Miracle-dz/UniFAR-FTUs huggingface dataset:
How cytoskeletal crosstalk makes cells move: bridging cell-free and cell studies

Cell migration is a fundamental process for life and is highly dependent on the dynamical and mechanical properties of the cytoskeleton. Intensive physical and biochemical crosstalk between actin, microtubules, and intermediate filaments ensures their coordination to facilitate and enable migration. In this review we discuss the different mechanical aspects that govern cell migration and provide, for each mechanical aspect, a novel perspective by juxtaposing two complementary approaches to the biophysical study of cytoskeletal crosstalk: live-cell studies (often referred to as top-down studies) and cell-free studies (often referred to as bottom-up studies). We summarize the main findings from both experimental approaches, and we provide our perspective on bridging the two perspectives to address the open questions of how cytoskeletal crosstalk governs cell migration and makes cells move.
FIT-SLAM - Fisher Information and Traversability estimation-based Active SLAM for exploration in 3D environments

Active visual SLAM finds a wide array of applications in GNSS-Denied sub-terrain environments and outdoor environments for ground robots. To achieve robust localization and mapping accuracy, it is imperative to incorporate the perception considerations in the goal selection and path planning towards the goal during an exploration mission. Through this work, we propose FIT-SLAM (Fisher Information and Traversability estimation-based Active SLAM), a new exploration method tailored for unmanned ground vehicles (UGVs) to explore 3D environments. This approach is devised with the dual objectives of sustaining an efficient exploration rate while optimizing SLAM accuracy. Initially, an estimation of a global traversability map is conducted, which accounts for the environmental constraints pertaining to traversability. Subsequently, we propose a goal candidate selection approach along with a path planning method towards this goal that takes into account the information provided by the landmarks used by the SLAM backend to achieve robust localization and successful path execution . The entire algorithm is tested and evaluated first in a simulated 3D world, followed by a real-world environment and is compared to pre-existing exploration methods. The results obtained during this evaluation demonstrate a significant increase in the exploration rate while effectively minimizing the localization covariance.
Luttinger Liquid phase in the Aubry-Andr\'e Hubbard chain

We study the interplay between an on-site Hubbard repulsion and quasiperiodic potential in one-dimensional fermion chains using the density matrix renormalization group. We find that, at half-filling, the quasiperiodic potential can destroy the Mott gap, leading to a metallic Luttinger liquid phase between the gapped Mott insulator at strong repulsion and localized gapless Aubry- Andr\'e insulator at strong quasiperiodic potential. Away from half-filing, the metallic phase of the interacting model persists to larger critical strengths of the potential than in the non-interacting case, suggesting interaction-stabilized delocalization at finite doping. We characterize the Luttinger liquid through its charge and spin correlations, structure factors, and entanglement entropy.
Predicting and Interpreting Energy Barriers of Metallic Glasses with Graph Neural Networks

Metallic Glasses (MGs) are widely used disordered materials. Understanding the relationship between the local structure and physical properties of MGs is one of the greatest challenges for both material science and condensed matter physics. In this work, we utilize Graph Neural Networks (GNNs) to model the atomic graph structure and study the connection between the structure and the corresponding local energy barrier, which is believed to govern many critical physical properties in MGs. One of our key contributions is to propose a novel Symmetrized GNN (SymGNN) model for predicting the energy barriers, which is invariant under orthogonal transformations of the structure, e.g., rotations and reflections. Such invariance is a desired property that standard GNNs like Graph Convolutional Networks cannot capture. SymGNNs handle the invariance by aggregating over orthogonal transformations of the graph structure for representation learning, and an optimal distribution over all 3D orthogonal transformations $\mathcal{O}_3$ is learned to maximize the benefit of invariance. We demonstrate in our experiments that SymGNN can significantly improve the energy barrier prediction over other GNNs and non-graph machine learning models. With such an accurate model, we also apply graph explanation algorithms to better reveal the structure-property relationship of MGs. Our GNN framework allows effective prediction of material physical properties and bolsters material science research through the use of AI models.
Distill-SODA: Distilling Self-Supervised Vision Transformer for Source-Free Open-Set Domain Adaptation in Computational Pathology.

Developing computational pathology models is essential for reducing manual tissue typing from whole slide images, transferring knowledge from the source domain to an unlabeled, shifted target domain, and identifying unseen categories. We propose a practical setting by addressing the above-mentioned challenges in one fell swoop, i.e., source-free open-set domain adaptation. Our methodology focuses on adapting a pre-trained source model to an unlabeled target dataset and encompasses both closed-set and open-set classes. Beyond addressing the semantic shift of unknown classes, our framework also deals with a covariate shift, which manifests as variations in color appearance between source and target tissue samples. Our method hinges on distilling knowledge from a self-supervised vision transformer (ViT), drawing guidance from either robustly pre-trained transformer models or histopathology datasets, including those from the target domain. In pursuit of this, we introduce a novel style-based adversarial data augmentation, serving as hard positives for self-training a ViT, resulting in highly contextualized embeddings. Following this, we cluster semantically akin target images, with the source model offering weak pseudo-labels, albeit with uncertain confidence. To enhance this process, we present the closed-set affinity score (CSAS), aiming to correct the confidence levels of these pseudo-labels and to calculate weighted class prototypes within the contextualized embedding space. Our approach establishes itself as state-of-the-art across three public histopathological datasets for colorectal cancer assessment. Notably, our self-training method seamlessly integrates with open-set detection methods, resulting in enhanced performance in both closed-set and open-set recognition tasks.
Large Language Models Are Neurosymbolic Reasoners

A wide range of real-world applications is characterized by their symbolic nature, necessitating a strong capability for symbolic reasoning. This paper investigates the potential application of Large Language Models (LLMs) as symbolic reasoners. We focus on text-based games, significant benchmarks for agents with natural language capabilities, particularly in symbolic tasks like math, map reading, sorting, and applying common sense in text-based worlds. To facilitate these agents, we propose an LLM agent designed to tackle symbolic challenges and achieve in-game objectives. We begin by initializing the LLM agent and informing it of its role. The agent then receives observations and a set of valid actions from the text-based games, along with a specific symbolic module. With these inputs, the LLM agent chooses an action and interacts with the game environments. Our experimental results demonstrate that our method significantly enhances the capability of LLMs as automated agents for symbolic reasoning, and our LLM agent is effective in text-based games involving symbolic tasks, achieving an average performance of 88% across all tasks.
Distribution, Speciation and Bioaccumulation of Hg and As in Mariculture Sediments from Dongshan Bay, China

ABSTRACT Hg and As are the major hazardous pollutants in marine sediments due to their high toxicity to benthonic organisms. Understanding the spatial distribution, speciation and bioaccumulation of these toxic elements in sediments is therefore of high environmental importance for identifying their potential risks. Sediments and bivalves Paphia undulata were collected from the mariculture area of Dongshan Bay, China, for characterizing geochemistry (by using the European Community Bureau of Reference (BCR) sequential extraction procedure) and bioaccumulation of Hg and As [by calculating the biota sediment accumulation factor (BSAF)]. Both elements in sediments were mostly associated with the residual fraction (69.52–95.06% and 88.22–91.12% of the total concentration, respectively), followed by the oxidizable (bound to sulfides and organic matter) fraction (1.25–25.32% and 3.62–6.00%, respectively). However, Hg presented a higher bioaccumulation than As. Correlation analysis indicated that As in residual fraction and Hg in oxidizable fraction exert positive contributions (R = 0.927, P < 0.01 and R = 0.869, P < 0.05, respectively) on their own bioaccumulation factor. This indicated that P. undulata could adsorb both Hg in organic fraction and As in residual fraction from the sediments. Therefore, we should pay more attention to the potential dissolution and release of metals bound to sediments in the digestive tracts of marine organisms.
Global dynamics of a size-structured forest model

We study a size-structured model proposed in [1] C. Barril, \`A. Calsina, O. Diekmann, J. Z. Farkas, On competition through growth reduction, e-print arXiv:2303.02981, to describe the dynamics of trees growth in the forest. Our approach to the associated renewal equation is rather different from the methods in [1] and is based on ideas developed in [2] F. Herrera, S. Trofimchuk, Dynamics of one-dimensional maps and Gurtin-MacCamy's population model. Part I: asymptotically constant solutions, Ukrainian Math. J., (in Memory of O. Sharkovsky), 75 (2023), 1635-1651, https://doi.org/10.3842/umzh.v75i12. 7678. Assuming relatively weak restrictions on the reproduction, death and growth rates $\beta, \mu, g$, we establish the permanence properties of the semiflow $\frak F^t$ generated by the renewal equation and prove that it possesses a compact global attractor of points $\mathcal A$. Next we show that the opposite types of monotonicity of $\beta, g$ assure that $\frak F^t$ is also monotone and that in this case $\mathcal A$ coincides with a unique asymptotically stable equilibrium attracting neighbourhoods of compact sets with non-zero initial data.
Vacuum Stability in the Standard Model and Beyond

We revisit the stability of the Standard Model vacuum, and investigate its quantum effective potential using the highest available orders in perturbation theory and the most accurate determination of input parameters to date. We observe that the stability of the electroweak vacuum centrally depends on the values of the top mass and the strong coupling constant. We estimate that reducing their uncertainties by a factor of two is sufficient to establish or refute SM vacuum stability at the $5\sigma$ level. We further investigate vacuum stability for a variety of singlet scalar field extensions with and without flavor using the Higgs portal mechanism. We identify the BSM parameter spaces for stability and find sizable room for new physics. We further study the phenomenology of Planck-safe models at colliders, and determine the impact on the Higgs trilinear, the Higgs-to-electroweak-boson, and the Higgs quartic couplings, some of which can be significant. The former two can be probed at the HL-LHC, the latter requires a future collider with sufficient energy and precision such as the FCC-hh.
A Novel Interpretable Fusion Analytic Framework for Investigating Functional Brain Connectivity Differences in Cognitive Impairments

Functional magnetic resonance imaging (fMRI) data is characterized by its complexity and high--dimensionality, encompassing signals from various regions of interests (ROIs) that exhibit intricate correlations. Analyzing fMRI data directly proves challenging due to its intricate structure. Nevertheless, ROIs convey crucial information about brain activities through their connections, offering insights into distinctive brain activity characteristics between different groups. To address this, we propose a cutting-edge interpretable fusion analytic framework that facilitates the identification and understanding of ROI connectivity disparities between two groups, thereby revealing their unique features. Our novel approach encompasses three key steps. Firstly, we construct ROI functional connectivity networks (FCNs) to effectively manage fMRI data. Secondly, employing the FCNs, we utilize a self--attention deep learning model for binary classification, generating an attention distribution that encodes group differences. Lastly, we employ a latent space item-response model to extract group representative ROI features, visualizing these features on the group summary FCNs. We validate the effectiveness of our framework by analyzing four types of cognitive impairments, showcasing its capability to identify significant ROIs contributing to the differences between the two disease groups. This novel interpretable fusion analytic framework holds immense potential for advancing our understanding of cognitive impairments and could pave the way for more targeted therapeutic interventions.
Dark showers from Z-dark Z' mixing

We discuss dark shower signals at the LHC from a dark QCD sector, containing GeV-scale dark pions. The portal with the Standard Model is given by the mixing of the Z boson with a dark Z' coupled to the dark quarks. Both mass and kinetic mixings are included, but the mass mixing is the essential ingredient, as it is the one mediating visible decays of the long-lived dark pions. We focus especially on the possibility that the dark Z' is lighter than the Z. Indirect constraints are dominated by electroweak precision tests, which we thoroughly discuss, showing that both Z-pole and low-energy observables are important. We then recast CMS and LHCb searches for displaced dimuon resonances to dark shower signals initiated by the production of on-shell Z or Z', where the visible signature is left by a dark pion decaying to $\mu^+ \mu^-$. We demonstrate how dark shower topologies have already tested new parameter space in Run 2, reaching better sensitivity on a light dark Z' compared to the flavor-changing decays of B mesons, which can produce a single dark pion at a time, and the electroweak precision tests.
Preparing Lessons for Progressive Training on Language Models

The rapid progress of Transformers in artificial intelligence has come at the cost of increased resource consumption and greenhouse gas emissions due to growing model sizes. Prior work suggests using pretrained small models to improve training efficiency, but this approach may not be suitable for new model structures. On the other hand, training from scratch can be slow, and progressively stacking layers often fails to achieve significant acceleration. To address these challenges, we propose a novel method called Apollo, which prepares lessons for expanding operations by learning high-layer functionality during training of low layers. Our approach involves low-value-prioritized sampling (LVPS) to train different depths and weight sharing to facilitate efficient expansion. We also introduce an interpolation method for stable model depth extension. Experiments demonstrate that Apollo achieves state-of-the-art acceleration ratios, even rivaling methods using pretrained models, making it a universal and efficient solution for training deep models while reducing time, financial, and environmental costs.
Fast Kernel Summation in High Dimensions via Slicing and Fourier Transforms

Kernel-based methods are heavily used in machine learning. However, they suffer from $O(N^2)$ complexity in the number $N$ of considered data points. In this paper, we propose an approximation procedure, which reduces this complexity to $O(N)$. Our approach is based on two ideas. First, we prove that any radial kernel with analytic basis function can be represented as sliced version of some one-dimensional kernel and derive an analytic formula for the one-dimensional counterpart. It turns out that the relation between one- and $d$-dimensional kernels is given by a generalized Riemann-Liouville fractional integral. Hence, we can reduce the $d$-dimensional kernel summation to a one-dimensional setting. Second, for solving these one-dimensional problems efficiently, we apply fast Fourier summations on non-equispaced data, a sorting algorithm or a combination of both. Due to its practical importance we pay special attention to the Gaussian kernel, where we show a dimension-independent error bound and represent its one-dimensional counterpart via a closed-form Fourier transform. We provide a run time comparison and error estimate of our fast kernel summations.
New data on a cultural control method against coconut lethal yellowing in Ghana

Lethal yellowing disease has been rife in West Africa, especially Ghana, since 1932. In the 1990s, the first operations to fell diseased coconut palms at an early stage showed that this substantially slowed down the spread of the disease. Trial conducted in 1995 showed that early felling of diseased coconut palms, even without prior treatment, considerably slowed down the spread of the disease. Replications of this action in several other plots kept them healthy for many years. These positive results made it possible to obtain funding from Agence francaise de Developpement (AFD) to maintain a "sanitary cordon" in the far West of the Western Region of Ghana, near the Ivorian border, where there is a wide area of coconut palms. (Resume d'auteur)
THE ANATOMY AND RELATIONSHIPS OF ENDENNASAURUS ACUTIROSTRIS (REPTILIA, NEODIAPSIDA), FROM THE NORIAN (LATE TRIASSIC) OF LOMBARDY

A careful preparation of the holotype of Endennasautus acutirostris Renesto, 1984, that revealed part of the skull roof, along with the finding of a new specimen, allow a more complete description of the skeletal anatomy of this reptile and a better definition of its taxonomic position. The new specimen (paratype) is partially exposed from dorsal side (the holotype is exposed from the ventral side) and retains almost the entire tail, lacking in the holotype. Analysis of the newly available characters suggests close relationships between Endennasaurus and a group of Triassic marine reptiles, the Thalattosauria Merriam. Both Endennasaurus specimens were found in the Zorzino Limestone (Norian, Late Triassic), in the small quarry of Endenna, near Zogno, in the Bergamo Prealps (Lombardy, Northern ltaly).
Antipersistent energy current correlations in strong long-ranged Fermi-Pasta-Ulam-Tsingou type models

We study heat transfer in one-dimensional Fermi-Pasta-Ulam-Tsingou type systems with long-range (LR) interactions. The strength of the LR interaction between two lattice sites decays as a power $\sigma$ of the inverse of their distance. We focus on the strong LR regime ($0\leq \sigma \leq1$) and show that the thermal transport behaviors are remarkably nuanced. Specifically, we observe that the antipersistent (negative) energy current correlation in this regime is intricately dependent on $\sigma$, displaying a nonmonotonic variation. Notably, a significant qualitative change occurs at $\sigma_c=0.5$, where with respect to other $\sigma$ values, the correlation shows a minimum negative value. Furthermore, our findings also demonstrate that within the long-time range considered, these antipersistent correlations will eventually vanish for certain $\sigma>0.5$. The underlying mechanisms behind these intriguing phenomena are related to the crossover of two diverse space-time scaling properties of equilibrium heat correlations and the various scattering processes of phonons and discrete breathers.
Topological properties of caustics in five-dimensional spaces

We give a list of universal linear relations between the Euler characteristics of manifolds consisting of multisingularities of a generic Lagrangian map into a five-dimensional space. From these relations it follows, in particular, that the numbers $D_5A_2, A_4A_3, A_4A_2^2$ of isolated self-intersection points of the corresponding types on any generic compact four-dimensional caustic are even. The numbers $D_4^+A_3+D_4^-A_3+E_6$, $D_4^+A_2^2+D_4^-A_2^2+\frac12A_4A_3$ are even as well.
On algebraic properties of some non-prime ideals of collections of cells

In this work, we consider a family of coordinate rings obtained as a quotient ring of a binomial ideal associated to a collection of cells. The algebraic properties of Cohen-Macaulayness, Gorensteiness and the Hilbert-Poincar\'{e} series have been studied in literature only for some classes of collections of cells whose related binomial ideals are prime. We study these algebraic properties for some classes of collections of cells and polyominoes whose coordinate rings are not domain, considering a new strategy involving the combinatorics of the collections of cells and a recent result of Conca and Varbaro on the radical initial ideals.
Revealing Vulnerabilities in Stable Diffusion via Targeted Attacks

Recent developments in text-to-image models, particularly Stable Diffusion, have marked significant achievements in various applications. With these advancements, there are growing safety concerns about the vulnerability of the model that malicious entities exploit to generate targeted harmful images. However, the existing methods in the vulnerability of the model mainly evaluate the alignment between the prompt and generated images, but fall short in revealing the vulnerability associated with targeted image generation. In this study, we formulate the problem of targeted adversarial attack on Stable Diffusion and propose a framework to generate adversarial prompts. Specifically, we design a gradient-based embedding optimization method to craft reliable adversarial prompts that guide stable diffusion to generate specific images. Furthermore, after obtaining successful adversarial prompts, we reveal the mechanisms that cause the vulnerability of the model. Extensive experiments on two targeted attack tasks demonstrate the effectiveness of our method in targeted attacks. The code can be obtained in https://github.com/datar001/Revealing-Vulnerabilities-in-Stable-Diffusion-via-Targeted-Attacks.
The First Polarimetric View on Quasiperiodic Oscillations in a Black Hole X-Ray Binary

We present the first polarimetric analysis of quasiperiodic oscillations (QPOs) in a black hole binary utilizing IXPE data. Our study focuses on Swift J1727.8–1613, which experienced a massive outburst that was observed by various telescopes across different wavelengths. The IXPE observation we studied was conducted during the hard-intermediate state. The polarization degree (PD) and polarization angle (PA) were measured at 4.28% ± 0.20% and 1. °9 ± 1. °4, respectively. Remarkably, significant QPO signals were detected during this observation, with a QPO frequency of approximately 1.34 Hz and a fractional rms amplitude of about 12.3%. Furthermore, we conducted a phase-resolved analysis of the QPO using the Hilbert–Huang transform technique. The photon index showed a strong modulation with respect to the QPO phase. In contrast, the PD and PA exhibit no modulations in relation to the QPO phase, which is inconsistent with the expectation of the Lense–Thirring precession of the inner flow. Further theoretical studies are needed to conform with the observational results.