We explore a possible time variation of the fine structure constant ($alpha equiv e^2/hbar c$) using the Sunyaev-Zeldovich effect measurements of galaxy clusters along with their X-ray observations. Specifically, the ratio of the integrated Compto-io
nization parameter $Y_{SZ}D_A^2$ and its X-ray counterpart $Y_X$ is used as an observable to constrain the bounds on the variation of $alpha$. Considering the violation of cosmic distance duality relation, this ratio depends on the fine structure constant as $sim alpha^3$. We use the quintessence model to provide the origin of $alpha$ time variation. In order to give a robust test on $alpha$ variation, two galaxy cluster samples, the 61 clusters provided by the Planck collaboration and the 58 clusters detected by the South Pole Telescope, are collected for analysis. Their X-ray observations are given by the XMM-Newton survey. Our results give $zeta=-0.203^{+0.101}_{-0.099}$ for the Planck sample and $zeta=-0.043^{+0.165}_{-0.148}$ for the SPT sample, indicating that $alpha$ is constant with redshift within $3sigma$ and $1sigma$ for the two samples, respectively.
As many robot automation applications increasingly rely on multi-core processing or deep-learning models, cloud computing is becoming an attractive and economically viable resource for systems that do not contain high computing power onboard. Despite
its immense computing capacity, it is often underused by the robotics and automation community due to lack of expertise in cloud computing and cloud-based infrastructure. Fog Robotics balances computing and data between cloud edge devices. We propose a software framework, FogROS, as an extension of the Robot Operating System (ROS), the de-facto standard for creating robot automation applications and components. It allows researchers to deploy components of their software to the cloud with minimal effort, and correspondingly gain access to additional computing cores, GPUs, FPGAs, and TPUs, as well as predeployed software made available by other researchers. FogROS allows a researcher to specify which components of their software will be deployed to the cloud and to what type of computing hardware. We evaluate FogROS on 3 examples: (1) simultaneous localization and mapping (ORB-SLAM2), (2) Dexterity Network (Dex-Net) GPU-based grasp planning, and (3) multi-core motion planning using a 96-core cloud-based server. In all three examples, a component is deployed to the cloud and accelerated with a small change in system launch configuration, while incurring additional latency of 1.2 s, 0.6 s, and 0.5 s due to network communication, the computation speed is improved by 2.6x, 6.0x and 34.2x, respectively. Code, videos, and supplementary material can be found at https://github.com/BerkeleyAutomation/FogROS.
This paper gives a localized method for the multi-commodity flow problem. We relax both the capacity constraints and flow conservation constraints, and introduce a congestion function $psi$ for each arc and $height$ function $h$ for each vertex and c
ommodity. If the flow exceeds the capacity on arc $a$, arc $a$ would have a congestion cost. If the flow into the vertex $i$ is not equal to that out of the vertex for commodity $k$, vertex $i$ would have a height, which is positively related to the difference between the amount of the commodity $k$ into the vertex $i$ and that out of the vertex. Based on the height function $h$ and the congestion function $psi$, a new conception, stable pseudo-flow, is introduced, which satisfies the following conditions: ($mathrm{i}$) for any used arc of commodity $k$, the height difference between vertex $i$ and vertex $j$ is equal to the congestion of arc $(i,j)$; ($mathrm{ii}$) for any unused arc of commodity $k$, the height difference between vertex $i$ and vertex $j$ is less than or equal to the congestion of arc $(i,j)$. If the stable pseudo-flow is a nonzero-stable pseudo-flow, there exists no feasible solution for the multi-commodity flow problem; if the stable pseudo-flow is a zero-stable pseudo-flow, there exists feasible solution for the multi-commodity flow problem and the zero-stable pseudo-flow is the feasible solution. Besides, a non-linear description of the multi-commodity flow problem is given, whose solution is stable pseudo-flow. And the non-linear description could be rewritten as convex quadratic programming with box constraints. Rather than examine the entire network to find path, the conclusion in this paper shows that the multi-commodity flow problem could be solved in a localized manner by looking only at the vertex and its neighbors.
The separation of the connected and disconnected sea partons, which were uncovered in the Euclidean path-integral formulation of the hadronic tensor, is accommodated with the CT18 parametrization of the global analysis of the parton distribution func
tions (PDFs). This is achieved with the help of the distinct small $x$ behaviors of these two sea parton components and the constraint from the lattice calculation of the ratio of the strange momentum fraction to that of the ${bar u}$ or ${bar d}$ in the disconnected insertion. This allows lattice calculations of separate flavors in both the connected and disconnected insertions to be directly compared with the global analysis results term by term.
Dynamic objects in the environment, such as people and other agents, lead to challenges for existing simultaneous localization and mapping (SLAM) approaches. To deal with dynamic environments, computer vision researchers usually apply some learning-b
ased object detectors to remove these dynamic objects. However, these object detectors are computationally too expensive for mobile robot on-board processing. In practical applications, these objects output noisy sounds that can be effectively detected by on-board sound source localization. The directional information of the sound source object can be efficiently obtained by direction of sound arrival (DoA) estimation, but depth estimation is difficult. Therefore, in this paper, we propose a novel audio-visual fusion approach that fuses sound source direction into the RGB-D image and thus removes the effect of dynamic obstacles on the multi-robot SLAM system. Experimental results of multi-robot SLAM in different dynamic environments show that the proposed method uses very small computational resources to obtain very stable self-localization results.
For loop integrals, the standard method is reduction. A well-known reduction method for one-loop integrals is the Passarino-Veltman reduction. Inspired by the recent paper [1] where the tadpole reduction coefficients have been solved, in this paper w
e show the same technique can be used to give a complete integral reduction for any one-loop integrals. The differential operator method is an improved version of the PV-reduction method. Using this method, analytic expressions of all reduction coefficients of the master integrals can be given by algebraic recurrence relation easily. We demonstrate our method explicitly with several examples.
We propose a new method for self-injection of high-quality electron bunches in the plasma wakefield structure in the blowout regime utilizing a flying focus produced by a drive-beam with an energy-chirp. In a flying focus the speed of the density cen
troid of the drive bunch can be superluminal or subluminal by utilizing the chromatic dependence of the focusing optics. We first derive the focal velocity and the characteristic length of the focal spot in terms of the focal length and an energy chirp. We then demonstrate using multi-dimensional particle-in-cell simulations that a wake driven by a superluminally propagating flying focus of an electron beam can generate GeV-level electron bunches with ultra-low normalized slice emittance ($sim$30 nm rad), high current ($sim$ 17 kA), low slice energy-spread ($sim$0.1%) and therefore high normalized brightness ($>10^{19}$ A/rad$^2$/m$^2$) in a plasma of density $sim10^{19}$ cm$^{-3}$. The injection process is highly controllable and tunable by changing the focal velocity and shaping the drive beam current. Near-term experiments using the new FACET II beam could potentially produce beams with brightness exceeding $10^{20}$ A/rad$^2$/m$^2$.
Deep neural networks have significantly improved appearance-based gaze estimation accuracy. However, it still suffers from unsatisfactory performance when generalizing the trained model to new domains, e.g., unseen environments or persons. In this pa
per, we propose a plug-and-play gaze adaptation framework (PnP-GA), which is an ensemble of networks that learn collaboratively with the guidance of outliers. Since our proposed framework does not require ground-truth labels in the target domain, the existing gaze estimation networks can be directly plugged into PnP-GA and generalize the algorithms to new domains. We test PnP-GA on four gaze domain adaptation tasks, ETH-to-MPII, ETH-to-EyeDiap, Gaze360-to-MPII, and Gaze360-to-EyeDiap. The experimental results demonstrate that the PnP-GA framework achieves considerable performance improvements of 36.9%, 31.6%, 19.4%, and 11.8% over the baseline system. The proposed framework also outperforms the state-of-the-art domain adaptation approaches on gaze domain adaptation tasks.
This paper surveys and organizes research works in a new paradigm in natural language processing, which we dub prompt-based learning. Unlike traditional supervised learning, which trains a model to take in an input x and predict an output y as P(y|x)
, prompt-based learning is based on language models that model the probability of text directly. To use these models to perform prediction tasks, the original input x is modified using a template into a textual string prompt x that has some unfilled slots, and then the language model is used to probabilistically fill the unfilled information to obtain a final string x, from which the final output y can be derived. This framework is powerful and attractive for a number of reasons: it allows the language model to be pre-trained on massive amounts of raw text, and by defining a new prompting function the model is able to perform few-shot or even zero-shot learning, adapting to new scenarios with few or no labeled data. In this paper we introduce the basics of this promising paradigm, describe a unified set of mathematical notations that can cover a wide variety of existing work, and organize existing work along several dimensions, e.g.the choice of pre-trained models, prompts, and tuning strategies. To make the field more accessible to interested beginners, we not only make a systematic review of existing works and a highly structured typology of prompt-based concepts, but also release other resources, e.g., a website http://pretrain.nlpedia.ai/ including constantly-updated survey, and paperlist.
This paper addresses the problem of microphone array generalization for deep-learning-based end-to-end multichannel speech enhancement. We aim to train a unique deep neural network (DNN) potentially performing well on unseen microphone arrays. The mi
crophone array geometry shapes the networks parameters when training on a fixed microphone array, and thus restricts the generalization of the trained network to another microphone array. To resolve this problem, a single network is trained using data recorded by various microphone arrays of different geometries. We design three variants of our recently proposed narrowband network to cope with the agnostic number of microphones. Overall, the goal is to make the network learn the universal information for speech enhancement that is available for any array geometry, rather than learn the one-array-dedicated characteristics. The experiments on both simulated and real room impulse responses (RIR) demonstrate the excellent across-array generalization capability of the proposed networks, in the sense that their performance measures are very close to, or even exceed the network trained with test arrays. Moreover, they notably outperform various beamforming methods and other advanced deep-learning-based methods.
