Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning Arm-Assisted Fall Damage Reduction and Recovery for Legged Mobile Manipulators

Adaptive falling and recovery skills greatly extend the applicability of robot deployments. In the case of legged mobile manipulators, the robot arm could adaptively stop the fall and assist the recovery. Prior works on falling and recovery strategies for legged mobile manipulators usually rely on assumptions such as inelastic collisions and falling in defined directions to enable real-time computation. This paper presents a learning-based approach to reducing fall damage and recovery. An asymmetric actor-critic training structure is used to train a time-invariant policy with time-varying reward functions. In simulated experiments, the policy recovers from 98.9% of initial falling configurations. It reduces base contact impulse, peak joint internal forces, and base acceleration during the fall compared to the baseline methods. The trained control policy is deployed and extensively tested on the ALMA robot hardware. A video summarizing the proposed method and the hardware tests is available at https://youtu.be/avwg2HqGi8s
Superpixel-guided multiscale kernel collaborative representation for hyperspectral image classification

ABSTRACT This article presents a superpixel-guided multiscale kernel collaborative representation method for robust classification of hyperspectral images. This novel method first exploits the spatial multiscale information of hyperspectral images by extending a superpixel segmentation algorithm, and then proposes a spatial-spectral information fusion technique to encode the spatial multiscale similarities and the spectral similarities between the pixels in the framework of kernel collaborative representation classification. The advantages of it mainly consist in (1) avoiding choosing empirical parameters in the spatial feature extraction process of superpixels and (2) enhanced classification accuracy as compared to traditional spatial-spectral kernel techniques. Experimental results with two widely used hyperspectral images demonstrate the effectiveness of the proposed method.
PDSketch: Integrated Planning Domain Programming and Learning

This paper studies a model learning and online planning approach towards building flexible and general robots. Specifically, we investigate how to exploit the locality and sparsity structures in the underlying environmental transition model to improve model generalization, data-efficiency, and runtime-efficiency. We present a new domain definition language, named PDSketch. It allows users to flexibly define high-level structures in the transition models, such as object and feature dependencies, in a way similar to how programmers use TensorFlow or PyTorch to specify kernel sizes and hidden dimensions of a convolutional neural network. The details of the transition model will be filled in by trainable neural networks. Based on the defined structures and learned parameters, PDSketch automatically generates domain-independent planning heuristics without additional training. The derived heuristics accelerate the performance-time planning for novel goals.
A Host--Kra ${\mathbf F}_2^\omega$-system of order $5$ that is not Abramov of order $5$, and non-measurability of the inverse theorem for the $U^6({\mathbf F}_2^n)$ norm

It was conjectured by Bergelson, Tao, and Ziegler that every Host--Kra ${\mathbf F}_p^\omega$-system of order $k$ is an Abramov system of order $k$. This conjecture has been verified for $k \leq p+1$. In this paper we show that the conjecture fails when $k=5, p=2$. We in fact establish a stronger (combinatorial) statement, in that we produce a bounded function $f: {\mathbf F}_2^n \to {\mathbf C}$ of large Gowers norm $\|f\|_{U^6({\mathbf F}_2^n)}$ which (as per the inverse theorem for that norm) correlates with a non-classical quintic phase polynomial $e(P)$, but with the property that all such phase polynomials $e(P)$ are ``non-measurable'' in the sense that they cannot be well approximated by functions of a bounded number of random translates of $f$.
Scalable Stochastic Gradient Riemannian Langevin Dynamics in Non-Diagonal Metrics

Stochastic-gradient sampling methods are often used to perform Bayesian inference on neural networks. It has been observed that the methods in which notions of differential geometry are included tend to have better performances, with the Riemannian metric improving posterior exploration by accounting for the local curvature. However, the existing methods often resort to simple diagonal metrics to remain computationally efficient. This loses some of the gains. We propose two non-diagonal metrics that can be used in stochastic-gradient samplers to improve convergence and exploration but have only a minor computational overhead over diagonal metrics. We show that for fully connected neural networks (NNs) with sparsity-inducing priors and convolutional NNs with correlated priors, using these metrics can provide improvements. For some other choices the posterior is sufficiently easy also for the simpler metrics.
Chirostrip Antenna: Line Source Problem

Recently, we introduced the idea of chirostrip antennas which are microstrip radiators with chiral materials as substrates and/or superstrates. Owing to the handedness of chiral substrates, novel features are associated with this type of antenna. In this paper, the problem of chirostrip line antennas is analyzed in detail. These antennas consist of an electric line source printed on top of a grounded chiral slab. Both the surface wave and radiation modes of such substrates are obtained and their important features are discussed. Excitation of these surface waves and radiation by the line source are studied and their notable characteristics, such as the power and polarization state of the radiation fields, and the power carried by surface waves, are addressed. It is found that for the two-dimensional chirostrip antenna the chirality can, under certain circumstances, decrease the undesirable surface-wave power and increase the radiated power, thus giving rise to higher radiation resistance and radiation eff.
Improved bounds on the bosonic dark matter with pulsars in the Milky Way

Neutron stars (NSs) can be used to constrain dark matter (DM) since a NS can transform into a black hole (BH) if it captures sufficient DM particles and exceeds the Chandrasekhar limit. We extend earlier work and for the first time take into account the Galactic motion of individual NSs, which changes the amount of the captured DM by as large as one to two orders of magnitude. We systematically apply the analysis to 413 NSs in the Milky Way, and constrain the DM particle mass and its interaction with nucleon simultaneously. We find that the most stringent bound is placed by a few NSs and the bound becomes stronger after considering the Galactic motion. The survival of observed NSs already excludes a cross section σ nX ≳ 10-45 cm2 for DM particles with mass from 100 MeV to 103 GeV. Especially for a mass around 10 GeV, the constraint on the cross section is as stringent as σ nX ≲ 10-49 cm2.
Matching curved 3D object models to 2D images

Presents a method of locating known rigid 3D objects with arbitrary curved surfaces using a single image. A 3D object is modeled by a covering set of 2D silhouettes together with important internal edges. The model silhouette is derived by the curvature method of Basri and Ullman. Internal edges are computed using a stereo matching strategy. The pose of the observed object is determined by fitting the edgemap derived from the model images to the edgemap of the object. No salient matching primitives are used: correspondence is guided by the minimization of the over-all Euclidean distance between the model edgemap and the observed edgemap. Bench tests and simulations show that the matching technique converges for a broad range (entire aspect) of starting poses. <<ETX>>
Collapse models and gravitational decoherence at test: how far can we push the limits of quantum mechanics?

Collapse models describe the breakdown of the quantum superposition principle when moving from microscopic to macroscopic scales. They are among the possible solutions to the quantum measurement problem and thus describe the emergence of classical mechanics from the quantum one. Testing collapse models is equivalent to test the limits of quantum mechanics. I will provide an overview on how one can test collapse models, and which are the future theoretical and experimental challenges ahead.
Cloudless-Training: A Framework to Improve Efficiency of Geo-Distributed ML Training

Geo-distributed ML training can benefit many emerging ML scenarios (e.g., large model training, federated learning) with multi-regional cloud resources and wide area network. However, its efficiency is limited due to 2 challenges. First, efficient elastic scheduling of multi-regional cloud resources is usually missing, affecting resource utilization and performance of training. Second, training communication on WAN is still the main overhead, easily subjected to low bandwidth and high fluctuations of WAN. In this paper, we propose a framework, Cloudless-Training, to realize efficient PS-based geo-distributed ML training in 3 aspects. First, it uses a two-layer architecture with control and physical training planes to support elastic scheduling and communication for multi-regional clouds in a serverless maner.Second, it provides an elastic scheduling strategy that can deploy training workflows adaptively according to the heterogeneity of available cloud resources and distribution of pre-existing training datasets. Third, it provides 2 new synchronization strategies for training partitions among clouds, including asynchronous SGD with gradient accumulation (ASGD-GA) and inter-PS model averaging (MA). It is implemented with OpenFaaS and evaluated on Tencent Cloud. Experiments show that Cloudless-Training can support general ML training in a geo-distributed way, greatly improve resource utilization (e.g., 9.2%-24.0% training cost reduction) and synchronization efficiency (e.g., 1.7x training speedup over baseline at most) with model correctness guarantees.
Cal-QL: Calibrated Offline RL Pre-Training for Efficient Online Fine-Tuning

A compelling use case of offline reinforcement learning (RL) is to obtain a policy initialization from existing datasets followed by fast online fine-tuning with limited interaction. However, existing offline RL methods tend to behave poorly during fine-tuning. In this paper, we devise an approach for learning an effective initialization from offline data that also enables fast online fine-tuning capabilities. Our approach, calibrated Q-learning (Cal-QL), accomplishes this by learning a conservative value function initialization that underestimates the value of the learned policy from offline data, while also being calibrated, in the sense that the learned Q-values are at a reasonable scale. We refer to this property as calibration, and define it formally as providing a lower bound on the true value function of the learned policy and an upper bound on the value of some other (suboptimal) reference policy, which may simply be the behavior policy. We show that offline RL algorithms that learn such calibrated value functions lead to effective online fine-tuning, enabling us to take the benefits of offline initializations in online fine-tuning. In practice, Cal-QL can be implemented on top of the conservative Q learning (CQL) for offline RL within a one-line code change. Empirically, Cal-QL outperforms state-of-the-art methods on 9/11 fine-tuning benchmark tasks that we study in this paper. Code and video are available at https://nakamotoo.github. io/Cal-QL
Let's Get Personal: Personal Questions Improve SocialBot Performance in the Alexa Prize

There has been an increased focus on creating conversational open-domain dialogue systems in the spoken dialogue community. Unlike traditional dialogue systems, these conversational systems cannot assume any specific information need or domain restrictions, i.e., the only inherent goal is to converse with the user on an unknown set of topics. While massive improvements in Natural Language Understanding (NLU) and the growth of available knowledge resources can partially support a robust conversation, these conversations generally lack the rapport between two humans that know each other. We developed a robust open-domain conversational system, Athena, that real Amazon Echo users access and evaluate at scale in the context of the Alexa Prize competition. We experiment with methods intended to increase intimacy between Athena and the user by heuristically developing a rule-based user model that personalizes both the current and subsequent conversations and evaluating specific personal opinion question strategies in A/B studies. Our results show a statistically significant positive impact on perceived conversation quality and length when employing these strategies.
Deep Equilibrium Learning of Explicit Regularization Functionals for Imaging Inverse Problems

There has been significant recent interest in the use of deep learning for regularizing imaging inverse problems. Most work in the area has focused on regularization imposed implicitly by convolutional neural networks (CNNs) pre-trained for image reconstruction. In this work, we follow an alternative line of work based on learning explicit regularization functionals that promote preferred solutions. We develop the Explicit Learned Deep Equilibrium Regularizer (ELDER) method for learning explicit regularization functionals that minimize a mean-squared error (MSE) metric. ELDER is based on a regularization functional parameterized by a CNN and a deep equilibrium learning (DEQ) method for training the functional to be MSE-optimal at the fixed points of the reconstruction algorithm. The explicit regularizer enables ELDER to directly inherit fundamental convergence results from optimization theory. On the other hand, DEQ training enables ELDER to improve over existing explicit regularizers without prohibitive memory complexity during training. We use ELDER to train several approaches to parameterizing explicit regularizers and test their performance on three distinct imaging inverse problems. Our results show that ELDER can greatly improve the quality of explicit regularizers compared to existing methods, and show that learning explicit regularizers does not compromise performance relative to methods based on implicit regularization.
Investigating the fast spectral diffusion of a quantum emitter in hBN using resonant excitation and photon correlations

The ability to identify and characterize homogeneous and inhomogeneous dephasing processes is crucial in solid-state quantum optics. In particular, spectral diffusion leading to line broadening is difficult to evidence when the associated timescale is shorter than the inverse of the photon detection rate. Here, we show that a combination of resonant laser excitation and second-order photon correlations allows to access such fast dynamics. The resonant laser drive converts spectral diffusion into intensity fluctuations, leaving a signature in the second-order coherence function $g^{(2)}(\tau)$ of the scattered light that can be characterized using two-photon coincidences -- which simultaneously provides the homogeneous dephasing time. We experimentally implement this method to investigate the fast spectral diffusion of a color center generated by an electron beam in the two-dimensional material hexagonal boron nitride. The $g^{(2)}(\tau)$ function of the quantum emitter measured over more than ten orders of magnitude of delay times, at various laser powers, establishes that the color center experiences spectral diffusion at a characteristic timescale of a few tens of microseconds, while emitting Fourier-limited single photons ($T_2/2T_1 \sim 1$) between spectral jumps.
3D wind field profiles from hyperspectral sounders: revisiting optic-flow from a meteorological perspective

In this work, we present an efficient optic flow algorithm for the extraction of vertically resolved 3D atmospheric motion vector (AMV) fields from incomplete hyperspectral image data measures by infrared sounders. The model at the heart of the energy to be minimized is consistent with atmospheric dynamics, incorporating ingredients of thermodynamics, hydrostatic equilibrium and statistical turbulence. Modern optimization techniques are deployed to design a low-complexity solver for the energy minimization problem, which is non-convex, non-differentiable, high-dimensional and subject to physical constraints. In particular, taking advantage of the alternate direction of multipliers methods (ADMM), we show how to split the original high-dimensional problem into a recursion involving a set of standard and tractable optic-flow sub-problems. By comparing with the ground truth provided by the operational numerical simulation of the European Centre for Medium-Range Weather Forecasts (ECMWF), we show that the performance of the proposed method is superior to state-of-the-art optical flow algorithms in the context of real infrared atmospheric sounding interferometer (IASI) observations.
A COEFFICIENT FORMULA OF AN INFINITE PRODUCT OF POWER SERIES FROM ITS PERMUTATION AND REORDERING NATURE

We give an exact coeﬃcients formula of any inﬁnite product of power series with constant term equal to 1, by using structures from partitions of integers and permutation groups. This is an universal theorem for various of Binomial-type theorems in many sense. In particular, we give the new formulas as the double counting of Bell polynomial, Binomial Theorem and Multinomial Theorem.
A Theory for Semantic Channel Coding With Many-to-one Source

As one of the potential key technologies of 6G, semantic communication is still in its infancy and there are many open problems, such as semantic entropy definition and semantic channel coding theory. To address these challenges, we investigate semantic information measures and semantic channel coding theorem. Specifically, we propose a semantic entropy definition as the uncertainty in the semantic interpretation of random variable symbols in the context of knowledge bases, which can be transformed into existing semantic entropy definitions under given conditions. Moreover, different from traditional communications, semantic communications can achieve accurate transmission of semantic information under a non-zero bit error rate. Based on this property, we derive a semantic channel coding theorem for a typical semantic communication with many-to-one source (i.e., multiple source sequences express the same meaning), and prove its achievability and converse based on a generalized Fano's inequality. Finally, numerical results verify the effectiveness of the proposed semantic entropy and semantic channel coding theorem.
Electron addition spectral functions of low-density polaron liquids

Spectral functions are important quantities that contain a wealth of information about the quasiparticles of a system, and that can also be measured experimentally. For systems with electron-phonon coupling, good approximations for the spectral function are available only in the Migdal limit (at Fermi energies much larger than the typical phonon frequency, E_F\gg \OmegaEF≫Ω, requiring a large carrier concentration xx) and in the single polaron limit (at x=0x=0). Here we show that the region with x\ll 1x≪1(E_F<\OmegaEF<Ω) can also be reliably investigated with the Momentum Average (MA) variational approximation, which essentially describes the formation of a polaron above an inert Fermi sea. Specifically, we show that for the one-dimensional spinless Holstein model, the MA spectral functions compare favorably with those calculated using variationally exact density matrix renormalization group simulations (DMRG) evaluated directly in frequency-space, so long as x<0.1x<0.1 and the adiabaticity ratio \Omega/t>0.5Ω/t>0.5. Unlike in the Migdal limit, here 'polaronic physics’ emerges already at moderate couplings. The relevance of these results for a spinful low-xx metal is also discussed.
3D hydrodynamic simulations of massive main-sequence stars. I. Dynamics and mixing of convection and internal gravity waves

We performed 3D hydrodynamic simulations of the inner ≈ 50% radial extent of a 25 M⊙ star in the early phase of the main sequence and investigate core convection and internal gravity waves in the core-envelope boundary region. Simulations for different grid resolutions and driving luminosities establish scaling relations to constrain models of mixing for 1D applications. As in previous works, the turbulent mass entrainment rate extrapolated to nominal heating is unrealistically high (1.58 × 10−4 M⊙ yr−1), which is discussed in terms of the non-equilibrium response of the simulations to the initial stratification. We measure quantitatively the effect of mixing due to internal gravity waves excited by core convection interacting with the boundary in our simulations. The wave power spectral density as a function of frequency and wavelength agrees well with the GYRE eigenmode predictions based on the 1D spherically averaged radial profile. A diffusion coefficient profile that reproduces the spherically averaged abundance distribution evolution is determined for each simulation. Through a combination of eigenmode analysis and scaling relations it is shown that in the N2-peak region, mixing is due to internal gravity waves and follows the scaling relation DIGW-hydro∝L4/3 over a $\gtrapprox 2\,\,\mathrm{\mathrm{dex}}$ range of heating factors. Different extrapolations of the mixing efficiency down to nominal heating are discussed. If internal gravity wave mixing is due to thermally-enhanced shear mixing, an upper limit is $D_\mathrm{IGW}\lessapprox 2$ to 3 × 104 cm2/s at nominal heating in the N2-peak region above the convective core.
Power and Interference Control for VLC-Based UDN: A Reinforcement Learning Approach

Visible light communication (VLC) has been widely applied as a promising solution for modern short range communication. When it comes to the deployment of LED arrays in VLC networks, the emerging ultra-dense network (UDN) technology can be adopted to expand the VLC network’s capacity. However, the problem of inter-cell interference (ICI) mitigation and efficient power control in the VLC-based UDN is still a critical challenge. To this end, a reinforcement learning (RL) based VLC UDN architecture is devised in this paper. The deployment of the cells is optimized via spatial reuse to mitigate ICI. An RL-based algorithm is proposed to dynamically optimize the policy of power and interference control, maximizing the system utility in the complicated and dynamic environment. Simulation results demonstrate the superiority of the proposed scheme, it increase the system utility and achievable data rate while reducing the energy consumption and ICI, which outperforms the benchmark scheme.