Miracle-dz/UniFAR-FTUs huggingface dataset:
DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset

The creation of large, diverse, high-quality robot manipulation datasets is an important stepping stone on the path toward more capable and robust robotic manipulation policies. However, creating such datasets is challenging: collecting robot manipulation data in diverse environments poses logistical and safety challenges and requires substantial investments in hardware and human labour. As a result, even the most general robot manipulation policies today are mostly trained on data collected in a small number of environments with limited scene and task diversity. In this work, we introduce DROID (Distributed Robot Interaction Dataset), a diverse robot manipulation dataset with 76k demonstration trajectories or 350 hours of interaction data, collected across 564 scenes and 84 tasks by 50 data collectors in North America, Asia, and Europe over the course of 12 months. We demonstrate that training with DROID leads to policies with higher performance and improved generalization ability. We open source the full dataset, policy learning code, and a detailed guide for reproducing our robot hardware setup.
FootstepNet: an Efficient Actor-Critic Method for Fast On-line Bipedal Footstep Planning and Forecasting

Designing a humanoid locomotion controller is challenging and classically split up in sub-problems. Footstep planning is one of those, where the sequence of footsteps is defined. Even in simpler environments, finding a minimal sequence, or even a feasible sequence, yields a complex optimization problem. In the literature, this problem is usually addressed by search-based algorithms (e.g. variants of A*). However, such approaches are either computationally expensive or rely on hand-crafted tuning of several parameters. In this work, at first, we propose an efficient footstep planning method to navigate in local environments with obstacles, based on state-of-the art Deep Reinforcement Learning (DRL) techniques, with very low computational requirements for on-line inference. Our approach is heuristic-free and relies on a continuous set of actions to generate feasible footsteps. In contrast, other methods necessitate the selection of a relevant discrete set of actions. Second, we propose a forecasting method, allowing to quickly estimate the number of footsteps required to reach different candidates of local targets. This approach relies on inherent computations made by the actor-critic DRL architecture. We demonstrate the validity of our approach with simulation results, and by a deployment on a kid-size humanoid robot during the RoboCup 2023 competition.
Consistency of Value of Information: Effects of Packet Loss and Time Delay in Networked Control Systems Tasks

In this chapter, we study the consistency of the value of information$\unicode{x2014}$a semantic metric that claims to determine the right piece of information in networked control systems tasks$\unicode{x2014}$in a lossy and delayed communication regime. Our analysis begins with a focus on state estimation, and subsequently extends to feedback control. To that end, we make a causal tradeoff between the packet rate and the mean square error. Associated with this tradeoff, we demonstrate the existence of an optimal policy profile, comprising a symmetric threshold scheduling policy based on the value of information for the encoder and a non-Gaussian linear estimation policy for the decoder. Our structural results assert that the scheduling policy is expressible in terms of $3d-1$ variables related to the source and the channel, where $d$ is the time delay, and that the estimation policy incorporates no residual related to signaling. We then construct an optimal control policy by exploiting the separation principle.
Non-coercive Neumann boundary control problems

The article examines a linear-quadratic Neumann control problem that is governed by a non-coercive elliptic equation. Due to the non-self-adjoint nature of the linear control-to-state operator, it is necessary to independently study both the state and adjoint state equations. The article establishes the existence and uniqueness of solutions for both equations, with minimal assumptions made about the problem's data. Next, the regularity of these solutions is studied in three frameworks: Hilbert-Sobolev spaces, Sobolev-Slobodecki\u\i{} spaces, and weighted Sobolev spaces. These regularity results enable a numerical analysis of the finite element approximation of both the state and adjoint state equations. The results cover both convex and non-convex domains and quasi-uniform and graded meshes. Finally, the optimal control problem is analyzed and discretized. Existence and uniqueness of the solution, first-order optimality conditions, and error estimates for the finite element approximation of the control are obtained. Numerical experiments confirming these results are included. A significant highlight is that the discretization error estimates known from the literature, are improved even for the coercive case.
Zero-Reference Low-Light Enhancement via Physical Quadruple Priors

Understanding illumination and reducing the need for supervision pose a significant challenge in low-light enhancement. Current approaches are highly sensitive to data usage during training and illumination-specific hyper-parameters, limiting their ability to handle unseen scenarios. In this paper, we propose a new zero-reference low-light enhancement framework trainable solely with normal light images. To accomplish this, we devise an illumination-invariant prior inspired by the theory of physical light transfer. This prior serves as the bridge between normal and low-light images. Then, we develop a prior-to-image framework trained without low-light data. During testing, this framework is able to restore our illumination-invariant prior back to images, automatically achieving low-light enhancement. Within this framework, we leverage a pretrained generative diffusion model for model ability, introduce a bypass decoder to handle detail distortion, as well as offer a lightweight version for practicality. Extensive experiments demonstrate our framework's superiority in various scenarios as well as good interpretability, robustness, and efficiency. Code is available on our project homepage: http://daooshee.github. io/QuadPrior-Website/
DNA Double-Strand Breaks Coupled with PARP1 and HNRNPA2B1 Binding Sites Flank Coordinately Expressed Domains in Human Chromosomes

Genome instability plays a key role in multiple biological processes and diseases, including cancer. Genome-wide mapping of DNA double-strand breaks (DSBs) is important for understanding both chromosomal architecture and specific chromosomal regions at DSBs. We developed a method for precise genome-wide mapping of blunt-ended DSBs in human chromosomes, and observed non-random fragmentation and DSB hot spots. These hot spots are scattered along chromosomes and delimit protected 50–250 kb DNA domains. We found that about 30% of the domains (denoted forum domains) possess coordinately expressed genes and that PARP1 and HNRNPA2B1 specifically bind DNA sequences at the forum domain termini. Thus, our data suggest a novel type of gene regulation: a coordinated transcription or silencing of gene clusters delimited by DSB hot spots as well as PARP1 and HNRNPa2B1 binding sites.
Aerial Lifting: Neural Urban Semantic and Building Instance Lifting from Aerial Imagery

We present a neural radiance field method for urban-scale semantic and building-level instance segmentation from aerial images by lifting noisy 2D labels to 3D. This is a challenging problem due to two primary reasons. Firstly, objects in urban aerial images exhibit substantial variations in size, including buildings, cars, and roads, which pose a significant challenge for accurate 2D segmentation. Secondly, the 2D labels generated by existing segmentation methods suffer from the multi-view inconsistency problem, especially in the case of aerial images, where each image captures only a small portion of the entire scene. To overcome these limitations, we first introduce a scale-adaptive semantic label fusion strategy that enhances the segmentation of objects of varying sizes by combining labels predicted from different altitudes, harnessing the novel-view synthesis capabilities of NeRF. We then introduce a novel cross-view instance label grouping strategy based on the 3D scene representation to mitigate the multi-view inconsistency problem in the 2D instance labels. Furthermore, we exploit multi-view reconstructed depth priors to improve the geometric quality of the reconstructed radiance field, resulting in enhanced segmentation results. Experiments on multiple real-world urban-scale datasets demonstrate that our approach outperforms existing methods, highlighting its effectiveness.
Differences in brain structure in patients with distinct sites of chronic pain: A voxel-based morphometric analysis

A reduction in gray matter volume is common in patients with chronic back pain, and different types of pain are associated with gray matter abnormalities in distinct brain regions. To examine differences in brain morphology in patients with low back pain or neck and upper back pain, we investigated changes in gray matter volume in chronic back pain patients having different sites of pain using voxel-based morphometry. A reduction in cortical gray matter volume was found primarily in the left postcentral gyrus and in the left precuneus and bilateral cuneal cortex of patients with low back pain. In these patients, there was an increase in subcortical gray matter volume in the bilateral putamen and accumbens, right pallidum, right caudate nucleus, and left amygdala. In upper back pain patients, reduced cortical gray matter volume was found in the left precentral and left postcentral cortices. Our findings suggest that regional gray matter volume abnormalities in low back pain patients are more extensive than in upper back pain patients. Subcortical gray matter volume increases are found only in patients with low back pain.
FSO artificial low-cost fog attenuation experiment design

A low-cost experiment, feasible without the use of laboratory-grade atmospheric chamber and optical spectral analyzer, was designed to experimentally evaluate the influence of fog and smoke on free-space optical systems. Low-visibility conditions were simulated using an artificial fog machine and smoke flares in a 50 m PVC corridor with several bi-directional wireless optical links established at wavelengths in the 1270 nm-1550 nm range with an additional green laser based measurement system. In low visibility conditions the variation in attenuation with wavelength was found to be larger than predicted by the standard model. We propose a new empirical model for this region and show that longer infra-red wavelengths are more suitable for free space optical communication.
Let's Focus on Neuron: Neuron-Level Supervised Fine-tuning for Large Language Model

Large Language Models (LLMs) are composed of neurons that exhibit various behaviors and roles, which become increasingly diversified as models scale. Recent studies have revealed that not all neurons are active across different datasets, and this sparsity correlates positively with the task-specific ability, leading to advancements in model pruning and training efficiency. Traditional fine-tuning methods engage all parameters of LLMs, which is computationally expensive and may not be necessary. In contrast, Parameter-Efficient Fine-Tuning (PEFT) approaches aim to minimize the number of trainable parameters, yet they still operate at a relatively macro scale (e.g., layer-level). We introduce Neuron-Level Fine-Tuning (NeFT), a novel approach that refines the granularity of parameter training down to the individual neuron, enabling more precise and computationally efficient model updates. The experimental results show that NeFT not only exceeded the performance of full-parameter fine-tuning and PEFT but also provided insights into the analysis of neurons.
ReGenNet: Towards Human Action-Reaction Synthesis

Humans constantly interact with their surrounding environments. Current human-centric generative models mainly focus on synthesizing humans plausibly interacting with static scenes and objects, while the dynamic human action-reaction synthesis for ubiquitous causal human-human interactions is less explored. Human-human interactions can be regarded as asymmetric with actors and reactors in atomic interaction periods. In this paper, we comprehensively analyze the asymmetric, dynamic, synchronous, and detailed nature of human-human interactions and propose the first multi-setting human action-reaction synthesis benchmark to generate human reactions conditioned on given human actions. To begin with, we propose to annotate the actor-reactor order of the interaction sequences for the NTU120, InterHuman, and Chi3D datasets. Based on them, a diffusion-based generative model with a Transformer decoder architecture called ReGenNet together with an explicit distance-based interaction loss is proposed to predict human reactions in an online manner, where the future states of actors are unavailable to reactors. Quantitative and qualitative results show that our method can generate instant and plausible human reactions compared to the baselines, and can generalize to unseen actor motions and viewpoint changes.
Numerical method for nonlinear Kolmogorov PDEs via sensitivity analysis

We examine nonlinear Kolmogorov partial differential equations (PDEs). Here the nonlinear part of the PDE comes from its Hamiltonian where one maximizes over all possible drift and diffusion coefficients which fall within a $\varepsilon$-neighborhood of pre-specified baseline coefficients. Our goal is to quantify and compute how sensitive those PDEs are to such a small nonlinearity, and then use the results to develop an efficient numerical method for their approximation. We show that as $\varepsilon\downarrow 0$, the nonlinear Kolmogorov PDE equals the linear Kolmogorov PDE defined with respect to the corresponding baseline coefficients plus $\varepsilon$ times a correction term which can be also characterized by the solution of another linear Kolmogorov PDE involving the baseline coefficients. As these linear Kolmogorov PDEs can be efficiently solved in high-dimensions by exploiting their Feynman-Kac representation, our derived sensitivity analysis then provides a Monte Carlo based numerical method which can efficiently solve these nonlinear Kolmogorov equations. We provide numerical examples in up to 100 dimensions to empirically demonstrate the applicability of our numerical method.
The Value of Reward Lookahead in Reinforcement Learning

In reinforcement learning (RL), agents sequentially interact with changing environments while aiming to maximize the obtained rewards. Usually, rewards are observed only after acting, and so the goal is to maximize the expected cumulative reward. Yet, in many practical settings, reward information is observed in advance -- prices are observed before performing transactions; nearby traffic information is partially known; and goals are oftentimes given to agents prior to the interaction. In this work, we aim to quantifiably analyze the value of such future reward information through the lens of competitive analysis. In particular, we measure the ratio between the value of standard RL agents and that of agents with partial future-reward lookahead. We characterize the worst-case reward distribution and derive exact ratios for the worst-case reward expectations. Surprisingly, the resulting ratios relate to known quantities in offline RL and reward-free exploration. We further provide tight bounds for the ratio given the worst-case dynamics. Our results cover the full spectrum between observing the immediate rewards before acting to observing all the rewards before the interaction starts.
Beyond the flow decomposition barrier

We introduce a new approach to the maximum flow problem. This approach is based on assigning arc lengths based on the residual flow value and the residual are capacities. Our approach leads to an O(min(n/sup 2/3/, m/sup 1/2/)m log(n/sup 2//m) log U) time bound for a network with n vertices, m arcs, and integral arc capacities in the range [1,. ,U]. This is a fundamental improvement over the previous time bounds. We also improve bounds for the Gomory-Hu tree problem, the parametric flow problem, and the approximate s-t cut problems.
Continual Domain Randomization

Domain Randomization (DR) is commonly used for sim2real transfer of reinforcement learning (RL) policies in robotics. Most DR approaches require a simulator with a fixed set of tunable parameters from the start of the training, from which the parameters are randomized simultaneously to train a robust model for use in the real world. However, the combined randomization of many parameters increases the task difficulty and might result in sub-optimal policies. To address this problem and to provide a more flexible training process, we propose Continual Domain Randomization (CDR) for RL that combines domain randomization with continual learning to enable sequential training in simulation on a subset of randomization parameters at a time. Starting from a model trained in a non-randomized simulation where the task is easier to solve, the model is trained on a sequence of randomizations, and continual learning is employed to remember the effects of previous randomizations. Our robotic reaching and grasping tasks experiments show that the model trained in this fashion learns effectively in simulation and performs robustly on the real robot while matching or outperforming baselines that employ combined randomization or sequential randomization without continual learning. Our code and videos are available at https://continual-dr.github. io/.
Guiding the generation of counterfactual explanations through temporal background knowledge for Predictive Process Monitoring

Counterfactual explanations suggest what should be different in the input instance to change the outcome of an AI system. When dealing with counterfactual explanations in the field of Predictive Process Monitoring, however, control flow relationships among events have to be carefully considered. A counterfactual, indeed, should not violate control flow relationships among activities (temporal background knowledege). Within the field of Explainability in Predictive Process Monitoring, there have been a series of works regarding counterfactual explanations for outcome-based predictions. However, none of them consider the inclusion of temporal background knowledge when generating these counterfactuals. In this work, we adapt state-of-the-art techniques for counterfactual generation in the domain of XAI that are based on genetic algorithms to consider a series of temporal constraints at runtime. We assume that this temporal background knowledge is given, and we adapt the fitness function, as well as the crossover and mutation operators, to maintain the satisfaction of the constraints. The proposed methods are evaluated with respect to state-of-the-art genetic algorithms for counterfactual generation and the results are presented. We showcase that the inclusion of temporal background knowledge allows the generation of counterfactuals more conformant to the temporal background knowledge, without however losing in terms of the counterfactual traditional quality metrics.
Ant Colony Algorithm for Swarm Systems

The paper presents a swarm model inspired in ant colonies where ants perform different tasks. The model emphasizes explorer and worker ants responsible for food search and bringing food back to the nest, respectively. The model is based on ACO - ant colony optimization providing a shortest path algorithm during route exploration. Results from ant colony model simulations are presented and contrasted
Tuning of the ultrafast demagnetization by ultrashort spin polarized currents in multi-sublattice ferrimagnets

Femtosecond laser pulses can be used to induce ultrafast changes of the magnetization in magnetic materials. Several microscopic mechanisms have been proposed to explain the observations, including the transport of ultrashort spin-polarized hot-electrons (SPHE). Such ultrafast spin currents find growing interest because of the recent challenges in ultrafast spintronics however they are only poorly characterized. One of the key challenges is to characterize the spin-polarized ultrafast currents and the microscopic mechanisms behind SPHE induced manipulation of the magnetization, especially in the case of technologically relevant ferrimagnetic alloys. Here, we have used a combined approach using time- and element-resolved X-ray magnetic circular dichroism and theoretical calculations based on atomistic spin-dynamics simulations to address the ultrafast transfer of the angular momentum from spin-polarized currents into ferrimagnetic Fe74Gd26 films and the concomitant reduction of sub-lattice magnetization. Our study shows that using a Co/Pt multilayer as a polarizer in a spin-valve structure, the SPHE drives the demagnetization of the two sub-lattices of the Fe74Gd26 film. This behaviour is explained based on two physical mechanisms, i.e., spin transfer torque and thermal fluctuations induced by the SPHE. We provide a quantitative description of the heat transfer of the ultrashort SPHE pulse to the Fe74Gd26 films, as well as the degree of spin-polarization of the SPHE current density responsible for the observed magnetization dynamics. Our work finally characterizes the spin-polarization of the SPHEs revealing unexpected opposite spin polarization to the Co magnetization, explaining our experimental results.
Inter-individual and inter-site neural code conversion and image reconstruction without shared stimuli

The human brain demonstrates substantial inter-individual variability in fine-grained functional topography, posing challenges in identifying common neural representations across individuals. Functional alignment has the potential to harmonize these individual differences. However, it typically requires an identical set of stimuli presented to different individuals, which is often unavailable. To address this, we propose a content loss-based neural code converter, designed to convert brain activity from one subject to another representing the same content. The converter is optimized so that the source subject's converted brain activity is decoded into a latent image representation that closely resembles that of the stimulus given to the source subject. We show that converters optimized using hierarchical image representations achieve conversion accuracy comparable to those optimized by paired brain activity as in conventional methods. The brain activity converted from a different individual and even from a different site sharing no stimuli produced reconstructions that approached the quality of within-individual reconstructions. The converted brain activity had a generalizable representation that can be read out by different decoding schemes. The converter required much fewer training samples than that typically required for decoder training to produce recognizable reconstructions. These results demonstrate that our method can effectively combine image representations to convert brain activity across individuals without the need for shared stimuli, providing a promising tool for flexibly aligning data from complex cognitive tasks and a basis for brain-to-brain communication.