Miracle-dz/UniFAR-FTUs huggingface dataset:
On the It\^o-Stratonovich Diffusion Limit for the Magnetic Field in a 3D Thin Domain

We introduce a stochastic model for a passive magnetic field in a three dimensional thin domain. The velocity field, white in time and modelling phenomenologically a turbulent fluid, acts on the magnetic field as a transport-stretching noise. We prove, in a quantitative way, that, in the simultaneous scaling limit of the thickness of the thin layer and the separation of scales, the mean on the thin direction of the magnetic field is close to the solution of the equation of the magnetic field with additional dissipation. In certain choice of noises with correlation between their components, without mirror symmetry and with a non zero mean helicity, we identify an alpha-term, in addition to the extra dissipation term. However, it does not produce dynamo; consequently, we extend a no-dynamo theorem to thin layers.
Challenges in computing matrix functions

This manuscript summarizes the outcome of the focus groups at"The f(A)bulous workshop on matrix functions and exponential integrators", held at the Max Planck Institute for Dynamics of Complex Technical Systems in Magdeburg, Germany, on 25-27 September 2023. There were three focus groups in total, each with a different theme: knowledge transfer, high-performance and energy-aware computing, and benchmarking. We collect insights, open issues, and perspectives from each focus group, as well as from general discussions throughout the workshop. Our primary aim is to highlight ripe research directions and continue to build on the momentum from a lively meeting.
Dynamic Transformer Architecture for Continual Learning of Multimodal Tasks

Transformer neural networks are increasingly replacing prior architectures in a wide range of applications in different data modalities. The increasing size and computational demands of fine-tuning large pre-trained transformer neural networks pose significant challenges for the widespread adoption of these models for applications that demand on-edge computing. To tackle this challenge, continual learning (CL) emerges as a solution by facilitating the transfer of knowledge across tasks that arrive sequentially for an autonomously learning agent. However, current CL methods mainly focus on learning tasks that are exclusively vision-based or language-based. We propose a transformer-based CL framework focusing on learning tasks that involve both vision and language, known as Vision-and-Language (VaL) tasks. Due to the success of transformers in other modalities, our architecture has the potential to be used in multimodal learning settings. In our framework, we benefit from introducing extra parameters to a base transformer to specialize the network for each task. As a result, we enable dynamic model expansion to learn several tasks in a sequence. We also use knowledge distillation to benefit from relevant past experiences to learn the current task more efficiently. Our proposed method, Task Attentive Multimodal Continual Learning (TAM-CL), allows for the exchange of information between tasks while mitigating the problem of catastrophic forgetting. Notably, our approach is scalable, incurring minimal memory and time overhead. TAM-CL achieves state-of-the-art (SOTA) performance on challenging multimodal tasks
Towards Global Glacier Mapping with Deep Learning and Open Earth Observation Data

Accurate global glacier mapping is critical for understanding climate change impacts. It is challenged by glacier diversity, difficult-to-classify debris and big data processing. Here we propose Glacier-VisionTransformer-U-Net (GlaViTU), a convolutional-transformer deep learning model, and five strategies for multitemporal global-scale glacier mapping using open satellite imagery. Assessing the spatial, temporal and cross-sensor generalisation shows that our best strategy achieves intersection over union>0.85 on previously unobserved images in most cases, which drops to>0.75 for debris-rich areas such as High-Mountain Asia and increases to>0.90 for regions dominated by clean ice. Additionally, adding synthetic aperture radar data, namely, backscatter and interferometric coherence, increases the accuracy in all regions where available. The calibrated confidence for glacier extents is reported making the predictions more reliable and interpretable. We also release a benchmark dataset that covers 9% of glaciers worldwide. Our results support efforts towards automated multitemporal and global glacier mapping.
Accurate detection of diffusion anisotropy from an individual short particle trajectory

In parallel with advances in microscale imaging techniques, the fields of biology and materials science have focused on precisely extracting particle properties based on their diffusion behavior. Although the majority of real-world particles exhibit anisotropy, their behavior has been studied less than that of isotropic particles. In this study, we introduce a new method for estimating the diffusion coefficients of individual anisotropic particles using short-trajectory data on the basis of a maximum likelihood framework. Traditional estimation techniques often use mean-squared displacement (MSD) values or other statistical measures that inherently remove angular information. Instead, we treated the angle as a latent variable and used belief propagation to estimate it while maximizing the likelihood using the expectation-maximization algorithm. Compared to conventional methods, this approach facilitates better estimation of shorter trajectories and faster rotations, as confirmed by numerical simulations and experimental data involving bacteria and quantum rods. Additionally, we performed an analytical investigation of the limits of detectability of anisotropy and provided guidelines for the experimental design. In addition to serving as a powerful tool for analyzing complex systems, the proposed method will pave the way for applying maximum likelihood methods to more complex diffusion phenomena.
E-EVAL: A Comprehensive Chinese K-12 Education Evaluation Benchmark for Large Language Models

With the accelerating development of Large Language Models (LLMs), many LLMs are beginning to be used in the Chinese K-12 education domain. The integration of LLMs and education is getting closer and closer, however, there is currently no benchmark for evaluating LLMs that focuses on the Chinese K-12 education domain. Therefore, there is an urgent need for a comprehensive natural language processing benchmark to accurately assess the capabilities of various LLMs in the Chinese K-12 education domain. To address this, we introduce the E-EVAL, the first comprehensive evaluation benchmark specifically designed for the Chinese K-12 education field. The E-EVAL consists of 4,351 multiple-choice questions at the primary, middle, and high school levels across a wide range of subjects, including Chinese, English, Politics, History, Ethics, Physics, Chemistry, Mathematics, and Geography. We conducted a comprehensive evaluation of E-EVAL on advanced LLMs, including both English-dominant and Chinese-dominant models. Findings show that Chinese-dominant models perform well compared to English-dominant models, with many scoring even above the GPT 4.0. However, almost all models perform poorly in complex subjects such as mathematics. We also found that most Chinese-dominant LLMs did not achieve higher scores at the primary school level compared to the middle school level. We observe that the mastery of higher-order knowledge by the model does not necessarily imply the mastery of lower-order knowledge as well. Additionally, the experimental results indicate that the Chain of Thought (CoT) technique is effective only for the challenging science subjects, while Few-shot prompting is more beneficial for liberal arts subjects. With E-EVAL, we aim to analyze the strengths and limitations of LLMs in educational applications, and to contribute to the progress and development of Chinese K-12 education and LLMs.
Together We Go Further: LLMs and IDE Static Analysis for Extract Method Refactoring

Excessively long methods that encapsulate multiple responsibilities within a single method are challenging to comprehend, debug, reuse, and maintain. The solution to this problem, a hallmark refactoring called Extract Method, consists of two phases: (i) choosing the statements to extract and (ii) applying the mechanics to perform this refactoring. While the application part has been a staple feature of all modern IDEs, they leave it up to developers to choose the statements to extract. Choosing which statements are profitable to extract has been the subject of many research tools that employ hard-coded rules to optimize software quality metrics. Despite steady improvements, these tools often fail to generate refactorings that align with developers' preferences and acceptance criteria. In this paper, we introduce EM-Assist, a tool that augments the refactoring capabilities of IDEs with the power of LLMs to perform Extract Method refactoring. We empirically evaluated EM-Assist on a diverse, publicly available corpus that other researchers used in the past. The results show that EM-Assist outperforms previous state-of-the-art tools: at 1% tolerance, EM-Assist suggests the correct refactoring among its top-5 suggestions 60.6% of the time, compared to 54.2% reported by existing ML models, and 52.2% reported by existing static analysis tools. When we replicated 2,849 actual Extract Method instances from open-source projects, EM-Assist's recall rate was 42.1% compared to 6.5% for its peers. Furthermore, we conducted warehouse surveys with 20 industrial developers and suggested refactorings on their recent commits. 81.3% of the respondents agreed with the recommendations provided by EM-Assist. This shows the usefulness of our approach and ushers us into a new era of refactoring when LLMs.
Choiceless Polynomial Space

Abstract State Machines (ASMs) provide a model of computations on structures rather than strings. Blass, Gurevich and Shelah showed that deterministic PTIME-bounded ASMs define the choiceless fragment of PTIME, but cannot capture PTIME. In this article deterministic PSPACE-bounded ASMs are introduced, and it is proven that they cannot capture PSPACE. The key for the proof is a characterisation by partial fixed-point formulae over the St\"ark/Nanchen logic for deterministic ASMs and a construction of transitive structures, in which such formulae must hold. This construction exploits that the decisive support theorem for choiceless polynomial time holds under slightly weaker assumptions.
"May I Speak?": Multi-modal Attention Guidance in Social VR Group Conversations

In this paper, we present a novel multi-modal attention guidance method designed to address the challenges of turn-taking dynamics in meetings and enhance group conversations within virtual reality (VR) environments. Recognizing the difficulties posed by a confined field of view and the absence of detailed gesture tracking in VR, our proposed method aims to mitigate the challenges of noticing new speakers attempting to join the conversation. This approach tailors attention guidance, providing a nuanced experience for highly engaged participants while offering subtler cues for those less engaged, thereby enriching the overall meeting dynamics. Through group interview studies, we gathered insights to guide our design, resulting in a prototype that employs light as a diegetic guidance mechanism, complemented by spatial audio. The combination creates an intuitive and immersive meeting environment, effectively directing users' attention to new speakers. An evaluation study, comparing our method to state-of-the-art attention guidance approaches, demonstrated significantly faster response times (p < 0.001), heightened perceived conversation satisfaction (p < 0.001), and preference (p < 0.001) for our method. Our findings contribute to the understanding of design implications for VR social attention guidance, opening avenues for future research and development.
Transfer Learning for the Prediction of Entity Modifiers in Clinical Text: Application to Opioid Use Disorder Case Detection

Background: The semantics of entities extracted from a clinical text can be dramatically altered by modifiers, including entity negation, uncertainty, conditionality, severity, and subject. Existing models for determining modifiers of clinical entities involve regular expression or features weights that are trained independently for each modifier. Methods: We develop and evaluate a multi-task transformer architecture design where modifiers are learned and predicted jointly using the publicly available SemEval 2015 Task 14 corpus and a new Opioid Use Disorder (OUD) data set that contains modifiers shared with SemEval as well as novel modifiers specific for OUD. We evaluate the effectiveness of our multi-task learning approach versus previously published systems and assess the feasibility of transfer learning for clinical entity modifiers when only a portion of clinical modifiers are shared. Results: Our approach achieved state-of-the-art results on the ShARe corpus from SemEval 2015 Task 14, showing an increase of 1.1% on weighted accuracy, 1.7% on unweighted accuracy, and 10% on micro F1 scores. Conclusions: We show that learned weights from our shared model can be effectively transferred to a new partially matched data set, validating the use of transfer learning for clinical text modifiers
Near-Optimal Policy Optimization for Correlated Equilibrium in General-Sum Markov Games

We study policy optimization algorithms for computing correlated equilibria in multi-player general-sum Markov Games. Previous results achieve $O(T^{-1/2})$ convergence rate to a correlated equilibrium and an accelerated $O(T^{-3/4})$ convergence rate to the weaker notion of coarse correlated equilibrium. In this paper, we improve both results significantly by providing an uncoupled policy optimization algorithm that attains a near-optimal $\tilde{O}(T^{-1})$ convergence rate for computing a correlated equilibrium. Our algorithm is constructed by combining two main elements (i) smooth value updates and (ii) the optimistic-follow-the-regularized-leader algorithm with the log barrier regularizer.
Using multiple Dirac delta points to describe inhomogeneous flux density over a cell boundary in a single-cell diffusion model

Biological cells can release compounds into their direct environment, generally inhomogeneously over their cell membrane, after which the compounds spread by diffusion. In mathematical modelling and simulation of a collective of such cells, it is theoretically and numerically advantageous to replace spatial extended cells with point sources, in particular when cell numbers are large, but still so small that a continuum density description cannot be justified, or when cells are moving. We show that inhomogeneous flux density over the cell boundary may be realized in a point source approach, thus maintaining computational efficiency, by utilizing multiple, clustered point sources (and sinks). In this report, we limit ourselves to a sinusoidal function as flux density in the spatial exclusion model, and we show how to determine the amplitudes of the Dirac delta points in the point source model, such that the deviation between the point source model and the spatial exclusion model is small.
Byte Pair Encoding Is All You Need For Automatic Bengali Speech Recognition

Byte pair encoding (BPE) emerges as an effective tokenization method for tackling the out-of-vocabulary (OOV) challenge in various natural language and speech processing tasks. Recent research highlights the dependency of BPE subword tokenization's efficacy on the morphological nature of the language, particularly in languages rich in inflectional morphology, where fewer BPE merges suffice for generating highly productive tokens. Motivated by this, our study empirically identifies the optimal number of BPE tokens for Bengali, a language known for its morphological complexity, thus enhancing out-of-distribution automatic speech recognition (ASR) performance. Experimental evaluation reveals that an excessively high number of BPE tokens can lead to overfitting, while approximately 500-1000 tokens result in superior OOV performance. Furthermore, we conduct a comparative analysis of BPE with character-based and unigram-based tokenization methods. By introducing BPE tokenization to Bengali ASR, we achieve a substantial reduction in the word error rate (WER) from 66.44% in our character-based baseline system to 63.80% on the LB-ASRTD eval set and from 46.34% to 42.80% on the SHRUTI eval set, both of which include out-of-distribution data.
Multi-Interval Energy-Reserve Co-Optimization with SoC-Dependent Bids from Battery Storage

We consider the problem of co-optimized energy-reserve market clearing with state-of-charge (SoC) dependent bids from battery storage participants. While SoC-dependent bidding accurately captures storage's degradation and opportunity costs, such bids result in a non-convex optimization in the market clearing process. More challenging is the regulation reserve capacity clearing, where the SoC-dependent cost is uncertain as it depends on the unknown regulation trajectories ex-post of the market clearing. Addressing the nonconvexity and uncertainty in a multi-interval co-optimized real-time energy-reserve market, we introduce a simple restriction on the SoC-dependent bids along with a robust optimization formulation, transforming the non-convex market clearing under uncertainty into a standard convex piece-wise linear program and making it possible for large-scale storage integration. Under reasonable assumptions, we show that SoC-dependent bids yield higher profit for storage participants than that from SoC-independent bids. Numerical simulations demonstrate a 28%-150% profit increase of the proposed SoC-dependent bids compared with the SoC-independent counterpart.
Electronic structure and superconductivity in bilayer La$_3$Ni$_2$O$_7$

We show that the crystal field splitting, the bilayer coupling, and electron-electron correlations all play a crucial role in creating an electronic environment similar to hole-doped cuprates for high temperature superconductivity in bilayer La$_3$Ni$_2$O$_7$ under high pressure. The previous density functional theory calculations highly underestimated the crystal field splitting and bilayer coupling. Employing the hybrid exchange-correlation functional, we show that the exchange-correlation pushes the antibonding $d_{z^2}$ bands below the Fermi level to be fully occupied in both the low-pressure (LP) non-superconducting phase and the high-pressure (HP) phase exhibiting superconductivity. In the LP phase, the calculated Fermi surfaces and the correlation normalized band structure match well with the experimental findings at ambient pressure. Moreover, the electronic susceptibility calculated for this new band structure features nesting-induced peaks near the wave vector $Q=(\pi/2, \pi/2)$, suggesting a possible density wave instability in agreement with recent experiments. In the HP phase, an inversion symmetry between the bilayer emerges and produces a very different band structure and Fermi surface, which can be described by low-energy effective models for the inversion antisymmetric $\beta$ and symmetric $\alpha$ bands. The $\beta$ band turns out to be close to half-filling while the $\alpha$ band is highly overdoped. Considering the strong local Coulomb repulsion, we find a dominant pairing with $B_{1g}$ symmetry in the $\beta$ band that, upon Josephson coupling to the $\alpha$ band, drives a superconducting ground state with a congruent $d$-wave symmetry. Our results capture the essential physics of La$_3$Ni$_2$O$_7$ in both the LP and HP phases and reveal its similarity and difference from the hole-doped cuprate high-temperature superconductors.
Challenges in the rheology of glasses

In this contribution to the proceedings of the 29th Solvay Conference on Physics I will give an overview of some key challenges in our theoretical understanding of the rheology of glasses, focussing on (i) steady shear flow curves and their relation to the glass and jamming transitions, (ii) ductile versus brittle yielding in shear startup and (iii) yielding under oscillatory shear. I will also briefly discuss connections to the reversible-irreversible and random organization transitions as well as to the broad field of memory formation in materials.
Self-Supervised Learning in Event Sequences: A Comparative Study and Hybrid Approach of Generative Modeling and Contrastive Learning

This study investigates self-supervised learning techniques to obtain representations of Event Sequences. It is a key modality in various applications, including but not limited to banking, e-commerce, and healthcare. We perform a comprehensive study of generative and contrastive approaches in self-supervised learning, applying them both independently. We find that there is no single supreme method. Consequently, we explore the potential benefits of combining these approaches. To achieve this goal, we introduce a novel method that aligns generative and contrastive embeddings as distinct modalities, drawing inspiration from contemporary multimodal research. Generative and contrastive approaches are often treated as mutually exclusive, leaving a gap for their combined exploration. Our results demonstrate that this aligned model performs at least on par with, and mostly surpasses, existing methods and is more universal across a variety of tasks. Furthermore, we demonstrate that self-supervised methods consistently outperform the supervised approach on our datasets.
FeCuNbSiB thin films with sub-Oersted coercivity

Nanocrystalline FeCuNbSiB thin films were fabricated through magnetron sputtering followed by heat treatment, resulting in samples characterized by low coercivity and high effective magnetization. Comprehensive microstructural analysis, employing X-ray diffraction and transmission electron microscopy techniques such as selected area electron diffraction, high-resolution imaging, and Fourier transform, was conducted. Magnetic properties were investigated using an alternating gradient field magnetometer and broadband ferromagnetic resonance. The structural analysis revealed a well-defined microstructure of nanograins within an amorphous matrix in all of our films. However, the coercivity of the 80 nm films did not exhibit as low values as observed for the 160 nm films
Fabric and Sedimentology of the Upper Siwalik Boulder Conglomerate Exposed in the Neighbourhood of Pinjaur (Kumaon Himalayas)

The megaclast representing boulder, cobble and pebble sized fragments was derived from the Tertiary formations exposed immediately northeast of Pinjaur along the course of the Koshallia River. The detritus did not travel far from the source rocks and was transported by southwesterly flowing currents. The megaclast was deposited in shallow turbulent water where it could acquire well sorted to moderately sorted character.
Maps between schematic semi-graded rings

Motivated by Smith's work \cite{Smith2003, Smith2016} on maps between non-commu\-tative projective spaces of the form ${\rm Proj}_{nc} A$ in the setting of non-commutative projective geometry developed by Rosenberg and Van den Bergh, and the notion of schematicness introduced by Van Oystaeyen and Willaert \cite{VanOystaeyenWillaert1995} to $\mathbb{N}$-graded rings with the aim of formulating a non-commutative scheme theory \`a la Grothendieck \cite{EGAII1961}, in this paper we consider a first approach to maps in the Smith's sense in the more general setting of non-commutative projective spaces over semi-graded rings defined by Lezama and Latorre \cite{LezamaLatorre2017}. We extend Smith's key result \cite[Theorem 3.2]{Smith2003}, \cite[Theorem 1.2]{Smith2016} from the category of schematic $\mathbb{N}$-graded rings to the category of schematic semi-graded rings.