Miracle-dz/UniFAR-FTUs huggingface dataset:
Moment potentials for spectral density functional theory: Exploiting the momentum distribution of the uniform electron gas

In standard Kohn-Sham (KS) density-functional theory (DFT) the valence band satellites in Ni and Pd are missing, the band widths in Ni and Na are too large, and the formation of lower and upper Hubbard bands in SrVO$_3$ is not described. These spectral features may be corrected by constructing the spectral function from the first four spectral moments, which may be obtained numerically efficiently within a \textit{moment-functional based spectral density functional theory} (MFbSDFT). In order to obtain a suitable potential for the second moment, one may use existing models of the uniform electron gas (UEG). However, models for the third moment of the UEG are not yet available. Therefore, we show that reproducing the momentum distribution of the UEG within the two-pole approximation of the spectral function determines the third moment, when the second moment is given. This allows us to find a model for the second and third moment potentials, which reproduces the experimental spectra of Ni, Pd, Na, and SrVO$_3$, and which is consistent with the second moment of the UEG at low density. Additionally, we describe an efficient algorithm to compute the spectral function from the first $2P$ spectral moment matrices, which paves the way to increasing the accuracy of MFbSDFT by using more than 4 spectral moments.
Multiday User Equilibrium with Strategic Commuters

In the era of connected and automated mobility, commuters will possess strong computation power, enabling them to strategically make sequential travel choices over a planning horizon. This paper investigates the multiday traffic patterns that arise from such decision-making behavior. In doing so, we frame the commute problem as a mean-field Markov game and introduce a novel concept of multiday user equilibrium to capture the steady state of commuters' interactions. The proposed model is general and can be tailored to various travel choices such as route or departure time. We explore a range of properties of the multiday user equilibrium under mild conditions. The study reveals the fingerprint of user inertia on network flow patterns, particularly for foresighted commuters, causing between-day variations even at a steady state. Furthermore, our analysis establishes critical connections between the multiday user equilibrium and conventional Wardrop equilibrium.
Bridging Carrollian and celestial holography

Gravity in $4d$ asymptotically flat spacetime constitutes the archetypal example of a gravitational system with leaky boundary conditions. Pursuing our analysis of [1], we argue that the holographic description of such a system requires the coupling of the dual theory living at null infinity to some external sources encoding the radiation reaching the conformal boundary and responsible for the non-conservation of the charges. In particular, we show that the sourced Ward identities of a conformal Carrollian field theory living at null infinity reproduce the BMS flux-balance laws. We also derive the general form of low-point correlation functions for conformal Carrollian field theories and exhibit a new branch of solutions, which is argued to be the relevant one for holographic purposes. We then relate our Carrollian approach to the celestial holography proposal by mapping the Carrollian Ward identities to those constraining celestial operators through a suitable integral transform.
Frequency Regularization for Improving Adversarial Robustness

Deep neural networks are incredibly vulnerable to crafted, human-imperceptible adversarial perturbations. Although adversarial training (AT) has proven to be an effective defense approach, we find that the AT-trained models heavily rely on the input low-frequency content for judgment, accounting for the low standard accuracy. To close the large gap between the standard and robust accuracies during AT, we investigate the frequency difference between clean and adversarial inputs, and propose a frequency regularization (FR) to align the output difference in the spectral domain. Besides, we find Stochastic Weight Averaging (SWA), by smoothing the kernels over epochs, further improves the robustness. Among various defense schemes, our method achieves the strongest robustness against attacks by PGD-20, C\&W and Autoattack, on a WideResNet trained on CIFAR-10 without any extra data.
Milnor Fiber Consistency via Flatness of the Critical Locus

We describe a new algebro-geometric perspective on the study of the Milnor ﬁbration and, as a ﬁrst step toward putting it into practice, prove a powerful criterion for a deformation of a holomorphic function germ to preserve the Milnor ﬁbration in an appropriate sense. As a corollary, we obtain a method of partitioning the space of homogeneous polynomials of a ﬁxed degree into ﬁnitely many locally closed subsets such that the ﬁber diﬀeomorphism type of the Milnor ﬁbration is constant along each subset.
Derived special cycles on Shimura varieties

I employ methods from derived algebraic geometry to give a uniform moduli-theoretic construction of special cycle classes on integral models many Shimura varieties of Hodge type, including unitary, quaternionic, and orthogonal Shimura varieties. All desired properties of these cycles, even for those corresponding to degenerate Fourier coefficients under the Kudla correspondence, follow naturally from the construction. I formulate Kudla's modularity conjectures in this general framework, and give some preliminary evidence towards their validity.
Nucleation of Laboratory Earthquakes: Quantitative Analysis and Scalings

In this study we use the precursory acoustic emission (AE) activity during the nucleation of stick‐slip instability as a proxy to investigate foreshock occurrence prior to natural earthquakes. We report on three stick‐slip experiments performed on cylindrical samples of Indian metagabbro under upper crustal stress conditions (30–60 MPa). AEs were continuously recorded by eight calibrated acoustic sensors during the experiments. Seismological parameters (moment magnitude, corner frequency and stress‐drop) of the detected AEs (−8.8 ≤ Mw ≤ −7) follow the scaling law between moment magnitude and corner frequency that characterizes natural earthquakes. AE activity always increases toward failure and is driven by along fault slip velocity. The stacked AE foreshock sequences follow an inverse Omori type law, with a characteristic Omori time c inversely proportional to normal stress. AEs moment magnitudes increase toward failure, as manifested by a decrease in b‐value from ∼1 to ∼0.5 at the end of the nucleation process. During nucleation, foreshocks migrate toward the mainshock epicenter location, and stabilize at a distance from the latter compatible with the predicted Rate‐and‐State nucleation size. Importantly, the nucleation characteristic timescale also scales inversely with applied normal stress and the expected nucleation size. Finally, we infer that foreshocks are the byproducts of the nucleation phase which is an almost fully aseismic process. Nevertheless, the seismic/aseismic energy release ratio continuously increases during nucleation, highlighting that, the nucleation process starts as a fully aseismic process, and evolves toward a cascading process at the onset of dynamic rupture.
Dual Spacetime Symmetry Breaking down to Einstein Gravity

Einstein action of gravity is obtained from a gauge theory, if our spacetime was once in two folds with a double Lorentz symmetry. After the dual symmetry breaks spontaneously, Lorentz symmetry absorbs gauge symmetry, while the gauge ﬁeld begins to drive the vierbein and the spin connection. This gauge model of gravity has many bosons and fermions with negative norms, which will be undetectable. The consistency of these particles with the Copenhagen interpretation of wave functions, and their relation to the presence of dark matter as well as the absence of antimatter in the universe are discussed.
Doubly Smoothed GDA for Constrained Nonconvex-Nonconcave Minimax Optimization

Nonconvex-nonconcave minimax optimization has received intense attention over the last decade due to its broad applications in machine learning. Unfortunately, most existing algorithms cannot be guaranteed to converge globally and even suffer from limit cycles . To address this issue, we propose a novel single-loop algorithm called doubly smoothed gradient descent ascent method (DSGDA), which naturally balances the primal and dual updates. The proposed DSGDA can get rid of limit cycles in various challenging nonconvex-nonconcave examples in the literature, including “ Forsaken ”, “ Bilinearly-coupled minimax ”, “ Sixth-order polynomial ”, and “ PolarGame ”. We further show that under an one-sided Kurdyka-Łojasiewicz condition with exponent θ ∈ (0 , 1) (resp. convex primal/concave dual function), DSGDA can find a game-stationary point with an iteration complexity of O ( ϵ − 2max { 2 θ, 1 } ) (resp. O ( ϵ − 4 ) ). These match the best results for single-loop algorithms that solve nonconvex-concave or convex-nonconcave minimax problems, or problems satisfying the rather restrictive one-sided Polyak-Łojasiewicz condition. Our work demonstrates, for the first time, the possibility of having a simple and unified single-loop algorithm for solving nonconvex-nonconcave, nonconvex-concave, and convex-nonconcave minimax problems.
Improving Continuous Sign Language Recognition with Consistency Constraints and Signer Removal

Most deep-learning-based continuous sign language recognition (CSLR) models share a similar backbone consisting of a visual module, a sequential module, and an alignment module. However, due to limited training samples, a connectionist temporal classification loss may not train such CSLR backbones sufficiently. In this work, we propose three auxiliary tasks to enhance the CSLR backbones. The first task enhances the visual module, which is sensitive to the insufficient training problem, from the perspective of consistency. Specifically, since the information of sign languages is mainly included in signers' facial expressions and hand movements, a keypoint-guided spatial attention module is developed to enforce the visual module to focus on informative regions, i.e., spatial attention consistency. Second, noticing that both the output features of the visual and sequential modules represent the same sentence, to better exploit the backbone's power, a sentence embedding consistency constraint is imposed between the visual and sequential modules to enhance the representation power of both features. We name the CSLR model trained with the above auxiliary tasks as consistency-enhanced CSLR, which performs well on signer-dependent datasets in which all signers appear during both training and testing. To make it more robust for the signer-independent setting, a signer removal module based on feature disentanglement is further proposed to remove signer information from the backbone. Extensive ablation studies are conducted to validate the effectiveness of these auxiliary tasks. More remarkably, with a transformer-based backbone, our model achieves state-of-the-art or competitive performance on five benchmarks, PHOENIX-2014, PHOENIX-2014-T, PHOENIX-2014-SI, CSL, and CSL-Daily. Code and Models are available at https://github.com/2000ZRL/LCSA_C2SLR_SRM.
Pruning On-the-Fly: A Recoverable Pruning Method without Fine-tuning

Most existing pruning works are resource-intensive, requiring retraining or fine-tuning of the pruned models for accuracy. We propose a retraining-free pruning method based on hyperspherical learning and loss penalty terms. The proposed loss penalty term pushes some of the model weights far from zero, while the rest weight values are pushed near zero and can be safely pruned with no need for retraining and a negligible accuracy drop. In addition, our proposed method can instantly recover the accuracy of a pruned model by replacing the pruned values with their mean value. Our method obtains state-of-the-art results in retraining-free pruning and is evaluated on ResNet-18/50 and MobileNetV2 with ImageNet dataset. One can easily get a 50\% pruned ResNet18 model with a 0.47\% accuracy drop. With fine-tuning, the experiment results show that our method can significantly boost the accuracy of the pruned models compared with existing works. For example, the accuracy of a 70\% pruned (except the first convolutional layer) MobileNetV2 model only drops 3.5\%, much less than the 7\% $\sim$ 10\% accuracy drop with conventional methods.
MRTNet: Multi-Resolution Temporal Network for Video Sentence Grounding

Given an untrimmed video and natural language query, video sentence grounding aims to localize the target temporal moment in the video. Existing methods mainly tackle this task by matching and aligning semantics of the descriptive sentence and video segments on a single temporal resolution, while neglecting the temporal consistency of video content in different resolutions. In this work, we propose a novel multi-resolution temporal video sentence grounding network: MRTNet, which consists of a multi-modal feature encoder, a Multi-Resolution Temporal (MRT) module, and a predictor module. MRT module is an encoder-decoder network, and output features in the decoder part are in conjunction with Transformers to predict the final start and end timestamps. Particularly, our MRT module is hot-pluggable, which means it can be seamlessly incorporated into any anchor-free models. Besides, we utilize a hybrid loss to supervise cross-modal features in MRT module for more accurate grounding in three scales: frame-level, clip-level and sequence-level. Extensive experiments on three prevalent datasets have shown the effectiveness of MRTNet.
Link-Level Simulator for 5G Localization

Channel-state-information-based localization using 5G systems has been a promising way to achieve high-accuracy positioning results compared to previous communication systems. However, current 5G simulation platforms neglect the impact of hardware impairments and RF environment on 5G localization. Therefore, designing a unified and effective link-level simulation platform is very important to evaluate the performance of advanced positioning algorithms. This paper releases a link-level simulator for 5G localization, where all communication modules are closely compatible with the latest new radio physical-layer standards. The released simulator supports the link modeling at sub-6GHz and millimeter-wave frequency bands. Particularly, the critical simulation components that determine the localization perfomance are designed and integrated. Finally, we present an application case to evaluate the simulator, i.e., two-dimensional mobile terminal localization. The numerical results present the performance comparison of localization algorithms in various impairment conditions.
Improved Kernel Alignment Regret Bound for Online Kernel Learning

In this paper, we improve the kernel alignment regret bound for online kernel learning in the regime of the Hinge loss function. Previous algorithm achieves a regret of O((A_TT ln T)^{1/4}) at a computational complexity (space and per-round time) of O((A_TT ln T)^{1/2}), where A_T is called kernel alignment. We propose an algorithm whose regret bound and computational complexity are better than previous results. Our results depend on the decay rate of eigenvalues of the kernel matrix. If the eigenvalues of the kernel matrix decay exponentially, then our algorithm enjoys a regret of O((A_T)^{1/2}) at a computational complexity of O((ln T)^2). Otherwise, our algorithm enjoys a regret of O((A_TT)^{1/4}) at a computational complexity of O((A_TT)^{1/2}). We extend our algorithm to batch learning and obtain a O(T^{-1}(E[A_T])^{1/2}) excess risk bound which improves the previous O(T^{-1/2}) bound.
BD-KD: Balancing the Divergences for Online Knowledge Distillation

Knowledge distillation (KD) has gained a lot of attention in the field of model compression for edge devices thanks to its effectiveness in compressing large powerful networks into smaller lower-capacity models. Online distillation, in which both the teacher and the student are learning collaboratively, has also gained much interest due to its ability to improve on the performance of the networks involved. The Kullback-Leibler (KL) divergence ensures the proper knowledge transfer between the teacher and student. However, most online KD techniques present some bottlenecks under the network capacity gap. By cooperatively and simultaneously training, the models the KL distance becomes incapable of properly minimizing the teacher's and student's distributions. Alongside accuracy, critical edge device applications are in need of well-calibrated compact networks. Confidence calibration provides a sensible way of getting trustworthy predictions. We propose BD-KD: Balancing of Divergences for online Knowledge Distillation. We show that adaptively balancing between the reverse and forward divergences shifts the focus of the training strategy to the compact student network without limiting the teacher network's learning process. We demonstrate that, by performing this balancing design at the level of the student distillation loss, we improve upon both performance accuracy and calibration of the compact student network. We conducted extensive experiments using a variety of network architectures and show improvements on multiple datasets including CIFAR-10, CIFAR-100, Tiny-ImageNet, and ImageNet. We illustrate the effectiveness of our approach through comprehensive comparisons and ablations with current state-of-the-art online and offline KD techniques.
Measuring Price Impact and Information Content of Trades in a Time-Varying Setting

We propose a non-linear observation-driven version of the Hasbrouck (1991) model for dynamically estimating trades' market impact and information content. We find that market impact displays an intraday pattern superimposed with large fluctuations. Some of them are exogenous, and, as an example, we investigate market impact dynamics around FOMC announcements. Contrary to Hasbrouck (1991), we find that the information content of trades depends on the local liquidity level and the recent history of prices and trades. Finally, we use the model to estimate the time-varying permanent impact parameter, which allows performing a dynamic transaction cost analysis.
The Stokes resistance of a spherical cap to translational and rotational motions in a linear shear flow

Brenner's general results for the force and torque experienced by an arbitrary particle simultaneously translating and rotating in a linear shear flow are applied to the spherical cap. The cap's five fundamental resistance tensors are determined and the corresponding tensors for the sphere and circular disk are recovered as special cases. The problem of a freely moving cap in a linear shear is considered and the resulting translational and rotational motions are analysed. The cap's centre of free rotation is found and trajectories of this point are plotted in a few instances. The cap is also found to possess a ‘point of planar motion’ which always moves in a plane perpendicular to the vorticity vector of the undisturbed shear regardless of the initial orientation of the cap. It is shown that the motion of the cap actually serves as a model for the motions of all ‘oblate’ asymmetric bodies of revolution which are moving freely in a linear shear.
LMFLOSS: A Hybrid Loss For Imbalanced Medical Image Classification

Automatic medical image classiﬁcation is a very important ﬁeld where the use of AI has the potential to have a real so-cial impact. However, there are still many challenges that act as obstacles to making practically effective solutions. One of those is the fact that most of the medical imaging datasets have a class imbalance problem. This leads to the fact that ex-isting AI techniques, particularly neural network-based deep-learning methodologies, often perform poorly in such scenar-ios. Thus this makes this area an interesting and active research focus for researchers. In this study, we propose a novel loss function to train neural network models to mitigate this critical issue in this important ﬁeld. Through rigorous experiments on three independently collected datasets of three different medical imaging domains, we empirically show that our proposed loss function consistently performs well with an improvement between 2%-10% macro f1 when compared to the baseline models. We hope that our work will precipitate new research toward a more generalized approach to medical image classiﬁcation 1 .
Real or Fake Text?: Investigating Human Ability to Detect Boundaries Between Human-Written and Machine-Generated Text

As text generated by large language models proliferates, it becomes vital to understand how humans engage with such text, and whether or not they are able to detect when the text they are reading did not originate with a human writer. Prior work on human detection of generated text focuses on the case where an entire passage is either human-written or machine-generated. In this paper, we study a more realistic setting where text begins as human-written and transitions to being generated by state-of-the-art neural language models. We show that, while annotators often struggle at this task, there is substantial variance in annotator skill and that given proper incentives, annotators can improve at this task over time. Furthermore, we conduct a detailed comparison study and analyze how a variety of variables (model size, decoding strategy, fine-tuning, prompt genre, etc.) affect human detection performance. Finally, we collect error annotations from our participants and use them to show that certain textual genres influence models to make different types of errors and that certain sentence-level features correlate highly with annotator selection. We release the RoFT dataset: a collection of over 21,000 human annotations paired with error classifications to encourage future work in human detection and evaluation of generated text.
Data‐driven linear complexity low‐rank approximation of general kernel matrices: A geometric approach

A general, rectangular kernel matrix may be defined as Kij=κ(xi,yj)$$ {K}_{ij}=\kappa \left({x}_i,{y}_j\right) $$ where κ(x,y)$$ \kappa \left(x,y\right) $$ is a kernel function and where X={xi}i=1m$$ X={\left\{{x}_i\right\}}_{i=1}^m $$ and Y={yi}i=1n$$ Y={\left\{{y}_i\right\}}_{i=1}^n $$ are two sets of points. In this paper, we seek a low‐rank approximation to a kernel matrix where the sets of points X$$ X $$ and Y$$ Y $$ are large and are arbitrarily distributed, such as away from each other, “intermingled”, identical, and so forth. Such rectangular kernel matrices may arise, for example, in Gaussian process regression where X$$ X $$ corresponds to the training data and Y$$ Y $$ corresponds to the test data. In this case, the points are often high‐dimensional. Since the point sets are large, we must exploit the fact that the matrix arises from a kernel function, and avoid forming the matrix, and thus ruling out most algebraic techniques. In particular, we seek methods that can scale linearly or nearly linearly with respect to the size of data for a fixed approximation rank. The main idea in this paper is to geometrically select appropriate subsets of points to construct a low rank approximation. An analysis in this paper guides how this selection should be performed.