Recently, there have been efforts to compute the scene flow from 3D point clouds. A common approach is to train a regression model that consumes source and target point clouds and outputs the per-point translation vector. An alternative is to learn point matches between the point clouds concurrently with regressing a refinement of the initial correspondence flow. In both cases, the learning task is very challenging since the flow regression is done in the free 3D space, and a typical solution is to resort to a large annotated synthetic dataset. We introduce SCOOP, a new method for scene flow estimation that can be learned on a small amount of data without employing ground-truth flow supervision. In contrast to previous work, we train a pure correspondence model focused on learning point feature representation and initialize the flow as the difference between a source point and its softly corresponding target point. Then, in the run-time phase, we directly optimize a flow refinement component with a self-supervised objective, which leads to a coherent and accurate flow field between the point clouds. Experiments on widespread datasets demonstrate the performance gains achieved by our method compared to existing leading techniques while using a fraction of the training data. Our code is publicly available11https://github.com/itailang/SCOOP .
TRANSIENT ANALYSIS OF THE TOWNSEND DISCHARGE

The transient growth of currents in a one-dimensional Townsend gas discharge system under uniform d-c field conditions is examined. It is shown that a general solution for arbitrary external conditions can be given in terms of a''unit pulse'' solution. Two cases are examined in detail; in one case the discharge is initiated by a single pulse from the cathode, in the second case the discharge develops under constant external illumination of the cathode. Expressions are derived which permit reasonably rapid calculation of quantities pertinent to the transient characterization of the discharge. For the two cases considered in detail, numerical illustrations are given and comparison is made with the results of other investigators. (auth.)
Boundary Adversarial Examples Against Adversarial Overfitting

Standard adversarial training approaches suffer from robust overﬁtting where the robust accuracy decreases when models are adversarially trained for too long. The origin of this problem is still unclear and conﬂicting explanations have been reported, i.e., memorization effects induced by large loss data or because of small loss data and growing differences in loss distribution of training samples as the adversarial training progresses. Consequently, several mitigation approaches including early stopping, temporal ensembling and weight perturbations on small loss data have been proposed to mitigate the effect of robust overﬁtting. However, a side effect of these strategies is a larger reduction in clean accuracy compared to standard adversarial training. In this paper, we investigate if these mitigation approaches are complimentary to each other in improving adversarial training performance. We further propose the use of helper adversarial examples that can be obtained with minimal cost in the adversarial example generation, and show how they increase the clean accuracy in the existing approaches without compromising the robust accuracy.
Multi-Task Learning of Object State Changes from Uncurated Videos

We aim to learn to temporally localize object state changes and the corresponding state-modifying actions by observing people interacting with objects in long uncurated web videos. We introduce three principal contributions. First, we explore alternative multi-task network architectures and identify a model that enables efficient joint learning of multiple object states and actions such as pouring water and pouring coffee. Second, we design a multi-task self-supervised learning procedure that exploits different types of constraints between objects and state-modifying actions enabling end-to-end training of a model for temporal localization of object states and actions in videos from only noisy video-level supervision. Third, we report results on the large-scale ChangeIt and COIN datasets containing tens of thousands of long (un)curated web videos depicting various interactions such as hole drilling, cream whisking, or paper plane folding. We show that our multi-task model achieves a relative improvement of 40% over the prior single-task methods and significantly outperforms both image-based and video-based zero-shot models for this problem. We also test our method on long egocentric videos of the EPIC-KITCHENS and the Ego4D datasets in a zero-shot setup demonstrating the robustness of our learned model.