Miracle-dz/UniFAR-FTUs huggingface dataset:
Algebraic curves as a source of separable multi-Hamiltonian systems

In this paper we systematically consider various ways of generating integrable and separable Hamiltonian systems in canonical and in non-canonical representations from algebraic curves on the plane. In particular, we consider St\"ackel transform between two pairs of St\"ackel systems, generated by 2n-parameter algebraic curves on the plane, as well as Miura maps between St\"ackel systems generated by (n+N)-parameter algebraic curves, leading to multi-Hamiltonian representation of these systems.
Path-slice star-product on non-axially symmetric domains in several quaternionic variables

This paper extends the $*$-product from slice analysis to weakly slice analysis in several quaternionic variables, focusing on non-axially symmetric domains. It diverges from traditional applications in axially symmetric domains to address slice regularity in more complicated cases. The approach involves redefining the $*$-product for path-slice functions, borrowing techniques from strongly slice analysis. Key to this work is the introduction of relative stem-preserving set pairs and real-path-connected sets, which help establish a direct link between path-slice functions and their stem functions. The study culminates in conditions under which weakly slice regular functions form an algebra in specific slice domains, broadening the scope of slice analysis.
Optimizing the Fundamental Eigenvalue Gap of Quantum Graphs

We study the problem of minimizing or maximizing the fundamental spectral gap of Schr\"odinger operators on metric graphs with either a convex potential or a ``single-well'' potential on an appropriate specified subset. (In the case of metric trees, such a subset can be the entire graph.) In the convex case we find that the minimizing and maximizing potentials are piecewise linear with only a finite number of points of non-smoothness, but give examples showing that the optimal potentials need not be constant. This is a significant departure from the usual scenarios on intervals and domains where the constant potential is typically minimizing. In the single-well case we show that the optimal potentials are piecewise constant with a finite number of jumps, and in both cases give an explicit estimate on the number of points of non-smoothness, respectively jumps, the minimizing potential can have. Furthermore, we show that, unlike on domains, it is not generally possible to find nontrivial bounds on the fundamental gap in terms of the diameter of the graph alone, within the given classes.
New twisted van der Waals fabrication method based on strongly adhesive polymer

Observations of emergent quantum phases in twisted bilayer graphene prompted a flurry of activities in van-der-Waals (vdW) materials beyond graphene. Most current twisted experiments use a so-called tear-and-stack method using a polymer called PPC. However, despite the clear advantage of the current PPC tear-and-stack method, there are also technical limitations, mainly a limited number of vdW materials that can be studied using this PPC-based method. This technical bottleneck has been preventing further development of the exciting field beyond a few available vdW samples. To overcome this challenge and facilitate future expansion, we developed a new tear-and-stack method using a strongly adhesive polycaprolactone (PCL). With similar angular accuracy, our technology allows fabrication without a capping layer, facilitating surface analysis and ensuring inherently clean interfaces and low operating temperatures. More importantly, it can be applied to many other vdW materials that have remained inaccessible with the PPC-based method. We present our results on twist homostructures made with a wide choice of vdW materials – from two well-studied vdW materials (graphene and MoS2) to the first-ever demonstrations of other vdW materials (NbSe2, NiPS3, and Fe3GeTe2). Therefore, our new technique will help expand moiré physics beyond few selected vdW materials and open up more exciting developments.
A Survey on Efficient Federated Learning Methods for Foundation Model Training

Federated Learning (FL) has become an established technique to facilitate privacy-preserving collaborative training across a multitude of clients. However, new approaches to FL often discuss their contributions involving small deep-learning models only and focus on training full models on clients. In the wake of Foundation Models (FM), the reality is different for many deep learning applications. Typically, FMs have already been pre-trained across a wide variety of tasks and can be fine-tuned to specific downstream tasks over significantly smaller datasets than required for full model training. However, access to such datasets is often challenging. By its design, FL can help to open data silos. With this survey, we introduce a novel taxonomy focused on computational and communication efficiency, the vital elements to make use of FMs in FL systems. We discuss the benefits and drawbacks of parameter-efficient fine-tuning (PEFT) for FL applications, elaborate on the readiness of FL frameworks to work with FMs and provide future research opportunities on how to evaluate generative models in FL as well as the interplay of privacy and PEFT.
Serendipitous Archival Observations of a New Ultradistant Comet C/2019 E3 (ATLAS)

We identified a new ultradistant comet C/2019 E3 (ATLAS) exhibiting preperihelion cometary activity at heliocentric distances ≳20 au, making it the fourth member of this population after C/2010 U3 (Boattini), C/2014 UN271 (Bernardinelli–Bernstein), and C/2017 K2 (PANSTARRS). From serendipitous archival data, we conducted analyses of the comet, finding that the activity was consistent with steady-state behavior, suggestive of sublimation of supervolatiles; that the cross section of dust increased gradually on the inbound leg of the orbit, varying with heliocentric distances as r H − 1.5 ± 0.4 ; and that the dust was produced at a rate of ≳102 kg s−1 within the observed timespan. Our modeling of the largely symmetric morphology of the comet suggests that the dust environment was likely dominated by mm-scale dust grains ejected at speeds ≲0.4 m s−1 from the sunlit hemisphere of the nucleus. Assuming a typical geometric albedo of 0.05 and adopting several simplistic thermophysical models, we estimated the nucleus to be at least ∼3 km across. We also measured the color of the comet to be consistent with other long-period comets, except being slightly bluer in g − r. With our astrometric measurements, we determined an improved orbit of the comet, based upon which we derived that the comet is dynamically new and that its perihelion distance will further shrink due to the Galactic tide. We conclude the paper by comparing the known characteristics of the known ultradistant comets.
Towards Real-World Aerial Vision Guidance with Categorical 6D Pose Tracker

Tracking the object 6-DoF pose is crucial for various downstream robot tasks and real-world applications. In this paper, we investigate the real-world robot task of aerial vision guidance for aerial robotics manipulation, utilizing category-level 6-DoF pose tracking. Aerial conditions inevitably introduce special challenges, such as rapid viewpoint changes in pitch and roll and inter-frame differences. To support these challenges in task, we firstly introduce a robust category-level 6-DoF pose tracker (Robust6DoF). This tracker leverages shape and temporal prior knowledge to explore optimal inter-frame keypoint pairs, generated under a priori structural adaptive supervision in a coarse-to-fine manner. Notably, our Robust6DoF employs a Spatial-Temporal Augmentation module to deal with the problems of the inter-frame differences and intra-class shape variations through both temporal dynamic filtering and shape-similarity filtering. We further present a Pose-Aware Discrete Servo strategy (PAD-Servo), serving as a decoupling approach to implement the final aerial vision guidance task. It contains two servo action policies to better accommodate the structural properties of aerial robotics manipulation. Exhaustive experiments on four well-known public benchmarks demonstrate the superiority of our Robust6DoF. Real-world tests directly verify that our Robust6DoF along with PAD-Servo can be readily used in real-world aerial robotic applications.
Dense Hopfield Networks in the Teacher-Student Setting

Dense Hopfield networks are known for their feature to prototype transition and adversarial robustness. However, previous theoretical studies have been mostly concerned with their storage capacity. We bridge this gap by studying the phase diagram of p-body Hopfield networks in the teacher-student setting of an unsupervised learning problem, uncovering ferromagnetic phases reminiscent of the prototype and feature learning regimes. On the Nishimori line, we find the critical size of the training set necessary for efficient pattern retrieval. Interestingly, we find that that the paramagnetic to ferromagnetic transition of the teacher-student setting coincides with the paramagnetic to spin-glass transition of the direct model, i.e. with random patterns. Outside of the Nishimori line, we investigate the learning performance in relation to the inference temperature and dataset noise. Moreover, we show that using a larger p for the student than the teacher gives the student an extensive tolerance to noise. We then derive a closed-form expression measuring the adversarial robustness of such a student at zero temperature, corroborating the positive correlation between number of parameters and robustness observed in large neural networks. We also use our model to clarify why the prototype phase of modern Hopfield networks is adversarially robust.
Language-Conditioned Robotic Manipulation with Fast and Slow Thinking

The language-conditioned robotic manipulation aims to transfer natural language instructions into executable actions, from simple pick-and-place to tasks requiring intent recognition and visual reasoning. Inspired by the dual process theory in cognitive science, which suggests two parallel systems of fast and slow thinking in human decision-making, we introduce Robotics with Fast and Slow Thinking (RFST), a framework that mimics human cognitive architecture to classify tasks and makes decisions on two systems based on instruction types. Our RFST consists of two key components: 1) an instruction discriminator to determine which system should be activated based on the current user instruction, and 2) a slow-thinking system that is comprised of a fine-tuned vision language model aligned with the policy networks, which allows the robot to recognize user intention or perform reasoning tasks. To assess our methodology, we built a dataset featuring real-world trajectories, capturing actions ranging from spontaneous impulses to tasks requiring deliberate contemplation. Our results, both in simulation and real-world scenarios, confirm that our approach adeptly manages intricate tasks that demand intent recognition and reasoning. The project is available at https://jlm-z.github. io/RSFT/
Kondo breakdown in multi-orbital Anderson lattices induced by destructive hybridization interference

In this paper we consider a multi band extension to the periodic Anderson model. We use a single site DMFT(NRG) in order to study the impact of the conduction band mediated effective hopping of the correlated electrons between the correlated orbitals onto the heavy Fermi liquid formation. Whereas the hybridization of a single impurity model with two distinct conduction bands always adds up constructively, $T_{K}\propto \exp(-\mathrm{const}\, U/(\Gamma_1+\Gamma_2))$, we show that this does not have to be the case in lattice models, where, in remarkable contrast, also an low-energy Fermi liquid scale $T_0\propto \exp(-\mathrm{const}\, U/(\Gamma_1-\Gamma_2))$ can emerge due to quantum interference effects in multi band models, where $U$ denotes the local Coulomb matrix element of the correlated orbitals and $\Gamma_i$ the local hybridization strength of band $i$. At high symmetry points, heavy Fermi liquid formation is suppressed which is associated with a breakdown of the Kondo effect. This results in an asymptotically scale-invariant (i.e., power-law) spectrum of the correlated orbitals $\propto|\omega|^{1/3}$, indicating non-Fermi liquid properties of the quantum critical point, and a small Fermi surface including only the light quasi-particles. This orbital selective Mott phase demonstrates the possibility of metallic local criticality within the general framework of ordinary single site DMFT.
Knowledge-enhanced Multi-perspective Video Representation Learning for Scene Recognition

With the explosive growth of video data in real-world applications, a comprehensive representation of videos becomes increasingly important. In this paper, we address the problem of video scene recognition, whose goal is to learn a high-level video representation to classify scenes in videos. Due to the diversity and complexity of video contents in realistic scenarios, this task remains a challenge. Most existing works identify scenes for videos only from visual or textual information in a temporal perspective, ignoring the valuable information hidden in single frames, while several earlier studies only recognize scenes for separate images in a non-temporal perspective. We argue that these two perspectives are both meaningful for this task and complementary to each other, meanwhile, externally introduced knowledge can also promote the comprehension of videos. We propose a novel two-stream framework to model video representations from multiple perspectives, i.e. temporal and non-temporal perspectives, and integrate the two perspectives in an end-to-end manner by self-distillation. Besides, we design a knowledge-enhanced feature fusion and label prediction method that contributes to naturally introducing knowledge into the task of video scene recognition. Experiments conducted on a real-world dataset demonstrate the effectiveness of our proposed method.
Cross-Speaker Encoding Network for Multi-Talker Speech Recognition

End-to-end multi-talker speech recognition has garnered great interest as an effective approach to directly transcribe overlapped speech from multiple speakers. Current methods typically adopt either 1) single-input multiple-output (SIMO) models with a branched encoder, or 2) single-input single-output (SISO) models based on attention-based encoder-decoder architecture with serialized output training (SOT). In this work, we propose a Cross-Speaker Encoding (CSE) network to address the limitations of SIMO models by aggregating cross-speaker representations. Furthermore, the CSE model is integrated with SOT to leverage both the advantages of SIMO and SISO while mitigating their drawbacks. To the best of our knowledge, this work represents an early effort to integrate SIMO and SISO for multi-talker speech recognition. Experiments on the two-speaker LibrispeechMix dataset show that the CES model reduces word error rate (WER) by 8% over the SIMO baseline. The CSE-SOT model reduces WER by 10% overall and by 16% on high-overlap speech compared to the SOT model.
Estimating an Executive Summary of a Time Series: The Tendency

In this paper we revisit the problem of decomposing a signal into a tendency and a residual. The tendency describes an executive summary of a signal that encapsulates its notable characteristics while disregarding seemingly random, less interesting aspects. Building upon the Intrinsic Time Decomposition (ITD) and information-theoretical analysis, we introduce two alternative procedures for selecting the tendency from the ITD baselines. The first is based on the maximum extrema prominence, namely the maximum difference between extrema within each baseline. Specifically this method selects the tendency as the baseline from which an ITD step would produce the largest decline of the maximum prominence. The second method uses the rotations from the ITD and selects the tendency as the last baseline for which the associated rotation is statistically stationary. We delve into a comparative analysis of the information content and interpretability of the tendencies obtained by our proposed methods and those obtained through conventional low-pass filtering schemes, particularly the Hodrik-Prescott (HP) filter. Our findings underscore a fundamental distinction in the nature and interpretability of these tendencies, highlighting their context-dependent utility with emphasis in multi-scale signals. Through a series of real-world applications, we demonstrate the computational robustness and practical utility of our proposed tendencies, emphasizing their adaptability and relevance in diverse time series contexts.
Evolution of potential evapotranspiration in the northern Loess Plateau of China: recent trends and climatic drivers

Potential evapotranspiration represents the maximum evaporative capacity for a region, which underscores the importance of analysing its changes and attribution to provide information for water resource management. This study focused on the northern Loess Plateau of China, investigated the changes in potential evapotranspiration (ET0) using data from 34 stations during 1960–2013 and conducted its temporal and spatial attribution through the differentiation equation method. The results show that the annual mean ET0 is 1004.9 mm with a decreasing trend at the rate of 0.33 mm year−1. The lowest values were found in the northeast and southwest regions, while the greatest ET0 was in the northwest region. An ‘evaporation paradox’ existed as a whole, but not obviously. The ET0 was most sensitive to actual vapour pressure (ea), followed by solar radiation (Rs), mean temperature (Tmean) and wind speed (U2). The inter‐annual variation in the sensitivity of ET0 in relation to climatic factors showed that ET0 became more sensitive to U2 and Tmean but less sensitive to Rs and ea from 1960 to 2013. Temporally, the increasing Tmean resulted in the increase of ET0; while its effect was weakened by the other three factors, especially by decreasing U2. The contributions of climatic factors to the ET0 spatial variation trends were also analysed along two transects, but the dominant factors were different for each transect. Furthermore, the possible impacts of human activities on ET0 changes were also discussed.
Modeling dynamic crack branching in unsaturated porous media through multi-phase micro-periporomechanics

Dynamic crack branching in unsaturated porous media holds significant relevance in various fields, including geotechnical engineering, geosciences, and petroleum engineering. This article presents a numerical investigation into dynamic crack branching in unsaturated porous media using a recently developed coupled micro-periporomechanics paradigm. This paradigm extends the periporomechanics model by incorporating the micro-rotation of the solid skeleton. Within this framework, each material point is equipped with three degrees of freedom: displacement, micro-rotation, and fluid pressure. Consistent with the Cosserat continuum theory, a length scale associated with the micro-rotation of material points is inherently integrated into the model. This study encompasses several key aspects: (1) Validation of the coupled micro-periporomechanics paradigm for effectively modeling crack branching in deformable porous media, (2) Examination of the transition from a single branch to multiple branches in porous media under drained conditions, (3) Simulation of single crack branching in unsaturated porous media under dynamic loading conditions, and (4) Investigation of multiple crack branching in unsaturated porous media under dynamic loading conditions. The numerical results obtained in this study are systematically analyzed to elucidate the factors that influence dynamic crack branching in porous media subjected to dynamic loading. Furthermore, the comprehensive numerical findings underscore the efficacy and robustness of the coupled micro-periporomechanics paradigm in accurately modeling dynamic crack branching in variably saturated porous media.
Generic Knowledge Boosted Pretraining for Remote Sensing Images

Deep learning models are essential for scene classification, change detection, land cover segmentation, and other remote sensing (RS) image understanding tasks. Most backbones of existing RS deep learning models are typically initialized by pretrained weights obtained from ImageNet pretraining (IMP). However, domain gaps exist between RS images and natural images (e.g., ImageNet), making deep learning models initialized by pretrained weights of IMP perform poorly for RS image understanding. Although some pretraining methods are studied in the RS community, current RS pretraining (RSP) methods face the problem of vague generalization by only using RS images. In this article, we propose a novel RSP framework, generic knowledge boosted RSP (GeRSP), to learn robust representations from RS and natural images for RS understanding tasks. GeRSP contains two pretraining branches: 1) a self-supervised pretraining branch is adopted to learn domain-related representations from unlabeled RS images and 2) a supervised pretraining branch is integrated into GeRSP for general knowledge learning from labeled natural images. Moreover, GeRSP combines two pretraining branches using a teacher–student architecture to simultaneously learn representations with general and special knowledge, which generates a powerful pretrained model for deep learning model initialization. Finally, we evaluate GeRSP and other RSP methods on three downstream tasks, i.e., object detection, semantic segmentation, and scene classification. The extensive experimental results consistently demonstrate that GeRSP can effectively learn robust representations in a unified manner, improving the performance of RS downstream tasks. Code and pretrained models: https://github.com/floatingstarZ/GeRSP.
Learning with Noisy Labels: Interconnection of Two Expectation-Maximizations

Labor-intensive labeling becomes a bottleneck in developing computer vision algorithms based on deep learning. For this reason, dealing with imperfect labels has increasingly gained attention and has become an active field of study. We address learning with noisy labels (LNL) problem, which is formalized as a task of finding a structured manifold in the midst of noisy data. In this framework, we provide a proper objective function and an optimization algorithm based on two expectation-maximization (EM) cycles. The separate networks associated with the two EM cycles collaborate to optimize the objective function, where one model is for distinguishing clean labels from corrupted ones while the other is for refurbishing the corrupted labels. This approach results in a non-collapsing LNL-flywheel model in the end. Experiments show that our algorithm achieves state-of-the-art performance in multiple standard benchmarks with substantial margins under various types of label noise.
On the Nuisance of Control Variables in Causal Regression Analysis

Control variables are included in regression analyses to estimate the causal effect of a treatment on an outcome. In this article, we argue that the estimated effect sizes of controls are unlikely to have a causal interpretation themselves, though. This is because even valid controls are possibly endogenous and represent a combination of several different causal mechanisms operating jointly on the outcome, which is hard to interpret theoretically. Therefore, we recommend refraining from interpreting the marginal effects of controls and focusing on the main variables of interest, for which a plausible identification argument can be established. To prevent erroneous managerial or policy implications, coefficients of control variables should be clearly marked as not having a causal interpretation or omitted from regression tables altogether. Moreover, we advise against using control variable estimates for subsequent theory building and meta-analyses.