Miracle-dz/UniFAR-FTUs huggingface dataset:
Low-Rank Modular Reinforcement Learning via Muscle Synergy

Modular Reinforcement Learning (RL) decentralizes the control of multi-joint robots by learning policies for each actuator. Previous work on modular RL has proven its ability to control morphologically different agents with a shared actuator policy. However, with the increase in the Degree of Freedom (DoF) of robots, training a morphology-generalizable modular controller becomes exponentially difficult. Motivated by the way the human central nervous system controls numerous muscles, we propose a Synergy-Oriented LeARning (SOLAR) framework that exploits the redundant nature of DoF in robot control. Actuators are grouped into synergies by an unsupervised learning method, and a synergy action is learned to control multiple actuators in synchrony. In this way, we achieve a low-rank control at the synergy level. We extensively evaluate our method on a variety of robot morphologies, and the results show its superior efficiency and generalizability, especially on robots with a large DoF like Humanoids++ and UNIMALs.
On the Performance of Irregular Repetition Slotted ALOHA with an Age of Information Threshold

—The present paper focuses on an IoT setting in which a large number of devices generate time-stamped updates addressed to a common gateway. Medium access is regulated fol- lowing a grant-free approach, and the system aims at maintaining an up-to-date knowledge at the receiver, measured through the average network age of information (AoI). In this context, we propose a variation of the irregular repetition slotted ALOHA (IRSA) protocol. The scheme, referred to as age-threshold IRSA (AT-IRSA), leverages feedback provided by the receiver to admit to the channel only devices whose AoI exceeds a dynamically adapted target value. By means of detailed networks simulations, as well as of a simple yet tight analytical approximation, we demonstrate that the approach can more than halve the average network AoI compared to plain IRSA, and offers notable improvements over feedback-based state-of-the-art slotted ALOHA solutions recently proposed in the literature.
Slowly evolving horizons in Einstein gravity and beyond

We study event horizon candidates for slowly evolving dynamical black holes (BHs) in General Relativity and Einstein–Gauss–Bonnet (EGB) gravity. Such a type of horizon candidate has been termed as slowly evolving null surface (SENS). It signifies a near-equilibrium state of a dynamic BH. We demonstrate the time evolution of such surfaces for three different metrics. First, we locate such a surface for a charged Vaidya metric and show that the parameter space of the BH gets constrained to allow a physically admissible SENS. We then consider a supertranslated Vaidya solution that contains a non-spherical horizon and study the properties of the SENS. This spacetime generates a non-vanishing shear at the SENS due to the presence of the supertranslation field. The SENS for a spherically symmetric Vaidya-like solution in EGB gravity yields a bound on the accretion rate that depends on the size of the horizon. We also show that the first and second laws of BH mechanics can be established for these slowly evolving surfaces.
Constraining Magnetic Field Amplification in SN Shocks Using Radio Observations of SNe 2011fe and 2014J

We modeled the radio non-detection of two Type Ia supernovae (SNe), SN 2011fe and SN 2014J, considering synchrotron emission from the interaction between SN ejecta and the circumstellar medium. For ejecta whose outer parts have a power-law density structure, we compare synchrotron emission with radio observations. Assuming that 20% of the bulk shock energy is being shared equally between electrons and magnetic fields, we found a very low-density medium around both the SNe. A less tenuous medium with particle density ∼1 cm−3, which could be expected around both SNe, can be estimated when the magnetic field amplification is less than that presumed for energy equipartition. This conclusion also holds if the progenitor of SN 2014J was a rigidly rotating white dwarf (WD) with a main-sequence (MS) or red giant companion. For a He star companion, or a MS for SN 2014J, with 10% and 1% of bulk kinetic energy in magnetic fields, we obtain mass-loss rates of and for a wind velocity of 100 . The former requires a mass accretion efficiency of >99% onto the WD, but is less restricted for the latter case. However, if the tenuous medium is due to a recurrent nova, it is difficult from our model to predict synchrotron luminosities. Although the formation channels of SNe 2011fe and 2014J are not clear, the null detection in radio wavelengths could point toward a low amplification efficiency for magnetic fields in SN shocks.
Pricing and Electric Vehicle Charging Equilibria

We study equilibria in an Electric Vehicle (EV) charging game, a cost minimization game inherent to decentralized charging control strategy for EV power demand management. In our model, each user optimizes its total cost which is sum of direct power cost and the indirect dissatisfaction cost. We show that, taking player specific price independent dissatisfaction cost in to account, contrary to popular belief, herding only happens at lower EV uptake. Moreover, this is true for both linear and logistic dissatisfaction functions. We study the question of existence of price profiles to induce a desired equilibrium. We define two types of equilibria, distributed and non-distributed equilibria, and show that under logistic dissatisfaction, only non-distributed equilibria are possible by feasibly setting prices. In linear case, both type of equilibria are possible but price discrimination is necessary to induce distributed equilibria. Finally, we show that in the case of symmetric EV users, mediation cannot improve upon Nash equilibria.
Isometric 3D Adversarial Examples in the Physical World

3D deep learning models are shown to be as vulnerable to adversarial examples as 2D models. However, existing attack methods are still far from stealthy and suffer from severe performance degradation in the physical world. Although 3D data is highly structured, it is difficult to bound the perturbations with simple metrics in the Euclidean space. In this paper, we propose a novel $\epsilon$-isometric ($\epsilon$-ISO) attack to generate natural and robust 3D adversarial examples in the physical world by considering the geometric properties of 3D objects and the invariance to physical transformations. For naturalness, we constrain the adversarial example to be $\epsilon$-isometric to the original one by adopting the Gaussian curvature as a surrogate metric guaranteed by a theoretical analysis. For invariance to physical transformations, we propose a maxima over transformation (MaxOT) method that actively searches for the most harmful transformations rather than random ones to make the generated adversarial example more robust in the physical world. Experiments on typical point cloud recognition models validate that our approach can significantly improve the attack success rate and naturalness of the generated 3D adversarial examples than the state-of-the-art attack methods.
Block Codes on Pomset Metric

Given a regular multiset [Formula: see text] on [Formula: see text], a partial order [Formula: see text] on [Formula: see text], and a label map [Formula: see text] defined by [Formula: see text] with [Formula: see text], we define a pomset block metric [Formula: see text] on the direct sum [Formula: see text] of [Formula: see text] based on the pomset [Formula: see text]. The pomset block metric extends the classical pomset metric introduced by Sudha and Selvaraj and generalizes the poset block metric introduced by Alves et al. over [Formula: see text]. The space [Formula: see text] is called the pomset block space and we determine the complete weight distribution of it. Further, [Formula: see text]-perfect pomset block codes for ideals with partial and full counts are described. Then, for block codes with chain pomset, the packing radius and Singleton bound are established. The relation between maximum distance separable (MDS) codes and [Formula: see text]-perfect codes for any ideal [Formula: see text] is investigated. Moreover, the duality theorem for an MDS pomset block code is established when all the blocks have the same size.
Human Guided Exploitation of Interpretable Attention Patterns in Summarization and Topic Segmentation

The multi-head self-attention mechanism of the transformer model has been thoroughly investigated recently. In one vein of study, researchers are interested in understanding why and how transformers work. In another vein, researchers propose new attention augmentation methods to make transformers more accurate, efficient and interpretable. In this paper, we combine these two lines of research in a human-in-the-loop pipeline to first discover important task-specific attention patterns. Then those patterns are injected, not only to smaller models, but also to the original model. The benefits of our pipeline and discovered patterns are demonstrated in two case studies with extractive summarization and topic segmentation. After discovering interpretable patterns in BERT-based models fine-tuned for the two downstream tasks, experiments indicate that when we inject the patterns into attention heads, the models show considerable improvements in accuracy and efficiency.
A shallow layer laboratory model of large-scale atmospheric circulation

A new shallow layer laboratory model of global atmospheric circulation is realised and studied by experiments and numerical simulations. A shallow rotating cylindrical fluid layer of 30 mm thickness and 690 mm diameter, with a localised heater at the bottom periphery and localised cooler in the central part of the upper boundary is considered. The rim heater imitates the equator heating and disc cooler – the North pole cooling. The flow transforms from the Hadley-like regime to the baroclinic wave regime through transitional states. The decrease in the thermal Rossby number for the fixed value of Taylor number results in the regularisation of the baroclinic waves. All wave regimes, even with regular wave structures, are characterised by strong non-periodic fluctuations. The observed baroclinic wave structures are a combination of temporarily evolving different baroclinic modes. The important outcome of the shallow layer model is a realisation of the Earth-like meridional three-cell structure. It is shown that the three-cell structure with analogs of polar, Ferrel and Hadley cells exist only in a limited range of parameters. A comparison of the results for the water and silicon oil demonstrated that the physical properties of the fluid can have a strong impact on the baroclinic wave structure.
Extended calculation of electronic excitations for direct detection of dark matter

Direct detection experiments utilizing electronic excitations are spearheading the search for light, sub-GeV, dark matter (DM). It is thus crucial to have accurate predictions for any DM-electron interaction rate in this regime. EXCEED-DM (EXtended Calculation of Electronic Excitations for Direct detection of Dark Matter) computes DM-electron interaction rates with inputs from a variety of ab initio electronic structure calculations. The purpose of this manuscript is two-fold: to familiarize the user with the formalism and inputs of EXCEED-DM, and perform novel calculations to showcase what EXCEED-DM is capable of. We perform four calculations which extend previous results: the scattering rate in the dark photon model, screened with the numerically computed dielectric function, the scattering rate with an interaction potential dependent on the electron velocity, an extended absorption calculation for scalar, pseudoscalar, and vector DM, and the annual modulation of the scattering rate in the dark photon model.
Longshortnet: Exploring Temporal and Semantic Features Fusion In Streaming Perception

Streaming perception is a fundamental task in autonomous driving that requires a careful balance between the latency and accuracy of the autopilot system. However, current methods for streaming perception are limited as they rely only on the current and adjacent two frames to learn movement patterns, which restricts their ability to model complex scenes, often leading to poor detection results. To address this limitation, we propose LongShortNet, a novel dual-path network that captures long-term temporal motion and integrates it with short-term spatial semantics for real-time perception. Our proposed LongShortNet is notable as it is the first work to extend long-term temporal modeling to streaming perception, enabling spatiotemporal feature fusion. We evaluate LongShortNet on the challenging Argoverse-HD dataset and demonstrate that it outperforms existing state-of-the-art methods with almost no additional computational cost.1
Smoothed Online Optimization with Unreliable Predictions

We examine the problem of smoothed online optimization, where a decision maker must sequentially choose points in a normed vector space to minimize the sum of per-round, non-convex hitting costs and the costs of switching decisions between rounds. The decision maker has access to a black-box oracle, such as a machine learning model, that provides untrusted and potentially inaccurate predictions of the optimal decision in each round. The goal of the decision maker is to exploit the predictions if they are accurate, while guaranteeing performance that is not much worse than the hindsight optimal sequence of decisions, even when predictions are inaccurate. We impose the standard assumption that hitting costs are globally α-polyhedral. We propose a novel algorithm, Adaptive Online Switching (AOS), and prove that, for a large set of feasible δ > 0, it is (1+δ)-competitive if predictions are perfect, while also maintaining a uniformly bounded competitive ratio of 2~O (1/(α δ)) even when predictions are adversarial. Further, we prove that this trade-off is necessary and nearly optimal in the sense that any deterministic algorithm which is (1+δ)-competitive if predictions are perfect must be at least 2~Ω (1/(α δ)) -competitive when predictions are inaccurate. In fact, we observe a unique threshold-type behavior in this trade-off: if δ is not in the set of feasible options, then no algorithm is simultaneously (1 + δ)-competitive if predictions are perfect and ζ-competitive when predictions are inaccurate for any ζ < ∞. Furthermore, we discuss that memory is crucial in AOS by proving that any algorithm that does not use memory cannot benefit from predictions. We complement our theoretical results by a numerical study on a microgrid application.
Distributed Machine Learning with Strategic Network Design: A Game-Theoretic Perspective

—This paper considers a game-theoretic framework for distributed machine learning problems over networks where the information acquisition at a node is modeled as a ratio- nal choice of a player. In the proposed game, players decide both the learning parameters and the network structure. The Nash equilibrium characterizes the tradeoff between the local performance and the global agreement of the learned classiﬁers. We ﬁrst introduce a commutative approach which features a joint learning process that integrates the iterative learning at each node and the network formation. We show that our game is equivalent to a generalized potential game in the setting of undirected networks. We study the convergence of the proposed commutative algorithm, analyze the network structures deter- mined by our game, and show the improvement of the social welfare in comparison with standard distributed learning over ﬁxed networks. To adapt our framework to streaming data, we derive a distributed Kalman ﬁlter. A concurrent algorithm based on the online mirror descent algorithm is also introduced for solving for Nash equilibria in a holistic manner. In the case study, we use telemonitoring of Parkinson’s disease to corroborate the results.
Minimax Optimal Algorithms for Fixed-Budget Best Arm Identification

We consider the fixed-budget best arm identification problem where the goal is to find the arm of the largest mean with a fixed number of samples. It is known that the probability of misidentifying the best arm is exponentially small to the number of rounds. However, limited characterizations have been discussed on the rate (exponent) of this value. In this paper, we characterize the minimax optimal rate as a result of an optimization over all possible parameters. We introduce two rates, $R^{\mathrm{go}}$ and $R^{\mathrm{go}}_{\infty}$, corresponding to lower bounds on the probability of misidentification, each of which is associated with a proposed algorithm. The rate $R^{\mathrm{go}}$ is associated with $R^{\mathrm{go}}$-tracking, which can be efficiently implemented by a neural network and is shown to outperform existing algorithms. However, this rate requires a nontrivial condition to be achievable. To address this issue, we introduce the second rate $R^{\mathrm{go}}_\infty$. We show that this rate is indeed achievable by introducing a conceptual algorithm called delayed optimal tracking (DOT).
Simple Mechanisms for Agents with Non-linear Utilities

We show that economic conclusions derived from Bulow and Roberts (1989) for linear utility models approximately extend to non-linear utility models. Speciﬁcally, we quantify the extent to which agents with non-linear utilities resemble agents with linear utilities, and we show that the approximation of mechanisms for agents with linear utilities approximately extend for agents with non-linear utilities. We illustrate the framework for the objectives of revenue and welfare on non-linear models that include agents with budget constraints, agents with risk aversion, and agents with endogenous valuations. We derive bounds on how much these models resemble the linear utility model and combine these bounds with well-studied approximation results for linear utility models. We conclude that simple mechanisms are approximately optimal for these non-linear agent models.
On the Importance and Applicability of Pre-Training for Federated Learning

Pre-training is prevalent in nowadays deep learning to improve the learned model's performance. However, in the literature on federated learning (FL), neural networks are mostly initialized with random weights. These attract our interest in conducting a systematic study to explore pre-training for FL. Across multiple visual recognition benchmarks, we found that pre-training can not only improve FL, but also close its accuracy gap to the counterpart centralized learning, especially in the challenging cases of non-IID clients' data. To make our findings applicable to situations where pre-trained models are not directly available, we explore pre-training with synthetic data or even with clients' data in a decentralized manner, and found that they can already improve FL notably. Interestingly, many of the techniques we explore are complementary to each other to further boost the performance, and we view this as a critical result toward scaling up deep FL for real-world applications. We conclude our paper with an attempt to understand the effect of pre-training on FL. We found that pre-training enables the learned global models under different clients' data conditions to converge to the same loss basin, and makes global aggregation in FL more stable. Nevertheless, pre-training seems to not alleviate local model drifting, a fundamental problem in FL under non-IID data.
X-ray observations of FO Aqr during the 2016 low state

We present the first ever X-ray data taken of an intermediate polar, FO Aqr, when in a low accretion state and during the subsequent recovery. The Swift and Chandra X-ray data taken during the low accretion state in July 2016 both show a softer spectrum when compared to archival data taken when FO Aqr was in a high state. The X-ray spectrum in the low state showed a significant increase in the ratio of the soft X-ray flux to the hard X-ray flux due to a change in the partial covering fraction of the white dwarf from $>85\%$ to $70^{+5}_{-8}\%$ and a change in the hydrogen column density within the disc from 19$^{+1.2}_{-0.9}\times 10^{22}$ cm$^{-2}$ to 1.3$^{+0.6}_{-0.3}\times 10^{22}$ cm$^{-2}$. XMM-Newton observations of FO Aqr during the subsequent recovery suggest that the system had not yet returned to its typical high state by November 2016, with the hydrogen column density within the disc found to be 15$^{+3.0}_{-2.0}$ cm$^{-2}$. The partial covering fraction varied in the recovery state between $85\%$ and $95\%$. The spin period of the white dwarf in 2014 and 2015 has also been refined to 1254.3342(8) s. Finally, we find an apparent phase difference between the high state X-ray pulse and recovery X-ray pulse of 0.17, which may be related to a restructuring of the X-ray emitting regions within the system.
A Slowly rotating charged black hole in five dimensions

Black hole solutions in higher dimensional Einstein and Einstein{Maxwell gravity have been discussed by Tangherlini as well as Myers and Perry a long time ago. These so- lutions are the generalizations of the familiar Schwarzschild, Reissner{Nordstrand Kerr solutions of four-dimensional general relativity. However, higher dimensional gen- eralization of the Kerr{Newman solution in four dimensions has not been found yet. As a rst step in this direction we shall report on a new solution of the Einstein{Maxwell system of equations that describes an electrically charged and slowly rotating black hole in v e dimensions.
Herschel and ALMA Observations of Massive SZE-selected Clusters

We present new Herschel observations of four massive, Sunyaev–Zel’dovich effect–selected clusters at , two of which have also been observed with the Atacama Large Millimeter/submillimeter Array (ALMA). We detect 19 Herschel/Photoconductor Array Camera and Spectrometer (PACS) counterparts to spectroscopically confirmed cluster members, five of which have redshifts determined via CO (4–3) and [C i] ( ) lines. The mean [C i]/CO line ratio is 0.19 ± 0.07 in brightness temperature units, consistent with previous results for field samples. We do not detect significant stacked ALMA dust continuum or spectral-line emission, implying upper limits on mean interstellar medium (H2 + H i) and molecular gas masses. An apparent anticorrelation of with clustercentric radius is driven by the tight relation between star formation rate and stellar mass. We find an average specific star formation rate of log(sSFR/yr−1) = −10.36, which is below the correlation measured for field galaxies at similar redshifts. The fraction of infrared-bright galaxies (IRBGs; ) per cluster and average sSFR rise significantly with redshift. For CO detections, we find , comparable to those of field galaxies, and gas depletion timescales of about 2 Gyr. We use radio observations to distinguish active galactic nuclei (AGNs) from star-forming galaxies. At least four of our 19 Herschel cluster members have , implying an AGN fraction for our PACS-selected sample.