The growth of connected intelligent devices in the Internet of Things has created a pressing need for real-time processing and understanding of large volumes of analogue data. The difficulty in boosting the computing speed renders digital computing u
nable to meet the demand for processing analogue information that is intrinsically continuous in magnitude and time. By utilizing a continuous data representation in a nanoscale crossbar array, parallel computing can be implemented for the direct processing of analogue information in real time. Here, we propose a scalable massively parallel computing scheme by exploiting a continuous-time data representation and frequency multiplexing in a nanoscale crossbar array. This computing scheme enables the parallel reading of stored data and the one-shot operation of matrix-matrix multiplications in the crossbar array. Furthermore, we achieve the one-shot recognition of 16 letter images based on two physically interconnected crossbar arrays and demonstrate that the processing and modulation of analogue information can be simultaneously performed in a memristive crossbar array.
A connected graph $G$ is said to be $k$-connected if it has more than $k$ vertices and remains connected whenever fewer than $k$ vertices are deleted. In this paper, for a connected graph $G$ with sufficiently large order, we present a tight sufficie
nt condition for $G$ with fixed minimum degree to be $k$-connected based on the $Q$-index. Our result can be viewed as a spectral counterpart of the corresponding Dirac type condition.
Existing research for image text retrieval mainly relies on sentence-level supervision to distinguish matched and mismatched sentences for a query image. However, semantic mismatch between an image and sentences usually happens in finer grain, i.e.,
phrase level. In this paper, we explore to introduce additional phrase-level supervision for the better identification of mismatched units in the text. In practice, multi-grained semantic labels are automatically constructed for a query image in both sentence-level and phrase-level. We construct text scene graphs for the matched sentences and extract entities and triples as the phrase-level labels. In order to integrate both supervision of sentence-level and phrase-level, we propose Semantic Structure Aware Multimodal Transformer (SSAMT) for multi-modal representation learning. Inside the SSAMT, we utilize different kinds of attention mechanisms to enforce interactions of multi-grain semantic units in both sides of vision and language. For the training, we propose multi-scale matching losses from both global and local perspectives, and penalize mismatched phrases. Experimental results on MS-COCO and Flickr30K show the effectiveness of our approach compared to some state-of-the-art models.
Generating provably stable walking gaits that yield natural locomotion when executed on robotic-assistive devices is a challenging task that often requires hand-tuning by domain experts. This paper presents an alternative methodology, where we propos
e the addition of musculoskeletal models directly into the gait generation process to intuitively shape the resulting behavior. In particular, we construct a multi-domain hybrid system model that combines the system dynamics with muscle models to represent natural multicontact walking. Stable walking gaits can then be formally generated for this model via the hybrid zero dynamics method. We experimentally apply our framework towards achieving multicontact locomotion on a dual-actuated transfemoral prosthesis, AMPRO3. The results demonstrate that enforcing feasible muscle dynamics produces gaits that yield natural locomotion (as analyzed via electromyography), without the need for extensive manual tuning. Moreover, these gaits yield similar behavior to expert-tuned gaits. We conclude that the novel approach of combining robotic walking methods (specifically HZD) with muscle models successfully generates anthropomorphic robotic-assisted locomotion.
This work presents a sensitivity study of a reactor liquid scintillator detector to three kinds of dark bosons with masses below 1 MeV, such as dark photons, axion-like particles and light scalar bosons. The JUNO-TAO detector with Taishan nuclear rea
ctor is taken as a reference. With proposed 180 days data taking, the sensitivity to the dark bosons can reach $sim10^{-5}$ 95%C.L. for the optimized signal to background ratio for the electron coupling constant $it{g_X} $ through inverse Compton-like scattering. The background systematic uncertainty presents as the main limiting factor for the further sensitivity improvement. Additionally the differential and the inverse differential cross sections have been derived for all three boson types and their interactions with electrons in liquid scintillator.
A new strategy, namely the clean numerical simulation (CNS), was proposed (J. Computational Physics, 418:109629, 2020) to gain reliable/convergent simulations (with negligible numerical noises) of spatiotemporal chaotic systems in a long enough inter
val of time, which provide us benchmark solution for comparison. Here we illustrate that machine learning (ML) can always give good enough fitting predictions of a spatiotemporal chaos by using, separately, two quite different training sets: one is the clean database given by the CNS with negligible numerical noises, the other is the polluted database given by the traditional algorithms in single/double precision with considerably large numerical noises. However, even in statistics, the ML predictions based on the polluted database are quite different from those based on the clean database. It illustrates that the database noises have huge influences on ML predictions of some spatiotemporal chaos, even in statistics. Thus, we must use a clean database for machine learning of some spatiotemporal chaos. This surprising result might open a new door and possibility to study machine learning.
Materials exhibiting zero thermal expansion (ZTE), namely, volume invariance during temperature change, can resist thermal shock and are highly desired in modern industries as high-precision components. However, pure ZTE materials are rare, especiall
y those that are metallic. Here, we report the discovery of a pure metallic ZTE material: an orthorhombic Mn1-xNixCoSi spiral magnet. The introduction of Ni can efficiently enhance the ferromagnetic exchange interaction and construct the transition from a spiral magnetic state to a ferromagnetic-like state in MnCoSi-based alloys. Systematic in situ neutron powder diffraction revealed a new cycloidal spiral magnetic structure in bc plane at ground state which would transform to the helical spiral in the ab plane with increasing temperature. Combined with Lorentz transmission electron microscopy techniques, the cycloidal and helical spin order coherently rotated at varying periods along the c axis during the magnetic transition. This spin rotation drove the continuous movement of the coupled crystalline lattice and induced a large negative thermal expansion along the a axis, eventually leading to a wide-temperature ZTE effect. Our work not only introduces a new ZTE alloy but also presents a new mechanism by which to discover or design ZTE magnets.
Early and accurate severity assessment of Coronavirus disease 2019 (COVID-19) based on computed tomography (CT) images offers a great help to the estimation of intensive care unit event and the clinical decision of treatment planning. To augment the
labeled data and improve the generalization ability of the classification model, it is necessary to aggregate data from multiple sites. This task faces several challenges including class imbalance between mild and severe infections, domain distribution discrepancy between sites, and presence of heterogeneous features. In this paper, we propose a novel domain adaptation (DA) method with two components to address these problems. The first component is a stochastic class-balanced boosting sampling strategy that overcomes the imbalanced learning problem and improves the classification performance on poorly-predicted classes. The second component is a representation learning that guarantees three properties: 1) domain-transferability by prototype triplet loss, 2) discriminant by conditional maximum mean discrepancy loss, and 3) completeness by multi-view reconstruction loss. Particularly, we propose a domain translator and align the heterogeneous data to the estimated class prototypes (i.e., class centers) in a hyper-sphere manifold. Experiments on cross-site severity assessment of COVID-19 from CT images show that the proposed method can effectively tackle the imbalanced learning problem and outperform recent DA approaches.
There is currently much interest in the recycling of entangled systems, for use in quantum information protocols by sequential observers. In this work, we study the sequential generation of Bell nonlocality via recycling one or both components of two
-qubit states. We first give a description of two-valued qubit measurements in terms of measurement bias, strength, and reversibility, and derive useful tradeoff relations between them. Then, we derive one-sided monogamy relations that support the recent Conjecture in [S. Cheng {it et al.}, arXiv:2102.11574], that if the first pair of observers violate Bell nonlocality then a subsequent independent pair cannot. We also answer a question raised in [P. J. Brown and R. Colbeck, Phys. Rev. Lett. textbf{125}, 090401 (2020)], by showing that the conditions given therein for the recycling of one qubit by an arbitrarily large number of observers are sufficient but not necessary. Finally, we find that it is possible to share Bell nonlocality between multiple pairs of independent observers on both sides, if sufficiently many pairs of qubits are shared. Our results are based on a formalism that is applicable to more general problems in recycling entanglement, and hence is expected to aid progress in this field.
We propose the rhoLENT method, an extension of the unstructured Level Set / Front Tracking (LENT) method, based on the collocated Finite Volume equation discretization, that recovers exact numerical stability for the two-phase momentum convection wit
h a range of density ratios, namely $rho^-/rho^+in [1, 10000]$. We provide the theoretical basis for the numerical inconsistency in the collocated finite volume equation discretization of the single-field two-phase momentum convection. The cause of the numerical inconsistency lies in the way the cell-centered density is computed in the new time step ($rho_c^{n+1}$). Specifically, if $rho_c^{n+1}$ is computed from the approximation of the fluid interface at $t^{n+1}$, and it is not computed by solving a mass conservation equation (or its equivalent), the two-phase momentum convection term will automatically be inconsistently discretized. We provide the theoretical justification behind using the auxiliary mass conservation equation to stabilize flows with strong density ratios. The evaluation of the face-centered (mass flux) density we base on the fundamental principle of mass conservation, used to model the single-field density, contrary to the use of different weighted averages of cell-centered single-field densities and alternative reconstructions of the mass flux density by other contemporary methods. Implicit discretization of the two-phase momentum convection term is achieved, removing the CFL stability criterion. Numerical stability is demonstrated in terms of the relative $L_infty$ velocity error norm with realistic viscosity and strong surface tension forces. The stabilization technique in the rhoLENT method is also applicable to other two-phase flow simulation methods that utilize the collocated unstructured Finite Volume Method to discretize single-field two-phase Navier-Stokes Equations.
