Miracle-dz/UniFAR-FTUs huggingface dataset:
Spatiotemporal Attention-based Semantic Compression for Real-time Video Recognition

This paper studies the computational offloading of video action recognition in edge computing. To achieve effective semantic information extraction and compression, following semantic communication we propose a novel spatiotemporal attention-based autoencoder (STAE) architecture, including a frame attention module and a spatial attention module, to evaluate the importance of frames and pixels in each frame. Additionally, we use entropy encoding to remove statistical redundancy in the compressed data to further reduce communication overhead. At the receiver, we develop a lightweight decoder that leverages a 3D-2D CNN combined architecture to reconstruct missing information by simultaneously learning temporal and spatial information from the received data to improve accuracy. To fasten convergence, we use a step-by-step approach to train the resulting STAE-based vision transformer (ViT_STAE) models. Experimental results show that ViT_STAE can compress the video dataset HMDB51 by l04x with only 5% accuracy loss, outperforming the state-of-the-art baseline DeepISC. The proposed ViT_STAE achieves faster inference and higher accuracy than the DeepISC-based ViT model under time-varying wireless channel, which highlights the effectiveness of STAE in guaranteeing higher accuracy under time constraints.
Chest X-ray Image Classification: A Causal Perspective

The chest X-ray (CXR) is one of the most common and easy-to-get medical tests used to diagnose common diseases of the chest. Recently, many deep learning-based methods have been proposed that are capable of effectively classifying CXRs. Even though these techniques have worked quite well, it is difficult to establish whether what these algorithms actually learn is the cause-and-effect link between diseases and their causes or just how to map labels to photos.In this paper, we propose a causal approach to address the CXR classification problem, which constructs a structural causal model (SCM) and uses the backdoor adjustment to select effective visual information for CXR classification. Specially, we design different probability optimization functions to eliminate the influence of confounders on the learning of real causality. Experimental results demonstrate that our proposed method outperforms the open-source NIH ChestX-ray14 in terms of classification performance.
CopyNE: Better Contextual ASR by Copying Named Entities

Recent years have seen remarkable progress in automatic speech recognition (ASR). However, traditional token-level ASR models have struggled with accurately transcribing entities due to the problem of homophonic and near-homophonic tokens. This paper introduces a novel approach called CopyNE, which uses a span-level copying mechanism to improve ASR in transcribing entities. CopyNE can copy all tokens of an entity at once, effectively avoiding errors caused by homophonic or near-homophonic tokens that occur when predicting multiple tokens separately. Experiments on Aishell and ST-cmds datasets demonstrate that CopyNE achieves significant reductions in character error rate (CER) and named entity CER (NE-CER), especially in entity-rich scenarios. Furthermore, even when compared to the strong Whisper baseline, CopyNE still achieves notable reductions in CER and NE-CER. Qualitative comparisons with previous approaches demonstrate that CopyNE can better handle entities, effectively improving the accuracy of ASR.
Measuring Inductive Biases of In-Context Learning with Underspecified Demonstrations

In-context learning (ICL) is an important paradigm for adapting large language models (LLMs) to new tasks, but the generalization behavior of ICL remains poorly understood. We investigate the inductive biases of ICL from the perspective of feature bias: which feature ICL is more likely to use given a set of underspecified demonstrations in which two features are equally predictive of the labels. First, we characterize the feature biases of GPT-3 models by constructing underspecified demonstrations from a range of NLP datasets and feature combinations. We find that LLMs exhibit clear feature biases—for example, demonstrating a strong bias to predict labels according to sentiment rather than shallow lexical features, like punctuation. Second, we evaluate the effect of different interventions that are designed to impose an inductive bias in favor of a particular feature, such as adding a natural language instruction or using semantically relevant label words. We find that, while many interventions can influence the learner to prefer a particular feature, it can be difficult to overcome strong prior biases. Overall, our results provide a broader picture of the types of features that ICL may be more likely to exploit and how to impose inductive biases that are better aligned with the intended task.
Polarization modes of gravitational waves in generalized Proca theory

In this paper, we study polarization modes of gravitational waves in generalized Proca theory in the homogeneous and isotropic Minkowski background. The results show that the polarizations of gravitational waves depend on the parameter space of this gravity theory and can be divided into quite rich cases by parameters. In some parameter space, it only allows two tensor modes, i.e., the $+$ and $\times$ modes. In some parameter space, besides tensor modes, it also allows one scalar mode, or two vector (vector-$x$ and vector-$y$) modes, or both one scalar mode and two vector modes. The scalar mode is a mixture mode of a breathing mode and a longitudinal mode, or just a pure breathing mode. Interestingly, it is found that the amplitude of the vector modes is related to the speed of the tensor modes. This allows us to give the upper bound of the amplitude of the vector modes by detecting the speed of the tensor modes. Specifically, if the speed of tensor modes is strictly equal to the speed of light, then the amplitude of vector modes is zero.
Bidirectional Transformer Reranker for Grammatical Error Correction

Pre-trained seq2seq models have achieved state-of-the-art results in the grammatical error correction task. However, these models still suffer from a prediction bias due to their unidirectional decoding. Thus, we propose a bidirectional Transformer reranker (BTR), that re-estimates the probability of each candidate sentence generated by the pre-trained seq2seq model. The BTR preserves the seq2seq-style Transformer architecture but utilizes a BERT-style self-attention mechanism in the decoder to compute the probability of each target token by using masked language modeling to capture bidirectional representations from the target context. For guiding the reranking, the BTR adopts negative sampling in the objective function to minimize the unlikelihood. During inference, the BTR gives final results after comparing the reranked top-1 results with the original ones by an acceptance threshold. Experimental results show that, in reranking candidates from a pre-trained seq2seq model, T5-base, the BTR on top of T5-base could yield 65.47 and 71.27 F0.5 scores on the CoNLL-14 and BEA test sets, respectively, and yield 59.52 GLEU score on the JFLEG corpus, with improvements of 0.36, 0.76 and 0.48 points compared with the original T5-base. Furthermore, when reranking candidates from T5-large, the BTR on top of T5-base improved the original T5-large by 0.26 points on the BEA test set.
On the Trade-off of Intra-/Inter-class Diversity for Supervised Pre-training

Pre-training datasets are critical for building state-of-the-art machine learning models, motivating rigorous study on their impact on downstream tasks. In this work, we study the impact of the trade-off between the intra-class diversity (the number of samples per class) and the inter-class diversity (the number of classes) of a supervised pre-training dataset. Empirically, we found that with the size of the pre-training dataset fixed, the best downstream performance comes with a balance on the intra-/inter-class diversity. To understand the underlying mechanism, we show theoretically that the downstream performance depends monotonically on both types of diversity. Notably, our theory reveals that the optimal class-to-sample ratio (#classes / #samples per class) is invariant to the size of the pre-training dataset, which motivates an application of predicting the optimal number of pre-training classes. We demonstrate the effectiveness of this application by an improvement of around 2 points on the downstream tasks when using ImageNet as the pre-training dataset.
Modeling of Thermal Emission from ULX Pulsar Swift J0243.6+6124 with General Relativistic Radiation MHD Simulations

We perform general relativistic radiation magnetohydrodynamics simulations of super-Eddington accretion flows around a neutron star with a dipole magnetic field for modeling the Galactic ultraluminous X-ray source exhibiting X-ray pulsations, Swift J0243.6+6124. Our simulations show the accretion columns near the magnetic poles, the accretion disk outside the magnetosphere, and the outflows from the disk. It is revealed that the effectively optically thick outflows, consistent with the observed thermal emission at ∼107 K, are generated if the mass accretion rate is much higher than the Eddington rate ṀEdd and the magnetospheric radius is smaller than the spherization radius. In order to explain the blackbody radius (∼100–500 km) without contradicting the reported spin period (9.8 s) and spin-up rate ( Ṗ=−2.22×10−8ss−1 ), a mass accretion rate of (200–1200)ṀEdd is required. Since the thermal emission was detected in two observations with Ṗ of −2.22 × 10−8 s s−1 and −1.75 × 10−8 s s−1 but not in another with Ṗ=−6.8×10−9ss−1 , the surface magnetic field strength of the neutron star in Swift J0243.6+6124 is estimated to be between 3 × 1011 G and 4 × 1012 G. From this restricted range of magnetic field strength, the accretion rate would be (200–500)ṀEdd when the thermal emission appears and (60–100)ṀEdd when it is not detected. Our results support the hypothesis that the super-Eddington phase in the 2017–2018 giant outburst of Swift J0243.6+6124 is powered by highly super-Eddington accretion flows onto a magnetized neutron star.
Nondestructive Evaluation of Pesticide Residual in Tomatoes with Near Infrared Diffuse Reflection Spectrum

The NIR spectra of tomato samples from Hubei province were studied in this paper. Vector Normalization (VN) as the optimal pre-processing method was used to optimize the principal component factors of all spectra. With the analysis of principal component, the score vectors were obtained and utilized as input of the BP artificial neural network for pattern recognition to establish the nondestructive evaluation model of organophosphorus pesticides in tomato. The results indicated that when the number of principal components was 3, the best model with its correct discrimination rate of 96% , training error of 0.015 and correlation coefficient of 0.971 was obtained.
PINA: Leveraging Side Information in eXtreme Multi-label Classification via Predicted Instance Neighborhood Aggregation

The eXtreme Multi-label Classification~(XMC) problem seeks to find relevant labels from an exceptionally large label space. Most of the existing XMC learners focus on the extraction of semantic features from input query text. However, conventional XMC studies usually neglect the side information of instances and labels, which can be of use in many real-world applications such as recommendation systems and e-commerce product search. We propose Predicted Instance Neighborhood Aggregation (PINA), a data enhancement method for the general XMC problem that leverages beneficial side information. Unlike most existing XMC frameworks that treat labels and input instances as featureless indicators and independent entries, PINA extracts information from the label metadata and the correlations among training instances. Extensive experimental results demonstrate the consistent gain of PINA on various XMC tasks compared to the state-of-the-art methods: PINA offers a gain in accuracy compared to standard XR-Transformers on five public benchmark datasets. Moreover, PINA achieves a $\sim 5\%$ gain in accuracy on the largest dataset LF-AmazonTitles-1.3M. Our implementation is publicly available.
Screening condition in the core of neutron stars

The screening condition in neutron star core has been formulated as equality of velocities of superconducting protons and the electrons $\mathbf{v}_p=\mathbf{u}_e$ at wavenumbers $q\ll\lambda^{-1}$ ($\lambda$ is the London depth) and has been used to derive the force between the electronic flow past the flux tube, which has astrophysical applications. By calculating the current-current response, I find that $\mathbf{v}_p\neq\mathbf{u}_e$ for $l^{-1}<q\ll\lambda^{-1}$ ($l$ is the electron mean free path) at typical realistic parameters. Therefore, the momentum exchange between the electrons and the flux tubes in the core of neutron stars remains an open question.
Semantic Structure Enhanced Event Causality Identification

Event Causality Identification (ECI) aims to identify causal relations between events in unstructured texts.This is a very challenging task, because causal relations are usually expressed by implicit associations between events.Existing methods usually capture such associations by directly modeling the texts with pre-trained language models, which underestimate two kinds of semantic structures vital to the ECI task, namely, event-centric structure and event-associated structure.The former includes important semantic elements related to the events to describe them more precisely, while the latter contains semantic paths between two events to provide possible supports for ECI. In this paper, we study the implicit associations between events by modeling the above explicit semantic structures, and propose a Semantic Structure Integration model (SemSIn). It utilizes a GNN-based event aggregator to integrate the event-centric structure information, and employs an LSTM-based path aggregator to capture the event-associated structure information between two events.Experimental results on three widely used datasets show that SemSIn achieves significant improvements over baseline methods.
Long-lead forecasts of wintertime air stagnation index in southern China using oceanic memory effects

Stagnant weather condition is one of the major contributors to air pollution as it is favorable for the formation and accumulation of pollutants. To measure the atmosphere's ability to dilute air pollutants, Air Stagnation Index (ASI) has been introduced as an important meteorological index. Therefore, making long-lead ASI forecasts is vital to make plans in advance for air quality management. In this study, we found that autumn Ni\~no indices derived from sea surface temperature (SST) anomalies show a negative correlation with wintertime ASI in southern China, offering prospects for a prewinter forecast. We developed an LSTM-based model to predict the future wintertime ASI. Results demonstrated that multivariate inputs (past ASI and Ni\~no indices) achieve better forecast performance than univariate input (only past ASI). The model achieves a correlation coefficient of 0.778 between the actual and predicted ASI, exhibiting a high degree of consistency.
Data-Driven Methods Applied to Soft Robot Modeling and Control: A Review

Soft robots show compliance and have infinite degrees of freedom. Thanks to these properties, such robots can be leveraged for surgery, rehabilitation, biomimetics, unstructured environment exploring, and industrial grippers. In this case, they attract scholars from a variety of areas. However, nonlinearity and hysteresis effects also bring a burden to robot modeling. Moreover, following their flexibility and adaptation, soft robot control is more challenging than rigid robot control. In order to model and control soft robots, a large number of data-driven methods are utilized in pairs or separately. This review first briefly introduces two foundations for data-driven approaches, which are physical models and the Jacobian matrix, then summarizes three kinds of data-driven approaches, which are statistical method, neural network, and reinforcement learning. This review compares the modeling and controller features, e.g., model dynamics, data requirement, and target task, within and among these categories. Finally, we summarize the features of each method. A discussion about the advantages and limitations of the existing modeling and control approaches is presented, and we forecast the future of data-driven approaches in soft robots. A website (https://sites.google.com/view/23zcb) is built for this review and will be updated frequently.
Compactification on curved manifolds

The characterization of a m-dimensional internal manifold with metric as having positive, zero or negative curvature is known to be one of the most important aspects of warped compactifications in (4 + m)-dimensional supergravity models and hence that of matter content in an effective four-dimensional theory. In this context, Douglas and Kallosh in arXiv:1001.4008 argued that string compactifications using manifolds whose scalar curvature is everywhere negative must have significant warping or large stringy corrections, or both. Douglas-Kallosh argument may apply to some particular class of flux compactifications with strong constraints on the warp geometry or standard Kaluza-Klein compactifications (with constant warp factor), but perhaps not to a general class of warped solutions in curved manifolds. For clarity, we first present some explicit examples of 4D non-singular de Sitter solutions in ten and eleven dimensions, without source terms (fluxes or objects that violate positivity conditions), which give a finite warped volume and hence a finite four-dimensional effective Planck mass. We then explore the possibility of obtaining de Sitter solutions by introducing p-form gauge fields in a 6-dimensional warped manifold M. We show that four-dimensional de Sitter solutions can exist with almost any choice of internal space curvature, including manifolds whose 6D Ricci scalar curvature is negative.
ChatGPT Is More Likely to Be Perceived as Male Than Female

We investigate how people perceive ChatGPT, and, in particular, how they assign human-like attributes such as gender to the chatbot. Across five pre-registered studies (N = 1,552), we find that people are more likely to perceive ChatGPT to be male than female. Specifically, people perceive male gender identity (1) following demonstrations of ChatGPT's core abilities (e.g., providing information or summarizing text), (2) in the absence of such demonstrations, and (3) across different methods of eliciting perceived gender (using various scales and asking to name ChatGPT). Moreover, we find that this seemingly default perception of ChatGPT as male can reverse when ChatGPT's feminine-coded abilities are highlighted (e.g., providing emotional support for a user).
Uncertainty-based Detection of Adversarial Attacks in Semantic Segmentation

State-of-the-art deep neural networks have proven to be highly powerful in a broad range of tasks, including semantic image segmentation. However, these networks are vulnerable against adversarial attacks, i.e., non-perceptible perturbations added to the input image causing incorrect predictions, which is hazardous in safety-critical applications like automated driving. Adversarial examples and defense strategies are well studied for the image classification task, while there has been limited research in the context of semantic segmentation. First works however show that the segmentation outcome can be severely distorted by adversarial attacks. In this work, we introduce an uncertainty-based approach for the detection of adversarial attacks in semantic segmentation. We observe that uncertainty as for example captured by the entropy of the output distribution behaves differently on clean and perturbed images and leverage this property to distinguish between the two cases. Our method works in a light-weight and post-processing manner, i.e., we do not modify the model or need knowledge of the process used for generating adversarial examples. In a thorough empirical analysis, we demonstrate the ability of our approach to detect perturbed images across multiple types of adversarial attacks.
MHD in a Cylindrical Shearing Box. II. Intermittent Bursts and Substructures in MRI Turbulence

By performing ideal magnetohydrodynamical (MHD) simulations with weak vertical magnetic fields in unstratified cylindrical shearing boxes with modified boundary treatment, we investigate MHD turbulence excited by magnetorotational instability. The cylindrical simulation exhibits extremely large temporal variation in the magnetic activity compared with the simulation in a normal Cartesian shearing box, although the time-averaged field strengths are comparable in the cylindrical and Cartesian setups. Detailed analysis of the terms describing magnetic energy evolution with “triangle diagrams” surprisingly reveals that in the cylindrical simulation the compression of toroidal magnetic field is unexpectedly as important as the winding due to differential rotation in amplifying magnetic fields and triggering intermittent magnetic bursts, which are not seen in the Cartesian simulation. The importance of the compressible amplification is also true for a cylindrical simulation with tiny curvature; the evolution of magnetic fields in the nearly Cartesian shearing box simulation is fundamentally different from that in the exact Cartesian counterpart. The radial gradient of epicyclic frequency, κ, which cannot be considered in the normal Cartesian shearing box model, is the cause of this fundamental difference. An additional consequence of the spatial variation of κ is continuous and ubiquitous formation of narrow high-density (low-density) and weak-field (strong-field) localized structures; seeds of these ring gap structures are created by the compressible effect and subsequently amplified and maintained under the marginally unstable condition regarding “viscous-type” instability.
MFT: Long-Term Tracking of Every Pixel

We propose MFT -- Multi-Flow dense Tracker -- a novel method for dense, pixel-level, long-term tracking. The approach exploits optical flows estimated not only between consecutive frames, but also for pairs of frames at logarithmically spaced intervals. It selects the most reliable sequence of flows on the basis of estimates of its geometric accuracy and the probability of occlusion, both provided by a pre-trained CNN. We show that MFT achieves competitive performance on the TAP-Vid benchmark, outperforming baselines by a significant margin, and tracking densely orders of magnitude faster than the state-of-the-art point-tracking methods. The method is insensitive to medium-length occlusions and it is robustified by estimating flow with respect to the reference frame, which reduces drift.
From hexagonal to rocksalt structure: A computational study of gallium selenide under hydrostatic pressure

This article discusses the pressure-induced structural phase transition and related phonon, electronic and optical properties of hexagonal {\epsilon}-GaSe using first-principles calculations. The study focuses on optimizing geometric and electronic band structures, analyzing the charge density distributions, atomic vibrations, and phonon spectra, and characterizing the optical properties of GaSe under hydrostatic pressure. The work also includes an analysis of the phase transformation mechanism using the solid-state Nudged Elastic Band (SS-NEB) method. This research sheds light on the physics of structural phase transitions in layered materials and offers potential for the development of pressure-manipulated electronics or optoelectronics.