Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Keller-Segel models interacting with a stochastically forced incompressible viscous flow in $\mathbb{R}^2$

This paper considers the Keller-Segel model coupled to stochastic Navier-Stokes equations (KS-SNS, for short), which describes the dynamics of oxygen and bacteria densities evolving within a stochastically forced incompressible viscous flow. Our main results establish the existence and uniqueness results of global solutions (strong in probabilistic sense and weak in PDE's sense) to the KS-SNS system in $\mathbb{R}^2$. A novel approximate KS-SNS system with proper regularization and cut-off operators in $H^s(\mathbb{R}^2)$ is introduced, and the existence of approximate solution is proved by some a priori uniform bounds and a careful analysis on the approximation scheme. Under different assumptions, two types of entropy-energy functional inequality that seems to be new in their forms for the regularized KS-SNS system are derived, which together with the Prohorov's theorem and Jakubowski-Skorokhod theorem enable us to prove that the sequence of approximate solutions converges to a global martingale weak solution to the KS-SNS system. Moreover, when $\chi(\cdot)\equiv \textrm{const. }>0$, we prove that the solution is pathwise unique, and hence by the Yamada-Wantanabe theorem that the KS-SNS system admits a unique global pathwise weak solution.
Thermal stability of amorphous carbon films grown by pulsed laser deposition

The thermal stability in vacuum of amorphous tetrahedrally coordinated carbon (a‐tC) films grown on Si has been assessed by in situ Raman spectroscopy. Films were grown in vacuum on room‐temperature substrates using laser fluences of 12, 22, and 45 J/cm2 and in a background gas of either hydrogen or nitrogen using a laser fluence of 45 J/cm2. The films grown in vacuum at high fluence (≳20J/cm2) show little change in the a‐tC Raman spectra with temperature up to 800 °C. Above this temperature the films convert to glassy carbon (nanocrystalline graphite). Samples grown in vacuum at lower fluence or in a background gas (H2 or N2) at high fluence are not nearly as stable. For all samples, the Raman signal from the Si substrate (observed through the a‐tC film) decreases in intensity with annealing temperature indicating that the transparency of the a‐tC films is decreasing with temperature. These changes in transparency begin at much lower temperatures (∼200 °C) than the changes in the a‐tC Raman band shape an.
AsPOS: Assamese Part of Speech Tagger using Deep Learning Approach

Part of Speech (POS) tagging is crucial to Natural Language Processing (NLP). It is a well-studied topic in several resource-rich languages. However, the development of computational linguistic resources is still in its infancy despite the existence of numerous languages that are historically and literary rich. Assamese, an Indian scheduled language, spoken by more than 25 million people, falls under this category. In this paper, we present a Deep Learning (DL)-based POS tagger for Assamese. The development process is divided into two stages. In the first phase, several pretrained word embeddings are employed to train several tagging models. This allows us to evaluate the performance of the word embeddings in the POS tagging task. The top-performing model from the first phase is employed to annotate another set of new sentences. In the second phase, the model is trained further using the fresh dataset. Finally, we attain a tagging accuracy of 86.52 in F1 score. The model may serve as a baseline for further study on DL-based Assamese POS tagging.
On the Probability of Necessity and Sufficiency of Explaining Graph Neural Networks: A Lower Bound Optimization Approach

The explainability of Graph Neural Networks (GNNs) is critical to various GNN applications, yet it remains a significant challenge. A convincing explanation should be both necessary and sufficient simultaneously. However, existing GNN explaining approaches focus on only one of the two aspects, necessity or sufficiency, or a heuristic trade-off between the two. Theoretically, the Probability of Necessity and Sufficiency (PNS) holds the potential to identify the most necessary and sufficient explanation since it can mathematically quantify the necessity and sufficiency of an explanation. Nevertheless, the difficulty of obtaining PNS due to non-monotonicity and the challenge of counterfactual estimation limit its wide use. To address the non-identifiability of PNS, we resort to a lower bound of PNS that can be optimized via counterfactual estimation, and propose a framework of Necessary and Sufficient Explanation for GNN (NSEG) via optimizing that lower bound. Specifically, we depict the GNN as a structural causal model (SCM), and estimate the probability of counterfactual via the intervention under the SCM. Additionally, we leverage continuous masks with a sampling strategy to optimize the lower bound to enhance the scalability. Empirical results demonstrate that NSEG outperforms state-of-the-art methods, consistently generating the most necessary and sufficient explanations.
From Approximations to Exact Relations for Characteristic Impedances

Approximations for the characteristic impedance of a special two conductor stripline and for the general function K/K' are derived by a transformation method recently described in the literature; the first, second, and third approximations having a greatest relative error of the order of 10/sup -3/, 10/sup -6/, and 10/sup -12/, respectively. They can be introduced into an algorithm which is based on elementary conformal mappings, and thus further approximations with rapidly vanishing errors can be derived. The results agree with those for the function K/K' obtained by elliptic integral theory. Obviously no such theory is needed to calculate characteristic impedances or the function K/K' with arbitrary accuracy. The advantages of the new method are illustrated for shielded coupled-strip transmission lines, for which an extended diagram with extreme parameter values has been worked out.
Evidence of High-latitude Emission in the Prompt Phase of GRBs: How Far from the Central Engine are the GRBs Produced?

One of the difficulties in nailing down the physical mechanism of gamma-ray bursts (GRBs) comes from the fact that there has been no clear observational evidence on how far from the central engine the prompt gamma rays of GRBs are emitted. Here we present a simple study addressing this question by making use of the “high-latitude emission” (HLE). We show that our detailed numerical modeling exhibits a clear signature of HLE in the decaying phase of “broad pulses” of GRBs. We show that the HLE can emerge as a prominent spectral break in F ν spectra and dominate the peak of ν F ν spectra even while the “line-of-sight emission” (LoSE) is still ongoing. This finding provides a new view of HLE emergence since it has been believed so far that the HLE can show up and dominate the spectra only after the LoSE is turned off. We remark, however, that this “HLE break” can be hidden in some broad pulses, depending on the proximity between the peak energies of the LoSE and the HLE. Therefore, this new picture of HLE emergence explains both the detection and nondetection of HLE signature in observations of broad pulses. Also, we present three examples of Fermi Gamma-ray Burst Monitor GRBs with broad pulses that exhibit the HLE signature. We show that their gamma-ray-emitting region should be located at ∼1016 cm from the central engine, which places a constraint on the GRB models.
Understanding Zero-Shot Adversarial Robustness for Large-Scale Models

Pretrained large-scale vision-language models like CLIP have exhibited strong generalization over unseen tasks. Yet imperceptible adversarial perturbations can significantly reduce CLIP's performance on new tasks. In this work, we identify and explore the problem of \emph{adapting large-scale models for zero-shot adversarial robustness}. We first identify two key factors during model adaption -- training losses and adaptation methods -- that affect the model's zero-shot adversarial robustness. We then propose a text-guided contrastive adversarial training loss, which aligns the text embeddings and the adversarial visual features with contrastive learning on a small set of training data. We apply this training loss to two adaption methods, model finetuning and visual prompt tuning. We find that visual prompt tuning is more effective in the absence of texts, while finetuning wins in the existence of text guidance. Overall, our approach significantly improves the zero-shot adversarial robustness over CLIP, seeing an average improvement of over 31 points over ImageNet and 15 zero-shot datasets. We hope this work can shed light on understanding the zero-shot adversarial robustness of large-scale models.
A Generic Reinforced Explainable Framework with Knowledge Graph for Session-based Recommendation

Session-based recommendation (SR) has gained increasing attention in recent years. Quite a great amount of studies have been devoted to designing complex algorithms to improve recommendation performance, where deep learning methods account for the majority. However, most of these methods are black-box ones and ignore to provide moderate explanations to facilitate users’ understanding, which thus might lead to lowered user satisfaction and reduced system revenues. Therefore, in our study, we propose a generic Reinforced Explainable framework with Knowledge graph for Session-based recommendation (i.e., REKS), which strives to improve the existing black-box SR models (denoted as non-explainable ones) with Markov decision process. In particular, we construct a knowledge graph with session behaviors and treat SR models as part of the policy network of Markov decision process. Based on our particularly designed state vector, reward strategy, and loss function, the reinforcement learning (RL)-based framework not only achieves improved recommendation accuracy, but also provides appropriate explanations at the same time. Finally, we instantiate the REKS in five representative, state-of-the-art SR models (i.e., GRU4REC, NARM, SR-GNN, GCSAN, BERT4REC), whereby extensive experiments towards these methods on four datasets demonstrate the effectiveness of our framework on both recommendation and explanation tasks.
Fundamentals of Perpetual Futures

Perpetual futures -- swap contracts that never expire -- are the most popular derivative traded in cryptocurrency markets, with more than \$100 billion traded daily. Perpetuals provide investors with leveraged exposure to cryptocurrencies, which does not require rollover or direct cryptocurrency holding. To keep the gap between perpetual futures and spot prices small, long position holders periodically pay short position holders a funding rate proportional to this gap. The funding rate incentivizes trades that tend to narrow the futures-spot gap. But unlike fixed-maturity futures, perpetuals are not guaranteed to converge to the spot price of their underlying asset at any time, and familiar no-arbitrage prices for perpetuals are not available, as the contracts have no expiry date to enforce arbitrage. Here, using a weaker notion of random-maturity arbitrage, we derive no-arbitrage prices for perpetual futures in frictionless markets and no-arbitrage bounds for markets with trading costs. These no-arbitrage prices provide a valuable benchmark for perpetual futures and simultaneously prescribe a strategy to exploit divergence from these fundamental values. Empirically, we find that deviations of crypto perpetual futures from no-arbitrage prices are considerably larger than those documented in traditional currency markets. These deviations comove across cryptocurrencies and diminish over time as crypto markets develop and become more efficient. A simple trading strategy generates large Sharpe ratios even for investors paying the highest trading costs on Binance, which is currently the largest crypto exchange by volume.
Error-Aware B-PINNs: Improving Uncertainty Quantification in Bayesian Physics-Informed Neural Networks

Physics-Informed Neural Networks (PINNs) are gaining popularity as a method for solving differential equations. While being more feasible in some contexts than the classical numerical techniques, PINNs still lack credibility. A remedy for that can be found in Uncertainty Quantification (UQ) which is just beginning to emerge in the context of PINNs. Assessing how well the trained PINN complies with imposed differential equation is the key to tackling uncertainty, yet there is lack of comprehensive methodology for this task. We propose a framework for UQ in Bayesian PINNs (B-PINNs) that incorporates the discrepancy between the B-PINN solution and the unknown true solution. We exploit recent results on error bounds for PINNs on linear dynamical systems and demonstrate the predictive uncertainty on a class of linear ODEs.
EgoLoc: Revisiting 3D Object Localization from Egocentric Videos with Visual Queries

With the recent advances in video and 3D understanding, novel 4D spatio-temporal methods fusing both concepts have emerged. Towards this direction, the Ego4D Episodic Memory Benchmark proposed a task for Visual Queries with 3D Localization (VQ3D). Given an egocentric video clip and an image crop depicting a query object, the goal is to localize the 3D position of the center of that query object with respect to the camera pose of a query frame. Current methods tackle the problem of VQ3D by unprojecting the 2D localization results of the sibling task Visual Queries with 2D Localization (VQ2D) into 3D predictions. Yet, we point out that the low number of camera poses caused by camera re-localization from previous VQ3D methods severally hinders their overall success rate. In this work, we formalize a pipeline (we dub EgoLoc) that better entangles 3D multiview geometry with 2D object retrieval from egocentric videos. Our approach involves estimating more robust camera poses and aggregating multi-view 3D displacements by leveraging the 2D detection confidence, which enhances the success rate of object queries and leads to a significant improvement in the VQ3D baseline performance. Specifically, our approach achieves an overall success rate of up to 87.12%, which sets a new state-of-the-art result in the VQ3D task1. We provide a comprehensive empirical analysis of the VQ3D task and existing solutions, and highlight the remaining challenges in VQ3D. The code is available at https://github.com/Wayne-Mai/EgoLoc.
Robust Estimation of the non-Gaussian Dimension in Structural Linear Models

Statistical identification of possibly non-fundamental SVARMA models requires structural errors: (i) to be an i.i. d process, (ii) to be mutually independent across components, and (iii) each of them must be non-Gaussian distributed. Hence, provided the first two requisites, it is crucial to evaluate the non-Gaussian identification condition. We address this problem by relating the non-Gaussian dimension of structural errors vector to the rank of a matrix built from the higher-order spectrum of reduced-form errors. This makes our proposal robust to the roots location of the lag polynomials, and generalizes the current procedures designed for the restricted case of a causal structural VAR model. Simulation exercises show that our procedure satisfactorily estimates the number of non-Gaussian components.
A Data-driven, Physics-based Transport Model of Solar Energetic Particles Accelerated by Coronal Mass Ejection Shocks Propagating through the Solar Coronal and Heliospheric Magnetic Fields

In an effort to develop computational tools for predicting radiation hazards from solar energetic particles (SEPs), we have created a data-driven physics-based particle transport model to calculate the injection, acceleration, and propagation of SEPs from coronal mass ejection (CME) shocks traversing through the solar corona and interplanetary magnetic fields. The model runs on an input of corona and heliospheric plasma and magnetic field configuration from a magnetohydrodynamic model driven by solar photospheric magnetic field measurements superposed with observed CME shocks determined from coronagraph images. SEP source particles are injected at the shock using the result of diffusive shock acceleration formulation from a characteristic obliquity-dependent injection from a heated solar wind thermal tail population. With several advanced computation techniques involving stochastic simulation and integration, the model obtains the particle intensity at any location in interplanetary space through the rigorous solution to the time-dependent 5D focus transport equation in the phase space that includes perpendicular diffusion. We apply the model to the 2011 November 3 CME event. The calculation results reproduce multispacecraft SEP observations at Earth and STEREO-B reasonably well without normalization of particle flux. The observations at STEREO-A can be reproduced by rescaling particle energy or modified energy dependence of particle diffusion coefficients. This circumsolar SEP event seen by spacecraft at Earth, STEREO-A, and STEREO-B at widely separated longitudes can be explained by diffusive shock acceleration by a single CME shock with a moderate speed.
Design-time Fashion Popularity Forecasting in VR Environments

Being able to forecast the popularity of new garment designs is very important in an industry as fast paced as fashion, both in terms of profitability and reducing the problem of unsold inventory. Here, we attempt to address this task in order to provide informative forecasts to fashion designers within a virtual reality designer application that will allow them to fine tune their creations based on current consumer preferences within an interactive and immersive environment. To achieve this we have to deal with the following central challenges: (1) the proposed method should not hinder the creative process and thus it has to rely only on the garment's visual characteristics, (2) the new garment lacks historical data from which to extrapolate their future popularity and (3) fashion trends in general are highly dynamical. To this end, we develop a computer vision pipeline fine tuned on fashion imagery in order to extract relevant visual features along with the category and attributes of the garment. We propose a hierarchical label sharing (HLS) pipeline for automatically capturing hierarchical relations among fashion categories and attributes. Moreover, we propose MuQAR, a Multimodal Quasi-AutoRegressive neural network that forecasts the popularity of new garments by combining their visual features and categorical features while an autoregressive neural network is modelling the popularity time series of the garment's category and attributes. Both the proposed HLS and MuQAR prove capable of surpassing the current state-of-the-art in key benchmark datasets, DeepFashion for image classification and VISUELLE for new garment sales forecasting.
VINet: Lightweight, Scalable, and Heterogeneous Cooperative Perception for 3D Object Detection

Utilizing the latest advances in Artificial Intelligence (AI), the computer vision community is now witnessing an unprecedented evolution in all kinds of perception tasks, particularly in object detection. Based on multiple spatially separated perception nodes, Cooperative Perception (CP) has emerged to significantly advance the perception of automated driving. However, current cooperative object detection methods mainly focus on ego-vehicle efficiency without considering the practical issues of system-wide costs. In this paper, we introduce VINet, a unified deep learning-based CP network for scalable, lightweight, and heterogeneous cooperative 3D object detection. VINet is the first CP method designed from the standpoint of large-scale system-level implementation and can be divided into three main phases: 1) Global Pre-Processing and Lightweight Feature Extraction which prepare the data into global style and extract features for cooperation in a lightweight manner; 2) Two-Stream Fusion which fuses the features from scalable and heterogeneous perception nodes; and 3) Central Feature Backbone and 3D Detection Head which further process the fused features and generate cooperative detection results. An open-source data experimental platform is designed and developed for CP dataset acquisition and model evaluation. The experimental analysis shows that VINet can reduce 84% system-level computational cost and 94% system-level communication cost while improving the 3D detection accuracy.
MANTa: Efficient Gradient-Based Tokenization for Robust End-to-End Language Modeling

Static subword tokenization algorithms have been an essential component of recent works on language modeling. However, their static nature results in important flaws that degrade the models' downstream performance and robustness. In this work, we propose MANTa, a Module for Adaptive Neural TokenizAtion. MANTa is a differentiable tokenizer trained end-to-end with the language model. The resulting system offers a trade-off between the expressiveness of byte-level models and the speed of models trained using subword tokenization. In addition, our tokenizer is highly explainable since it produces an explicit segmentation of sequences into blocks. We evaluate our pre-trained model on several English datasets from different domains as well as on synthetic noise. We find that MANTa improves robustness to character perturbations and out-of-domain data. We then show that MANTa performs comparably to other models on the general-domain GLUE benchmark. Finally, we show that it is considerably faster than strictly byte-level models.
Resilient Terrain Navigation with a 5 DOF Metal Detector Drone

Micro aerial vehicles (MAVs) hold the potential for performing autonomous and contactless land surveys for the detection of landmines and explosive remnants of war (ERW). Metal detectors are the standard detection tool but must be operated close to and parallel to the terrain. A successful combination of MAVs with metal detectors has not been presented yet, as it requires advanced flight capabilities. To this end, we present an autonomous system to survey challenging undulated terrain using a metal detector mounted on a 5 degrees of freedom (DOF) MAV. Based on an online estimate of the terrain, our receding-horizon planner efficiently covers the area, aligning the detector to the surface while considering the kinematic and visibility constraints of the platform. As the survey requires resilient and accurate localization in diverse terrain, we also propose a factor graph-based online fusion of GNSS, IMU, and LiDAR measurements. We validate the robustness of the solution to individual sensor degeneracy by flying under the canopy of trees and over featureless fields. A simulated ablation study shows that the proposed planner reduces coverage duration and improves trajectory smoothness. Real-world flight experiments showcase autonomous mapping of buried metallic objects in undulated and obstructed terrain.
LGRASS: Linear Graph Spectral Sparsification for Final Task of the 3rd ACM-China International Parallel Computing Challenge

This paper presents our solution for optimization task of the 3rd ACM-China IPCC. By the complexity analysis, we identified three time-consuming subroutines of original algorithm: marking edges, computing pseudo inverse and sorting edges. These subroutines becomes the main performance bottleneck owing to their super-linear time complexity. To address this, we proposed LGRASS, a linear graph spectral sparsification algorithm to run in strictly linear time. LGRASS takes advantage of spanning tree properties and efficient algorithms to optimize bottleneck subroutines. Furthermore, we crafted a parallel processing scheme for LGRASS to make full use of multi-processor hardware. Experiment shows that our proposed method fulfils the task in dozens of milliseconds on official test cases and keep its linearity as graph size scales up on random test cases.