Miracle-dz/UniFAR-FTUs huggingface dataset:
The porous medium equation on noncompact manifolds with nonnegative Ricci curvature: a Green function approach

We consider the porous medium equation (PME) on complete noncompact manifolds $M$ of nonnegative Ricci curvature. We require nonparabolicity of the manifold and construct a natural space $X$ of functions, strictly larger than $L^1$, in which the Green function on $M$ appears as a weight, such that the PME admits a solution in the weak dual (i.e. potential) sense whenever the initial datum $u_0$ is nonnegative and belongs to $X$. Smoothing estimates are also proved to hold both for $L^1$ data, where they take into account the volume growth of Riemannian balls giving rise to bounds which are shown to be sharp in a suitable sense, and for data belonging to $X$ as well.
Articulated Object Manipulation with Coarse-to-fine Affordance for Mitigating the Effect of Point Cloud Noise

3D articulated objects are inherently challenging for manipulation due to the varied geometries and intricate functionalities associated with articulated objects.Point-level affordance, which predicts the per-point actionable score and thus proposes the best point to interact with, has demonstrated excellent performance and generalization capabilities in articulated object manipulation. However, a significant challenge remains: while previous works use perfect point cloud generated in simulation, the models cannot directly apply to the noisy point cloud in the real-world. To tackle this challenge, we leverage the property of real-world scanned point cloud that, the point cloud becomes less noisy when the camera is closer to the object. Therefore, we propose a novel coarse-to-fine affordance learning pipeline to mitigate the effect of point cloud noise in two stages. In the first stage, we learn the affordance on the noisy far point cloud which includes the whole object to propose the approximated place to manipulate. Then, we move the camera in front of the approximated place, scan a less noisy point cloud containing precise local geometries for manipulation, and learn affordance on such point cloud to propose fine-grained final actions. The proposed method is thoroughly evaluated both using large-scale simulated noisy point clouds mimicking real-world scans, and in the real world scenarios, with superiority over existing methods, demonstrating the effectiveness in tackling the noisy real-world point cloud problem.
Universal Scaling of Clustering Instability for Interacting Active Brownian Particles

Clustering is one of the mayor collective phenomena observed in active matter. We study the overdamped motion of interacting active Brownian particles in two dimensions. An instability in the pair correlation function causes the onset of clustering. This clustering mechanism depends mainly on the self-propulsion properties of the active particles and details of the interactions do not effect the scaling of the clustering instability. Theoretical predictions from repeated ring-kinetic theory are confirmed by agent-based simulations.
A Multi-Model Ensemble System for the outer Heliosphere (MMESH): Solar Wind Conditions near Jupiter

How the solar wind influences the magnetospheres of the outer planets is a fundamentally important question, but is difficult to answer in the absence of consistent, simultaneous monitoring of the upstream solar wind and the large-scale dynamics internal to the magnetosphere. To compensate for the relative lack of in-situ data, propagation models are often used to estimate the ambient solar wind conditions at the outer planets for comparison to remote observations or in-situ measurements. This introduces another complication: the propagation of near-Earth solar wind measurements introduces difficult-to-assess uncertainties. Here, we present the Multi-Model Ensemble System for the outer Heliosphere (MMESH) to begin to address these issues, along with the resultant multi-model ensemble (MME) of the solar wind conditions near Jupiter. MMESH accepts as input any number of solar wind models together with contemporaneous in-situ spacecraft data. From these, the system characterizes typical uncertainties in model timing, quantifies how these uncertainties vary under different conditions, attempts to correct for systematic biases in the input model timing, and composes a MME with uncertainties from the results. For the case of the Jupiter-MME presented here, three solar wind propagation models were compared to in-situ measurements from the near-Jupiter spacecraft Ulysses and Juno which span diverse geometries and phases of the solar cycle, amounting to more than 14,000 hours of data over 2.5 decades. The MME gives the most-probable near-Jupiter solar wind conditions for times within the tested epoch, outperforming the input models and returning quantified estimates of uncertainty.
RL-GPT: Integrating Reinforcement Learning and Code-as-policy

Large Language Models (LLMs) have demonstrated proficiency in utilizing various tools by coding, yet they face limitations in handling intricate logic and precise control. In embodied tasks, high-level planning is amenable to direct coding, while low-level actions often necessitate task-specific refinement, such as Reinforcement Learning (RL). To seamlessly integrate both modalities, we introduce a two-level hierarchical framework, RL-GPT, comprising a slow agent and a fast agent. The slow agent analyzes actions suitable for coding, while the fast agent executes coding tasks. This decomposition effectively focuses each agent on specific tasks, proving highly efficient within our pipeline. Our approach outperforms traditional RL methods and existing GPT agents, demonstrating superior efficiency. In the Minecraft game, it rapidly obtains diamonds within a single day on an RTX3090. Additionally, it achieves SOTA performance across all designated MineDojo tasks.
Generalized Pentagon Equations

Drinfeld defined the Knizhinik--Zamolodchikov (KZ) associator $\Phi_{\rm KZ}$ by considering the regularized holonomy of the KZ connection along the {\em droit chemin} $[0,1]$. The KZ associator is a group-like element of the free associative algebra with two generators, and it satisfies the pentagon equation. In this paper, we consider paths on $\mathbb{C}\backslash \{ z_1, \dots, z_n\}$ which start and end at tangential base points. These paths are not necessarily straight, and they may have a finite number of transversal self-intersections. We show that the regularized holonomy $H$ of the KZ connection associated to such a path satisfies a generalization of Drinfeld's pentagon equation. In this equation, we encounter $H$, $\Phi_{\rm KZ}$, and new factors associated to self-intersections, to tangential base points, and to the rotation number of the path.
Debiased Novel Category Discovering and Localization

In recent years, object detection in deep learning has experienced rapid development. However, most existing object detection models perform well only on closed-set datasets, ignoring a large number of potential objects whose categories are not defined in the training set. These objects are often identified as background or incorrectly classified as pre-defined categories by the detectors. In this paper, we focus on the challenging problem of Novel Class Discovery and Localization (NCDL), aiming to train detectors that can detect the categories present in the training data, while also actively discover, localize, and cluster new categories. We analyze existing NCDL methods and identify the core issue: object detectors tend to be biased towards seen objects, and this leads to the neglect of unseen targets. To address this issue, we first propose an Debiased Region Mining (DRM) approach that combines class-agnostic Region Proposal Network (RPN) and class-aware RPN in a complementary manner. Additionally, we suggest to improve the representation network through semi-supervised contrastive learning by leveraging unlabeled data. Finally, we adopt a simple and efficient mini-batch K-means clustering method for novel class discovery. We conduct extensive experiments on the NCDL benchmark, and the results demonstrate that the proposed DRM approach significantly outperforms previous methods, establishing a new state-of-the-art.
Deep Learning for 3D Human Pose Estimation and Mesh Recovery: A Survey

3D human pose estimation and mesh recovery have attracted widespread research interest in many areas, such as computer vision, autonomous driving, and robotics. Deep learning on 3D human pose estimation and mesh recovery has recently thrived, with numerous methods proposed to address different problems in this area. In this paper, to stimulate future research, we present a comprehensive review of recent progress over the past five years in deep learning methods for this area by delving into over 200 references. To the best of our knowledge, this survey is arguably the first to comprehensively cover deep learning methods for 3D human pose estimation, including both single-person and multi-person approaches, as well as human mesh recovery, encompassing methods based on explicit models and implicit representations. We also present comparative results on several publicly available datasets, together with insightful observations and inspiring future research directions. A regularly updated project page can be found at https://github.com/liuyangme/SOTA-3DHPE-HMR.
Transformation and amplification of light in optical waveguides and resonators modulated by a low frequency traveling wave

We investigate the transformation and amplification of a light wave with phase velocity $v$ and frequency $\omega$ propagating through an optical waveguide and a racetrack resonator parametrically modulated by a travelling wave with phase velocity $v_p$ and frequency $\omega_p \ll \omega$. We consider the propagation within a small bandwidth $\Delta\omega \ll \omega$ where the waveguide is assumed to be dispersionless and low loss. Close to the luminal condition, $v_p=v$, the bandwidth of the output frequency comb spectrum, as well as the propagation nonreciprocity, can significantly grow. However, for realistic low-loss optical waveguides and resonators, the light amplification remains small due to its essentially broadband character. For a lithium niobate waveguide modulated with a $0.1\%$ relative refractive index amplitude at frequency $\omega_p \sim 100$ GHz, the amplification of the light power integrated over the frequency band $\Delta\omega \ll \omega \sim 200$ THz is estimated as $\sim 1$ dB/m.
Achieving quantized transport in Floquet topological insulators via energy filters

Due to photon-assisted transport processes, chiral edge modes induced by periodic driving do not directly mediate quantized transport. Here we show how narrow bandwidth"energy filters"can restore quantization by suppressing photon assisted transport through Floquet sidebands. We derive a Floquet Landauer type equation to describe transport through such an energy-filtered setup, and show how the filter can be integrated out to yield a sharply energy-dependent renormalized system-lead coupling. We show analytically and through numerical simulations that a nearly quantized conductance can be achieved in both off-resonantly and resonantly induced quasienergy gaps when filters are introduced. The conductance approaches the appropriate quantized value on each plateau with increasing system and filter size. We introduce a"Floquet distribution function"and show both analytically and numerically that it approaches the equilibrium Fermi-Dirac form when narrow-band filters are introduced, highlighting the mechanism that restores quantized transport.
A conceptual model for constructing high‐resolution gauge‐satellite merged precipitation analyses

[1] A conceptual model has been developed to create high-resolution precipitation analyses over land by merging gauge-based analysis and CMORPH satellite estimates using data over China for a 5 month period from April to September 2007. A two-step strategy is adopted to remove the bias inherent in the CMORPH satellite precipitation estimates and to combine the bias-corrected satellite estimates with the gauge analysis. First, bias correction is performed for the CMORPH estimates by matching the probability density function (PDF) of the satellite data with that of the gauge analysis using colocated data pairs over a spatial domain of 5°lat/lon centering at the target grid box and over a time period of 30 days, ending at the target date. The spatial domain is expanded wherever necessary over gauge-sparse regions to ensure the collection of a sufficient number of gauge-satellite data pairs. The bias-corrected CMORPH precipitation estimates are then combined with the gauge analysis through the optimal interpolation (OI) technique, in which the bias-corrected CMORPH is used as the first guess while the gauge data are used as the observations to modify the first guess over regions with station coverage. Error statistics are computed for the input gauge and satellite data to maximize the performance of the high-resolution merged analysis of daily precipitation. Cross-validation tests and comparisons against independent gauge observations demonstrate feasibility and effectiveness of the conceptual algorithm in constructing merged precipitation analysis with substantially removed bias and significantly improved pattern agreements compared with those of the input gauge and satellite data.
How Platform Exchange and Safeguards Matter: The Case of Sexual Risk in Airbnb and Couchsurfing

Recent work in CHI and CSCW has devoted increasing attention to how the design of network hospitality platforms shapes user experiences and relational outcomes. In this article, I interrogate how different risk factors emerge based on the type of exchanges these platforms facilitate. To do so, I juxtapose two prominent network hospitality platforms: one facilitating negotiated exchange (i.e., Airbnb) with another facilitating reciprocal exchange (i.e., Couchsurfing) between users. Homing in on sexual risk, an underexplored form of platform danger, and drawing on interviews with 40 female dual-platform users, I argue that the provision of binding negotiated exchange and institutional safeguards by Airbnb reduces risk through three mechanisms: casting initial guest-host relation into a buyer-seller arrangement, stabilizing interactional scripts, and formalizing sexual violence recourse. Conversely, Couchsurfing's reciprocal exchange and lack of safeguards increase sexual precarity for users both on- and off-platform. This study demonstrates how platforms with strong prosocial motivations can jeopardize sociality and concludes with implications for designs that better protect vulnerable user populations.
Warming and Cooling: The Medieval Climate Anomaly in Africa and Arabia

The Medieval Climate Anomaly (MCA) is a well-recognized climate perturbation in many parts of the world, with a core period of 1000–1200 Common Era. Here we present a palaeotemperature synthesis for the MCA in Africa and Arabia, based on 44 published localities. The data sets have been thoroughly correlated and the MCA trends palaeoclimatologically mapped. The vast majority of available Afro-Arabian onshore sites suggest a warm MCA, with the exception of the southern Levant where the MCA appears to have been cold. MCA cooling has also been documented in many segments of the circum-Africa-Arabian upwelling systems, as a result of changes in the wind systems which were leading to an intensification of cold water upwelling. Offshore cores from outside upwelling systems mostly show warm MCA conditions. The most likely key drivers of the observed medieval climate change are solar forcing and ocean cycles. Conspicuous cold spikes during the earliest and latest MCA may help to discriminate between solar (Oort Minimum) and ocean cycle (Atlantic Multidecadal Oscillation, AMO) influence. Compared to its large share of nearly one quarter of the world's landmass, data from Africa and Arabia are significantly underrepresented in global temperature reconstructions of the past 2,000 years. Onshore data are still absent for most regions in Africa and Arabia, except for regional data clusters in Morocco, South Africa, the East African Rift, and the Levant coast. In order to reconstruct land palaeotemperatures more robustly over Africa and Arabia, a systematic research program is needed.
Geometry from Integrability: Multi-Leg Fishnet Integrals in Two Dimensions

We generalise the geometric analysis of square fishnet integrals in two dimensions to the case of hexagonal fishnets with three-point vertices. Our results support the conjecture that fishnet Feynman integrals in two dimensions, together with their associated geometry, are completely fixed by their Yangian and permutation symmetries. As a new feature for the hexagonal fishnets, the star-triangle identity introduces an ambiguity in the graph representation of a given Feynman integral. This translates into a map between different geometric interpretations attached to a graph. We demonstrate explicitly how these fishnet integrals can be understood as Calabi-Yau varieties, whose Picard-Fuchs ideals are generated by the Yangian over the conformal algebra. In analogy to elliptic curves, which represent the simplest examples of fishnet integrals with four-point vertices, we find that the simplest examples of three-point fishnets correspond to Picard curves with natural generalisations at higher loop orders.
Controlling polymer translocation with crowded medium and polymer length asymmetry

Polymer translocation in crowded environments is a ubiquitous phenomenon in biological systems. We studied polymer translocation through a pore in free, one-sided (asymmetric), and two-sided (symmetric) crowded environments. Extensive Langevin dynamics simulation is employed to model the dynamics of the flexible polymer and crowding particles. We studied how crowding size and packing fraction play a crucial role in the translocation process. After determining the standard scaling properties of the translocation probability, time, and MSD, we observed that the translocation rate and bead velocities are location-dependent as we move along the polymer backbone, even in a crowd-free environment. Counter-intuitively, translocation rate and bead velocities showed the opposite behavior; for example, middle monomers near the pore exhibit maximum bead velocity and minimum translocation rate. Free energy calculation for asymmetrically placed polymer indicates there exists a critical number of segments that make the polymer to prefer the receiver side for translocation. For one-sided crowding, we have identified a critical crowding size above which there exists a non-zero probability to translocate into the crowding side instead of the free side. Moreover, we have observed that shifting the polymer towards the crowded side compensates for one-sided crowding, yielding an equal probability akin to a crowder-free system. Under two-sided crowding, the mechanism of how a slight variation in crowder size and packing fraction can force a polymer to switch its translocation direction is proposed, which has not been explored before. Using this control we achieved an equal translocation probability like a crowd-free scenario. These conspicuous yet counter-intuitive phenomena are rationalized by simple theoretical arguments based on osmotic pressure and radial entropic forces.
Energy-Aware Heterogeneous Federated Learning via Approximate Systolic DNN Accelerators

In Federated Learning (FL), devices that participate in the training usually have heterogeneous resources, i.e., energy availability. In current deployments of FL, devices that do not fulfill certain hardware requirements are often dropped from the collaborative training. However, dropping devices in FL can degrade training accuracy and introduce bias or unfairness. Several works have tacked this problem on an algorithmic level, e.g., by letting constrained devices train a subset of the server neural network (NN) model. However, it has been observed that these techniques are not effective w.r. t. accuracy. Importantly, they make simplistic assumptions about devices' resources via indirect metrics such as multiply accumulate (MAC) operations or peak memory requirements. In this work, for the first time, we consider on-device accelerator design for FL with heterogeneous devices. We utilize compressed arithmetic formats and approximate computing, targeting to satisfy limited energy budgets. Using a hardware-aware energy model, we observe that, contrary to the state of the art's moderate energy reduction, our technique allows for lowering the energy requirements (by 4x) while maintaining higher accuracy.
A Model-Based Approach for Improving Reinforcement Learning Efficiency Leveraging Expert Observations

This paper investigates how to incorporate expert observations (without explicit information on expert actions) into a deep reinforcement learning setting to improve sample efficiency. First, we formulate an augmented policy loss combining a maximum entropy reinforcement learning objective with a behavioral cloning loss that leverages a forward dynamics model. Then, we propose an algorithm that automatically adjusts the weights of each component in the augmented loss function. Experiments on a variety of continuous control tasks demonstrate that the proposed algorithm outperforms various benchmarks by effectively utilizing available expert observations.
A SOUND APPROACH: Using Large Language Models to generate audio descriptions for egocentric text-audio retrieval

Video databases from the internet are a valuable source of text-audio retrieval datasets. However, given that sound and vision streams represent different"views"of the data, treating visual descriptions as audio descriptions is far from optimal. Even if audio class labels are present, they commonly are not very detailed, making them unsuited for text-audio retrieval. To exploit relevant audio information from video-text datasets, we introduce a methodology for generating audio-centric descriptions using Large Language Models (LLMs). In this work, we consider the egocentric video setting and propose three new text-audio retrieval benchmarks based on the EpicMIR and EgoMCQ tasks, and on the EpicSounds dataset. Our approach for obtaining audio-centric descriptions gives significantly higher zero-shot performance than using the original visual-centric descriptions. Furthermore, we show that using the same prompts, we can successfully employ LLMs to improve the retrieval on EpicSounds, compared to using the original audio class labels of the dataset. Finally, we confirm that LLMs can be used to determine the difficulty of identifying the action associated with a sound.
MaxCUCL: Max-Consensus with Deterministic Convergence in Networks with Unreliable Communication

In this paper, we present a novel distributed algorithm (herein called MaxCUCL) designed to guarantee that max-consensus is reached in networks characterized by unreliable communication links (i.e., links suffering from packet drops). Our proposed algorithm is the first algorithm that achieves max-consensus in a deterministic manner (i.e., nodes always calculate the maximum of their states regardless of the nature of the probability distribution of the packet drops). Furthermore, it allows nodes to determine whether convergence has been achieved (enabling them to transition to subsequent tasks). The operation of MaxCUCL relies on the deployment of narrowband error-free feedback channels used for acknowledging whether a packet transmission between nodes was successful. We analyze the operation of our algorithm and show that it converges after a finite number of time steps. Finally, we demonstrate our algorithm's effectiveness and practical applicability by applying it to a sensor network deployed for environmental monitoring.