Miracle-dz/UniFAR-FTUs huggingface dataset:
Negotiated Reasoning: On Provably Addressing Relative Over-Generalization

Over-generalization is a thorny issue in cognitive science, where people may become overly cautious due to past experiences. Agents in multi-agent reinforcement learning (MARL) also have been found to suffer relative over-generalization (RO) as people do and stuck to sub-optimal cooperation. Recent methods have shown that assigning reasoning ability to agents can mitigate RO algorithmically and empirically, but there has been a lack of theoretical understanding of RO, let alone designing provably RO-free methods. This paper first proves that RO can be avoided when the MARL method satisfies a consistent reasoning requirement under certain conditions. Then we introduce a novel reasoning framework, called negotiated reasoning, that first builds the connection between reasoning and RO with theoretical justifications. After that, we propose an instantiated algorithm, Stein variational negotiated reasoning (SVNR), which uses Stein variational gradient descent to derive a negotiation policy that provably avoids RO in MARL under maximum entropy policy iteration. The method is further parameterized with neural networks for amortized learning, making computation efficient. Numerical experiments on many RO-challenged environments demonstrate the superiority and efficiency of SVNR compared to state-of-the-art methods in addressing RO.
Magnetic anisotropy of the superconducting transition in superconductor/antiferromagnet heterostructures with spin-orbit coupling

The influence of Rashba spin-orbit coupling (SOC) on superconducting correlations in thin-film superconductor/antiferromagnet (S/AF) structures with compensated interfaces is studied. A unique effect of anisotropic enhancement of proximity-induced triplet correlations by the SOC is predicted. It manifests itself in the anisotropy of the superconducting critical temperature Tc with respect to orientation of the Neel vector relative to the S/AF interface, which is opposite to the behaviour of Tc in superconductor/ferromagnet structures. We show that the anisotropy is controlled by the chemical potential of the superconductor and, therefore, can be adjusted in (quasi)2D structures.
MIMO Detection under Hardware Impairments: Learning with Noisy Labels

This paper considers a data detection problem in multiple-input multiple-output (MIMO) communication systems with hardware impairments. To address challenges posed by nonlinear and unknown distortion in received signals, two learning-based detection methods, referred to as model-driven and data-driven, are presented. The model-driven method employs a generalized Gaussian distortion model to approximate the conditional distribution of the distorted received signal. By using the outputs of coarse data detection as noisy training data, the model-driven method avoids the need for additional training overhead beyond traditional pilot overhead for channel estimation. An expectation-maximization algorithm is devised to accurately learn the parameters of the distortion model from noisy training data. To resolve a model mismatch problem in the model-driven method, the data-driven method employs a deep neural network (DNN) for approximating a-posteriori probabilities for each received signal. This method uses the outputs of the model-driven method as noisy labels and therefore does not require extra training overhead. To avoid the overfitting problem caused by noisy labels, a robust DNN training algorithm is devised, which involves a warm-up period, sample selection, and loss correction. Simulation results demonstrate that the two proposed methods outperform existing solutions with the same overhead under various hardware impairment scenarios.
Fast and Effective GNN Training with Linearized Random Spanning Trees

We present a new effective and scalable framework for training GNNs in node classification tasks, based on the effective resistance, a powerful tool solidly rooted in graph theory. Our approach progressively refines the GNN weights on an extensive sequence of random spanning trees, suitably transformed into path graphs that retain essential topological and node information of the original graph. The sparse nature of these path graphs substantially lightens the computational burden of GNN training. This not only enhances scalability but also effectively addresses common issues like over-squashing, over-smoothing, and performance deterioration caused by overfitting in small training set regimes. We carry out an extensive experimental investigation on a number of real-world graph benchmarks, where we apply our framework to graph convolutional networks, showing simultaneous improvement of both training speed and test accuracy over a wide pool of representative baselines.
ShuttleSet: A Human-Annotated Stroke-Level Singles Dataset for Badminton Tactical Analysis

With the recent progress in sports analytics, deep learning approaches have demonstrated the effectiveness of mining insights into players' tactics for improving performance quality and fan engagement. This is attributed to the availability of public ground-truth datasets. While there are a few available datasets for turn-based sports for action detection, these datasets severely lack structured source data and stroke-level records since these require high-cost labeling efforts from domain experts and are hard to detect using automatic techniques. Consequently, the development of artificial intelligence approaches is significantly hindered when existing models are applied to more challenging structured turn-based sequences. In this paper, we present ShuttleSet, the largest publicly-available badminton singles dataset with annotated stroke-level records. It contains 104 sets, 3,685 rallies, and 36,492 strokes in 44 matches between 2018 and 2021 with 27 top-ranking men's singles and women's singles players. ShuttleSet is manually annotated with a computer-aided labeling tool to increase the labeling efficiency and effectiveness of selecting the shot type with a choice of 18 distinct classes, the corresponding hitting locations, and the locations of both players at each stroke. In the experiments, we provide multiple benchmarks (i.e., stroke influence, stroke forecasting, and movement forecasting) with baselines to illustrate the practicability of using ShuttleSet for turn-based analytics, which is expected to stimulate both academic and sports communities. Over the past two years, a visualization platform has been deployed to illustrate the variability of analysis cases from ShuttleSet for coaches to delve into players' tactical preferences with human-interactive interfaces, which was also used by national badminton teams during multiple international high-ranking matches.
Solitons induced by an in-plane magnetic field in rhombohedral multilayer graphene

We model the influence of an in-plane magnetic field on the orbital motion of electrons in rhombohedral graphene multilayers. For zero field, the low-energy band structure includes a pair of flat bands near zero energy which are localized on the surface layers of a finite thin film. For finite field, we find that the zero-energy bands persist and that level bifurcations occur at energies determined by the component of the in-plane wave vector $q$ that is parallel to the external field. The occurrence of level bifurcations is explained by invoking semiclassical quantization of the zero field Fermi surface of rhombohedral graphite. We find parameter regions with a single isoenergetic contour of Berry phase zero corresponding to a conventional Landau level spectrum and regions with two isoenergetic contours, each of Berry phase $\pi$, corresponding to a Dirac-like spectrum of levels. We write down an analogous one-dimensional tight-binding model and relate the persistence of the zero-energy bands in large magnetic fields to a soliton texture supporting zero-energy states in the Su-Schreiffer-Heeger model. We show that different states contributing to the zero-energy flat bands in rhombohedral graphene multilayers in a large field, as determined by the wave vector $q$, are localized on different bulk layers of the system, not just the surfaces.
Time-Optimal Path Tracking with ISO Safety Guarantees

One way of ensuring operator's safety during human-robot collaboration is through Speed and Separation Monitoring (SSM), as defined in ISO standard ISO/TS 15066. In general, it is impossible to avoid all human-robot collisions: consider for instance the case when the robot does not move at all, a human operator can still collide with it by hitting it of her own voluntary motion. In the SSM framework, it is possible however to minimize harm by requiring this: if a collision ever occurs, then the robot must be in a stationary state (all links have zero velocity) at the time instant of the collision. In this paper, we propose a time-optimal control policy based on Time-Optimal Path Parameterization (TOPP) to guarantee such a behavior. Specifically, we show that: for any robot motion that is strictly faster than the motion recommended by our policy, there exists a human motion that results in a collision with the robot in a non-stationary state. Correlatively, we show, in simulation, that our policy is strictly less conservative than state-of-the-art safe robot control methods. Additionally, we propose a parallelization method to reduce the computation time of our pre-computation phase (down to about 0.5 sec, practically), which enables the whole pipeline (including the pre-computation) to be executed at runtime, nearly in real-time. Finally, we demonstrate the application of our method in a scenario: time-optimal, safe control of a 6-dof industrial robot.
Double SSA spectrum and magnetic field strength of the FSRQ 3C 454.3

We present the results of a radio multifrequency ($\rm 3{-}340~GHz$) study of the blazar 3C 454.3. After subtracting the quiescent spectrum corresponding to optically thin emission, we found two individual synchrotron self-absorption (SSA) features in the wide-band spectrum. The one SSA had a relatively low turnover frequency (νm) in the range of $\rm 3{-}37~GHz$ (lower νm SSA spectrum, LSS), and the other one had a relatively high νm of $\rm 55{-}124~GHz$ (higher νm SSA spectrum, HSS). Using the SSA parameters, we estimated B-field strengths at the surface where optical depth τ = 1. The estimated B-field strengths were $\rm \gt 7$ and $\rm \gt 0.2~mG$ for the LSS and HSS, respectively. The LSS-emitting region was magnetically dominated before the 2014 June γ-ray flare. The quasi-stationary component (C), ∼0.6 mas apart from the 43 -GHz radio core, became brighter than the core with decreasing observing frequency, and we found that component C was related to the LSS. A decrease in jet width was found near component C. As a moving component, K14 approached component C, and the flux density of the component was enhanced while the angular size decreased. The high intrinsic brightness temperature in the fluid frame was obtained as TB, int ≈ (7.0 ± 1.0) × 1011 K from the jet component after the 2015 August γ-ray flare, suggesting that component C is a high-energy emitting region. The observed local minimum of jet width and re-brightening behaviour suggest a possible recollimation shock in component C.
Cooperated Bidding Algorithm for Spectrum Allocation Based on Maximal Independent Set

Graph theory is an effective way to solve the problem of dynamic spectrum allocation in cognitive radio.As the current distributed greedy algorithm based on capacity and the distributed random algorithm based on complexity are inefficient,an improved cooperated bidding algorithm based on maximal independent set is proposed.This mechanism can obtain the utility of primary users easily for the cooperated users in the MIS giving a bid higher than the maximum revenue of the users outside the MIS.Therefore this method can maximize the spectrum utilization including both primary and cognitive users.In addition,it also can resistant user collusion in some extent.
Multi-level Multiple Instance Learning with Transformer for Whole Slide Image Classification

Whole slide image (WSI) refers to a type of high-resolution scanned tissue image, which is extensively employed in computer-assisted diagnosis (CAD). The extremely high resolution and limited availability of region-level annotations make employing deep learning methods for WSI-based digital diagnosis challenging. Recently integrating multiple instance learning (MIL) and Transformer for WSI analysis shows very promising results. However, designing effective Transformers for this weakly-supervised high-resolution image analysis is an underexplored yet important problem. In this paper, we propose a Multi-level MIL (MMIL) scheme by introducing a hierarchical structure to MIL, which enables efficient handling of MIL tasks involving a large number of instances. Based on MMIL, we instantiated MMIL-Transformer, an efficient Transformer model with windowed exact self-attention for large-scale MIL tasks. To validate its effectiveness, we conducted a set of experiments on WSI classification tasks, where MMIL-Transformer demonstrate superior performance compared to existing state-of-the-art methods, i.e., 96.80% test AUC and 97.67% test accuracy on the CAMELYON16 dataset, 99.04% test AUC and 94.37% test accuracy on the TCGA-NSCLC dataset, respectively. All code and pre-trained models are available at: https://github.com/hustvl/MMIL-Transformer
The common fundamental plane of X-ray emissions from pulsars and magnetars in quiescence

Magnetars are a unique class of neutron stars characterized by their incredibly strong magnetic fields. Unlike normal pulsars whose X-ray emission was driven by rotational energy loss, magnetars exhibit distinct X-ray emissions thought to be driven by their strong magnetic fields. Here we present the results of magnetar X-ray spectra analysis in their quiescent state. In the sample studied in this paper, spectra of 17 magnetars can be fitted well with a model consisting of a power-law and a black body component. We found that the luminosity of the power-law component can be described by a function of black-body temperature and emission-region radius. The same relation was seen in pulsars whose X-ray emission mechanism is thought to be different. The fact that these magnetars and pulsars share a common fundamental plane in the space spanned by non-thermal X-ray luminosity, surface temperature and the radius of the thermally emitting region presents both challenges and hints to theoretical models for a complete comprehension of the magnetospheric emissions from these two classes of neutron stars.
Normalization-Equivariant Neural Networks with Application to Image Denoising

In many information processing systems, it may be desirable to ensure that any change of the input, whether by shifting or scaling, results in a corresponding change in the system response. While deep neural networks are gradually replacing all traditional automatic processing methods, they surprisingly do not guarantee such normalization-equivariance (scale + shift) property, which can be detrimental in many applications. To address this issue, we propose a methodology for adapting existing neural networks so that normalization-equivariance holds by design. Our main claim is that not only ordinary convolutional layers, but also all activation functions, including the ReLU (rectified linear unit), which are applied element-wise to the pre-activated neurons, should be completely removed from neural networks and replaced by better conditioned alternatives. To this end, we introduce affine-constrained convolutions and channel-wise sort pooling layers as surrogates and show that these two architectural modifications do preserve normalization-equivariance without loss of performance. Experimental results in image denoising show that normalization-equivariant neural networks, in addition to their better conditioning, also provide much better generalization across noise levels.
ReliableSwap: Boosting General Face Swapping Via Reliable Supervision

Almost all advanced face swapping approaches use reconstruction as the proxy task, i.e., supervision only exists when the target and source belong to the same person. Otherwise, lacking pixel-level supervision, these methods struggle for source identity preservation. This paper proposes to construct reliable supervision, dubbed cycle triplets, which serves as the image-level guidance when the source identity differs from the target one during training. Specifically, we use face reenactment and blending techniques to synthesize the swapped face from real images in advance, where the synthetic face preserves source identity and target attributes. However, there may be some artifacts in such a synthetic face. To avoid the potential artifacts and drive the distribution of the network output close to the natural one, we reversely take synthetic images as input while the real face as reliable supervision during the training stage of face swapping. Besides, we empirically find that the existing methods tend to lose lower-face details like face shape and mouth from the source. This paper additionally designs a FixerNet, providing discriminative embeddings of lower faces as an enhancement. Our face swapping framework, named ReliableSwap, can boost the performance of any existing face swapping network with negligible overhead. Extensive experiments demonstrate the efficacy of our ReliableSwap, especially in identity preservation. The project page is https://reliable-swap.github. io/.
Language-specific Acoustic Boundary Learning for Mandarin-English Code-switching Speech Recognition

Code-switching speech recognition (CSSR) transcribes speech that switches between multiple languages or dialects within a single sentence. The main challenge in this task is that different languages often have similar pronunciations, making it difficult for models to distinguish between them. In this paper, we propose a method for solving the CSSR task from the perspective of language-specific acoustic boundary learning. We introduce language-specific weight estimators (LSWE) to model acoustic boundary learning in different languages separately. Additionally, a non-autoregressive (NAR) decoder and a language change detection (LCD) module are employed to assist in training. Evaluated on the SEAME corpus, our method achieves a state-of-the-art mixed error rate (MER) of 16.29% and 22.81% on the test_man and test_sge sets. We also demonstrate the effectiveness of our method on a 9000-hour in-house meeting code-switching dataset, where our method achieves a relatively 7.9% MER reduction.
Designing Anthropomorphic Soft Hands Through Interaction

Modeling and simulating soft robot hands can aid in design iteration for complex and high degree-of-freedom (DoF) morphologies. This can be further supplemented by iterating on the design based on its performance in real world manipulation tasks. However, iterating in the real world requires an approach that allows us to test new designs quickly at low costs. In this paper, we leverage rapid prototyping of the hand using 3D-printing, and utilize teleoperation to evaluate the hand in real world manipulation tasks. Using this method, we design a 3D-printed 16-DoF dexterous anthropomorphic soft hand (DASH) and iteratively improve its design over five iterations. Rapid prototyping techniques such as 3D-printing allow us to directly evaluate the fabricated hand without modeling it in simulation. We show that the design improves over five design iterations through evaluating the hand's performance in 30 real-world teleoperated manipulation tasks. Testing over 900 demonstrations shows that our final version of DASH can solve 19 of the 30 tasks compared to Allegro, a popular rigid hand in the market, which can only solve 7 tasks. We open-source our CAD models as well as the teleoperated dataset for further study. They are made available on our website https://dash-through-interaction.github. io.
Reconciling Predictive and Statistical Parity: A Causal Approach

Since the rise of fair machine learning as a critical field of inquiry, many different notions on how to quantify and measure discrimination have been proposed in the literature. Some of these notions, however, were shown to be mutually incompatible. Such findings make it appear that numerous different kinds of fairness exist, thereby making a consensus on the appropriate measure of fairness harder to reach, hindering the applications of these tools in practice. In this paper, we investigate one of these key impossibility results that relates the notions of statistical and predictive parity. Specifically, we derive a new causal decomposition formula for the fairness measures associated with predictive parity, and obtain a novel insight into how this criterion is related to statistical parity through the legal doctrines of disparate treatment, disparate impact, and the notion of business necessity. Our results show that through a more careful causal analysis, the notions of statistical and predictive parity are not really mutually exclusive, but complementary and spanning a spectrum of fairness notions through the concept of business necessity. Finally, we demonstrate the importance of our findings on a real-world example.
RDumb: A simple approach that questions our progress in continual test-time adaptation

Test-Time Adaptation (TTA) allows to update pretrained models to changing data distributions at deployment time. While early work tested these algorithms for individual fixed distribution shifts, recent work proposed and applied methods for continual adaptation over long timescales. To examine the reported progress in the field, we propose the Continuously Changing Corruptions (CCC) benchmark to measure asymptotic performance of TTA techniques. We find that eventually all but one state-of-the-art methods collapse and perform worse than a non-adapting model, including models specifically proposed to be robust to performance collapse. In addition, we introduce a simple baseline,"RDumb", that periodically resets the model to its pretrained state. RDumb performs better or on par with the previously proposed state-of-the-art in all considered benchmarks. Our results show that previous TTA approaches are neither effective at regularizing adaptation to avoid collapse nor able to outperform a simplistic resetting strategy.
Room-temperature magnetoelectric effect in lead-free multiferroic $(1-x)$ Ba$_{0.95}$Ca$_{0.05}$Ti$_{0.89}$Sn$_{0.11}$O$_3$-$(x)$CoFe$_2$O$_4$ particulate composites

Multiferroic particulate composites $(1-x)$ Ba$_{0.95}$Ca$_{0.05}$Ti$_{0.89}$Sn$_{0.11}$O$_3$-$(x)$CoFe$_2$O$_4$ with ($x$ = 0.1, 0.2, 0.3, 0.4 and 0.5) have been prepared by mechanical mixing of the calcined and milled individual ferroic phases. X-ray diffraction and Raman spectroscopy analysis confirmed the formation of both perovskite Ba$_{0.95}$Ca$_{0.05}$Ti$_{0.89}$Sn$_{0.11}$O$_3$ (BCTSn) and spinel CoFe$_2$O$_4$ (CFO) phases without the presence of additional phases. The morphological properties of the composites were provided by using Field Emission Scanning Electron Microscopy. The BCTSn-CFO composites exhibit multiferroic behavior at room temperature, as evidenced by ferroelectric and ferromagnetic hysteresis loops. The magnetoelectric (ME) coupling was measured under a magnetic field up to 10 kOe and the maximum ME response found to be 0.1 mV /cm/ Oe for the composition 0.7 BCTSn-0.3 CFO exhibiting a high degree of pseudo-cubicity and large density.