Miracle-dz/UniFAR-FTUs huggingface dataset:
A Differentiable Newton-Euler Algorithm for Real-World Robotics

Obtaining dynamics models is essential for robotics to achieve accurate model-based controllers and simulators for planning. The dynamics models are typically obtained using model specification of the manufacturer or simple numerical methods such as linear regression. However, this approach does not guarantee physically plausible parameters and can only be applied to kinematic chains consisting of rigid bodies. In this article, we describe a differentiable simulator that can be used to identify the system parameters of real-world mechanical systems with complex friction models, holonomic as well as non-holonomic constraints. To guarantee physically consistent parameters, we utilize virtual parameters and gradient-based optimization. The described Differentiable Newton-Euler Algorithm (DiffNEA) can be applied to a class of dynamical systems and guarantees physically plausible predictions. The extensive experimental evaluation shows, that the proposed model learning approach learns accurate dynamics models of systems with complex friction and non-holonomic constraints. Especially in the offline reinforcement learning experiments, the identified DiffNEA models excel. For the challenging ball in a cup task, these models solve the task using model-based offline reinforcement learning on the physical system. The black-box baselines fail on this task in simulation and on the physical system despite using more data for learning the model.
Non-Asymptotic Error Bounds for Bidirectional GANs

We derive nearly sharp bounds for the bidirectional GAN (BiGAN) estimation error under the Dudley distance between the latent joint distribution and the data joint distribution with appropriately specified architecture of the neural networks used in the model. To the best of our knowledge, this is the first theoretical guarantee for the bidirectional GAN learning approach. An appealing feature of our results is that they do not assume the reference and the data distributions to have the same dimensions or these distributions to have bounded support. These assumptions are commonly assumed in the existing convergence analysis of the unidirectional GANs but may not be satisfied in practice. Our results are also applicable to the Wasserstein bidirectional GAN if the target distribution is assumed to have a bounded support. To prove these results, we construct neural network functions that push forward an empirical distribution to another arbitrary empirical distribution on a possibly different-dimensional space. We also develop a novel decomposition of the integral probability metric for the error analysis of bidirectional GANs. These basic theoretical results are of independent interest and can be applied to other related learning problems.
Noisy UGC Translation at the Character Level: Revisiting Open-Vocabulary Capabilities and Robustness of Char-Based Models

This work explores the capacities of character-based Neural Machine Translation to translate noisy User-Generated Content (UGC) with a strong focus on exploring the limits of such approaches to handle productive UGC phenomena, which almost by definition, cannot be seen at training time. Within a strict zero-shot scenario, we first study the detrimental impact on translation performance of various user-generated content phenomena on a small annotated dataset we developed and then show that such models are indeed incapable of handling unknown letters, which leads to catastrophic translation failure once such characters are encountered. We further confirm this behavior with a simple, yet insightful, copy task experiment and highlight the importance of reducing the vocabulary size hyper-parameter to increase the robustness of character-based models for machine translation.
The Nearby Evolved Stars Survey II: Constructing a volume-limited sample and first results from the James Clerk Maxwell Telescope

The Nearby Evolved Stars Survey (NESS) is a volume-complete sample of $\sim$850 Galactic evolved stars within 3\,kpc at (sub-)mm wavelengths, observed in the CO $J = $ (2$-$1) and (3$-$2) rotational lines, and the sub-mm continuum, using the James Clark Maxwell Telescope and Atacama Pathfinder Experiment. NESS consists of five tiers, based on distances and dust-production rate (DPR). We define a new metric for estimating the distances to evolved stars and compare its results to \emph{Gaia} EDR3. Replicating other studies, the most-evolved, highly enshrouded objects in the Galactic Plane dominate the dust returned by our sources, and we initially estimate a total DPR of $4.7\times 10^{-5}$ M$_\odot$ yr$^{-1}$ from our sample. Our sub-mm fluxes are systematically higher and spectral indices are typically shallower than dust models typically predict. The 450/850 $\mu$m spectral indices are consistent with the blackbody Rayleigh--Jeans regime, suggesting a large fraction of evolved stars have unexpectedly large envelopes of cold dust.
Learning convex regularizers satisfying the variational source condition for inverse problems

Variational regularization has remained one of the most successful approaches for reconstruction in imaging inverse problems for several decades. With the emergence and astonishing success of deep learning in recent years, a considerable amount of research has gone into data-driven modeling of the regularizer in the variational setting. Our work extends a recently proposed method, referred to as adversarial convex regularization (ACR), that seeks to learn data-driven convex regularizers via adversarial training in an attempt to combine the power of data with the classical convex regularization theory. Specifically, we leverage the variational source condition (SC) during training to enforce that the ground-truth images minimize the variational loss corresponding to the learned convex regularizer. This is achieved by adding an appropriate penalty term to the ACR training objective. The resulting regularizer (abbreviated as ACR-SC) performs on par with the ACR, but unlike ACR, comes with a quantitative convergence rate estimate.
Neural Relightable Participating Media Rendering

Learning neural radiance fields of a scene has recently allowed realistic novel view synthesis of the scene, but they are limited to synthesize images under the original fixed lighting condition. Therefore, they are not flexible for the eagerly desired tasks like relighting, scene editing and scene composition. To tackle this problem, several recent methods propose to disentangle reflectance and illumination from the radiance field. These methods can cope with solid objects with opaque surfaces but participating media are neglected. Also, they take into account only direct illumination or at most one-bounce indirect illumination, thus suffer from energy loss due to ignoring the high-order indirect illumination. We propose to learn neural representations for participating media with a complete simulation of global illumination. We estimate direct illumination via ray tracing and compute indirect illumination with spherical harmonics. Our approach avoids computing the lengthy indirect bounces and does not suffer from energy loss. Our experiments on multiple scenes show that our approach achieves superior visual quality and numerical performance compared to state-of-the-art methods, and it can generalize to deal with solid objects with opaque surfaces as well.
A Compact Coupling Interface Method with Accurate Gradient Approximation for Elliptic Interface Problems

Elliptic interface boundary value problems play a major role in numerous applications involving heat, fluids, materials, and proteins, to name a few. As an example, in implicit variational solvation, for the construction of biomolecular shapes, the electrostatic contributions satisfy the Poisson-Boltzmann equation with discontinuous dielectric constants across the interface. When interface motions are involved, one often needs not only accurate solution values, but accurate derivatives as well, such as the normal derivatives at the interface. We introduce here the Compact Coupling Interface Method (CCIM), a finite difference method for the elliptic interface problem with interfacial jump conditions. The CCIM can calculate solution values and their derivatives up to second-order accuracy in arbitrary ambient space dimensions. It combines elements of Chern and Shu's Coupling Interface Method and Mayo's approach for elliptic interface boundary value problems, leading to more compact finite difference stencils that are applicable to more general situations. Numerical results on a variety of geometric interfacial shapes and on complex protein molecules in three dimensions support the efficacy of our approach and reveal advantages in accuracy and robustness.
ZerO Initialization: Initializing Neural Networks with only Zeros and Ones

Deep neural networks are usually initialized with random weights, with adequately selected initial variance to ensure stable signal propagation during training. However, selecting the appropriate variance becomes challenging especially as the number of layers grows. In this work, we replace random weight initialization with a fully deterministic initialization scheme, viz., ZerO, which initializes the weights of networks with only zeros and ones (up to a normalization factor), based on identity and Hadamard transforms. Through both theoretical and empirical studies, we demonstrate that ZerO is able to train networks without damaging their expressivity. Applying ZerO on ResNet achieves state-of-the-art performance on various datasets, including ImageNet, which suggests random weights may be unnecessary for network initialization. In addition, ZerO has many benefits, such as training ultra deep networks (without batch-normalization), exhibiting low-rank learning trajectories that result in low-rank and sparse solutions, and improving training reproducibility.
A Layer-wise Adversarial-aware Quantization Optimization for Improving Robustness

Neural networks are getting better accuracy with higher energy and computational cost. After quantization, the cost can be greatly saved, and the quantized models are more hardware friendly with acceptable accuracy loss. On the other hand, recent research has found that neural networks are vulnerable to adversarial attacks, and the robustness of a neural network model can only be improved with defense methods, such as adversarial training. In this work, we find that adversarially-trained neural networks are more vulnerable to quantization loss than plain models. To minimize both the adversarial and the quantization losses simultaneously and to make the quantized model robust, we propose a layer-wise adversarial-aware quantization method, using the Lipschitz constant to choose the best quantization parameter settings for a neural network. We theoretically derive the losses and prove the consistency of our metric selection. The experiment results show that our method can effectively and efficiently improve the robustness of quantized adversarially-trained neural networks.
The Prevalence of Cybersecurity Misinformation on Social Media: Case Studies on Phishing Reports and Zoom's Threats

Recently, threat intelligence and security tools have been augmented to use the timely and relevant security information extracted from social media. However, both ordinary users and malicious actors may spread misinformation, which can misguide not only the end-users but also the threat intelligence tools. In this work, for the first time, we study the prevalence of cybersecurity and privacy misinformation on social media, focusing on two different topics: phishing websites and Zoom’s security & privacy . We collected Twitter posts that were warning users about phishing websites and tried to verify these claims. We found about 22% of these tweets to be not valid claims. We then investigated posts about Zoom’s security and privacy on multiple platforms, including Instagram, Reddit, Twitter, and Facebook. To detect misinformation related to Zoom, we first created a groundtruth dataset and a taxonomy of misinformation and identified the textual and contextual features to be used for training classifiers to detect posts that discuss the security and privacy of Zoom and detect misinformation. Our classifiers showed great performance, e.g., Reddit and Facebook misinformation classifier reached an accuracy of 99% while Twitter and Instagram reached an accuracy of 98%. Employing these classifiers on the posts from Instagram, Facebook, Reddit, and Twitter, we found that respectively about 3%, 10%, 4% and 0.4% of Zoom’s security and privacy posts as misinformation. This highlights the need for social media platforms to dedicate resources to curb the spread of misinformation, and for data-driven security tools to pro-pose methods to minimize the impact of such misinformation on their performance
Determining the Neutrino Mass Eigenstates and the Effective Majorana Mass

: This paper aims at solving several open questions in current neutrino physics: the neutrino mass hierarchy, the Dirac CP violating phase, the absolute mass of neutrinos, the nature of neutrinos (Dirac or Majorana), the Majorana matrix and the absolute value of the effective Majorana neutrino mass.In the research presented in this paper, we have shown that the precise definition of the mass splittings between neutrino mass eigenstates, done in the latest analysis of experimental data, can be of crucial importance for defining the nature of neutrino mass hierarchy. The Standard Model has three generations of fundamental matter particles. Three generations of the charged lepton mass show a hierarchical structure: . Owing to that, there is a belief and it is considered that neutrinos may follow such hierarchical structure. In our calculations, we have also included the latest data obtained, based on the processing of measurement results, which showed that even with such data, obtained results favor the normal neutrino mass hierarchy. As for the individual neutrino mass calculated in this paper, in today's neutrino physics it is only known that neutrino mass scale is bounded only from above, and both the Dirac and the Majorana character of neutrinos are compatible with all observations. Among some of the questions resolved in this paper, which are related to the properties of neutrinos, a positive answer was also given to the question of whether light neutrinos are self-conjugate particles or not.
SILT: Self-supervised Lighting Transfer Using Implicit Image Decomposition

We present SILT, a Self-supervised Implicit Lighting Transfer method. Unlike previous research on scene relighting, we do not seek to apply arbitrary new lighting configurations to a given scene. Instead, we wish to transfer the lighting style from a database of other scenes, to provide a uniform lighting style regardless of the input. The solution operates as a two-branch network that first aims to map input images of any arbitrary lighting style to a unified domain, with extra guidance achieved through implicit image decomposition. We then remap this unified input domain using a discriminator that is presented with the generated outputs and the style reference, i.e. images of the desired illumination conditions. Our method is shown to outperform supervised relighting solutions across two different datasets without requiring lighting supervision.
Local supersymmetry: variations on a theme by Volkov and Soroka

We revisit the work by Volkov and Soroka on spontaneously broken local supersymmetry. It is demonstrated for the first time that, for specially chosen parameters of the theory, the Volkov–Soroka action is invariant under two different local supersymmetries. One of them is present for arbitrary values of the parameters and acts on the Goldstino, while the other supersymmetry emerges only in a special case and leaves the Goldstino invariant. The former can be used to gauge away the Goldstino, and then the resulting action coincides with that proposed by Deser and Zumino for consistent supergravity in the first-order formalism. In this sense, pure N=1 supergravity is a special case of the Volkov–Soroka theory, although it was not discovered by these authors. We also explain how the Volkov–Soroka approach allows one to naturally arrive at the 1.5 formalism. Our analysis provides a nonlinear realization approach to construct unbroken N=1 Poincaré supergravity.
A CMOS SoC for Wireless Ultrasonic Power/Data Transfer and SHM Measurements on Structures

This paper describes a highly-integrated CMOS system-on-chip (SoC) for active structural health monitoring (SHM). The chip integrates ultrasonic power and bidirectional half-duplex data transfer, a power management unit (PMU), and an ultrasound transceiver to enable wireless ultrasonically-coupled sensor SHM networks on structures. The PMU includes an active bias-flip rectifier with off-delay compensation, a high-efficiency dual-path DC-DC converter with inductor time-sharing, and five switched-capacitor DC-DC converters to generate multi-level spectrally band-limited pulses for guided-wave SHM. The chip was fabricated in a standard 180 nm process and has a die area of $2\times 2$ mm2. Test results show power conversion efficiency (PCE) > 85% for the active rectifier, > 70% for the inductive DC-DC converter, and > 60% for the switched-capacitor DC-DC converters. Output pulses have a peak-to-sidelobe ratio (PSL) > 30 dB and worst-case out-of-band emissions < −30 dB, respectively. The SoC was integrated with a low-power microcontroller and passive components to realize miniaturized (15 mm $\times 30$ mm) wireless SHM nodes. A set of nodes was deployed on an SHM test-bed (carbon fiber reinforced polymer sheet) representing an airframe panel. Tests on this wireless network confirm both long-range ultrasound power/data transfer and the ability to detect structural damage.
Sufficient reductions in regression with mixed predictors

Most data sets comprise of measurements on continuous and categorical variables. In regression and classification Statistics literature, modeling high-dimensional mixed predictors has received limited attention. In this paper we study the general regression problem of inferring on a variable of interest based on high dimensional mixed continuous and binary predictors. The aim is to find a lower dimensional function of the mixed predictor vector that contains all the modeling information in the mixed predictors for the response, which can be either continuous or categorical. The approach we propose identifies sufficient reductions by reversing the regression and modeling the mixed predictors conditional on the response. We derive the maximum likelihood estimator of the sufficient reductions, asymptotic tests for dimension, and a regularized estimator, which simultaneously achieves variable (feature) selection and dimension reduction (feature extraction). We study the performance of the proposed method and compare it with other approaches through simulations and real data examples.
Enhancing Haptic Distinguishability of Surface Materials With Boosting Technique

Discriminative features are crucial for several learning applications, such as object detection and classification. Neural networks are extensively used for extracting discriminative features of images and speech signals. However, the lack of large datasets in the haptics domain often limits the applicability of such techniques. This paper presents a general framework for the analysis of the discriminative properties of haptic signals. We demonstrate the effectiveness of spectral features and a boosted embedding technique in enhancing the distinguishability of haptic signals. Experiments indicate our framework needs less training data, generalizes well for different predictors, and outperforms the related state-of-the-art.
Learning in Multi-Stage Decentralized Matching Markets

Matching markets are often organized in a multi-stage and decentralized manner. Moreover, participants in real-world matching markets often have uncertain preferences. This article develops a framework for learning optimal strategies in such settings, based on a nonparametric statistical approach and variational analysis. We propose an efficient algorithm, built upon concepts of"lower uncertainty bound"and"calibrated decentralized matching,"for maximizing the participants' expected payoff. We show that there exists a welfare-versus-fairness trade-off that is characterized by the uncertainty level of acceptance. Participants will strategically act in favor of a low uncertainty level to reduce competition and increase expected payoff. We prove that participants can be better off with multi-stage matching compared to single-stage matching. We demonstrate aspects of the theoretical predictions through simulations and an experiment using real data from college admissions.
Ladder-GNN: Hop-Aware Representation Learning for Graph Neural Networks

In the representation learning of Graph Neural Networks (GNNs), as the messages passed among nodes contain both information and noise, it is critical to retrieve information effectively while suppressing noise. Generally speaking, interactions with distant nodes introduce more noise for a particular node than those with close neighbours. However, in most existing works, the messages being passed among nodes are mingled together, which is inefficient from a communication perspective. Motivated by the above, we propose a simple yet effective hop-aware aggregation scheme, resulting in a ladder-style GNN architecture, namely Ladder-GNN. Specifically, we separate messages from different hops, assign different dimensions for them, and then concatenate them to obtain the node representation. Such disentan-gled representations facilitate improving the information-to-noise ratio of messages passed from different hops. To explore an effective hop-dimension relationship, we propose a conditionally progressive neural architecture search strategy. The resulting hop-aware representations generally contain more dimensions for low-order neighbours and fewer dimensions for high-order neighbours, leading to a ladder-style architecture. This observation motivates us to introduce an efficient approximate hop-dimension relation function used in Ladder-GNN design. We verify the proposed Ladder-GNN on seven semi-supervised node classification datasets, including both homogeneous and heterogeneous graphs. Experimental results show that the proposed simple hop-aware representation learning solution outperforms existing techniques.
Physics Based GNNs for Locating Faults in Power Grids

The reducing cost of renewable energy resources, such as solar photovoltaics (PV) and wind farms, is accelerating global energy transformation to mitigate climate change. However, a high level of intermittent renewable energy causes power grids to have more stability issues. This accentuates the need for quick location of system failures and follow-up control actions. In recent events such as in California, line failures have resulted in large-scale wildfires leading to loss of life and property. In this article, we propose a two-stage graph learning framework to locate power grid faults in the challenging but practical regime characterized by (a) sparse observations, (b) low label rates, and (c) system variability. Our approach embeds the geometrical structure of power grids into the graph neural networks (GNN) in stage I for fast fault location, and then stage II further enhances the location accuracy by employing the physical similarity of the labeled and unlabeled data samples. We compare our approach with three baselines in the IEEE 123-node benchmark system and show that it outperforms the others by significant margins in various scenarios.