Miracle-dz/UniFAR-FTUs huggingface dataset:
Taxonomy-based differences in feeding guilds of fish

Abstract It has been taken for granted that feeding guilds and behavior in animals are linked to the taxonomic relatedness of species, but empirical evidence supporting such relationship is virtually missing. To examine the importance of taxonomy on trophic ecology, I here present the first well-resolved dietary taxonomy analysis based on feeding guilds (predation, herbivory, and filtering) among families and genera within the fish order Perciformes. Taxonomic relatedness in feeding did not vary with ecosystem dimension (marine vs. freshwater). Although predation dominates among Perciformes fishes, this study shows that in most cases taxonomic units (family or genus) are composed by species with several feeding guilds. Related species are more similar in feeding compared with species that are taxonomically more distant, demonstrating that there is a greater variation of feeding guilds within families than genera. Thus, there is no consistency in feeding guilds between family- and genus-level taxonomy. This study provides empirical support for the notion that genera are more informative than families, underlining that family-level taxonomy should be avoided to infer feeding habits of fish species at finer taxonomic resolution. Thus, the choice of taxonomic resolution (family or genus level) in ecological studies is key to avoid information loss and misleading results. I conclude that high-rank taxonomic units (i.e., above the generic level) are not appropriate to test research hypotheses about the feeding of fish.
Detect-Order-Construct: A Tree Construction based Approach for Hierarchical Document Structure Analysis

Document structure analysis (aka document layout analysis) is crucial for understanding the physical layout and logical structure of documents, with applications in information retrieval, document summarization, knowledge extraction, etc. In this paper, we concentrate on Hierarchical Document Structure Analysis (HDSA) to explore hierarchical relationships within structured documents created using authoring software employing hierarchical schemas, such as LaTeX, Microsoft Word, and HTML. To comprehensively analyze hierarchical document structures, we propose a tree construction based approach that addresses multiple subtasks concurrently, including page object detection (Detect), reading order prediction of identified objects (Order), and the construction of intended hierarchical structure (Construct). We present an effective end-to-end solution based on this framework to demonstrate its performance. To assess our approach, we develop a comprehensive benchmark called Comp-HRDoc, which evaluates the above subtasks simultaneously. Our end-to-end system achieves state-of-the-art performance on two large-scale document layout analysis datasets (PubLayNet and DocLayNet), a high-quality hierarchical document structure reconstruction dataset (HRDoc), and our Comp-HRDoc benchmark. The Comp-HRDoc benchmark will be released to facilitate further research in this field.
A Training-Free Defense Framework for Robust Learned Image Compression

We study the robustness of learned image compression models against adversarial attacks and present a training-free defense technique based on simple image transform functions. Recent learned image compression models are vulnerable to adversarial attacks that result in poor compression rate, low reconstruction quality, or weird artifacts. To address the limitations, we propose a simple but effective two-way compression algorithm with random input transforms, which is conveniently applicable to existing image compression models. Unlike the na\"ive approaches, our approach preserves the original rate-distortion performance of the models on clean images. Moreover, the proposed algorithm requires no additional training or modification of existing models, making it more practical. We demonstrate the effectiveness of the proposed techniques through extensive experiments under multiple compression models, evaluation metrics, and attack scenarios.
Quality-Aware Hydraulic Control in Drinking Water Networks via Controllability Proxies

The operation of water distribution networks is a complex procedure aimed at efficiently delivering consumers with adequate water quantity while ensuring its safe quality. An added challenge is the dependency of the water quality dynamics on the system's hydraulics, which influences the performance of the water quality controller. Prior research has addressed either solving the optimum operational hydraulic setting problem or regulating the water quality dynamics as separate problems. Additionally, there have been efforts to couple these two problems and solve one compact problem resulting in trade-offs between the contradictory objectives. In contrast, this paper examines the dependency and influence from a control-theoretic standpoint. More specifically, we explore the influence of accountability for water quality controllability improvement when addressing the pump scheduling problem. We examine its effects on the cumulative cost of the interconnected systems as well as the subsequent performance of the water quality controller. To achieve this, we develop a framework that incorporates different controllability metrics within the operational hydraulic optimization problem; its aim is attaining an adequate level of water quality control across the system. We assess the aforementioned aspects' performance on various scaled networks with a wide range of numerical scenarios.
Inner Structure of Many-Body Localization Transition and Fulfillment of Harris Criterion

We treat disordered Heisenberg model in 1D as the"standard model"of many-body localization (MBL). Two independent order parameters stemming purely from the half-chain von Neumann entanglement entropy $S_{\textrm{vN}}$ are introduced to probe its eigenstate transition. From symmetry-endowed entropy decomposition, they are probability distribution deviation $|d(p_n)|$ and von Neumann entropy $S_{\textrm{vN}}^{n}(D_n\! =\! \mbox{max})$ of the maximum-dimensional symmetry subdivision. Finite-size analyses reveal that $\{p_n\}$ drives the localization transition, preceded by a thermalization breakdown transition governed by $\{S_{\textrm{vN}}^{n}\}$. For noninteracting case, these transitions coincide, but in interacting situation they separate. Such separability creates an intermediate phase region and may help discriminate between the Anderson and MBL transitions. An obstacle whose solution eludes community to date is the violation of Harris criterion in nearly all numeric investigations of MBL so far. Upon elucidating the mutually independent components in $S_{\textrm{vN}}$, it is clear that previous studies of eigenspectra, $S_{\textrm{vN}}$, and the like lack resolution to pinpoint (thus completely overlook) the crucial internal structures of the transition. We show, for the first time, that after this necessary decoupling, the universal critical exponents for both transitions of $|d(p_n)|$ and $S_{\textrm{vN}}^{n}(D_n\! =\! \mbox{max})$ fulfill the Harris criterion: $\nu\approx2.0\ (\nu\approx1.5)$ for quench (quasirandom) disorder. Our work puts forth"symmetry combined with entanglement"as the missing organization principle for the generic eigenstate matter and transition.
The Geometry of Multi-curve Interest Rate Models

We study the problems of consistency and of the existence of finite-dimensional realizations for multi-curve interest rate models of Heath-Jarrow-Morton type, generalizing the geometric approach developed by T. Bj\"ork and co-authors in the classical single-curve setting. We characterize when a multi-curve interest rate model is consistent with a given parameterized family of forward curves and spreads and when a model can be realized by a finite-dimensional state process. We illustrate the general theory in a number of model classes and examples, providing explicit constructions of finite-dimensional realizations. Based on these theoretical results, we perform the calibration of a three-curve Hull-White model to market data and analyse the stability of the estimated parameters.
CARE: Ensemble Adversarial Robustness Evaluation Against Adaptive Attackers for Security Applications

Ensemble defenses, are widely employed in various security-related applications to enhance model performance and robustness. The widespread adoption of these techniques also raises many questions: Are general ensembles defenses guaranteed to be more robust than individuals? Will stronger adaptive attacks defeat existing ensemble defense strategies as the cybersecurity arms race progresses? Can ensemble defenses achieve adversarial robustness to different types of attacks simultaneously and resist the continually adjusted adaptive attacks? Unfortunately, these critical questions remain unresolved as there are no platforms for comprehensive evaluation of ensemble adversarial attacks and defenses in the cybersecurity domain. In this paper, we propose a general Cybersecurity Adversarial Robustness Evaluation (CARE) platform aiming to bridge this gap.
Wavelet Networks: Scale-Translation Equivariant Learning From Raw Time-Series

Leveraging the symmetries inherent to specific data domains for the construction of equivariant neural networks has lead to remarkable improvements in terms of data efficiency and generalization. However, most existing research focuses on symmetries arising from planar and volumetric data, leaving a crucial data source largely underexplored: time-series. In this work, we fill this gap by leveraging the symmetries inherent to time-series for the construction of equivariant neural network. We identify two core symmetries: *scale and translation*, and construct scale-translation equivariant neural networks for time-series learning. Intriguingly, we find that scale-translation equivariant mappings share strong resemblance with the wavelet transform. Inspired by this resemblance, we term our networks Wavelet Networks, and show that they perform nested non-linear wavelet-like time-frequency transforms. Empirical results show that Wavelet Networks outperform conventional CNNs on raw waveforms, and match strongly engineered spectrogram techniques across several tasks and time-series types, including audio, environmental sounds, and electrical signals. Our code is publicly available at https://github.com/dwromero/wavelet_networks.
Atomistic calculation of the f0 attempt frequency in Fe3O4 magnetite nanoparticles

The Arrhenius law predicts the transition time between equilibrium states in physical systems due to thermal activation, with broad applications in material science, magnetic hyperthermia and paleomagnetism where it is used to estimate the transition time and thermal stability of assemblies of magnetic nanoparticles. Magnetite is a material of great importance in paleomagnetic studies and magnetic hyperthermia but existing estimates of the attempt frequency $f_0$ vary by several orders of magnitude in the range $10^7-10^{13}$ Hz, leading to significant uncertainty in their relaxation rate. Here we present a dynamical method enabling full parameterization of the Arrhenius-N\'eel law using atomistic spin dynamics. We determine the temperature and volume dependence of the attempt frequency of magnetite nanoparticles with cubic anisotropy and find a value of $f_0 = 0.562 \pm 0.059$ GHz at room temperature. For particles with enhanced anisotropy we find a significant increase in the attempt frequency and a strong temperature dependence suggesting an important role of anisotropy. The method is applicable to a wide range of dynamical systems where different states can be clearly identified and enables robust estimates of domain state stabilities, with particular importance in the rapidly developing field of micromagnetic analysis of paleomagnetic recordings where samples can be numerically reconstructed to provide a better understanding of geomagnetic recording fidelity over geological time scales.
Personalized Over-the-Air Federated Learning with Personalized Reconfigurable Intelligent Surfaces

Over-the-air federated learning (OTA-FL) provides bandwidth-efficient learning by leveraging the inherent superposition property of wireless channels. Personalized federated learning balances performance for users with diverse datasets, addressing real-life data heterogeneity. We propose the first personalized OTA-FL scheme through multi-task learning, assisted by personal reconfigurable intelligent surfaces (RIS) for each user. We take a cross-layer approach that optimizes communication and computation resources for global and personalized tasks in time-varying channels with imperfect channel state information, using multi-task learning for non-i.i. d data. Our PROAR-PFed algorithm adaptively designs power, local iterations, and RIS configurations. We present convergence analysis for non-convex objectives and demonstrate that PROAR-PFed outperforms state-of-the-art on the Fashion-MNIST dataset.
Distributed Multi-Task Learning for Stochastic Bandits with Context Distribution and Stage-wise Constraints

We present the problem of conservative distributed multi-task learning in stochastic linear contextual bandits with heterogeneous agents. This extends conservative linear bandits to a distributed setting where M agents tackle different but related tasks while adhering to stage-wise performance constraints. The exact context is unknown, and only a context distribution is available to the agents as in many practical applications that involve a prediction mechanism to infer context, such as stock market prediction and weather forecast. We propose a distributed upper confidence bound (UCB) algorithm, DiSC-UCB. Our algorithm constructs a pruned action set during each round to ensure the constraints are met. Additionally, it includes synchronized sharing of estimates among agents via a central server using well-structured synchronization steps. We prove the regret and communication bounds on the algorithm. We extend the problem to a setting where the agents are unaware of the baseline reward. For this setting, we provide a modified algorithm, DiSC-UCB2, and we show that the modified algorithm achieves the same regret and communication bounds. We empirically validated the performance of our algorithm on synthetic data and real-world Movielens-100K data.
Report of the AMS Sample Preparation Workshop Saturday 13 August 1994

Le compte-rendu d'un groupe de travail reuni le 13 aout 1994 est detaille. Il traite de la preparation des echantillons destines a etre traites par spectrometrie de masse, de la separation du pollen des sediments, de la graphitisation d'echantillons de pollen et du comportement du pollen en presence d'ions hemispheriques
Expansion of Green's function and regularity of Robin's function for elliptic operators in divergence form

We consider Green's function $ G_K $ of the elliptic operator in divergence form $ \mathcal{L}_K=-\text{div}(K(x)\nabla ) $ on a bounded smooth domain $ \Omega\subseteq\mathbb{R}^n (n\geq 2) $ with zero Dirichlet boundary condition, where $ K $ is a smooth positively definite matrix-valued function on $ \Omega $. We obtain a high-order asymptotic expansion of $ G_K(x, y) $, which defines uniquely a regular part $ H_K(x, y) $. Moreover, we prove that the associated Robin's function $ R_K(x) = H_K(x, x) $ is smooth in $ \Omega $, despite the regular part $ H_K\notin C^1(\Omega\times\Omega) $ in general.
Diffusion Model Conditioning on Gaussian Mixture Model and Negative Gaussian Mixture Gradient

Diffusion models (DMs) are a type of generative model that has a huge impact on image synthesis and beyond. They achieve state-of-the-art generation results in various generative tasks. A great diversity of conditioning inputs, such as text or bounding boxes, are accessible to control the generation. In this work, we propose a conditioning mechanism utilizing Gaussian mixture models (GMMs) as feature conditioning to guide the denoising process. Based on set theory, we provide a comprehensive theoretical analysis that shows that conditional latent distribution based on features and classes is significantly different, so that conditional latent distribution on features produces fewer defect generations than conditioning on classes. Two diffusion models conditioned on the Gaussian mixture model are trained separately for comparison. Experiments support our findings. A novel gradient function called the negative Gaussian mixture gradient (NGMG) is proposed and applied in diffusion model training with an additional classifier. Training stability has improved. We also theoretically prove that NGMG shares the same benefit as the Earth Mover distance (Wasserstein) as a more sensible cost function when learning distributions supported by low-dimensional manifolds.
Flexible Models for Simple Longitudinal Data

We propose a new method for estimating subject-specific mean functions from longitudinal data. We aim to do this in a flexible manner (without restrictive assumptions about the shape of the subject-specific mean functions), while exploiting similarities in the mean functions between different subjects. Functional principal components analysis fulfils both requirements, and methods for functional principal components analysis have been developed for longitudinal data. However, we find that these existing methods sometimes give fitted mean functions which are more complex than needed to provide a good fit to the data. We develop a new penalised likelihood approach to flexibly model longitudinal data, with a penalty term to control the balance between fit to the data and smoothness of the subject-specific mean curves. We run simulation studies to demonstrate that the new method substantially improves the quality of inference relative to existing methods across a range of examples, and apply the method to data on changes in body composition in adolescent girls.
Efficient local linearity regularization to overcome catastrophic overfitting

Catastrophic overfitting (CO) in single-step adversarial training (AT) results in abrupt drops in the adversarial test accuracy (even down to 0%). For models trained with multi-step AT, it has been observed that the loss function behaves locally linearly with respect to the input, this is however lost in single-step AT. To address CO in single-step AT, several methods have been proposed to enforce local linearity of the loss via regularization. However, these regularization terms considerably slow down training due to Double Backpropagation. Instead, in this work, we introduce a regularization term, called ELLE, to mitigate CO effectively and efficiently in classical AT evaluations, as well as some more difficult regimes, e.g., large adversarial perturbations and long training schedules. Our regularization term can be theoretically linked to curvature of the loss function and is computationally cheaper than previous methods by avoiding Double Backpropagation. Our thorough experimental validation demonstrates that our work does not suffer from CO, even in challenging settings where previous works suffer from it. We also notice that adapting our regularization parameter during training (ELLE-A) greatly improves the performance, specially in large $\epsilon$ setups. Our implementation is available in https://github.com/LIONS-EPFL/ELLE .
On an invariant for colored classical and singular links

A colored link, as defined by Francesca Aicardi, is an oriented classical link together with a coloration, which is a function defined on the set of link components and whose image is a finite set of colors. An oriented classical link can be regarded as a colored link with its components colored with a sole color. Aicardi constructed an invariant $F(L)$ of colored links $L$ defined via skein relations. When the components of a colored link are colored with the same color or when the colored link is a knot, $F(L)$ is a specialization of the HOMFLY-PT polynomial. Aicardi also showed that $F(L)$ is a stronger invariant than the HOMFLY-PT polynomial when evaluated on colored links whose components have different colors. In this paper, we provide a state-sum model for the invariant $F(L)$ of colored links using a graphical calculus for oriented, colored, 4-valent planar graphs. We also extend $F(L)$ to an invariant of oriented colored singular links.
Generalized Minkowski formulas and rigidity results for anisotropic capillary hypersurfaces

We show the generalization of Hsiung-Minkowski integral formula for anisotropic capillary hypersurfaces in the half-space, which includes the weighted Hsiung-Minkowski formula and classical anisotropic Minkowski identity for closed hypersurfaces as special cases. As applications, we prove some anisotropic Alexandrov-type theorems and rigidity results for anisotropic capillary hypersurfaces. Specially, the uniqueness of the solution to the anisotropic Orlicz-Christoffel-Minkowski problem is obtained.
LW-FedSSL: Resource-efficient Layer-wise Federated Self-supervised Learning

Many recent studies integrate federated learning (FL) with self-supervised learning (SSL) to take advantage of raw training data distributed across edge devices. However, edge devices often struggle with high computation and communication costs imposed by SSL and FL algorithms. To tackle this hindrance, we propose LW-FedSSL, a layer-wise federated self-supervised learning approach that allows edge devices to incrementally train one layer of the model at a time. LW-FedSSL comprises server-side calibration and representation alignment mechanisms to maintain comparable performance with end-to-end FedSSL while significantly lowering clients' resource requirements. The server-side calibration mechanism takes advantage of the resource-rich server in an FL environment to assist in global model training. Meanwhile, the representation alignment mechanism encourages closeness between representations of FL local models and those of the global model. Our experiments show that LW-FedSSL has a $3.3 \times$ lower memory requirement and a $3.2 \times$ cheaper communication cost than its end-to-end counterpart. We also explore a progressive training strategy called Prog-FedSSL that outperforms end-to-end training with a similar memory requirement and a $1.8 \times$ cheaper communication cost.
FAIR Enough: How Can We Develop and Assess a FAIR-Compliant Dataset for Large Language Models' Training?

The rapid evolution of Large Language Models (LLMs) underscores the critical importance of ethical considerations and data integrity in AI development, emphasizing the role of FAIR (Findable, Accessible, Interoperable, Reusable) data principles. While these principles have long been a cornerstone of ethical data stewardship, their application in LLM training data is less prevalent, an issue our research aims to address. Our study begins with a review of existing literature, highlighting the significance of FAIR principles in data management for model training. Building on this foundation, we introduce a novel framework that incorporates FAIR principles into the LLM training process. A key aspect of this approach is a comprehensive checklist, designed to assist researchers and developers in consistently applying FAIR data principles throughout the model development lifecycle. The practicality and effectiveness of our framework are demonstrated through a case study that involves creating a FAIR-compliant dataset to detect and reduce biases. This case study not only validates the usefulness of our framework but also establishes new benchmarks for more equitable, transparent, and ethical practices in LLM training. We offer this framework to the community as a means to promote technologically advanced, ethically sound, and socially responsible AI models.