Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning robust perceptive locomotion for quadrupedal robots in the wild

Legged robots that can operate autonomously in remote and hazardous environments will greatly increase opportunities for exploration into underexplored areas. Exteroceptive perception is crucial for fast and energy-efficient locomotion: Perceiving the terrain before making contact with it enables planning and adaptation of the gait ahead of time to maintain speed and stability. However, using exteroceptive perception robustly for locomotion has remained a grand challenge in robotics. Snow, vegetation, and water visually appear as obstacles on which the robot cannot step or are missing altogether due to high reflectance. In addition, depth perception can degrade due to difficult lighting, dust, fog, reflective or transparent surfaces, sensor occlusion, and more. For this reason, the most robust and general solutions to legged locomotion to date rely solely on proprioception. This severely limits locomotion speed because the robot has to physically feel out the terrain before adapting its gait accordingly. Here, we present a robust and general solution to integrating exteroceptive and proprioceptive perception for legged locomotion. We leverage an attention-based recurrent encoder that integrates proprioceptive and exteroceptive input. The encoder is trained end to end and learns to seamlessly combine the different perception modalities without resorting to heuristics. The result is a legged locomotion controller with high robustness and speed. The controller was tested in a variety of challenging natural and urban environments over multiple seasons and completed an hour-long hike in the Alps in the time recommended for human hikers. Description A legged locomotion controller achieves high robustness and speed in the wild by combining multimodal information.
The half mass radius of MaNGA galaxies: Effect of IMF gradients

Gradients in the stellar populations (SP) of galaxies -- e.g., in age, metallicity, stellar Initial Mass Function (IMF) -- can result in gradients in the stellar mass to light ratio, $M_*/L$. Such gradients imply that the distribution of the stellar mass and light are different. For old SPs, e.g., in early-type galaxies at $z\sim 0$, the $M_*/L$ gradients are weak if driven by variations in age and metallicity, but significantly larger if driven by the IMF. A gradient which has larger $M_*/L$ in the center increases the estimated total stellar mass ($M_*$) and reduces the scale which contains half this mass ($R_{e,*}$), compared to when the gradient is ignored. For the IMF gradients inferred from fitting MILES simple SP models to the H$_\beta$, $\langle$Fe$\rangle$, [MgFe] and TiO$_{\rm 2SDSS}$ absorption lines measured in spatially resolved spectra of early-type galaxies in the MaNGA survey, the fractional change in $R_{e,*}$ can be significantly larger than that in $M_*$, especially when the light is more centrally concentrated. The $R_{e,*}-M_*$ correlation which results is offset by 0.3 dex to smaller sizes compared to when these gradients are ignored. Comparisons with `quiescent' galaxies at higher-$z$ must account for evolution in SP gradients (especially age and IMF) and the light profile before drawing conclusions about how $R_{e,*}$ and $M_*$ evolve. The implied merging between higher-$z$ and the present is less contrived if $R_{e,*}/R_e$ at $z\sim 0$ is closer to our IMF-driven gradient calibration than to unity.
CELESTIAL: Classification Enabled via Labelless Embeddings with Self-supervised Telescope Image Analysis Learning

A common class of problems in remote sensing is scene classification, a fundamentally important task for natural hazards identification, geographic image retrieval, and environment monitoring. Recent developments in this field rely label-dependent supervised learning techniques which is antithetical to the 35 petabytes of unlabelled satellite imagery in NASA GIBS. To solve this problem, we establish CELESTIAL-a self-supervised learning pipeline for effectively leveraging sparsely-labeled satellite imagery. This pipeline successfully adapts SimCLR, an algorithm that first learns image representations on unlabelled data and then fine-tunes this knowledge on the provided labels. Our results show CELESTIAL requires only a third of the labels that the supervised method needs to attain the same accuracy on an experimental dataset. The first unsupervised tier can enable applications such as reverse image search for NASA Worldview (i.e. searching similar atmospheric phenomenon over years of unlabelled data with minimal samples) and the second supervised tier can lower the necessity of expensive data annotation significantly. In the future, we hope we can generalize the CELESTIAL pipeline to other data types, algorithms, and applications.
Quantum dual-path interferometry scheme for axion dark matter searches

We propose a dual-path interferometry amplification configuration in cavity axion dark matter searches. We show quantum-mechanically that in a low-temperature cavity permeated by a magnetic field, the single axion-photon conversion rate is enhanced by the cavity quality factor $Q$ and quantitatively larger than the classical result by a factor $\pi/2$. Under modern cryogenic conditions, thermal photons in the cavity are negligible; thus, the axion cavity can be considered a quantum device emitting single photons with temporal separations. This differs from the classical picture in which axions transition in batches and the converted energy accumulates in the electromagnetic field inside the cavity. It reveals a possibility for the axion cavity experiment to handle the signal sensitivity at the quantum level, e.g., cross-power and second-order correlation measurements. The correlation of photon field quadratures in the amplification chain within current technology enhances the signal-to-noise ratio compared with a single-path amplification scheme based on a high electronic mobility transistor amplifier or Josephson Parametric Amplifiers (JPAs). In particular, it is useful to combine the dual-path interferometry scheme with other techniques, e.g., JPAs, to overcome the inevitable microwave signal insertion loss (normally $\sim-3\,$dB) in the channels between the cavity and the quantum-limited amplifiers. This enhancement would greatly reduce the signal scanning time to improve the sensitivity of the axion-photon coupling. In addition, the second-order correlation function measurement in the dual-path scheme can provide an additional verification of whether the candidate signals are axion converted or other noises.
Crystal structure evolution in the van der Waals vanadium trihalides

Most transition-metal trihalides are dimorphic. The representative chromium-based triad, CrCl3, CrBr3, CrI3, is characterized by the low-temperature (LT) phase adopting the trigonal BiI3-type while the structure of the high-temperature (HT) phase is monoclinic of AlCl3 type (C2/m). The structural transition between the two crystallographic phases is of the first-order type with large thermal hysteresis in CrCl3 and CrI3. We studied crystal structures and structural phase transitions of vanadium-based counterparts VCl3, VBr3, and VI3 by measuring specific heat, magnetization, and x-ray diffraction as functions of temperature and observed an inverse situation. In these cases, the HT phase has a higher symmetry while the LT structure reveals a lower symmetry. The structural phase transition between them shows no measurable hysteresis in contrast to CrX3. Possible relations of the evolution of the ratio c/a of the unit cell parameters, types of crystal structures, and nature of the structural transitions in V and Cr trihalides are discussed.
Variational Bayesian Filtering with Subspace Information for Extreme Spatio-Temporal Matrix Completion

Missing data is a common problem in real-world sensor data collection. The performance of various approaches to impute data degrade rapidly in the extreme scenarios of low data sampling and noisy sampling, a case present in many real-world problems in the field of traffic sensing and environment monitoring, etc. However, jointly exploiting the spatiotemporal and periodic structure, which is generally not captured by classical matrix completion approaches, can improve the imputation performance of sensor data in such real-world conditions. We present a Bayesian approach towards spatiotemporal matrix completion wherein we estimate the underlying temporarily varying subspace using a Variational Bayesian technique. We jointly couple the low-rank matrix completion with the state space autoregressive framework along with a penalty function on the slowly varying subspace to model the temporal and periodic evolution in the data. A major advantage of our method is that a critical parameter like the rank of the model is automatically tuned using the automatic relevance determination (ARD) approach, unlike most matrix/tensor completion techniques. We also propose a robust version of the above formulation, which improves the performance of imputation in the presence of outliers. We evaluate the proposed Variational Bayesian Filtering with Subspace Information (VBFSI) method to impute matrices in real-world traffic and air pollution data. Simulation results demonstrate that the proposed method outperforms the recent state-of-the-art methods and provides a sufficiently accurate imputation for different sampling rates. In particular, we demonstrate that fusing the subspace evolution over days can improve the imputation performance with even 15% of the data sampling.
Cross-Lingual Text-to-Speech Using Multi-Task Learning and Speaker Classifier Joint Training

In cross-lingual speech synthesis, the speech in various languages can be synthesized for a monoglot speaker. Normally, only the data of monoglot speakers are available for model training, thus the speaker similarity is relatively low between the synthesized cross-lingual speech and the native language recordings. Based on the multilingual transformer text-to-speech model, this paper studies a multi-task learning framework to improve the cross-lingual speaker similarity. To further improve the speaker similarity, joint training with a speaker classifier is proposed. Here, a scheme similar to parallel scheduled sampling is proposed to train the transformer model efficiently to avoid breaking the parallel training mechanism when introducing joint training. By using multi-task learning and speaker classifier joint training, in subjective and objective evaluations, the cross-lingual speaker similarity can be consistently improved for both the seen and unseen speakers in the training set.
Identifying critical nodes in complex networks by graph representation learning

Because of its wide application, critical nodes identification has become an important research topic at the micro level of network science. Influence maximization is one of the main problems in critical nodes mining and is usually handled with heuristics. In this paper, a deep graph learning framework IMGNN is proposed and the corresponding training sample generation scheme is designed. The framework takes centralities of nodes in a network as input and the probability that nodes in the optimal initial spreaders as output. By training on a large number of small synthetic networks, IMGNN is more efficient than human-based heuristics in minimizing the size of initial spreaders under the fixed infection scale. The experimental results on one synthetic and five real networks show that, compared with traditional non-iterative node ranking algorithms, IMGNN has the smallest proportion of initial spreaders under different infection probabilities when the final infection scale is fixed. And the reordered version of IMGNN outperforms all the latest critical nodes mining algorithms.
Adaptive Energy Management for Self-Sustainable Wearables in Mobile Health

Wearable devices that integrate multiple sensors, processors, and communication technologies have the potential to transform mobile health for remote monitoring of health parameters. However, the small form factor of the wearable devices limits the battery size and operating lifetime. As a result, the devices require frequent recharging, which has limited their widespread adoption. Energy harvesting has emerged as an effective method towards sustainable operation of wearable devices. Unfortunately, energy harvesting alone is not sufficient to fulfill the energy requirements of wearable devices. This paper studies the novel problem of adaptive energy management towards the goal of self-sustainable wearables by using harvested energy to supplement the battery energy and to reduce manual recharging by users. To solve this problem, we propose a principled algorithm referred as AdaEM. There are two key ideas behind AdaEM. First, it uses machine learning (ML) methods to learn predictive models of user activity and energy usage patterns. These models allow us to estimate the potential of energy harvesting in a day as a function of the user activities. Second, it reasons about the uncertainty in predictions and estimations from the ML models to optimize the energy management decisions using a dynamic robust optimization (DyRO) formulation. We propose a light-weight solution for DyRO to meet the practical needs of deployment. We validate the AdaEM approach on a wearable device prototype consisting of solar and motion energy harvesting using real-world data of user activities. Experiments show that AdaEM achieves solutions that are within 5% of the optimal with less than 0.005% execution time and energy overhead.
Distributed Stochastic Model Predictive Control for an Urban Traffic Network

In this paper, we design a stochastic Model Predictive Control (MPC) traffic signal control method for an urban traffic network when the uncertainties in the estimation of the exogenous (in/out)-flows and the turning ratios of downstream traffic flows are taken into account. Assuming that the traffic model parameters are random variables with known expectations and variance, the traffic signal control and coordination problem is formulated as a quadratic program with linear and second-order cone constraints. In order to reduce computational complexity, we suggest a way to decompose the optimization problem corresponding to the whole traffic network into multiple subproblems. By applying Alternating Direction Method of Multipliers (ADMM), the optimal stochastic traffic signal splits are found in distributed manner. The effectiveness of the designed control method is validated via some simulations using VISSIM and MATLAB.
A simple mechanism for stable oscillations in an intermediate complexity Earth System Model

The global ocean circulation plays a pivotal role in the regulation of the Earth’s climate. The specific pattern and strength of circulation also determines how carbon and nutrients are cycled and via the resulting distribution of dissolved oxygen, where habitats suitable for marine animals occur. However, from data and models, transitions in circulation patterns are known to have occurred in the past. Understanding the controls on marine environmental conditions and biological productivity then requires a full appreciation of the nature and drivers of such state transitions. Here we present stable oscillations observed in the ocean circulation of an Earth System Model of intermediate complexity. The presence of the oscillations depends on a circumpolar current. We adapt a simple ocean box model to include a delayed feedback to represent a circumpolar current and investigate stable oscillatory solutions via bifurcation analysis. Our results provide insight into oscillations observed in simulations based on land mass configurations typifying the geological past and also highlight the potential influence of changing circumpolar current speed on the stability of the ocean’s meridional overturning circulation.
GAN-based matrix factorization for recommender systems

Proposed in 2014, Generative Adversarial Networks (GAN) initiated a fresh interest in generative modelling. They immediately achieved state-of-the-art in image synthesis, image-to-image translation, text-to-image generation, image inpainting and have been used in sciences ranging from medicine to high-energy particle physics. Despite their popularity and ability to learn arbitrary distributions, GAN have not been widely applied in recommender systems (RS). Moreover, only few of the techniques that have introduced GAN in RS have employed them directly as a collaborative filtering (CF) model. In this work we propose a new GAN-based approach that learns user and item latent factors in a matrix factorization setting for the generic top-N recommendation problem. Following the vector-wise GAN training approach for RS introduced by CFGAN, we identify 2 unique issues when utilizing GAN for CF. We propose solutions for both of them by using an autoencoder as discriminator and incorporating an additional loss function for the generator. We evaluate our model, GANMF, through well-known datasets in the RS community and show improvements over traditional CF approaches and GAN-based models. Through an ablation study on the components of GANMF we aim to understand the effects of our architectural choices. Finally, we provide a qualitative evaluation of the matrix factorization performance of GANMF.
Several Coercivity Proofs of First-Order System Least-Squares Methods for Second-Order Elliptic PDEs

In this paper, we present three versions of proofs of the coercivity for first-order system least-squares methods for second-order elliptic PDEs. The first version is based on the a priori error estimate of the PDEs, which has the weakest assumption. For the second and third proofs, a sufficient condition on the coefficients ensuring the coercivity of the standard variational formulation is assumed. The second proof is a simple direct proof and the third proof is based on a lemma introduced in the discontinuous Petrov-Galerkin method. By pointing out the advantages and limitations of different proofs, we hope that the paper will provide a guide for future proofs. As an application, we also discuss least-squares finite element methods for problems with H righthand side.
Complex In-Hand Manipulation via Compliance-Enabled Finger Gaiting and Multi-Modal Planning

Constraining contacts to remain fixed on an object during manipulation limits the potential workspace size, as motion is subject to the hand's kinematic topology. Finger gaiting is one way to alleviate such restraints. It allows contacts to be freely broken and remade so as to operate on different manipulation manifolds. This capability, however, has traditionally been difficult or impossible to practically realize. A finger gaiting system must simultaneously plan for and control forces on the object while maintaining stability during contact switching. This work alleviates the traditional requirement by taking advantage of system compliance, allowing the hand to more easily switch contacts while maintaining a stable grasp. Our method achieves complete SO(3) finger gaiting control of grasped objects against gravity by developing a manipulation planner that operates via orthogonal safe modes of a compliant, underactuated hand absent of tactile sensors or joint encoders. During manipulation, a low-latency 6D pose object tracker provides feedback via vision, allowing the planner to update its plan online so as to adaptively recover from trajectory deviations. The efficacy of this method is showcased by manipulating both convex and non-convex objects on a real robot. Its robustness is evaluated via perturbation rejection and long trajectory goals. To the best of the authors' knowledge, this is the first work that has autonomously achieved full SO(3) control of objects within-hand via finger gaiting and without a support surface, elucidating a valuable step towards realizing true robot in-hand manipulation capabilities.
What can we learn from misclassified ImageNet images?

Understanding the patterns of misclassified ImageNet images is particularly important, as it could guide us to design deep neural networks (DNN) that generalize better. However, the richness of ImageNet imposes difficulties for researchers to visually find any useful patterns of misclassification. Here, to help find these patterns, we propose"Superclassing ImageNet dataset". It is a subset of ImageNet which consists of 10 superclasses, each containing 7-116 related subclasses (e.g., 52 bird types, 116 dog types). By training neural networks on this dataset, we found that: (i) Misclassifications are rarely across superclasses, but mainly among subclasses within a superclass. (ii) Ensemble networks trained each only on subclasses of a given superclass perform better than the same network trained on all subclasses of all superclasses. Hence, we propose a two-stage Super-Sub framework, and demonstrate that: (i) The framework improves overall classification performance by 3.3%, by first inferring a superclass using a generalist superclass-level network, and then using a specialized network for final subclass-level classification. (ii) Although the total parameter storage cost increases to a factor N+1 for N superclasses compared to using a single network, with finetuning, delta and quantization aware training techniques this can be reduced to 0.2N+1. Another advantage of this efficient implementation is that the memory cost on the GPU during inference is equivalent to using only one network. The reason is we initiate each subclass-level network through addition of small parameter variations (deltas) to the superclass-level network. (iii) Finally, our framework promises to be more scalable and generalizable than the common alternative of simply scaling up a vanilla network in size, since very large networks often suffer from overfitting and gradient vanishing.
Low-Pass Filtering SGD for Recovering Flat Optima in the Deep Learning Optimization Landscape

In this paper, we study the sharpness of a deep learning (DL) loss landscape around local minima in order to reveal systematic mechanisms underlying the generalization abilities of DL models. Our analysis is performed across varying network and optimizer hyper-parameters, and involves a rich family of different sharpness measures. We compare these measures and show that the low-pass filter-based measure exhibits the highest correlation with the generalization abilities of DL models, has high robustness to both data and label noise, and furthermore can track the double descent behavior for neural networks. We next derive the optimization algorithm, relying on the low-pass filter (LPF), that actively searches the flat regions in the DL optimization landscape using SGD-like procedure. The update of the proposed algorithm, that we call LPF-SGD, is determined by the gradient of the convolution of the filter kernel with the loss function and can be efficiently computed using MC sampling. We empirically show that our algorithm achieves superior generalization performance compared to the common DL training strategies. On the theoretical front, we prove that LPF-SGD converges to a better optimal point with smaller generalization error than SGD.
Sensing Method for Two-Target Detection in Time-Constrained Vector Poisson Channel

It is an experimental design problem in which there are two Poisson sources with two possible and known rates, and one counter. Through a switch, the counter can observe the sources individually or the counts can be combined so that the counter observes the sum of the two. The sensor scheduling problem is to determine an optimal proportion of the available time to be allocated toward individual and joint sensing, under a total time constraint. Two different metrics are used for optimization: mutual information between the sources and the observed counts, and probability of detection for the associated source detection problem. Our results, which are primarily computational, indicate similar but not identical results under the two cost functions.