Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimating the Evolution of Sparks in the Partially Screened Gap of Pulsars from Subpulse Drifting

A novel scheme has been developed to show that the observed phase behavior associated with subpulse drifting from two pulsars, J1034−3224 and J1720−2933, can be used to obtain the magnetic field configuration in the partially screened gap (PSG). The outflowing plasma along the open magnetic field line region of pulsars is generated as a result of spark discharges in an inner acceleration region (IAR) above the polar cap. The IAR has been modeled as a PSG with a steady supply of positively charged ions emitted from the heated polar cap surface dominated by strong non-dipolar magnetic fields. In a PSG the sparks are tightly packed and constrained to be present along the polar cap boundary. The sparks lag behind the rotation of the star during their lifetimes. As a result, the sparking pattern evolves along two different directions in a clockwise and counterclockwise manner around a stationary central spark and can be associated with the observed phenomenon of subpulse drifting. PSR J1034−3224 has four prominent components and exhibits bi-drifting where alternate components show the opposite sense of drifting, while PSR J1720−2933 has a single component profile and shows systematically coherent drift bands. We show that the differences in their drifting behavior can be directly linked to different natures of the non-dipolar surface magnetic field configurations.
Error Analysis of Physics-Informed Neural Networks for Approximating Dynamic PDEs of Second Order in Time

We consider the approximation of a class of dynamic partial differential equations (PDE) of second order in time by the physics-informed neural network (PINN) approach, and provide an error analysis of PINN for the wave equation, the Sine-Gordon equation and the linear elastodynamic equation. Our analyses show that, with feed-forward neural networks having two hidden layers and the $\tanh$ activation function, the PINN approximation errors for the solution field, its time derivative and its gradient field can be effectively bounded by the training loss and the number of training data points (quadrature points). Our analyses further suggest new forms for the training loss function, which contain certain residuals that are crucial to the error estimate but would be absent from the canonical PINN loss formulation. Adopting these new forms for the loss function leads to a variant PINN algorithm. We present ample numerical experiments with the new PINN algorithm for the wave equation, the Sine-Gordon equation and the linear elastodynamic equation, which show that the method can capture the solution well.
Machine learning-informed structuro-elastoplasticity predicts ductility of disordered solids

All solids yield under sufficiently high mechanical loads. Below yield, the mechanical responses of all disordered solids are nearly alike, but above yield every different disordered solid responds in its own way. Brittle systems can shatter without warning, like ordinary window glass, or exhibit strain localization prior to fracture, like metallic or polymeric glasses. Ductile systems, e.g. foams like shaving cream or emulsions like mayonnaise, can flow indefinitely with no strain localization. While there are empirical strategies for tuning the degree of strain localization, there is no framework that explains their effectiveness or limitations. We show that Structuro-Elastoplastic (StEP) models provide microscopic understanding of how strain localization depends on the interplay of structure, plasticity and elasticity.
Scalable bayesian bi-level variable selection in generalized linear models

Motivated by a real-world application in cardiology, we develop an algorithm to perform Bayesian bi-level variable selection in a generalized linear model, for datasets that may be large both in terms of the number of individuals and the number of predictors. Our algorithm relies on the waste-free SMC Sequential Monte Carlo methodology of Dau and Chopin (2022), a new proposal mechanism to deal with the constraints specific to bi-level selection (which forbid to select an individual predictor if its group is not selected), and the ALA (approximate Laplace approximation) approach of Rossell et al. (2021). We show in our numerical study that the algorithm may offer reliable performance on large datasets within a few minutes, on both simulated data and real data related to the aforementioned cardiology application.
A multi-class large margin classifier

Currently there are two approaches for a multi-class support vector classifier (SVC). One is to construct and combine several binary classifiers while the other is to directly consider all classes of data in one optimization formulation. For a K-class problem (K>2), the first approach has to construct at least K classifiers, and the second approach has to solve a much larger optimization problem proportional to K by the algorithms developed so far. In this paper, following the second approach, we present a novel multi-class large margin classifier (MLMC). This new machine can solve K-class problems in one optimization formulation without increasing the size of the quadratic programming (QP) problem proportional to K. This property allows us to construct just one classifier with as few variables in the QP problem as possible to classify multi-class data, and we can gain the advantage of speed from it especially when K is large. Our experiments indicate that MLMC almost works as well as (sometimes better than) many other multi-class SVCs for some benchmark data classification problems, and obtains a reasonable performance in face recognition application on the AR face database.
Un experimento de demostraci\'on formal de un teorema de nivel intermedio en \'algebra (Formalizing the proof of an intermediate-level algebra theorem -- An experiment)

Proof assistants are computer softwares that allow us to write mathematical proofs so as to assess their correctness. In November 2021, I started the project of checking the simplicity of the alternating groups within the Lean theorem prover and its mathlib library. This text aims at reviewing this experiment. -- (French) Les assistants de preuves sont des logiciels qui permettent de r\'ediger des d\'emonstrations math\'ematiques et d'en garantir leur correction. En novembre 2021, j'ai d\'ebut\'e un projet de v\'erification de la simplicit\'e des groupes altern\'es au sein de l'assistant de preuve Lean, et de sa librairie mathlib. Ce texte est un essai de compte rendu de cette exp\'erience. -- Published version in Spanish
Soil Moisture Active/Passive (SMAP) Mission concept

Soil Moisture Active/Passive (SMAP) Mission is one of the first satellites being developed by NASA in response to the National Research Council's Decadal Survey. SMAP will make global measurements of the moisture present at Earth's land surface and will distinguish frozen from thawed land surfaces. Direct observations of soil moisture and freeze/thaw state from space will allow better estimates of water and energy transfers between Earth's surface and atmosphere, which are primary driving factors for weather and climate. Soil moisture measurements are also of great importance in assessing flooding potential and as input to flood prediction models. Conversely, observations of widespread low soil moisture levels can provide early warning of drought conditions, reduced water supply and crop loss. SMAP observations can help mitigate these natural hazards, resulting in potentially great economic and social benefits. SMAP freeze/thaw timing observations will also reduce a major uncertainty in quantifying the global carbon balance and will help resolve the problem of the missing carbon sink. The SMAP mission concept would utilize L-band radar and radiometry. These instruments will share a rotating 6-meter mesh antenna to provide high-resolution and high-accuracy global maps of soil moisture and freeze/thaw state every two to three days.
Disturbance Injection Under Partial Automation: Robust Imitation Learning for Long-Horizon Tasks

Partial Automation (PA) with intelligent support systems has been introduced in industrial machinery and advanced automobiles to reduce the burden of long hours of human operation. Under PA, operators perform manual operations (providing actions) and operations that switch to automatic/manual mode (mode-switching). Since PA reduces the total duration of manual operation, these two action and mode-switching operations can be replicated by imitation learning with high sample efficiency. To this end, this letter proposes Disturbance Injection under Partial Automation (DIPA) as a novel imitation learning framework. In DIPA, mode and actions (in the manual mode) are assumed to be observables in each state and are used to learn both action and mode-switching policies. The above learning is robustified by injecting disturbances into the operator's actions to optimize the disturbance's level for minimizing the covariate shift under PA. We experimentally validated the effectiveness of our method for long-horizon tasks in two simulations and a real robot environment and confirmed that our method outperformed the previous methods and reduced the demonstration burden.
The Complexity of Why-Provenance for Datalog Queries

Explaining why a database query result is obtained is an essential task towards the goal of Explainable AI, especially nowadays where expressive database query languages such as Datalog play a critical role in the development of ontology-based applications. A standard way of explaining a query result is the so-called why-provenance, which essentially provides information about the witnesses to a query result in the form of subsets of the input database that are sufficient to derive that result. To our surprise, despite the fact that the notion of why-provenance for Datalog queries has been around for decades and intensively studied, its computational complexity remains unexplored. The goal of this work is to fill this apparent gap in the why-provenance literature. Towards this end, we pinpoint the data complexity of why-provenance for Datalog queries and key subclasses thereof. The takeaway of our work is that why-provenance for recursive queries, even if the recursion is limited to be linear, is an intractable problem, whereas for non-recursive queries is highly tractable. Having said that, we experimentally confirm, by exploiting SAT solvers, that making why-provenance for (recursive) Datalog queries work in practice is not an unrealistic goal.
Sixth problem of the millennium: Navier-Stokes equations, existence and smoothness

This paper presents the main results concerning solubility of the basic initial-boundary value problem and the Cauchy problem for the three-dimensional non-stationary Navier-Stokes equations, together with a list of what to prove in order to solve the sixth problem of the seven problems of the millennium proposed on the Internet at the site http://claymath.org/.
Multi-view Feature Extraction based on Triple Contrastive Heads

Multi-view feature extraction is an efficient approach for alleviating the issue of dimensionality in highdimensional multi-view data. Contrastive learning (CL), which is a popular self-supervised learning method, has recently attracted considerable attention. In this study, we propose a novel multi-view feature extraction method based on triple contrastive heads, which combines the sample-, recovery- , and feature-level contrastive losses to extract the sufficient yet minimal subspace discriminative information in compliance with information bottleneck principle. In MFETCH, we construct the feature-level contrastive loss, which removes the redundent information in the consistency information to achieve the minimality of the subspace discriminative information. Moreover, the recovery-level contrastive loss is also constructed in MFETCH, which captures the view-specific discriminative information to achieve the sufficiency of the subspace discriminative information.The numerical experiments demonstrate that the proposed method offers a strong advantage for multi-view feature extraction.
Stabilization of Online Linear Matrix Inequality based Model Predictive Control of Cart in Inverted Pendulum System

The Model Predictive Control (MPC) schemes are employed in process industries, mainly because the constraints in the system are handled more effectively. The online Linear Matrix Inequality (LMI) based MPC is a recent development in the synthesis approach of model predictive controllers that uses the state space model of the system for predicting the future state and the control input. This method involves updating the weights (decision variables) and finding the optimal cost during each sampling instant. Stabilization of a system by a controller is very important as this directly affects the safety of a system. If the system is not stabilized by the controller, the plant might become unstable and pose a threat for the safe operation of the process. Hence, the stabilization of a plant plays a critical role in the process industry. As the optimum conditions are arrived at each instant by this controller, the system is stabilized over a wide operating range and is thus more secure. The inverted pendulum system which has the pendulum mounted over the moving cart is a standard control problem. In this analysis, the existing LMI based MPC and the online LMI based MPC methods are employed for stabilizing the cart in the inverted pendulum system and their performance under the same initial conditions are evaluated and compared. Keywords—Inverted Pendulum, Linear Matrix Inequality, Model Predictive Control, Stabilization.
Interpretable histopathology-based prediction of disease relevant features in Inflammatory Bowel Disease biopsies using weakly-supervised deep learning

Crohn's Disease (CD) and Ulcerative Colitis (UC) are the two main Inflammatory Bowel Disease (IBD) types. We developed deep learning models to identify histological disease features for both CD and UC using only endoscopic labels. We explored fine-tuning and end-to-end training of two state-of-the-art self-supervised models for predicting three different endoscopic categories (i) CD vs UC (AUC=0.87), (ii) normal vs lesional (AUC=0.81), (iii) low vs high disease severity score (AUC=0.80). We produced visual attention maps to interpret what the models learned and validated them with the support of a pathologist, where we observed a strong association between the models' predictions and histopathological inflammatory features of the disease. Additionally, we identified several cases where the model incorrectly predicted normal samples as lesional but were correct on the microscopic level when reviewed by the pathologist. This tendency of histological presentation to be more severe than endoscopic presentation was previously published in the literature. In parallel, we utilised a model trained on the Colon Nuclei Identification and Counting (CoNIC) dataset to predict and explore 6 cell populations. We observed correlation between areas enriched with the predicted immune cells in biopsies and the pathologist's feedback on the attention maps. Finally, we identified several cell level features indicative of disease severity in CD and UC. These models can enhance our understanding about the pathology behind IBD and can shape our strategies for patient stratification in clinical trials.
MEDIMP: Medical Images and Prompts for renal transplant representation learning

Renal transplantation emerges as the most eﬀective solution for end-stage renal disease. Occurring from complex causes, a substantial risk of transplant chronic dysfunction persists and may lead to graft loss. Medical imaging plays a substantial role in renal transplant monitoring in clinical practice. However, graft supervision is multi-disciplinary, notably joining nephrology, urology, and radiology, while identifying robust biomarkers from such high-dimensional and complex data for prognosis is challenging. In this work, taking inspiration from the recent success of Large Language Models (LLMs), we propose MEDIMP – Medical Images and Prompts – a model to learn meaningful multi-modal representations of renal transplant Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE MRI) by incorporating structural clinicobiological data after translating them into text prompts. MEDIMP is based on contrastive learning from joint text-image paired embeddings to perform this challenging task. Moreover, we propose a framework that generates medical prompts using automatic textual data augmentations from LLMs. Our goal is to learn meaningful manifolds of renal transplant DCE MRI, interesting for the prognosis of the transplant or patient status (2, 3, and 4 years after the transplant), fully exploiting the available multi-modal data in the most eﬃcient way. Extensive experiments and comparisons with other renal transplant representation learning methods with limited data prove the eﬀectiveness of MEDIMP in a relevant clinical setting, giving new directions toward medical prompts. Our code is available at https://github.com/leomlck/MEDIMP .
Rigidity-Aware Detection for 6D Object Pose Estimation

Most recent 6D object pose estimation methods first use object detection to obtain 2D bounding boxes before actually regressing the pose. However, the general object detection methods they use are ill-suited to handle cluttered scenes, thus producing poor initialization to the subsequent pose network. To address this, we propose a rigidity-aware detection method exploiting the fact that, in 6D pose estimation, the target objects are rigid. This lets us introduce an approach to sampling positive object regions from the entire visible object area during training, instead of naively drawing samples from the bounding box center where the object might be occluded. As such, every visible object part can contribute to the final bounding box prediction, yielding better detection robustness. Key to the success of our approach is a visibility map, which we propose to build using a minimum barrier distance between every pixel in the bounding box and the box boundary. Our results on seven challenging 6D pose estimation datasets evidence that our method outperforms general detection frameworks by a large margin. Furthermore, combined with a pose regression network, we obtain state-of-the-art pose estimation results on the challenging BOP benchmark.
LocalEyenet: Deep Attention framework for Localization of Eyes

Development of human machine interface has become a necessity for modern day machines to catalyze more autonomy and more efficiency. Gaze driven human intervention is an effective and convenient option for creating an interface to alleviate human errors. Facial landmark detection is very crucial for designing a robust gaze detection system. Regression based methods capacitate good spatial localization of the landmarks corresponding to different parts of the faces. But there are still scope of improvements which have been addressed by incorporating attention. In this paper, we have proposed a deep coarse-to-fine architecture called LocalEyenet for localization of only the eye regions that can be trained end-to-end. The model architecture, build on stacked hourglass backbone, learns the self-attention in feature maps which aids in preserving global as well as local spatial dependencies in face image. We have incorporated deep layer aggregation in each hourglass to minimize the loss of attention over the depth of architecture. Our model shows good generalization ability in cross-dataset evaluation and in real-time localization of eyes.
Practice of the Conformer Enhanced Audio-Visual Hubert on Mandarin and English

Considering the bimodal nature of human speech perception, lips, and teeth movement has a pivotal role in automatic speech recognition. Benefiting from the correlated and noise-invariant visual information, audio-visual recognition systems enhance robustness in multiple scenarios. In previous work, audio-visual HuBERT appears to be the finest practice incorporating modality knowledge. This paper outlines a mixed methodology, named conformer enhanced AV-HuBERT, boosting the AV-HuBERT system’s performance a step further. Compared with baseline AV-HuBERT, our method in the one-phase evaluation of clean and noisy conditions achieves 7% and 16% relative WER reduction on the English AVSR benchmark dataset LRS3. Furthermore, we establish a novel 1000h Mandarin AVSR dataset CSTS. On top of the baseline AV-HuBERT, we exceed the WeNet ASR system by 14% and 18% relatively on MISP and CMLR by pre-training with this dataset. The conformer-enhanced AV-HuBERT we proposed brings 7% on MISP and 6% CER reduction on CMLR, compared with the baseline AV-HuBERT system.
Can We Identify Stance Without Target Arguments? A Study for Rumour Stance Classification

Considering a conversation thread, rumour stance classification aims to identify the opinion (e.g. agree or disagree) of replies towards a target (rumour story). Although the target is expected to be an essential component in traditional stance classification, we show that rumour stance classification datasets contain a considerable amount of real-world data whose stance could be naturally inferred directly from the replies, contributing to the strong performance of the supervised models without awareness of the target. We find that current target-aware models underperform in cases where the context of the target is crucial. Finally, we propose a simple yet effective framework to enhance reasoning with the targets, achieving state-of-the-art performance on two benchmark datasets.
Inhomogeneous Galactic Chemical Evolution: Modelling Ultra-Faint Dwarf Galaxies of the Large Magellanic Cloud

Ultra-faint dwarf galaxies are among the oldest and most metal-poor galaxies in the cosmos, observed to contain no gas and a high dark matter mass fraction. Understanding the chemical abundance dispersion in such extreme environments could shed light on the very first generations of stars. We present a novel inhomogeneous chemical evolution model, i-GEtool, that we apply to two ultra-faint dwarf galaxies, Carina II and Reticulum II, both satellites of the Large Magellanic Cloud. Our model is based on the Monte Carlo sampling of the initial mass function as star formation proceeds in different gas cells of the galaxy volume. We account for the chemical enrichment of Supernova bubbles as they spread in the interstellar medium, causing dispersion in the elemental abundances. We recreate the abundance patterns of α- and odd-Z elements, predicting two sequences in [C/Fe] and [N/Fe] at all metallicities. Our models underestimate [C/Fe] and [Ti/Fe] because of the large uncertainty in the adopted stellar nucleosynthesis yields. We discuss that the observed C and N abundances had likely been affected by internal mixing processes, which changed the initial surface abundances in the red giants. Our Supernova feedback scheme is responsible for driving galactic outflows, which quench the star formation activity at early times. We predict an average outflow mass-loading factor ≈103, which extrapolates towards very low galaxy stellar masses the trend observed at high masses. Finally, by combining our model with the MIST isochrone database, we compare our synthetic colour-magnitude diagrams to observations.