Elastic fractal higher-order topological states

In this work, elastic fractal higher-order topological states are investigated. Bott index is adopted to characterize the topological property of elastic fractal structures. The topological corner and edge states of elastic waves in fractal structures are realized theoretically and experimentally. Different from traditional two-dimension (2D) high-order topological insulators based on periodic structures, the high-order topological states based on elastic fractal structures in this work intuitively reflect the fractal dimension in physics, supporting not only abundant topological outer corner states, but also rich inner corner states. The richness of corner states is much higher than that of topological insulators based on periodic structures. The strong robustness of the topological corner states in the fractal structure are verified by introducing disorders and defects. The topological phenomenon of in elastic fractal structures revealed in this work enriches the topological physics of elastic systems and breaks the limitation of that relies on periodic elastic structures. The results have important application prospects in energy harvesting, information transmissions, elastic energy acquisitions and high-sensitivity detections

Anomalous size effects of effective stiffnesses in bistable counter-rotating mechanical metamaterials

Counter-rotating mechanical metamaterials have previously been found to have anomalous characteristics or functions such as auxetics effects, shape changers, and soliton transports, which are all under monostable conditions. The properties of counter-rotating mechanical metamaterials under bistable conditions have not yet been explored. Here, we found that for a bistable counter-rotating metamaterial chain, the effective stiffnesses of the two steady states are different in the chain with even-numbered nodes. For the chain with odd-numbered nodes, the effective stiffnesses corresponding to the two steady states are exactly the same. This special property is not characterized by the characteristic attenuation lengths of the underlying mechanism, but depends on the different symmetries of the underlying mechanism of the chains with odd and even nodes. In addition, the relationship between the abnormal non-monotonic size effect and equilibrium angle are clarified. More interestingly, for one-dimensional chains with even-numbered nodes, the size effect of effective stiffness bifurcates at a specific equilibrium angle, and the according mechanisms are revealed

CRISPR-Cas and catalytic hairpin assembly technology for target-initiated amplification detection of pancreatic cancer specific tsRNAs

Transfer RNA-derived small RNAs (tsRNAs) tRF-LeuCAG-002 (ts3011a RNA) is a novel class of non-coding RNAs biomarker for pancreatic cancer (PC). Reverse transcription polymerase chain reaction (RT-qPCR) has been unfit for community hospitals that are short of specialized equipment or laboratory setups. It has not been reported whether isothermal technology can be used for detection, because the tsRNAs have rich modifications and secondary structures compared with other non-coding RNAs. Herein, we have employed a catalytic hairpin assembly (CHA) circuit and clustered regularly interspaced short palindromic repeats (CRISPR) to develop an isothermal and target-initiated amplification method for detecting ts3011a RNA. In the proposed assay, the presence of target tsRNA triggers the CHA circuit that transforms new DNA duplexes to activate collateral cleavage activity of CRISPR-associated proteins (CRISPR-Cas) 12a, achieving cascade signal amplification. This method showed a low detection limit of 88 aM at 37 °C within 2 h. Moreover, it was demonstrated for the first time that, this method is less likely to produce aerosol contamination than RT-qPCR by simulating aerosol leakage experiments. This method has good consistency with RT-qPCR in the detection of serum samples and showed great potential for PC-specific tsRNAs point-of-care testing (POCT)

Convergence and Privacy of Decentralized Nonconvex Optimization with Gradient Clipping and Communication Compression

Achieving communication efficiency in decentralized machine learning has been attracting significant attention, with communication compression recognized as an effective technique in algorithm design. This paper takes a first step to understand the role of gradient clipping, a popular strategy in practice, in decentralized nonconvex optimization with communication compression. We propose PORTER, which considers two variants of gradient clipping added before or after taking a mini-batch of stochastic gradients, where the former variant PORTER-DP allows local differential privacy analysis with additional Gaussian perturbation, and the latter variant PORTER-GC helps to stabilize training. We develop a novel analysis framework that establishes their convergence guarantees without assuming the stringent bounded gradient assumption. To the best of our knowledge, our work provides the first convergence analysis for decentralized nonconvex optimization with gradient clipping and communication compression, highlighting the trade-offs between convergence rate, compression ratio, network connectivity, and privacy

DESTRESS: Computation-optimal and communication-efficient decentralized nonconvex finite-sum optimization

Emerging applications in multi-agent environments such as internet-of-things, networked sensing, autonomous systems and federated learning, call for decentralized algorithms for finite-sum optimizations that are resource-efficient in terms of both computation and communication. In this paper, we consider the prototypical setting where the agents work collaboratively to minimize the sum of local loss functions by only communicating with their neighbors over a predetermined network topology. We develop a new algorithm, called DEcentralized STochastic REcurSive gradient methodS (DESTRESS) for nonconvex finite-sum optimization, which matches the optimal incremental first-order oracle (IFO) complexity of centralized algorithms for finding first-order stationary points, while maintaining communication efficiency. Detailed theoretical and numerical comparisons corroborate that the resource efficiencies of DESTRESS improve upon prior decentralized algorithms over a wide range of parameter regimes. DESTRESS leverages several key algorithm design ideas including randomly activated stochastic recursive gradient updates with mini-batches for local computation, gradient tracking with extra mixing (i.e., multiple gossiping rounds) for per-iteration communication, together with careful choices of hyper-parameters and new analysis frameworks to provably achieve a desirable computation-communication trade-off

BEER: Fast O(1/T) rate for decentralized nonconvex optimization with communication compression

Communication efficiency has been widely recognized as the bottleneck for large-scale decentralized machine learning applications in multi-agent or federated environments. To tackle the communication bottleneck, there have been many efforts to design communication-compressed algorithms for decentralized nonconvex optimization, where the clients are only allowed to communicate a small amount of quantized information (aka bits) with their neighbors over a predefined graph topology. Despite significant efforts, the state-of-the-art algorithm in the nonconvex setting still suffers from a slower rate of convergence $O((G/T)^{2/3})$ compared with their uncompressed counterpart, where $G$ measures the data heterogeneity across different clients, and $T$ is the number of communication rounds. This paper proposes BEER, which adopts communication compression with gradient tracking, and shows it converges at a faster rate of $O(1/T)$. This significantly improves over the state-of-the-art rate, by matching the rate without compression even under arbitrary data heterogeneity. Numerical experiments are also provided to corroborate our theory and confirm the practical superiority of BEER in the data heterogeneous regime.Comment: NeurIPS 202