On the Landscape of One-hidden-layer Sparse Networks and Beyond

Sparse neural networks have received increasing interests due to their small size compared to dense networks. Nevertheless, most existing works on neural network theory have focused on dense neural networks, and our understanding of sparse networks is very limited. In this paper, we study the loss landscape of one-hidden-layer sparse networks. We first consider sparse networks with linear activations. We show that sparse linear networks can have spurious strict minima, which is in sharp contrast to dense linear networks which do not even have spurious minima. Second, we show that spurious valleys can exist for wide sparse non-linear networks. This is different from wide dense networks which do not have spurious valleys under mild assumptions

Motion State of Fuel within Shell in Projection Acceleration Process

The fuel-air explosive (FAE) warheads are charged with the liquid-solid mixture fuel. The fuel is different 'om conventional solid explosives in physical and mechanical properties. The mass centre of the charged fuel changes during projecting the projectile. In this study, a method to calculate the mass centre change of the charged fuel is suggested and the  influence of this change on the projectile motion state in the projection process is discussed. The results show that in projection, the fuel mass centre varies with the projection acceleration and the deformation characteristics of the mixture fuel. The higher is the acceleration, the larger is the displacement of the mass centre. This displacement also increases with the compressibility of the fuel. It constitutes an influence on the state of motion for the whole projectile in the projection process, whose calculation approach is also proposed. The result provides a theoretical basis for the design of the FAE weapons

Stochastic Distributed Optimization under Average Second-order Similarity: Algorithms and Analysis

We study finite-sum distributed optimization problems involving a master node and $n-1$ local nodes under the popular $\delta$-similarity and $\mu$-strong convexity conditions. We propose two new algorithms, SVRS and AccSVRS, motivated by previous works. The non-accelerated SVRS method combines the techniques of gradient sliding and variance reduction and achieves a better communication complexity of $\tilde{\mathcal{O}}(n {+} \sqrt{n}\delta/\mu)$ compared to existing non-accelerated algorithms. Applying the framework proposed in Katyusha X, we also develop a directly accelerated version named AccSVRS with the $\tilde{\mathcal{O}}(n {+} n^{3/4}\sqrt{\delta/\mu})$ communication complexity. In contrast to existing results, our complexity bounds are entirely smoothness-free and exhibit superiority in ill-conditioned cases. Furthermore, we establish a nearly matched lower bound to verify the tightness of our AccSVRS method.Comment: Camera-ready version for NeurIPS 202

A Model for Components Library Based on Multi- level Assemblies and Parts Family

Abstract-Parts (and components) family is presented to realize another information expression method for the tabular layouts of article characteristics, and given a uniform and formal definition, including the template model, the attribute set and the family member. The principles and methods of grading to construct components family is discussed, which components, sub-assembly and parts are described with associated attributes in their respective levels. The links between components, subassembly and parts are established through member attribute, reducing attributes number of components and subassembly, reusing parts library, and achieving efficiently for building components library. For an example, the reuse library of diestamping components is established for the mold design

Low reliable and low latency communications for mission critical distributed industrial Internet of Things

Achieving ubiquitous ultra-reliable low latency consensus in centralized wireless communication systems can be costly and hard to scale up. The consensus mechanism, which has been widely utilized in distributed systems, can provide fault tolerance to the critical consensus, even though the individual communication link reliability is relatively low. In this paper, a widely used consensus mechanism, Raft, is introduced to the Industrial Internet of Things (IIoT) to achieve ultra-reliable and low latency consensus, where the consensus reliability performance in terms of nodes number and link transmission reliability is investigated. We propose a new concept, Reliability Gain, to show the linear relationship between consensus reliability and communication link transmission reliability. We also find that the time latency of consensus is contradictory to consensus reliability. These conclusions can provide guides to deploy Raft protocol in distributed IIoT systems

Studies on the alternative evaluation of improving hypoxia tolerance function in health foods

ObjectiveTo explore the feasibility of new test methods for evaluation of improving hypoxia tolerance function in health food.MethodsHypoxia tolerance experiments were carried out on mice. The zebrafish hypoxia model was established， and the improvement of zebrafish hypoxia movement and erythrocytosis were tested. A chemical hypoxia cell model was constructed with sodium disulfite， and cell activity and lactate dehydrogenase （LDH） activity were detected to verify their effect on hypoxia tolerance.ResultsCompared with normal control group， the hypoxia tolerance was improved in mice by health food sample. Compared with hypoxia model control groups， the sample improved zebrafish hypoxia， hypoxia-induced erycytosis， and alleviated the reduced cellular activity and increased LDH activity caused by cardiomyocyte hypoxia.ConclusionThe ability of the sample to improve hypoxia tolerance can be detected by three test systems in vitro as well as in vivo， and the introduction of alternative methods to animal testing for hypoxia tolerance tests is feasible

Experimental quantum computational chemistry with optimised unitary coupled cluster ansatz

Simulation of quantum chemistry is one of the most promising applications of quantum computing. While recent experimental works have demonstrated the potential of solving electronic structures with variational quantum eigensolver (VQE), the implementations are either restricted to nonscalable (hardware efficient) or classically simulable (Hartree-Fock) ansatz, or limited to a few qubits with large errors for the more accurate unitary coupled cluster (UCC) ansatz. Here, integrating experimental and theoretical advancements of improved operations and dedicated algorithm optimisations, we demonstrate an implementation of VQE with UCC for H_2, LiH, F_2 from 4 to 12 qubits. Combining error mitigation, we produce high-precision results of the ground-state energy with error suppression by around two orders of magnitude. For the first time, we achieve chemical accuracy for H_2 at all bond distances and LiH at small bond distances in the experiment. Our work demonstrates a feasible path towards a scalable solution to electronic structure calculation, validating the key technological features and identifying future challenges for this goal.Comment: 8 pages, 4 figures in the main text, and 29 pages supplementary materials with 16 figure