Miracle-dz/UniFAR-FTUs huggingface dataset:
Coupled thermal-hydraulic-chemical modelling at the Soultz-sous-Forêts HDR Reservoir (France)

The present study is part of the European concerted action for the support of the HDR geothermal energy R&D activities at Soultz-sous-Forêts (Alsace, France) from 1998-2001 and 2001-2004 representing one of the Swiss HDR R&D group’s contributions. It was funded by the Swiss Federal Office for Education and Science and carried out in close co-operation with the Centre of Hydrogeology at the University of Neuchâtel (CHYN) in Switzerland. The aim of the study was to investigate thermal-, hydraulicand chemically coupled processes and their impact on the Soultz HDR reservoir. The regional and local temperature distribution in the Rhine Graben was investigated, in order to link the Soultz HDR site to a wider geological context. The study highlights the impact of the graben-parallel faults on the hydrothermal convection. In addition, it was found that the graben-perpendicular fluid flux is negligible compared to the graben-parallel flux. This has to be taken into account when investigating temperature anomalies in the Rhine Graben. Moreover, such graben-wide features are important when delineating the general framework for HDR energy use at Soultz. The numerical code FRACHEM was developed, to simulate coupled thermal-, hydraulicand chemical processes, accounting for the Soultz specific conditions such as the high salinity of the reservoir fluid and the high temperatures. This was based on the codes FRACTure and the geochemical module of CHEMTOUGH. FRACTure calculates the thermal and hydraulic and the geochemical module the chemical processes. An interface links both parts. According to the characteristics of the Soultz HDR reservoir, the geochemical module was modified: 1) the Debye-Huckel approach was replaced by the Pitzer formalism to calculate the activity coefficients, accounting for the high salinity of the fluids. 2) New kinetic laws for calcite, dolomite, quartz and pyrite were implemented. These also required modifying the calculations of the reaction surface. 3) The porosity-permeability relation was replaced by a new relation for fractured rock. 4) The possibility of re-injecting the produced fluid was implemented. The sequential-non-iteration approach (SNIA) was used to couple transport and reactions. Sensitivity analyses that were conducted to test the numerical behaviour of FRACHEM, proved its proper functionality but highlighted the sensitivity of the SNIA approach to time steps. To quantify the FRACHEM results, a comparative
Randomized Coordinate Subgradient Method for Nonsmooth Composite Optimization

Coordinate-type subgradient methods for addressing nonsmooth optimization problems are relatively underexplored due to the set-valued nature of the subdifferential. In this work, our study focuses on nonsmooth composite optimization problems, encompassing a wide class of convex and weakly convex (nonconvex nonsmooth) problems. By utilizing the chain rule of the composite structure properly, we introduce the Randomized Coordinate Subgradient method (RCS) for tackling this problem class. To the best of our knowledge, this is the first coordinate subgradient method for solving general nonsmooth composite optimization problems. In theory, we consider the linearly bounded subgradients assumption for the objective function, which is more general than the traditional Lipschitz continuity assumption, to account for practical scenarios. We then conduct convergence analysis for RCS in both convex and weakly convex cases based on this generalized Lipschitz-type assumption. Specifically, we establish the $\widetilde{\mathcal{O}}$$(1/\sqrt{k})$ convergence rate in expectation and the $\tilde o(1/\sqrt{k})$ almost sure asymptotic convergence rate in terms of the suboptimality gap when $f$ is convex. For the case when $f$ is weakly convex and its subdifferential satisfies the global metric subregularity property, we derive the $\mathcal{O}(\varepsilon^{-4})$ iteration complexity in expectation. We also establish an asymptotic convergence result. To justify the global metric subregularity property utilized in the analysis, we establish this error bound condition for the concrete (real-valued) robust phase retrieval problem. We also provide a convergence lemma and the relationship between the global metric subregularity properties of a weakly convex function and its Moreau envelope. Finally, we conduct several experiments to demonstrate the possible superiority of RCS over the subgradient method.
Extremal, enumerative and probabilistic results on ordered hypergraph matchings

An ordered $r$-matching is an $r$-uniform hypergraph matching equipped with an ordering on its vertices. These objects can be viewed as natural generalisations of $r$-dimensional orders. The theory of ordered 2-matchings is well-developed and has connections and applications to extremal and enumerative combinatorics, probability, and geometry. On the other hand, in the case $r \ge 3$ much less is known, largely due to a lack of powerful bijective tools. Recently, Dudek, Grytczuk and Ruci\'nski made some first steps towards a general theory of ordered $r$-matchings, and in this paper we substantially improve several of their results and introduce some new directions of study. Many intriguing open questions remain.
Irreducible momentum-space spin structure of Weyl semimetals and its signatures in Friedel oscillations

Materials that break time-reversal or inversion symmetry possess nondegenerate electronic bands, which can touch at so-called Weyl points. The spinor eigenstates in the vicinity of a Weyl point exhibit a well-defined chirality $\pm 1$. Numerous works have studied the consequences of this chirality, for example in unconventional magnetoelectric transport. However, even a Weyl point with isotropic dispersion is not only characterized by its chirality but also by the momentum dependence of the spinor eigenstates. For a single Weyl point, this momentum-space spin structure can be brought into standard"hedgehog"form by a unitary transformation, but for two or more Weyl points, this is not possible. In this work, we show that the relative spin structure of a pair of Weyl points has strong qualitative signatures in the electromagnetic response. Specifically, we investigate the Friedel oscillations in the induced charge density due to a test charge for a centrosymmetric system consisting of two Weyl points with isotropic dispersion. The most pronounced signature is that the amplitude of the Friedel oscillations falls off as $1/r^4$ in directions in which both Weyl points exhibit the same spin structure, while for directions with inverted spin structures, the amplitude of the Friedel oscillations decreases as $1/r^3$.
Modeling excitable cells with the EMI equations: spectral analysis and iterative solution strategy

In this work, we are interested in solving large linear systems stemming from the extra–membrane–intra model, which is employed for simulating excitable tissues at a cellular scale. After setting the related systems of partial differential equations equipped with proper boundary conditions, we provide its finite element discretization and focus on the resulting large linear systems. We first give a relatively complete spectral analysis using tools from the theory of Generalized Locally Toeplitz matrix sequences. The obtained spectral information is used for designing appropriate preconditioned Krylov solvers. Through numerical experiments, we show that the presented solution strategy is robust w.r. t. problem and discretization parameters, efficient and scalable.
Magnetic-order-mediated carrier and phonon dynamics in MnBi2Te4

We investigate the quasiparticle dynamics in MnBi2Te4 single crystal using the ultrafast optical spectroscopy. Our results show that there exist anomalous dynamical optical responses below the antiferromagnetic (AFM) ordering temperature TN. In specific, we reveal that both the initial carrier decay and recombination processes can be modulated via introducing the AFM order in sub-picosecond and picosecond timescales, respectively. We also discover a long relaxation process emerging below TN with a timescale approaching to the nanosecond regime, and can be attributed to the T-dependent spin-lattice interaction. There also emerges an unusual phonon energy renormalization below TN , which is found to arise from its coupling the spin degree via the exchange interaction and magnetic anisotropy. Our findings provide key information for understanding the dynamical properties of non-equilibrium carrier, spin and lattice in MnBi2Te4.
A First-Order Algorithm for Decentralised Min-Max Problems

In this work, we consider a connected network of finitely many agents working cooperatively to solve a min-max problem with convex-concave structure. We propose a decentralised first-order algorithm which can be viewed as a non-trivial combination of two algorithms: PG-EXTRA for decentralised minimisation problems and the forward reflected backward method for (non-distributed) min-max problems. In each iteration of our algorithm, each agent computes the gradient of the smooth component of its local objective function as well as the proximal operator of its nonsmooth component, following by a round of communication with its neighbours. Our analysis shows that the sequence generated by the method converges under standard assumptions with non-decaying stepsize.
Coarse-to-Fine Multi-Scene Pose Regression With Transformers

Absolute camera pose regressors estimate the position and orientation of a camera given the captured image alone. Typically, a convolutional backbone with a multi-layer perceptron (MLP) head is trained using images and pose labels to embed a single reference scene at a time. Recently, this scheme was extended to learn multiple scenes by replacing the MLP head with a set of fully connected layers. In this work, we propose to learn multi-scene absolute camera pose regression with Transformers, where encoders are used to aggregate activation maps with self-attention and decoders transform latent features and scenes encoding into pose predictions. This allows our model to focus on general features that are informative for localization, while embedding multiple scenes in parallel. We extend our previous MS-Transformer approach Shavit et al. (2021) by introducing a mixed classification-regression architecture that improves the localization accuracy. Our method is evaluated on commonly benchmark indoor and outdoor datasets and has been shown to exceed both multi-scene and state-of-the-art single-scene absolute pose regressors.
Anisotropic Hybrid Networks for liver tumor segmentation with uncertainty quantification

The burden of liver tumors is important, ranking as the fourth leading cause of cancer mortality. In case of hepatocellular carcinoma (HCC), the delineation of liver and tumor on contrast-enhanced magnetic resonance imaging (CE-MRI) is performed to guide the treatment strategy. As this task is time-consuming, needs high expertise and could be subject to inter-observer variability there is a strong need for automatic tools. However, challenges arise from the lack of available training data, as well as the high variability in terms of image resolution and MRI sequence. In this work we propose to compare two different pipelines based on anisotropic models to obtain the segmentation of the liver and tumors. The first pipeline corresponds to a baseline multi-class model that performs the simultaneous segmentation of the liver and tumor classes. In the second approach, we train two distinct binary models, one segmenting the liver only and the other the tumors. Our results show that both pipelines exhibit different strengths and weaknesses. Moreover we propose an uncertainty quantification strategy allowing the identification of potential false positive tumor lesions. Both solutions were submitted to the MICCAI 2023 Atlas challenge regarding liver and tumor segmentation.
Prompt-Based Length Controlled Generation with Reinforcement Learning

Large language models (LLMs) like ChatGPT and GPT-4 have attracted great attention given their surprising performance on a wide range of NLP tasks. Length controlled generation of LLMs emerges as an important topic, which enables users to fully leverage the capability of LLMs in more real-world scenarios like generating a proper answer or essay of a desired length. In addition, the autoregressive generation in LLMs is extremely time-consuming, while the ability of controlling this generated length can reduce the inference cost by limiting the length. Therefore, we propose a prompt-based length control method to achieve high-accuracy length controlled generation. In particular, we adopt reinforcement learning with the reward signal given by either trainable or rule-based reward models, which further enhances the length-control ability of LLMs by rewarding outputs that follows pre-defined control instruction. To enable rule-based inference, we also introduce standard prompt extractor to collect the standard control information from users' input. Experiments show that our method significantly improves the accuracy of prompt-based length control for summarization task on popular datasets like CNNDM and NYT. Both the standard prompt extractor and the RL-tuned model have show strong generalization ability to unseen control prompt templates.
iGLU 4.0: A continuous glucose monitoring and balancing paradigm with physiological parameters

The conventional method of glucose measurement such as pricking blood from the body is prevalent which brings pain and trauma. Invasive methods of measurement sometimes raise the risk of blood infection to the patient. Sometimes, some of the physiological parameters such as body temperature and systolic blood pressure (SBP) are responsible for blood glucose level fluctuations. Moreover, diabetes for a long duration usually becomes a critical issue. In such situation, patients need to consult diabetologist frequently, which is not possible in normal life. Therefore, it is required to develop non-invasive glucose balancing paradigm, which measures blood glucose without pricking blood along with physiological parameters measurement and decision model. The proposed paradigm helps to doctor, who is even available at remote location. There will not be any need to consult frequently. In the way of optimized non-invasive system design, an NIRS technique with specific wavelengths along with physiological parameters is taken to predict the precise glucose value. The all parameters (glucose, Blood pressure and body temperature), food intake and insulin levels are parts of decision model, which would help to the doctor to take decision related to the further medicine doses and diet plan. The patients would have suggestions according to maintain their blood glucose level. The proposed system demonstrated an accurate model with MARD and AvgE 12.50% and 12.10% respectively using DNN model. Coefficient of determination R2 has been found 0.97.
Few-shot Anomaly Detection in Text with Deviation Learning

Most current methods for detecting anomalies in text concentrate on constructing models solely relying on unlabeled data. These models operate on the presumption that no labeled anomalous examples are available, which prevents them from utilizing prior knowledge of anomalies that are typically present in small numbers in many real-world applications. Furthermore, these models prioritize learning feature embeddings rather than optimizing anomaly scores directly, which could lead to suboptimal anomaly scoring and inefficient use of data during the learning process. In this paper, we introduce FATE, a deep few-shot learning-based framework that leverages limited anomaly examples and learns anomaly scores explicitly in an end-to-end method using deviation learning. In this approach, the anomaly scores of normal examples are adjusted to closely resemble reference scores obtained from a prior distribution. Conversely, anomaly samples are forced to have anomalous scores that considerably deviate from the reference score in the upper tail of the prior. Additionally, our model is optimized to learn the distinct behavior of anomalies by utilizing a multi-head self-attention layer and multiple instance learning approaches. Comprehensive experiments on several benchmark datasets demonstrate that our proposed approach attains a new level of state-of-the-art performance.
Quantum engines with interacting Bose-Einstein condensates

We consider a quantum Otto cycle with an interacting Bose-Einstein condensate at finite temperature. We present a procedure to evolve this system in time in three spatial dimensions, in which closed (adiabatic) strokes are described by the Gross-Pitaevskii equation, and open (isochoric) strokes are modeled using a stochastic Ginzburg-Landau equation. We analyze the effect on the thermodynamic efficiency of the strength of interactions, the frequency of the harmonic trap, and the temperatures of the reservoirs. The efficiency has little sensitivity to changes in the temperatures, but decreases as interactions increase. However, stronger interactions allow for faster cycles and for substantial increases in power.
Efficient Transfer Learning in Diffusion Models via Adversarial Noise

Diffusion Probabilistic Models (DPMs) have demonstrated substantial promise in image generation tasks but heavily rely on the availability of large amounts of training data. Previous works, like GANs, have tackled the limited data problem by transferring pre-trained models learned with sufficient data. However, those methods are hard to be utilized in DPMs since the distinct differences between DPM-based and GAN-based methods, showing in the unique iterative denoising process integral and the need for many timesteps with no-targeted noise in DPMs. In this paper, we propose a novel DPMs-based transfer learning method, TAN, to address the limited data problem. It includes two strategies: similarity-guided training, which boosts transfer with a classifier, and adversarial noise selection which adaptive chooses targeted noise based on the input image. Extensive experiments in the context of few-shot image generation tasks demonstrate that our method is not only efficient but also excels in terms of image quality and diversity when compared to existing GAN-based and DDPM-based methods.
Advancements in Point Cloud Data Augmentation for Deep Learning: A Survey

Deep learning (DL) has become one of the mainstream and effective methods for point cloud analysis tasks such as detection, segmentation and classification. To reduce overfitting during training DL models and improve model performance especially when the amount and/or diversity of training data are limited, augmentation is often crucial. Although various point cloud data augmentation methods have been widely used in different point cloud processing tasks, there are currently no published systematic surveys or reviews of these methods. Therefore, this article surveys these methods, categorizing them into a taxonomy framework that comprises basic and advanced point cloud data augmentation methods, according to their levels of complexity. Through a comprehensive evaluation of these augmentation methods, this article identifies their potentials and limitations, serving as a useful reference for choosing appropriate augmentation methods. In addition, potential directions for future research are recommended. This survey contributes to providing a holistic overview of the current state of point cloud data augmentation, promoting its wider application and development.
FlexKBQA: A Flexible LLM-Powered Framework for Few-Shot Knowledge Base Question Answering

Knowledge base question answering (KBQA) is a critical yet challenging task due to the vast number of entities within knowledge bases and the diversity of natural language questions posed by users. Unfortunately, the performance of most KBQA models tends to decline significantly in real-world scenarios where high-quality annotated data is insufficient. To mitigate the burden associated with manual annotation, we introduce FlexKBQA by utilizing Large Language Models (LLMs) as program translators for addressing the challenges inherent in the few-shot KBQA task. Specifically, FlexKBQA leverages automated algorithms to sample diverse programs, such as SPARQL queries, from the knowledge base, which are subsequently converted into natural language questions via LLMs. This synthetic dataset facilitates training a specialized lightweight model for the KB. Additionally, to reduce the barriers of distribution shift between synthetic data and real user questions, FlexKBQA introduces an executionguided self-training method to iterative leverage unlabeled user questions. Furthermore, we explore harnessing the inherent reasoning capability of LLMs to enhance the entire framework. Consequently, FlexKBQA delivers substantial flexibility, encompassing data annotation, deployment, and being domain agnostic. Through extensive experiments on GrailQA, WebQSP, and KQA Pro, we observe that under the few-shot even the more challenging zero-shot scenarios, FlexKBQA achieves impressive results with a few annotations, surpassing all previous baselines and even approaching the performance of supervised models, achieving a remarkable 93% performance relative to the fully-supervised models. We posit that FlexKBQA represents a significant advancement towards exploring better integration of large and lightweight models. Code is available at https://github.com/leezythu/FlexKBQA.
Equivariant Tangent Categories

In this paper we show that if $G$ is a smooth algebraic group and if $X$ is a left $G$-variety then under mild technical assumptions regarding a pre-equivariant pseudofunctor $F:\mathbf{SfResl}_G(X)^{\operatorname{op}} \to \mathfrak{Cat}$ on $X$ there is a tangent structure on the equivariant category $F_G(X)$ which is induced by equivariant descent through tangent categories over each principal $G$-resolution. In particular this allows us to describe a notion of equivariant tangent schemes over the variety $X$ and also give a general condition when the category of pseudocones $\mathsf{Bicat}(\mathscr{C}^{\operatorname{op}},\mathfrak{Cat})(1,F)$ has a tangent structure induced by local tangent structures that the pseudofunctor $F$ picks out. We also prove in both these cases that our constructions are functorial.
Assignment Based Metrics for Attributed Graphs

We introduce the Graph TT (GTT) and Graph OSPA (GOSPA) metrics based on optimal assignment, which allow us to compare not only the edge structures but also general vertex and edge attributes of graphs of possibly different sizes. We argue that this provides an intuitive and universal way to measure the distance between finite simple attributed graphs. Our paper discusses useful equivalences and inequalities as well as the relation of the new metrics to various existing quantifications of distance between graphs. By deriving a representation of a graph as a pair of point processes, we are able to formulate and study a new type of (finite) random graph convergence and demonstrate its applicability using general point processes of vertices with independent random edges. Computational aspects of the new metrics are studied in the form of an exact and two heuristic algorithms that are derived from previous algorithms for similar tasks. As an application, we perform a statistical test based on the GOSPA metric for functional differences in olfactory neurons of Drosophila flies.