Miracle-dz/UniFAR-FTUs huggingface dataset:
Numerical Study of MHD Natural Convection of Liquid Metal with Wall Effects

Three-dimensional natural convection of liquid metal under a uniform magnetic field in a cubic enclosure is numerically simulated. The opposite sides of the cubic enclosure are isothermal, and the other four walls are adiabatic. The temperature gradient is perpendicular to the gravity vector. Three different magnetic field directions are applied in the computing model, respectively; namely, parallel to the temperature gradient, parallel to the gravity vector, and mutually perpendicular to them. Different wall electrical conductivities are also adopted in the simulation. Based on the Bousssinesq assumption, the numerical simulation of natural convection with Rayleigh number of 105, 25,000, and 1000, Prandtl number (physical property of Pb-17Li under 573 K) of 0.0321, and Hartmann number of 100, 50, and 10 are performed using a projection method to simulate the incompressible liquid metal flow and a consistent and conservative scheme to calculate the electromagnetic force at a nonuniform rectangular collocated grid. The influences of magnetic field directions and wall electrical conductivities on MHD flows are also studied.
Ablation threshold and temperature dependent thermal conductivity of high entropy carbide thin films

High entropy carbides (HECs) are a promising new class of ultra-high temperature ceramics that could provide novel material solutions for leading edges of hypersonic vehicles, which can reach temperatures > 3,500 �C and experience extreme thermal gradients. Although the mechanical and thermal properties of HECs have been studied extensively at room temperature, few works have examined HEC properties at high temperatures or considered these materials� responses to thermal shock. In this work, we measure the thermal conductivity of a five-cation HEC up to 1200 �C. We find that thermal conductivity increases with temperature, consistent with trends demonstrated in single-metal carbides. We also measure thermal conductivity of an HEC deposited with varying CH4 flow rate, and find that although thermal conductivity is reduced when carbon content surpasses stoichiometric concentrations, the films all exhibited the same temperature dependent trends regardless of carbon content. To compare the thermal shock resistance of HECs with a refractory carbide, we conduct pulsed laser ablation measurements to determine the fluence threshold the HECs can withstand before damaging. We find that this metric for the average bond strength trends with the theoretical hardness of the HECs as expected.
Dual Arm Impact-Aware Grasping through Time-Invariant Reference Spreading Control

With the goal of increasing the speed and efficiency in robotic dual arm manipulation, a novel control approach is presented that utilizes intentional simultaneous impacts to rapidly grasp objects. This approach uses the time-invariant reference spreading framework, in which partly-overlapping ante- and post-impact reference vector fields are used. These vector fields are coupled via the impact dynamics in proximity of the expected impact area, minimizing the otherwise large velocity errors after the impact and the corresponding large control efforts. A purely spatial task is introduced to strongly encourage the synchronization of impact times of the two arms. An interim-impact control phase provides robustness in the execution against the inevitable lack of exact impact simultaneity and the corresponding unreliable velocity error. In this interim phase, a position feedback signal is derived from the ante-impact velocity reference, which is used to enforce sustained contact in all contact points without using velocity error feedback. With an eye towards real-life implementation, the approach is formulated using a QP control framework, and is validated using numerical simulations on a realistic robot model with flexible joints and low-level torque control.
Initial Results for Pairwise Causal Discovery Using Quantitative Information Flow

Pairwise Causal Discovery is the task of determining causal, anticausal, confounded or independence relationships from pairs of variables. Over the last few years, this challenging task has promoted not only the discovery of novel machine learning models aimed at solving the task, but also discussions on how learning the causal direction of variables may benefit machine learning overall. In this paper, we show that Quantitative Information Flow (QIF), a measure usually employed for measuring leakages of information from a system to an attacker, shows promising results as features for the task. In particular, experiments with real-world datasets indicate that QIF is statistically tied to the state of the art. Our initial results motivate further inquiries on how QIF relates to causality and what are its limitations.
Active galactic nuclei activity: self-regulation from backflow

We study the internal circulation within the cocoon carved out by the relativistic jet emanating from an active galactic nucleus (AGN) within the interstellar medium (ISM) of its host galaxy. First, we develop a model for the origin of the internal flow, noticing that a significant increase of large-scale velocity circulation within the cocoon arises as significant gradients in the density and entropy are created near the hotspot (a consequence of Crocco's vorticity generation theorem). We find simple and accurate approximate solutions for the large-scale flow, showing that a backflow towards the few inner parsec region develops. We solve the appropriate fluid dynamic equations, and we use these solutions to predict the mass inflow rates towards the central regions. We then perform a series of 2D simulations of the propagation of jets using FLASH 2.5, in order to validate the predictions of our model. In these simulations, we vary the mechanical input power between 10 43 and 10 45 erg s -1 , and assume a Navarro-Frenk-White (NFW) density profile for the dark matter halo, within which an isothermal diffuse ISM is embedded. The backflows which arise supply the central AGN region with very low angular-momentum gas, at average rates of the order of 0.1-0.8 M ⊙ yr -1 , the exact value seen to be strongly dependent on the central ISM density (for fixed input jet power). The time-scales of these inflows are apparently weakly dependent on the jet/ISM parameters, and are of the order of 3-5 x 10 7 yr. We then argue that these backflows could (at least partially) feed the AGN, and provide a self-regulatory mechanism of AGN activity, that is not directly controlled by, but instead controls, the star formation rate within the central circumnuclear disc.
Marginal integration for fully robust causal inference

We consider the problem of inferring the total causal effect of a single variable intervention on a (response) variable of interest. We propose a certain marginal integration regression technique for a very general class of potentially nonlinear structural equation models (SEMs) with known structure, or at least known superset of adjustment variables: we call the procedure S-mint regression. We prove that it achieves the convergence rate as for nonparametric regression: for example, single variable intervention effects can be estimated with convergence rate $n^{-2/5}$ assuming smoothness with twice differentiable functions. Our result can also be seen as a major robustness property with respect to model misspecification which goes much beyond the notion of double robustness. Furthermore, when the structure of the SEM is not known, we can estimate (the equivalence class of) the directed acyclic graph corresponding to the SEM, and then proceed by using S-mint based on these estimates. We empirically compare the S-mint regression method with more classical approaches and argue that the former is indeed more robust, more reliable and substantially simpler.
CLeBPI: Contrastive Learning for Bug Priority Inference

Automated bug priority inference can reduce the time overhead of bug triagers for priority assignments, improving the efficiency of software maintenance. Currently, there are two orthogonal lines for this task, i.e., traditional machine learning based (TML-based) and neural network based (NN-based) approaches. Although these approaches achieve competitive performance, our observation finds that existing approaches face the following two issues: 1) TML-based approaches require much manual feature engineering and cannot learn the semantic information of bug reports; 2) Both TML-based and NN-based approaches cannot effectively address the label imbalance problem because they are difficult to distinguish the semantic difference between bug reports with different priorities. In this paper, we propose CLeBPI (Contrastive Learning for Bug Priority Inference), which leverages pre-trained language model and contrastive learning to tackle the above-mentioned two issues. Specifically, CLeBPI is first pre-trained on a large-scale bug report corpus in a self-supervised way, thus it can automatically learn contextual representations of bug reports without manual feature engineering. Afterward, it is further pre-trained by a contrastive learning objective, which enables it to distinguish semantic differences between bug reports, learning more precise contextual representations for each bug report. When finishing pre-training, we can connect a classification layer to CLeBPI and fine-tune it for bug priority inference in a supervised way. To verify the effectiveness of CLeBPI, we choose four baseline approaches and conduct comparison experiments on a public dataset. The experimental results show that CLeBPI outperforms all baseline approaches by 23.86%-77.80% in terms of weighted average F1-score, showing its effectiveness.
SimpleMind adds thinking to deep neural networks

Deep neural networks (DNNs) detect patterns in data and have shown versatility and strong performance in many computer vision applications. However, DNNs alone are susceptible to obvious mistakes that violate simple, common sense concepts and are limited in their ability to use explicit knowledge to guide their search and decision making. While overall DNN performance metrics may be good, these obvious errors, coupled with a lack of explainability, have prevented widespread adoption for crucial tasks such as medical image analysis. The purpose of this paper is to introduce SimpleMind, an open-source software framework for Cognitive AI focused on medical image understanding. It allows creation of a knowledge base that describes expected characteristics and relationships between image objects in an intuitive human-readable form. The SimpleMind framework brings thinking to DNNs by: (1) providing methods for reasoning with the knowledge base about image content, such as spatial inferencing and conditional reasoning to check DNN outputs; (2) applying process knowledge, in the form of general-purpose software agents, that are chained together to accomplish image preprocessing, DNN prediction, and result post-processing, and (3) performing automatic co-optimization of all knowledge base parameters to adapt agents to specific problems. SimpleMind enables reasoning on multiple detected objects to ensure consistency, providing cross checking between DNN outputs. This machine reasoning improves the reliability and trustworthiness of DNNs through an interpretable model and explainable decisions. Example applications are provided that demonstrate how SimpleMind supports and improves deep neural networks by embedding them within a Cognitive AI framework.
Au–ZnO: A tunable localized surface plasmonic nanocomposite

In this letter, we report the thermal processing controlled tunability of localized surface plasmon resonance (LSPR) of Au nanoparticles embedded in ZnO matrix. Au–ZnO nanocomposite films were prepared by atom beam cosputtering and were annealed from 200to600°C in Ar. A regular redshift ∼110nm (from 505to615nm) in LSPR peak with increase in annealing temperature up to 600°C is observed. Transmission electron microscopy results confirm the formation of Au nanoparticles supported by ZnO nanorods at annealing temperature of 600°C. The Au–ZnO nanocomposite exhibits significant enhancement in the Raman signal for C70 molecules.
Focus! Relevant and Sufficient Context Selection for News Image Captioning

News Image Captioning requires describing an image by leveraging additional context from a news article. Previous works only coarsely leverage the article to extract the necessary context, which makes it challenging for models to identify relevant events and named entities. In our paper, we first demonstrate that by combining more fine-grained context that captures the key named entities (obtained via an oracle) and the global context that summarizes the news, we can dramatically improve the model's ability to generate accurate news captions. This begs the question, how to automatically extract such key entities from an image? We propose to use the pre-trained vision and language retrieval model CLIP to localize the visually grounded entities in the news article and then capture the non-visual entities via an open relation extraction model. Our experiments demonstrate that by simply selecting a better context from the article, we can significantly improve the performance of existing models and achieve new state-of-the-art performance on multiple benchmarks.
Ripple: Concept-Based Interpretation for Raw Time Series Models in Education

Time series is the most prevalent form of input data for educational prediction tasks. The vast majority of research using time series data focuses on hand-crafted features, designed by experts for predictive performance and interpretability. However, extracting these features is labor-intensive for humans and computers. In this paper, we propose an approach that utilizes irregular multivariate time series modeling with graph neural networks to achieve comparable or better accuracy with raw time series clickstreams in comparison to hand-crafted features. Furthermore, we extend concept activation vectors for interpretability in raw time series models. We analyze these advances in the education domain, addressing the task of early student performance prediction for downstream targeted interventions and instructional support. Our experimental analysis on 23 MOOCs with millions of combined interactions over six behavioral dimensions show that models designed with our approach can (i) beat state-of-the-art educational time series baselines with no feature extraction and (ii) provide interpretable insights for personalized interventions. Source code: https://github.com/epfl-ml4ed/ripple/.
Applications of Lattice Gauge Equivariant Neural Networks

The introduction of relevant physical information into neural network architectures has become a widely used and successful strategy for improving their performance. In lattice gauge theories, such information can be identified with gauge symmetries, which are incorporated into the network layers of our recently proposed Lattice Gauge Equivariant Convolutional Neural Networks (L-CNNs). L-CNNs can generalize better to differently sized lattices than traditional neural networks and are by construction equivariant under lattice gauge transformations. In these proceedings, we present our progress on possible applications of L-CNNs to Wilson flow or continuous normalizing flow. Our methods are based on neural ordinary differential equations which allow us to modify link configurations in a gauge equivariant manner. For simplicity, we focus on simple toy models to test these ideas in practice.
Model and Data Agreement for Learning with Noisy Labels

Learning with noisy labels is a vital topic for practical deep learning as models should be robust to noisy open-world datasets in the wild. The state-of-the-art noisy label learning approach JoCoR fails when faced with a large ratio of noisy labels. Moreover, selecting small-loss samples can also cause error accumulation as once the noisy samples are mistakenly selected as small-loss samples, they are more likely to be selected again. In this paper, we try to deal with error accumulation in noisy label learning from both model and data perspectives. We introduce mean point ensemble to utilize a more robust loss function and more information from unselected samples to reduce error accumulation from the model perspective. Furthermore, as the flip images have the same semantic meaning as the original images, we select small-loss samples according to the loss values of flip images instead of the original ones to reduce error accumulation from the data perspective. Extensive experiments on CIFAR-10, CIFAR-100, and large-scale Clothing1M show that our method outperforms state-of-the-art noisy label learning methods with different levels of label noise. Our method can also be seamlessly combined with other noisy label learning methods to further improve their performance and generalize well to other tasks. The code is available in https://github.com/zyh-uaiaaaa/MDA-noisy-label-learning.
A Learned Pixel-by-Pixel Lossless Image Compression Method with 59K Parameters and Parallel Decoding

This paper considers lossless image compression and presents a learned compression system that can achieve state-of-the-art lossless compression performance but uses only 59K parameters, which is more than 30x less than other learned systems proposed recently in the literature. The explored system is based on a learned pixel-by-pixel lossless image compression method, where each pixel's probability distribution parameters are obtained by processing the pixel's causal neighborhood (i.e. previously encoded/decoded pixels) with a simple neural network comprising 59K parameters. This causality causes the decoder to operate sequentially, i.e. the neural network has to be evaluated for each pixel sequentially, which increases decoding time significantly with common GPU software and hardware. To reduce the decoding time, parallel decoding algorithms are proposed and implemented. The obtained lossless image compression system is compared to traditional and learned systems in the literature in terms of compression performance, encoding-decoding times and computational complexity.
Rigidity of elliptic genera for non-spin manifolds

We discuss the rigidity of elliptic genera for non-spin manifolds $M$ with $S^1$-action. We show that if the universal covering of $M$ is spin, then the universal elliptic genus of $M$ is rigid. Moreover, we show that there is no condition which only depends on $\pi_2(M)$ that guarantees the rigidity in the case that the universal covering of $M$ is non-spin.
Kauffman bracket intertwiners and the volume conjecture

Bonahon-Wong-Yang introduced a new version of the volume conjecture by using the intertwiners between two isomorphic irreducible representations of the skein algebra. The intertwin- ers are built from surface diﬀeomorphisms; they made the volume conjecture when diﬀeomorphisms are pseudo-Anosov. In this paper we calculated all the intertwiners for the closed torus, producing some interesting results. Moreover we considered the periodic diﬀeomorphisms, and introduced the corresponding conjecture. For some special cases we made precise calculations for intertwiners and proved our conjecture.
Fake detection in imbalance dataset by Semi-supervised learning with GAN

As social media continues to grow rapidly, the prevalence of harassment on these platforms has also increased. This has piqued the interest of researchers in the field of fake detection. Social media data, often forms complex graphs with numerous nodes, posing several challenges. These challenges and limitations include dealing with a significant amount of irrelevant features in matrices and addressing issues such as high data dispersion and an imbalanced class distribution within the dataset. To overcome these challenges and limitations, researchers have employed auto-encoders and a combination of semi-supervised learning with a GAN algorithm, referred to as SGAN. Our proposed method utilizes auto-encoders for feature extraction and incorporates SGAN. By leveraging an unlabeled dataset, the unsupervised layer of SGAN compensates for the limited availability of labeled data, making efficient use of the limited number of labeled instances. Multiple evaluation metrics were employed, including the Confusion Matrix and the ROC curve. The dataset was divided into training and testing sets, with 100 labeled samples for training and 1,000 samples for testing. The novelty of our research lies in applying SGAN to address the issue of imbalanced datasets in fake account detection. By optimizing the use of a smaller number of labeled instances and reducing the need for extensive computational power, our method offers a more efficient solution. Additionally, our study contributes to the field by achieving an 81% accuracy in detecting fake accounts using only 100 labeled samples. This demonstrates the potential of SGAN as a powerful tool for handling minority classes and addressing big data challenges in fake account detection.
Discovery of a New Molecular Bubble–Outflow Structure in the Taurus B18 Cloud

Star formation can produce bubbles and outflows, as a result of stellar feedback. Outflows and bubbles inject momentum and energy into the surrounding interstellar medium, and so are related to the overall energy balance of the molecular cloud. Molecular bubbles can be resolved by higher-resolution radio telescopes to quantify the effect of star formation on molecular clouds. We report here the identification of a new molecular bubble with an outflow, and a Herbig–Haro object, HH 319, located at the bubble center. Multiwavelength data have been utilized to study its spatial structure, energy injection, and dynamical timescale. This bubble has a kinetic energy of 5.8 × 1043 erg within the smallest radius of a bubble in Taurus, 0.077 pc. The bubble formed ∼70,000 yr ago. According to the proper-motion velocities of protostars from Gaia EDR3, the T Tauri binary stars (FY Tau and FZ Tau) at the southwest edge of the bubble may have produced the outflow–bubble structure. This is an unusual new structure found in low- and intermediate-mass star formation regions. Only a bubble in Orion A, driven by V380 Ori, has a similar structure. The bubble–outflow structure provides additional observational evidence for the theory of stellar wind from T Tauri stars. It enhances our understanding of how stellar feedback acts on molecular clouds.
Randomized low-rank approximation for symmetric indefinite matrices

The Nystr\"om method is a popular choice for finding a low-rank approximation to a symmetric positive semi-definite matrix. The method can fail when applied to symmetric indefinite matrices, for which the error can be unboundedly large. In this work, we first identify the main challenges in finding a Nystr\"om approximation to symmetric indefinite matrices. We then prove the existence of a variant that overcomes the instability, and establish relative-error nuclear norm bounds of the resulting approximation that hold when the singular values decay rapidly. The analysis naturally leads to a practical algorithm, whose robustness is illustrated with experiments.