Miracle-dz/UniFAR-FTUs huggingface dataset:
AirIMU: Learning Uncertainty Propagation for Inertial Odometry

Accurate uncertainty estimation for inertial odometry is the foundation to achieve optimal fusion in multi-sensor systems, such as visual or LiDAR inertial odometry. Prior studies often simplify the assumptions regarding the uncertainty of inertial measurements, presuming fixed covariance parameters and empirical IMU sensor models. However, the inherent physical limitations and non-linear characteristics of sensors are difficult to capture. Moreover, uncertainty may fluctuate based on sensor rates and motion modalities, leading to variations across different IMUs. To address these challenges, we formulate a learning-based method that not only encapsulate the non-linearities inherent to IMUs but also ensure the accurate propagation of covariance in a data-driven manner. We extend the PyPose library to enable differentiable batched IMU integration with covariance propagation on manifolds, leading to significant runtime speedup. To demonstrate our method's adaptability, we evaluate it on several benchmarks as well as a large-scale helicopter dataset spanning over 262 kilometers. The drift rate of the inertial odometry on these datasets is reduced by a factor of between 2.2 and 4 times. Our method lays the groundwork for advanced developments in inertial odometry.
Aligning Language Models with Human Preferences via a Bayesian Approach

In the quest to advance human-centric natural language generation (NLG) systems, ensuring alignment between NLG models and human preferences is crucial. For this alignment, current popular methods leverage a reinforcement learning (RL) approach with a reward model trained on feedback from humans. However, inherent disagreements due to the subjective nature of human preferences pose a significant challenge for training the reward model, resulting in a deterioration of the NLG performance. To tackle this issue, previous approaches typically rely on majority voting or averaging to consolidate multiple inconsistent preferences into a merged one. Although straightforward to understand and execute, such methods suffer from an inability to capture the nuanced degrees of disaggregation among humans and may only represent a specialized subset of individuals, thereby lacking the ability to quantitatively disclose the universality of human preferences. To address this challenge, this paper proposes a novel approach, which employs a Bayesian framework to account for the distribution of disagreements among human preferences as training a preference model, and names it as d-PM. Besides, considering the RL strategy's inefficient and complex training process over the training efficiency, we further propose utilizing the contrastive learning strategy to train the NLG model with the preference scores derived from the d-PM model. Extensive experiments on two human-centric NLG tasks, i.e., emotional support conversation and integrity"Rule-of-Thumb"generation, show that our method consistently exceeds previous SOTA models in both automatic and human evaluations.
Cell Tracking-by-detection using Elliptical Bounding Boxes

Cell detection and tracking are paramount for bio-analysis. Recent approaches rely on the tracking-by-model evolution paradigm, which usually consists of training end-to-end deep learning models to detect and track the cells on the frames with promising results. However, such methods require extensive amounts of annotated data, which is time-consuming to obtain and often requires specialized annotators. This work proposes a new approach based on the classical tracking-by-detection paradigm that alleviates the requirement of annotated data. More precisely, it approximates the cell shapes as oriented ellipses and then uses generic-purpose oriented object detectors to identify the cells in each frame. We then rely on a global data association algorithm that explores temporal cell similarity using probability distance metrics, considering that the ellipses relate to two-dimensional Gaussian distributions. Our results show that our method can achieve detection and tracking results competitively with state-of-the-art techniques that require considerably more extensive data annotation. Our code is available at: https://github.com/LucasKirsten/Deep-Cell-Tracking-EBB.
Characterizing Barriers and Technology Needs in the Kitchen for Blind and Low Vision People

Cooking is a vital yet challenging activity for people with visual impairments (PVI). It involves tasks that can be dangerous or difficult without vision, such as handling a knife or adding a suitable amount of salt. A better understanding of these challenges can inform the design of technologies that mitigate safety hazards and improve the quality of the lives of PVI. Furthermore, there is a need to understand the effects of different visual abilities, including low vision and blindness, and the role of rehabilitation training where PVI learn cooking skills and assistive technologies. In this paper, we aim to comprehensively characterize PVI's challenges, strategies, and needs in the kitchen from the perspectives of both PVI and rehabilitation professionals. Through a contextual inquiry study, we observed 10 PVI, including six low vision and four blind participants, when they cooked dishes of their choices in their own kitchens. We then interviewed six rehabilitation professionals to explore their training strategies and technology recommendations. Our findings revealed the differences between low vision and blind people during cooking as well as the gaps between training and reality. We suggest improvements for rehabilitation training and distill design considerations for future assistive technology in the kitchen.
Age and origin of sillimanite schist from the Chinese Altai metamorphic belt: implications for late Palaeozoic tectonic evolution of the Central Asian Orogenic Belt

Reconstructing late Palaeozoic metamorphism of the Central Asian Orogenic Belt (CAOB) can provide a better understanding of how the CAOB formed. The petrology of sillimanite-bearing metapelitic schists from high-grade portions of the Permian Chinese Altai metamorphic belt (andalusite-type) reflects the effects of poorly understood high-T, low-P metamorphism. Phase equilibria modelling in the Na2O–CaO–K2O–FeO–MgO–Al2O3–SiO2–TiO2–O (NCKFMASHTO) system restricts P–T conditions of the sillimanite schists to approximately 635–670°C at approximately 5.8–6.8 kbar. SHRIMP U–Pb analyses of zircon from the rocks yield a concordant age of 299.2 ± 3.4 Ma. Combined with the slightly younger (292.8 ± 2.3 Ma) areally restricted pelitic granulite with peak P−T conditions of approximately 780–800°C at approximately 5–6 kbar and high-T granulite with P−T conditions of approximately 860°C at approximately 6 kbar, these metamorphic rocks reflect prograde heating at relatively low pressure in early Permian time. Together with contemporary and widespread magmatic activities, they are best explained in the context of a post-orogenic extensional environment related to a mantle plume.
PMNN: Physical Model-driven Neural Network for solving time-fractional differential equations

In this paper, an innovative Physical Model-driven Neural Network (PMNN) method is proposed to solve time-fractional differential equations. It establishes a temporal iteration scheme based on physical model-driven neural networks which effectively combines deep neural networks (DNNs) with interpolation approximation of fractional derivatives. Specifically, once the fractional differential operator is discretized, DNNs are employed as a bridge to integrate interpolation approximation techniques with differential equations. On the basis of this integration, we construct a neural-based iteration scheme. Subsequently, by training DNNs to learn this temporal iteration scheme, approximate solutions to the differential equations can be obtained. The proposed method aims to preserve the intrinsic physical information within the equations as far as possible. It fully utilizes the powerful fitting capability of neural networks while maintaining the efficiency of the difference schemes for fractional differential equations. Moreover, we validate the efficiency and accuracy of PMNN through several numerical experiments.
Detection of a locked zone at depth on the Parkfield, California, segment of the San Andreas Fault

The Parkfield, California, segment of the San Andreas fault is transitional in character between the creeping segment of the fault to the northwest and the locked Carrizo Plain segment to the southeast. The rate of shallow fault slip decreases from 25–30 mm/yr northwest of the epicenter of the 1966 Parkfield earthquake to zero at the southeastern end of the 1966 rupture zone. Data from a network of trilateration lines spanning the San Andreas fault near Parkfield and extending to the Pacific coast near San Luis Obispo shed light on the rate of fault slip at depth since the 1966 earthquake. In this study, average rates of line length change and shallow fault slip were inverted to determine the slip rate at depth on the Parkfield fault segment. The fault is taken to be a vertical surface with unknown distribution of strike-slip displacement in an elastic half-space. A striking result of the inversions is that all solutions providing acceptable fits to the data exhibit a locked zone essentially coincident with the rupture surface of the 1966 Parkfield earthquake. The data require that the locked zone extend nearly as far north as the 1966 epicenter; however, the vertical extent of the locked zone is not well resolved. Over much of the Parkfield segment the fault is slipping faster at the earth's surface than it is at seismogenic depths. In order to fit the trilateration measurements it is necessary to include a component of contraction normal to the trend of the San Andreas. The inversion results suggest a spatially uniform normal strain of −0.06 μstrain/yr. The orientation of the contraction is compatible with geologic and seismic evidence of active folding and reverse faulting in the region. The magnitude of the contraction is consistent with convergence rates inferred from global plate motion models.
Genetic prediction of quantitative traits: a machine learner's guide focused on height

Machine learning and deep learning have been celebrating many successes in the application to biological problems, especially in the domain of protein folding. Another equally complex and important question has received relatively little attention by the machine learning community, namely the one of prediction of complex traits from genetics. Tackling this problem requires in-depth knowledge of the related genetics literature and awareness of various subtleties associated with genetic data. In this guide, we provide an overview for the machine learning community on current state of the art models and associated subtleties which need to be taken into consideration when developing new models for phenotype prediction. We use height as an example of a continuous-valued phenotype and provide an introduction to benchmark datasets, confounders, feature selection, and common metrics.
An Analytic Model For Magnetically-Dominated Accretion Disks

Recent numerical cosmological radiation-magnetohydrodynamic-thermochemical-star formation simulations have resolved the formation of quasar accretion disks with Eddington or super-Eddington accretion rates onto supermassive black holes (SMBHs) down to a few hundred gravitational radii. These 'flux-frozen' and hyper-magnetized disks appear to be qualitatively distinct from classical $\alpha$ disks and magnetically-arrested disks: the midplane pressure is dominated by toroidal magnetic fields with plasma $\beta \ll 1$ powered by advection of magnetic flux from the interstellar medium (ISM), and they are super-sonically and trans-Alfvenically turbulent with cooling times short compared to dynamical times yet remain gravitationally stable owing to magnetic support. In this paper, we present a simple analytic similarity model for such disks. For reasonable assumptions, the model is entirely specified by the boundary conditions (inflow rate at the BH radius of influence [BHROI]). We show that the scalings from this model are robust to various detailed assumptions, agree remarkably well with the simulations (given their simplicity), and demonstrate the self-consistency and gravitational stability of such disks even in the outer accretion disk (approaching the BHROI) at hyper-Eddington accretion rates.
RoboHive: A Unified Framework for Robot Learning

We present RoboHive, a comprehensive software platform and ecosystem for research in the field of Robot Learning and Embodied Artificial Intelligence. Our platform encompasses a diverse range of pre-existing and novel environments, including dexterous manipulation with the Shadow Hand, whole-arm manipulation tasks with Franka and Fetch robots, quadruped locomotion, among others. Included environments are organized within and cover multiple domains such as hand manipulation, locomotion, multi-task, multi-agent, muscles, etc. In comparison to prior works, RoboHive offers a streamlined and unified task interface taking dependency on only a minimal set of well-maintained packages, features tasks with high physics fidelity and rich visual diversity, and supports common hardware drivers for real-world deployment. The unified interface of RoboHive offers a convenient and accessible abstraction for algorithmic research in imitation, reinforcement, multi-task, and hierarchical learning. Furthermore, RoboHive includes expert demonstrations and baseline results for most environments, providing a standard for benchmarking and comparisons. Details: https://sites.google. com/view/robohive
Robust Average Networks for Monte Carlo Denoising

We present a method for converting denoising neural networks from spatial into spatio-temporal ones by modifying the network architecture and loss function. We insert Robust Average blocks at arbitrary depths in the network graph. Each block performs latent space interpolation with trainable weights and works on the sequence of image representations from the preceding spatial components of the network. The temporal connections are kept live during training by forcing the network to predict a denoised frame from subsets of the input sequence. Using temporal coherence for denoising improves image quality and reduces temporal flickering independent of scene or image complexity.
WeatherDepth: Curriculum Contrastive Learning for Self-Supervised Depth Estimation under Adverse Weather Conditions

Depth estimation models have shown promising performance on clear scenes but fail to generalize to adverse weather conditions due to illumination variations, weather particles, etc. In this paper, we propose WeatherDepth, a self-supervised robust depth estimation model with curriculum contrastive learning, to tackle performance degradation in complex weather conditions. Concretely, we first present a progressive curriculum learning scheme with three simple-to-complex curricula to gradually adapt the model from clear to relative adverse, and then to adverse weather scenes. It encourages the model to gradually grasp beneficial depth cues against the weather effect, yielding smoother and better domain adaption. Meanwhile, to prevent the model from forgetting previous curricula, we integrate contrastive learning into different curricula. By drawing reference knowledge from the previous course, our strategy establishes a depth consistency constraint between different courses toward robust depth estimation in diverse weather. Besides, to reduce manual intervention and better adapt to different models, we designed an adaptive curriculum scheduler to automatically search for the best timing for course switching. In the experiment, the proposed solution is proven to be easily incorporated into various architectures and demonstrates state-of-the-art (SoTA) performance on both synthetic and real weather datasets. Source code and data are available at \url{https://github.com/wangjiyuan9/WeatherDepth}.
Initialization Bias of Fourier Neural Operator: Revisiting the Edge of Chaos

This paper investigates the initialization bias of the Fourier neural operator (FNO). A mean-field theory for FNO is established, analyzing the behavior of the random FNO from an \emph{edge of chaos} perspective. We uncover that the forward and backward propagation behaviors exhibit characteristics unique to FNO, induced by mode truncation, while also showcasing similarities to those of densely connected networks. Building upon this observation, we also propose an edge of chaos initialization scheme for FNO to mitigate the negative initialization bias leading to training instability. Experimental results show the effectiveness of our initialization scheme, enabling stable training of deep FNO without skip-connection.
Projected Push-Pull For Distributed Constrained Optimization Over Time-Varying Directed Graphs (extended version)

We introduce the Projected Push-Pull algorithm that enables multiple agents to solve a distributed constrained optimization problem with private cost functions and global constraints, in a collaborative manner. Our algorithm employs projected gradient descent to deal with constraints and a lazy update rule to control the trade-off between the consensus and optimization steps in the protocol. We prove that our algorithm achieves geometric convergence over time-varying directed graphs while ensuring that the decision variable always stays within the constraint set. We derive explicit bounds for step sizes that guarantee geometric convergence based on the strong-convexity and smoothness of cost functions, and graph properties. Moreover, we provide additional theoretical results on the usefulness of lazy updates, revealing the challenges in the analysis of any gradient tracking method that uses projection operators in a distributed constrained optimization setting. We validate our theoretical results with numerical studies over different graph types, showing that our algorithm achieves geometric convergence empirically.
Randomized Sparse Neural Galerkin Schemes for Solving Evolution Equations with Deep Networks

Training neural networks sequentially in time to approximate solution fields of time-dependent partial differential equations can be beneficial for preserving causality and other physics properties; however, the sequential-in-time training is numerically challenging because training errors quickly accumulate and amplify over time. This work introduces Neural Galerkin schemes that update randomized sparse subsets of network parameters at each time step. The randomization avoids overfitting locally in time and so helps prevent the error from accumulating quickly over the sequential-in-time training, which is motivated by dropout that addresses a similar issue of overfitting due to neuron co-adaptation. The sparsity of the update reduces the computational costs of training without losing expressiveness because many of the network parameters are redundant locally at each time step. In numerical experiments with a wide range of evolution equations, the proposed scheme with randomized sparse updates is up to two orders of magnitude more accurate at a fixed computational budget and up to two orders of magnitude faster at a fixed accuracy than schemes with dense updates.
Review of Machine Learning Techniques for Power Electronics Control and Optimization

In the rapidly advancing landscape of contemporary technology, power electronics assume a pivotal role across diverse applications, ranging from renewable energy systems to electric vehicles and consumer electronics. The efficacy and precision of these power electronics systems stand as cornerstones of their functionality. Within this context, the integration of machine learning techniques assumes paramount significance. This article endeavors to present an extensive and comprehensive review of the machine learning techniques that find application in power electronics control and optimization. Through meticulous exploration, we aim to elucidate the profound potential of these methods in shaping the future of power electronics control and optimization.
Multitask Learning for Time Series Data with 2D Convolution

Multitask learning (MTL) aims to develop a unified model that can handle a set of closely related tasks simultaneously. By optimizing the model across multiple tasks, MTL generally surpasses its non-MTL counterparts in terms of generalizability. Although MTL has been extensively researched in various domains such as computer vision, natural language processing, and recommendation systems, its application to time series data has received limited attention. In this paper, we investigate the application of MTL to the time series classification (TSC) problem. However, when we integrate the state-of-the-art 1D convolution-based TSC model with MTL, the performance of the TSC model actually deteriorates. By comparing the 1D convolution-based models with the Dynamic Time Warping (DTW) distance function, it appears that the underwhelming results stem from the limited expressive power of the 1D convolutional layers. To overcome this challenge, we propose a novel design for a 2D convolution-based model that enhances the model's expressiveness. Leveraging this advantage, our proposed method outperforms competing approaches on both the UCR Archive and an industrial transaction TSC dataset.
Variance Reduced Online Gradient Descent for Kernelized Pairwise Learning with Limited Memory

Pairwise learning is essential in machine learning, especially for problems involving loss functions defined on pairs of training examples. Online gradient descent (OGD) algorithms have been proposed to handle online pairwise learning, where data arrives sequentially. However, the pairwise nature of the problem makes scalability challenging, as the gradient computation for a new sample involves all past samples. Recent advancements in OGD algorithms have aimed to reduce the complexity of calculating online gradients, achieving complexities less than $O(T)$ and even as low as $O(1)$. However, these approaches are primarily limited to linear models and have induced variance. In this study, we propose a limited memory OGD algorithm that extends to kernel online pairwise learning while improving the sublinear regret. Specifically, we establish a clear connection between the variance of online gradients and the regret, and construct online gradients using the most recent stratified samples with a limited buffer of size of $s$ representing all past data, which have a complexity of $O(sT)$ and employs $O(\sqrt{T}\log{T})$ random Fourier features for kernel approximation. Importantly, our theoretical results demonstrate that the variance-reduced online gradients lead to an improved sublinear regret bound. The experiments on real-world datasets demonstrate the superiority of our algorithm over both kernelized and linear online pairwise learning algorithms.