The maximum recoverable strain of most crystalline solids is less than 1% because plastic deformation or fracture usually occurs at a small strain. In this work, we show that a SrNi$_2$P$_2$ micropillar exhibits pseudoelasticity with a large maximum
recoverable strain of ~14% under uniaxial compression via unique reversible structural transformation, double lattice collapse-expansion that is repeatable under cyclic loading. Its high yield strength (~3.8$pm$0.5 GPa) and large maximum recoverable strain bring out the ultrahigh modulus of resilience (~146$pm$19MJ/m$^3$) a few orders of magnitude higher than that of most engineering materials. The double lattice collapse-expansion mechanism shows stress-strain behaviors similar with that of conventional shape memory alloys, such as hysteresis and thermo-mechanical actuation, even though the structural changes involved are completely different. Our work suggests that the discovery of a new class of high performance ThCr$_2$Si$_2$-structured materials will open new research opportunities in the field of pseudoelasticity.
The power of the proton beam of a high-power spallation neutron source generally ranges from 100 kW to several MW. The distribution of the power density of the beam on the target is critical for the stable operation of the high-power spallation targe
t. This study proposes a beam monitoring method that involves restoring the image of a high-power proton beam spot on a target based on the principle of pinhole imaging by using the back-streaming of secondary neutrons from the spallation target. Fast and indirect imaging of the beam spot can be achieved at a distance of tens of meters from the target. The proposed method of beam monitoring can flexibly adjust the size of the pinhole and the measurement distance to control the intensity of flux of the secondary neutrons according to the demands of the detection system, which is far from the high-radiation target area. The results of simulations showed that the proposed method can be used to restore the beam spot of the incident proton by using the point response function and images of the secondary neutrons. Based on the target and the Back-n beamline in the CSNS, the effectiveness of this method has also been confirmed.
Magnon cat state represents a macroscopic quantum superposition of collective magnetic excitations of large number spins that not only provides fundamental tests of macroscopic quantum effects but also finds applications in quantum metrology and quan
tum computation. In particular, remote generation and manipulation of Schr{o}dinger cat states are particularly interesting for the development of long-distance and large-scale quantum information processing. Here, we propose an approach to remotely prepare magnon even/odd cat states by performing local non-Gaussian operations on the optical mode that is entangled with magnon mode through pulsed optomagnonic interaction. By evaluating key properties of the resulting cat states, we show that for experimentally feasible parameters they are generated with both high fidelity and nonclassicality, and with a size large enough to be useful for quantum technologies. Furthermore, the effects of experimental imperfections such as the error of projective measurements and dark count when performing single-photon operations have been discussed, where the lifetime of the created magnon cat states is expected to be $tsim1,mu$s.
The minimal supersymmetric standard model (MSSM) with complex parameters can contribute sizably to muon/electron anomalous magnetic dipole momemnt ($g-2$) and electric dipole moment (EDM). The electron $g-2$ interplays with electron EDM; the muon $g-
2$ can also interplay with electron EDM assuming the universality between smuon and selectron masses, either of which can constrain the relevant CP-phases in the MSSM. In this work, we first use such an interplay to derive an approximate analytical upper limit on the relevant CP-phase. Then we extensively scan the parameter space to obtain more accurate upper limits. We obtain the following observations: (i) The muon $g-2$ in the $2sigma$ range combined with the electron EDM upper limit (assuming the universality between smuon and selectron masses) typically constrains the relevant CP-phase under $1.9times 10^{-5} (text{rad})$; (ii) The electron $g-2$ in the $2sigma$ range (Berkeley) interplays with the electron EDM upper limit (without assuming the universality between smuon and selectron masses) constrains the relevant CP-phase under $3.9times 10^{-6}(text{rad})$ (also requiring muon $g-2$ in the allowed $2sigma$ range). We also find some special cancellations in the parameter space which can relax the constraints by a couple of orders. Such stringent limits on CP-phases may pose a challenge for model building of SUSY, i.e., how to naturally suppress these phases.
According to the FNAL+BNL measurements for the muon $g-2$ and the Berkeley $^{133}$Cs measurement for the electron $g-2$, the SM prediction for the muon (electron) $g-2$ is $4.2sigma$ ($2.4sigma$) below (above) the experimental value. A joint explana
tion requires a positive contribution to the muon $g-2$ and a negative contribution to the electron $g-2$. In this work we explore the possibility of such a joint explanation in the minimal supersymmetric standard model (MSSM). Assuming no universality between smuon and selectron soft masses, we find out a part of parameter space for a joint explanation of muon and electron $g-2$ anomalies at $2sigma$ level. This part of parameter space can survive the LHC and LEP constraints, but gives an over-abundance for the dark matter if the bino-like lightest neutralino is assumed to be the dark matter candidate. With the assumption that the dark matter candidate is a superWIMP (say a pseudo-goldstino in multi-sector SUSY breaking scenarios, whose mass can be as light as GeV and produced from the late-dacay of the thermally freeze-out lightest neutralino), the dark matter problem can be avoided. So, the MSSM may give a joint explanation for the muon and electron $g-2$ anomalies at $2sigma$ level (the muon $g-2$ anomaly can be ameliorated to $1sigma$).
In this work, we explore the predictive power of the recently proposed covariant chiral nuclear force. In particular, we focus on the $^3S_1$-$^3D_1$ coupled channel and show that it is capable to predict the $^3D_1$ phase shifts and mixing angle $va
repsilon_1$ by fitting to the $^3S_1$ phase shifts, or predict the $^3S_1$ phase shifts by fitting to the $^3D_1$ phase shifts and mixing angle $varepsilon_1$. For the physical nucleon-nucleon phase shifts, one can achieve a reasonably good description up to $T_mathrm{lab.}approx 100$ MeV. Qualitative descriptions can be achieved up to even higher energies. On the other hand, for the lattice QCD nucleon-nucleon phase shfits obtained with $M_pi=469$ MeV, the energy range for which a decent description can be achieved is much reduced, to only $T_mathrm{lab.}approx 10$ MeV. In addition, we show that with one more low energy constant, particularly, one for the mixing angle, the next-to-leading order heavy baryon chiral nuclear force can describe the lattice QCD data up to $T_mathrm{lab.}approx 40$ MeV, but the description of the physical nucleon-nucleon phase shifts is of similar quality to the leading order covariant chiral nuclear force. The present study is relevant to a better understanding of the lattice QCD nucleon-nucleon force or more general baryon-baryon interactions.
With the rapid development of NLP research, leaderboards have emerged as one tool to track the performance of various systems on various NLP tasks. They are effective in this goal to some extent, but generally present a rather simplistic one-dimensio
nal view of the submitted systems, communicated only through holistic accuracy numbers. In this paper, we present a new conceptualization and implementation of NLP evaluation: the ExplainaBoard, which in addition to inheriting the functionality of the standard leaderboard, also allows researchers to (i) diagnose strengths and weaknesses of a single system (e.g.~what is the best-performing system bad at?) (ii) interpret relationships between multiple systems. (e.g.~where does system A outperform system B? What if we combine systems A, B, and C?) and (iii) examine prediction results closely (e.g.~what are common errors made by multiple systems, or in what contexts do particular errors occur?). So far, ExplainaBoard covers more than 400 systems, 50 datasets, 40 languages, and 12 tasks. ExplainaBoard keeps updated and is recently upgraded by supporting (1) multilingual multi-task benchmark, (2) meta-evaluation, and (3) more complicated task: machine translation, which reviewers also suggested.} We not only released an online platform on the website url{http://explainaboard.nlpedia.ai/} but also make our evaluation tool an API with MIT Licence at Github url{https://github.com/neulab/explainaBoard} and PyPi url{https://pypi.org/project/interpret-eval/} that allows users to conveniently assess their models offline. We additionally release all output files from systems that we have run or collected to motivate output-driven research in the future.
The new FNAL result of the muon $g-2$, combined with the BNL result, shows a 4.2$sigma$ deviation from the SM. We use the new data of the muon $g-2$ to revisit several GUT-scale constrained SUSY models with the constraints from the LHC searches, the
dark matter detection, the flavor data and the electroweak vacuum stability. We first demonstrate the tension between the muon $g-2$ and other experimental measurements in the CMSSM/mSUGRA. Then after discussing the possible ways to alleviate such a tension and showing the muon $g-2$ in pMSSM under relevant experimental constraints, we survey several extensions of the CMSSM/mSUGRA with different types of universal boundary conditions at the GUT scale. Finally, we briefly discuss the muon $g-2$ in other popular SUSY breaking mechanisms, namely the GMSB and AMSB mechanisms and their extensions.
Speech enhancement aims to obtain speech signals with high intelligibility and quality from noisy speech. Recent work has demonstrated the excellent performance of time-domain deep learning methods, such as Conv-TasNet. However, these methods can be
degraded by the arbitrary scales of the waveform induced by the scale-invariant signal-to-noise ratio (SI-SNR) loss. This paper proposes a new framework called Time-domain Speech Enhancement Generative Adversarial Network (TSEGAN), which is an extension of the generative adversarial network (GAN) in time-domain with metric evaluation to mitigate the scaling problem, and provide model training stability, thus achieving performance improvement. In addition, we provide a new method based on objective function mapping for the theoretical analysis of the performance of Metric GAN, and explain why it is better than the Wasserstein GAN. Experiments conducted demonstrate the effectiveness of our proposed method, and illustrate the advantage of Metric GAN.
Federated learning (FL) is an emerging distributed machine learning paradigm that avoids data sharing among training nodes so as to protect data privacy. Under coordination of the FL server, each client conducts model training using its own computing
resource and private data set. The global model can be created by aggregating the training results of clients. To cope with highly non-IID data distributions, personalized federated learning (PFL) has been proposed to improve overall performance by allowing each client to learn a personalized model. However, one major drawback of a personalized model is the loss of generalization. To achieve model personalization while maintaining generalization, in this paper, we propose a new approach, named PFL-MoE, which mixes outputs of the personalized model and global model via the MoE architecture. PFL-MoE is a generic approach and can be instantiated by integrating existing PFL algorithms. Particularly, we propose the PFL-MF algorithm which is an instance of PFL-MoE based on the freeze-base PFL algorithm. We further improve PFL-MF by enhancing the decision-making ability of MoE gating network and propose a variant algorithm PFL-MFE. We demonstrate the effectiveness of PFL-MoE by training the LeNet-5 and VGG-16 models on the Fashion-MNIST and CIFAR-10 datasets with non-IID partitions.
