Search

We find Abelian gauge fields that double copy to a large class of black hole spacetimes with spherical horizon topology known as the Kerr-NUT-(A)dS family. Using a multi-Kerr-Schild prescription, we extend the previously-known double copy structure for arbitrarily rotating general dimension black holes, to include NUT charges and an arbitrary cosmological constant. In all cases, these single copy gauge fields are 'aligned fields', because their nonzero components align with the principal tensor which generates the Killing structure of the spacetime. In five dimensions, we additionally derive the same single-copy field strengths via the Weyl double copy procedure., Comment: 28 pages. v2: Added missing reference and fixed bibliography formatting

The International Air Transport Association (IATA) is one of several organizations that have presented goals for future CO2 emissions from commercial aviation with the intent of alleviating the associated environmental impacts. These goals include attaining carbon-neutral growth in the year 2020 and total aviation CO2 emissions in 2050 equal to 50% of 2005 aviation CO2 emissions. This paper presents the use of a simulation-based approach to predict future CO2 emissions from commercial aviation based upon a set of scenarios developed as part of the Aircraft Technology Modeling and Assessment project within ASCENT, the FAA Center of Excellence for Alternative Jet Fuels and the Environment. Results indicate that, in future scenarios with increasing demand for air travel, it is difficult to reduce CO2 emissions in 2050 to levels equal to or below 2005 levels, although neutral CO2 growth after 2020 may be possible., Comment: Presented at the Council of Engineering Systems Universities (CESUN) conference in 2018

This paper addresses a new semantic multi-robot planning problem in uncertain and dynamic environments. Particularly, the environment is occupied with non-cooperative, mobile, uncertain labeled targets. These targets are governed by stochastic dynamics while their current and future positions as well as their semantic labels are uncertain. Our goal is to control mobile sensing robots so that they can accomplish collaborative semantic tasks defined over the uncertain current/future positions and labels of these targets. We express these tasks using Linear Temporal Logic (LTL). We propose a sampling-based approach that explores the robot motion space, the mission specification space, as well as the future configurations of the labeled targets to design optimal paths. These paths are revised online to adapt to uncertain perceptual feedback. To the best of our knowledge, this is the first work that addresses semantic mission planning problems in uncertain and dynamic semantic environments. We provide extensive experiments that demonstrate the efficiency of the proposed method, Comment: arXiv admin note: text overlap with arXiv:2012.10490

This paper presents a new approach to design verified compositions of Neural Network (NN) controllers for autonomous systems with tasks captured by Linear Temporal Logic (LTL) formulas. Particularly, the LTL formula requires the system to reach and avoid certain regions in a temporal/logical order. We assume that the system is equipped with a finite set of trained NN controllers. Each controller has been trained so that it can drive the system towards a specific region of interest while avoiding others. Our goal is to check if there exists a temporal composition of the trained NN controllers - and if so, to compute it - that will yield composite system behaviors that satisfy a user-specified LTL task for any initial system state belonging to a given set. To address this problem, we propose a new approach that relies on a novel integration of automata theory and recently proposed reachability analysis tools for NN-controlled systems. We note that the proposed method can be applied to other controllers, not necessarily modeled by NNs, by appropriate selection of the reachability analysis tool. We focus on NN controllers due to their lack of robustness. The proposed method is demonstrated on navigation tasks for aerial vehicles.

Evacuation planning is a crucial part of disaster management. However, joint optimization of its two essential components, routing and scheduling, with objectives such as minimizing average evacuation time or evacuation completion time, is a computationally hard problem. To approach it, we present MIP-LNS, a scalable optimization method that utilizes heuristic search with mathematical optimization and can optimize a variety of objective functions. We also present the method MIP-LNS-SIM, where we combine agent-based simulation with MIP-LNS to estimate delays due to congestion, as well as, find optimized plans considering such delays. We use Harris County in Houston, Texas, as our study area. We show that, within a given time limit, MIP-LNS finds better solutions than existing methods in terms of three different metrics. However, when congestion dependent delay is considered, MIP-LNS-SIM outperforms MIP-LNS in multiple performance metrics. In addition, MIP-LNS-SIM has a significantly lower percent error in estimated evacuation completion time compared to MIP-LNS.

In most applications of model-based Markov decision processes, the parameters for the unknown underlying model are often estimated from the empirical data. Due to noise, the policy learnedfrom the estimated model is often far from the optimal policy of the underlying model. When applied to the environment of the underlying model, the learned policy results in suboptimal performance, thus calling for solutions with better generalization performance. In this work we take a Bayesian perspective and regularize the objective function of the Markov decision process with prior information in order to obtain more robust policies. Two approaches are proposed, one based on $L^1$ regularization and the other on relative entropic regularization. We evaluate our proposed algorithms on synthetic simulations and on real-world search logs of a large scale online shopping store. Our results demonstrate the robustness of regularized MDP policies against the noise present in the models.

We investigate the integer quantum Hall effect in Cd3As2 thin films under conditions of strong to moderate quantum confinement (thicknesses of 10 nm, 12 nm, 15 nm). In all the films, we observe the integer quantum Hall effect in the spin-polarized lowest Landau level (filling factor {\nu} = 1) and at spin-degenerate higher index Landau levels with even filling factors ({\nu} = 2,4,6). With increasing quantum confinement, we also observe a lifting of the Landau level spin degeneracy at {\nu} = 3, manifest as the emergence of an anomaly in the longitudinal and Hall resistivity. Tight-binding calculations show that the enhanced g-factor likely arises from a combination of quantum confinement and corrections from nearby subbands. We also comment on the magnetic field induced transition from an insulator to a quantum Hall liquid when the chemical potential is near the charge neutrality point.

We propose a new method for fine-grained few-shot recognition via deep object parsing. In our framework, an object is made up of K distinct parts and for each part, we learn a dictionary of templates, which is shared across all instances and categories. An object is parsed by estimating the locations of these K parts and a set of active templates that can reconstruct the part features. We recognize test instances by comparing its active templates and the relative geometry of its part locations against those of the presented few-shot instances. Our method is end-to-end trainable to learn part templates on-top of a convolutional backbone. To combat visual distortions such as orientation, pose and size, we learn templates at multiple scales, and at test-time parse and match instances across these scales. We show that our method is competitive with the state-of-the-art, and by virtue of parsing enjoys interpretability as well.

The power grid is going through significant changes with the introduction of renewable energy sources and incorporation of smart grid technologies. These rapid advancements necessitate new models and analyses to keep up with the various emergent phenomena they induce. A major prerequisite of such work is the acquisition of well-constructed and accurate network datasets for the power grid infrastructure. In this paper, we propose a robust, scalable framework to synthesize power distribution networks which resemble their physical counterparts for a given region. We use openly available information about interdependent road and building infrastructures to construct the networks. In contrast to prior work based on network statistics, we incorporate engineering and economic constraints to create the networks. Additionally, we provide a framework to create ensembles of power distribution networks to generate multiple possible instances of the network for a given region. The comprehensive dataset consists of nodes with attributes such as geo-coordinates, type of node (residence, transformer, or substation), and edges with attributes such as geometry, type of line (feeder lines, primary or secondary) and line parameters. For validation, we provide detailed comparisons of the generated networks with actual distribution networks. The generated datasets represent realistic test systems (as compared to standard IEEE test cases) that can be used by network scientists to analyze complex events in power grids and to perform detailed sensitivity and statistical analyses over ensembles of networks.

Payment channel networks (PCNs) are one of the most prominent solutions to the limited transaction throughput of blockchains. Nevertheless, PCNs suffer themselves from a throughput limitation due to the capital constraints of their channels. A similar dependence on high capital is also found in inter-bank payment settlements, where the so-called netting technique is used to mitigate liquidity demands. In this work, we alleviate this limitation by introducing the notion of transaction aggregation: instead of executing transactions sequentially through a PCN, we enable senders to aggregate multiple transactions and execute them simultaneously to benefit from several amounts that may "cancel out". Two direct advantages of our proposal is the decrease in intermediary fees paid by senders as well as the obfuscation of the transaction data from the intermediaries. We formulate the transaction aggregation as a computational problem, a generalization of the Bank Clearing Problem. We present a generic framework for the transaction aggregation execution, and thereafter we propose Wiser as an implementation of this framework in a specific hub-based setting. To overcome the NP-hardness of the transaction aggregation problem, in Wiser we propose a fixed-parameter linear algorithm for a special case of transaction aggregation as well as the Bank Clearing Problem. Wiser can also be seen as a modern variant of the Hawala money transfer system, as well as a decentralized implementation of the overseas remittance service of Wise.

A scalable platform to synthesize ultrathin heavy metals may enable high efficiency charge-to-spin conversion for next-generation spintronics. Here we report the synthesis of air-stable, epitaxially registered monolayer Pb underneath bilayer graphene on SiC (0001) by confinement heteroepitaxy (CHet). Diffraction, spectroscopy, and microscopy reveal CHet-based Pb intercalation predominantly exhibits a mottled hexagonal superstructure due to an ordered network of Frenkel-Kontorova-like domain walls. The system's air stability enables ex-situ spin torque ferromagnetic resonance (ST-FMR) measurements that demonstrate charge-to-spin conversion in graphene/Pb/ferromagnet heterostructures with a 1.5x increase in the effective field ratio compared to control samples., Comment: 27 pages, 5 figures. Supporting Information included (16 pages, 14 figures, 1 table)

We propose Recognition as Part Composition (RPC), an image encoding approach inspired by human cognition. It is based on the cognitive theory that humans recognize complex objects by components, and that they build a small compact vocabulary of concepts to represent each instance with. RPC encodes images by first decomposing them into salient parts, and then encoding each part as a mixture of a small number of prototypes, each representing a certain concept. We find that this type of learning inspired by human cognition can overcome hurdles faced by deep convolutional networks in low-shot generalization tasks, like zero-shot learning, few-shot learning and unsupervised domain adaptation. Furthermore, we find a classifier using an RPC image encoder is fairly robust to adversarial attacks, that deep neural networks are known to be prone to. Given that our image encoding principle is based on human cognition, one would expect the encodings to be interpretable by humans, which we find to be the case via crowd-sourcing experiments. Finally, we propose an application of these interpretable encodings in the form of generating synthetic attribute annotations for evaluating zero-shot learning methods on new datasets.

Soft robotic grippers facilitate contact-rich manipulation, including robust grasping of varied objects. Yet the beneficial compliance of a soft gripper also results in significant deformation that can make precision manipulation challenging. We present visual pressure estimation & control (VPEC), a method that infers pressure applied by a soft gripper using an RGB image from an external camera. We provide results for visual pressure inference when a pneumatic gripper and a tendon-actuated gripper make contact with a flat surface. We also show that VPEC enables precision manipulation via closed-loop control of inferred pressure images. In our evaluation, a mobile manipulator (Stretch RE1 from Hello Robot) uses visual servoing to make contact at a desired pressure; follow a spatial pressure trajectory; and grasp small low-profile objects, including a microSD card, a penny, and a pill. Overall, our results show that visual estimates of applied pressure can enable a soft gripper to perform precision manipulation., Comment: IROS 2022

In this paper, we present BESSIE (Behavior and Epidemic Simulator for Synthetic Information Environments), an open source, agent-based simulator for COVID-type epidemics. BESSIE uses a synthetic population where each person has demographic attributes, belong to a household, and has a base activity- and visit schedule covering seven days. The simulated disease spreads through contacts that arise from joint visits to the locations where activities take place. The simulation model has a plugin-type programmable behavioral model where, based on the dynamics and observables tracked by the simulator, agents decide on actions such as wearing a mask, engaging in social distancing, or refraining from certain activity types by staying at home instead. The plugins are supplied as Python code. To the best of our knowledge, BESSIE is a unique simulator supporting this feature set, and most certainly as open software. To illustrate the use of BESSIE, we provide a COVID-relevant example demonstrating some of its capabilities. The example uses a synthetic population for the City of Charlottesville, Virginia. Both this population and the Python plugin modules used in the example are made available. The Python implementation, which can run on anything from a laptop to a cluster, is made available under the Apache 2.0 license (https://www.apache.org/licenses/LICENSE-2.0.html). The example population accompanying this publication is made available under the CC BY 4.0 license (https://creativecommons.org/licenses/by/4.0/)., Comment: 24 pages; the git repository and accompanying data will be made openly available upon final journal publication

We propose a trainable Image Signal Processing (ISP) framework that produces DSLR quality images given RAW images captured by a smartphone. To address the color misalignments between training image pairs, we employ a color-conditional ISP network and optimize a novel parametric color mapping between each input RAW and reference DSLR image. During inference, we predict the target color image by designing a color prediction network with efficient Global Context Transformer modules. The latter effectively leverage global information to learn consistent color and tone mappings. We further propose a robust masked aligned loss to identify and discard regions with inaccurate motion estimation during training. Lastly, we introduce the ISP in the Wild (ISPW) dataset, consisting of weakly paired phone RAW and DSLR sRGB images. We extensively evaluate our method, setting a new state-of-the-art on two datasets., Comment: Accepted at ECCV 2022

People often interact with their surroundings by applying pressure with their hands. While hand pressure can be measured by placing pressure sensors between the hand and the environment, doing so can alter contact mechanics, interfere with human tactile perception, require costly sensors, and scale poorly to large environments. We explore the possibility of using a conventional RGB camera to infer hand pressure, enabling machine perception of hand pressure from uninstrumented hands and surfaces. The central insight is that the application of pressure by a hand results in informative appearance changes. Hands share biomechanical properties that result in similar observable phenomena, such as soft-tissue deformation, blood distribution, hand pose, and cast shadows. We collected videos of 36 participants with diverse skin tone applying pressure to an instrumented planar surface. We then trained a deep model (PressureVisionNet) to infer a pressure image from a single RGB image. Our model infers pressure for participants outside of the training data and outperforms baselines. We also show that the output of our model depends on the appearance of the hand and cast shadows near contact regions. Overall, our results suggest the appearance of a previously unobserved human hand can be used to accurately infer applied pressure. Data, code, and models are available online., Comment: ECCV 2022 oral

RNN-Transducer (RNN-T) models have become synonymous with streaming end-to-end ASR systems. While they perform competitively on a number of evaluation categories, rare words pose a serious challenge to RNN-T models. One main reason for the degradation in performance on rare words is that the language model (LM) internal to RNN-Ts can become overconfident and lead to hallucinated predictions that are acoustically inconsistent with the underlying speech. To address this issue, we propose a lightweight adaptive LM discounting technique AdaptLMD, that can be used with any RNN-T architecture without requiring any external resources or additional parameters. AdaptLMD uses a two-pronged approach: 1) Randomly mask the prediction network output to encourage the RNN-T to not be overly reliant on it's outputs. 2) Dynamically choose when to discount the implicit LM (ILM) based on rarity of recently predicted tokens and divergence between ILM and implicit acoustic model (IAM) scores. Comparing AdaptLMD to a competitive RNN-T baseline, we obtain up to 4% and 14% relative reductions in overall WER and rare word PER, respectively, on a conversational, code-mixed Hindi-English ASR task., Comment: Proceedings for ICASSP 2022

We report the measurement of efficient charge-to-spin conversion at room temperature in Weyl semimetal/ferromagnet heterostructures with both oxidized and pristine interfaces. Polycrystalline films of the Weyl semimetal, TaAs, are grown by molecular beam epitaxy on (001) GaAs and interfaced with a metallic ferromagnet (Ni$_{0.8}$Fe$_{0.2}$). Spin torque ferromagnetic resonance (ST-FMR) measurements in samples with an oxidized interface yield a spin torque efficiency as large as $\xi_{\mathrm{FMR}}=0.45\pm 0.25$ for a 8 nm Ni$_{0.8}$Fe$_{0.2}$ layer thickness. By studying ST-FMR in these samples with varying Ni$_{0.8}$Fe$_{0.2}$ layer thickness, we extract a damping-like torque efficiency as high as $\xi_{\mathrm{DL}}=1.36\pm 0.66$. In samples with a pristine (unoxidized) interface, the spin torque efficiency has opposite sign to that observed in oxidized samples ($\xi_{\mathrm{FMR}}=-0.27\pm 0.14$ for a 5 nm Ni$_{0.8}$Fe$_{0.2}$ layer thickness). We also find a lower bound on the spin Hall conductivity ($424 \pm 110 \frac{\hbar}{e}$ S/cm) which is surprisingly consistent with theoretical predictions for the single crystal Weyl semimetal state of TaAs.

Residential consumers have become active participants in the power distribution network after being equipped with residential EV charging provisions. This creates a challenge for the network operator tasked with dispatching electric power to the residential consumers through the existing distribution network infrastructure in a reliable manner. In this paper, we address the problem of scheduling residential EV charging for multiple consumers while maintaining network reliability. An additional challenge is the restricted exchange of information: where the consumers do not have access to network information and the network operator does not have access to consumer load parameters. We propose a distributed framework which generates an optimal EV charging schedule for individual residential consumers based on their preferences and iteratively updates it until the network reliability constraints set by the operator are satisfied. We validate the proposed approach for different EV adoption levels in a synthetically created digital twin of an actual power distribution network. The results demonstrate that the new approach can achieve a higher level of network reliability compared to the case where residential consumers charge EVs based solely on their individual preferences, thus providing a solution for the existing grid to keep up with increased adoption rates without significant investments in increasing grid capacity., Comment: 6 pages main conference paper, 3 pages appendix

Nitrogen-vacancy centers in diamond have recently been established as effective sensors of the magnetization dynamics in vicinal ferromagnetic materials. We demonstrate sub-100 nm placement accuracy of nitrogen-vacancy-containing nanodiamonds and use these as local sensors that probe optically detected ferromagnetic resonance in mesoscopically patterned Permalloy islands. These measurements reveal variations in the ferromagnetic resonance signal at different sites on these structures with distinct behavior in the edge and the bulk of patterned features. These test measurements establish an easily implemented approach for spatially targeted measurements of spin dynamics in mesoscale ferromagnets. In principle, the methodology can also be extended to local studies of nanoscale ferromagnets such as single magnetic nanowires and nanoparticles.

In classic graph signal processing, given a real-valued graph signal, its graph Fourier transform is typically defined as the series of inner products between the signal and each eigenvector of the graph Laplacian. Unfortunately, this definition is not mathematically valid in the cases of vector-valued graph signals which however are typical operands in the state-of-the-art graph learning modeling and analyses. Seeking a generalized transformation decoding the magnitudes of eigencomponents from vector-valued signals is thus the main objective of this paper. Several attempts are explored, and also it is found that performing the transformation at hierarchical levels of adjacency help profile the spectral characteristics of signals more insightfully. The proposed methods are introduced as a new tool assisting on diagnosing and profiling behaviors of graph learning models.

We consider the motion of a massive particle in a static, weakly-curved spacetime where the gravitational field is taken to be quantized. We find that Newton's law of free-fall is modified by quantum-gravitational corrections, in addition to the known special-relativistic and post-Newtonian modifications. The quantum-gravitational corrections take the form of stochastic noise in the particle trajectory, where the statistical properties of the noise depend on the quantum state of the gravitational field., Comment: 13 pages, LaTeX. v3: references added, expanded discussion