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Considering the high-performance and low-power requirements of edge AI, this study designs a specialized instruction set processor for edge AI based on the RISC-V instruction set architecture, addressing practical issues in digital signal processing for edge devices. This design enhances the execution efficiency of edge AI and reduces its energy consumption with limited hardware overhead, meeting the demands for efficient large language model (LLM) inference computation in edge AI applications. The main contributions of this paper are as follows: For the characteristics of large language models, custom instructions were extended based on the RISC-V instruction set to perform vector dot product calculations, accelerating the computation of large language models on dedicated vector dot product acceleration hardware. Based on the open-source high-performance RISC-V processor core XiangShan Nanhu architecture, the vector dot product specialized instruction set processor Nanhu-vdot was implemented, which adds vector dot product calculation units and pipeline processing logic on top of the XiangShan Nanhu.The Nanhu-vdot underwent FPGA hardware testing, achieving over four times the speed of scalar methods in vector dot product computation. Using a hardware-software co-design approach for second-generation Generative Pre-Trained Transformer (GPT-2) model inference, the speed improved by approximately 30% compared to pure software implementation with almost no additional consumption of hardware resources and power consumption., Comment: 10 pages, in Chinese language, 6 figures

The functionality of an entity frequently necessitates the support of a group situated in another layer of the system. To unravel the profound impact of such group support on a system's resilience against cascading failures, we devise a framework comprising a double-layer interdependent hypergraph system, wherein nodes are capable of receiving support via hyperedges. Our central hypothesis posits that the failure may transcend to another layer when all support groups of each dependent node fail, thereby initiating a potentially iterative cascade across layers. Through rigorous analytical methods, we derive the critical threshold for the initial node survival probability that marks the second-order phase transition point. A salient discovery is that as the prevalence of dependent nodes escalates, the system dynamics shift from a second-order to a first-order phase transition. Notably, irrespective of the collapse pattern, systems characterized by scale-free hyperdegree distributions within both hypergraph layers consistently demonstrate superior robustness compared to those adhering to Poisson hyperdegree distributions. In summary, our research underscores the paramount significance of group support mechanisms and intricate network topologies in determining the resilience of interconnected systems against the propagation of cascading failures. By exploring the interplay between these factors, we have gained insights into how systems can be designed or optimized to mitigate the risk of widespread disruptions, ensuring their continued functionality and stability in the face of adverse events., Comment: 18 pages, 5 figures

In this paper, the finite-time control and bumpless transfer control are investigated for switched systems under asynchronously switching. First, a class of dynamic output feedback controllers are designed to stabilize the switched system with measurable system outputs. Considering the improvement of transient performance, the bumpless transfer control and finite-time control are further studied in the controller design. To avoid the control bumps, a practical filter is introduced to make the control signal smoother and continuous. Furthermore, to derive a finite-time bounded system state over short-time intervals, the finite-time analysis is considered in managing the switching process with the average dwell time. New criteria are proposed to analyze the finite-time stability and finite-time boundedness for the closed-loop system and solvable conditions are newly proposed to optimize the controller gain. Finally, the superiorities of the proposed method are validated through an application to a boost converter.

Ferroelectricity is characterized by the presence of spontaneous and switchable macroscopic polarization. Scaling limits of ferroelectricity have been of both fundamental and technological importance, but the probes of ferroelectricity have often been indirect due to confounding factors such as leakage in the direct electrical measurements. Recent interest in low-voltage switching electronic devices squarely puts the focus on ultrathin limits of ferroelectricity in an electronic device form, specifically on the robustness of ferroelectric characteristics such as retention and endurance for practical applications. Here, we illustrate how manipulating the kinetic energy of the plasma plume during pulsed laser deposition can yield ultrathin ferroelectric capacitor heterostructures with high bulk and interface quality, significantly low leakage currents and a broad "growth window". These heterostructures venture into previously unexplored aspects of ferroelectric properties, showcasing ultralow switching voltages ($<$0.3 V), long retention times ($>$10$^{4}$s), and high endurance ($>$10$^{11}$cycles) in 20 nm films of the prototypical perovskite ferroelectric, BaTiO$_{3}$. Our work demonstrates that materials engineering can push the envelope of performance for ferroelectric materials and devices at the ultrathin limit and opens a direct, reliable and scalable pathway to practical applications of ferroelectrics in ultralow voltage switches for logic and memory technologies.

Aero-engine fault prediction aims to accurately predict the development trend of the future state of aero-engines, so as to diagnose faults in advance. Traditional aero-engine parameter prediction methods mainly use the nonlinear mapping relationship of time series data but generally ignore the adequate spatiotemporal features contained in aero-engine data. To this end, we propose a brain-inspired spike echo state network (Spike-ESN) model for aero-engine intelligent fault prediction, which is used to effectively capture the evolution process of aero-engine time series data in the framework of spatiotemporal dynamics. In the proposed approach, we design a spike input layer based on Poisson distribution inspired by the spike neural encoding mechanism of biological neurons, which can extract the useful temporal characteristics in aero-engine sequence data. Then, the temporal characteristics are input into a spike reservoir through the current calculation method of spike accumulation in neurons, which projects the data into a high-dimensional sparse space. In addition, we use the ridge regression method to read out the internal state of the spike reservoir. Finally, the experimental results of aero-engine states prediction demonstrate the superiority and potential of the proposed method.

The Weyl double copy builds the relation between gauge theory and gravity theory, especially the correspondence between gauge solutions and gravity solutions. In this paper, we obtain the slowly rotating charge solutions from Weyl double copy for the Kerr black hole with small Chern-Simons correction. Based on the Weyl double copy relation, for the Petrov type D solution, we find the additional correction to the electromagnetic field strength tensor of rotating charge. For the Petrov type I solution, we find that the additional electromagnetic field strength tensors have the exogenous properties, while the total sources vanish at the leading order., Comment: 9 pages

This study introduces a comprehensive framework that situates information cascades within the domain of higher-order interactions, utilizing a double-threshold hypergraph model. We propose that individuals (nodes) gain awareness of information through each communication channel (hyperedge) once the number of information adopters surpasses a threshold $\phi_m$. However, actual adoption of the information only occurs when the cumulative influence across all communication channels exceeds a second threshold, $\phi_k$. We analytically derive the cascade condition for both the case of a single seed node using percolation methods and the case of any seed size employing mean-field approximation. Our findings underscore that when considering the fractional seed size, $r_0 \in (0,1]$, the connectivity pattern of the random hypergraph, characterized by the hyperdegree, $k$, and cardinality, $m$, distributions, exerts an asymmetric impact on the global cascade boundary. This asymmetry manifests in the observed differences in the boundaries of the global cascade within the $(\phi_m, \langle m \rangle)$ and $(\phi_k, \langle k \rangle)$ planes. However, as $r_0 \to 0$, this asymmetric effect gradually diminishes. Overall, by elucidating the mechanisms driving information cascades within a broader context of higher-order interactions, our research contributes to theoretical advancements in complex systems theory., Comment: 11 pages, 10 figures

Iceberg melting is a critical factor for climate change, contributing to rising sea levels and climate change. However, the shape of an iceberg is an often neglected aspect of its melting process. Our study investigates the influence of different ice shapes and ambient flow velocities on melt rates by conducting direct numerical simulations. Our study focuses on the ellipsoidal shape, with the aspect ratio as the control parameter. It plays a crucial role in the melting process, resulting in significant variations in the melt rate between different shapes. Without flow, the optimal shape for a minimal melt rate is the disk (2D) or sphere (3D), due to the minimal surface area. However, as the ambient flow velocity increases, the optimal shape changes with the aspect ratio. We find that ice with an elliptical shape (when the long axis is aligned with the flow direction) can melt up to 10\% slower than a circular shape when exposed to flowing water. We provide a quantitative theoretical explanation for this optimal shape, based on the competition between surface area effects and convective heat transfer effects. Our findings provide insight into the interplay between phase transitions and ambient flows, contributing to our understanding of the iceberg melting process and highlighting the need to consider the aspect ratio effect in estimates of iceberg melt rates.

The atomic exchange effect will lead to a significant increase in the probability density of $\beta$ decays below a few keV. This effect is very important for scientific experiments that performed by low-energy electron spectroscopy measurements. However, the atomic exchange effect involves multi-electron interactions, especially for a system with 82 electrons such as lead. Different parameters will lead to different trends in the energy spectrum predicted by the theory, so it is urgent to carry out experimental measurements to provide parameter limits for the theory. The probability increase brought about by the atomic exchange effect is most obvious near 0keV, and the $\beta$ energy spectrum is accurately measured near this energy point, so as to provide constraints for the physical model of atomic exchange. However, it is extremely difficult to measure the $\beta$ energy spectrum at 0keV due to the limitations of electronic noise and internal conversion effects. The excited decay path of $^{210}$Pb was taken as the observation object,by measuring the total energy spectrum of $\beta$ rays and cascaded gamma rays, the precise measurement of the $\beta$ energy spectrum near 0keV has been completed. The analysis of the $\beta$ energy spectrum of $^{210}$Pb gives the following conclusions. The experimental results first verified the theory that the exchange effect causes the probability increase at the low energy end near 0keV. At the same time, the experimental results are higher than the existing predictions of the atomic exchange effect. At least for Pb element, all the electron shells have played a role in improving the probability density of the low end of the $\beta$ energy spectrum. This discovery will promote the theoretical calculation of the $\beta$ energy spectrum of $^{214}$Pb, at the same time, it also indicates that the reactor neutrinos have a higher probability density at the omnipotent end.

In many cases of attacks or failures, memory effects play a significant role. Therefore, we present a model that not only considers the dependencies between nodes but also incorporates the memory effects of attacks. Our research demonstrates that the survival probability of a random node reached by a random edge surpasses the inverse of the average degree ($1/{\langle k \rangle}$), and a giant component emerges regardless of the strength of dependencies. Moreover, if the dependency strength exceeds $1/{\langle k \rangle}$, the network experiences an abrupt collapse when an infinitesimally small fraction of nodes is removed, irrespective of the memory effect. Our proposed model provides insights into the interplay between dependencies between nodes, memory effects, and the network structures under attacks or failures. By considering these factors, we can better assess the vulnerability of complex systems and develop strategies to mitigate cascading failures., Comment: 6 pages, 6 figures

Social networks have provided a platform for the effective exchange of ideas or opinions but also served as a hotbed of polarization. While much research attempts to explore different causes of opinion polarization, the effect of perception bias caused by the network structure itself is largely understudied. To this end, we propose a threshold model that simulates the evolution of opinions by taking into account the perception bias, which is the gap between global information and locally available information from the neighborhood within networks. Our findings suggest that polarization occurs when the collective stubbornness of the population exceeds a critical value which is largely affected by the perception bias. In addition, as the level of stubbornness grows, the occurrence of first-order and second-order phase transitions between consensus and polarization becomes more prevalent, and the types of these phase transitions rely on the initial proportion of active opinions. Notably, for regular network structures, a step-wise pattern emerges that corresponds to various levels of polarization and is strongly associated with the formation of echo chambers. Our research presents a valuable framework for investigating the connection between perception bias and opinion polarization and provides valuable insights for mitigating polarization in the context of biased information.

The Grains and Oilseeds Index (GOI) and its sub-indices of wheat, maize, soyabeans, rice, and barley are daily price indexes reflect the price changes of the global spot markets of staple agro-food crops. In this paper, we carry out a visibility graph (VG) analysis of the GOI and its five sub-indices. Our findings reveal that the degree distributions of the VGs, except for rice, exhibit exponentially truncated power-law tails, while the rice VG conforms to a power-law tail. The average clustering coefficients of the six VGs are quite large ($>0.5$) and exhibit a nice power-law relation with respect to the average degrees of the VGs. For each VG, the clustering coefficients of nodes are inversely proportional to their degrees for large degrees and are correlated to their degrees as a power law for small degrees. All the six VGs exhibit small-world characteristics. The degree-degree correlation coefficients shows that the VGs for maize and soyabeans indices exhibit weak assortative mixing patterns, while the other four VGs are weakly disassortative. The average nearest neighbor degree functions have similar patterns, and each function shows a more complex mixing pattern which decreases for small degrees, increases for mediate degrees, and decreases again for large degrees., Comment: 16 pages, 9 figures

The presence of salt in ocean water strongly affects the melt rate and the shape evolution of ice, both of utmost relevance in geophysical and ocean flow and thus for the climate. To get a better quantitative understanding of the physical mechanics at play in ice melting in salty water, we numerically investigate the lateral melting of an ice block in stably stratified saline water, using a realistic, nonlinear equation of state (EOS). The developing ice shape from our numerical results shows good agreement with the experiments and theory from Huppert & Turner (J. Fluid Mech. 100, 367 (1980)). Furthermore, we find that the melt rate of ice depends non-monotonically on the mean ambient salinity: It first decreases for increasing salt concentration until a local minimum is attained, and then increases again. This non-monotonic behavior of the ice melt rate is due to the competition among salinity-driven buoyancy, temperature-driven buoyancy, and salinity-induced stratification. We develop a theoretical model based on the energy balance which gives a prediction of the salt concentration for which the melt rate is minimal, and is consistent with our data. Our findings give insight into the interplay between phase transitions and double-diffusive convective flows.

The arise of disagreement is an emergent phenomenon that can be observed within a growing social group and, beyond a certain threshold, can lead to group fragmentation. To better understand how disagreement emerges, we introduce an analytically tractable model of group formation where individuals have multidimensional binary opinions and the group grows through a noisy homophily principle, i.e., like-minded individuals attract each other with exceptions occurring with some small probability. Assuming that the level of disagreement is correlated with the number of different opinions coexisting within the group, we find analytically and numerically that in growing groups disagreement emerges spontaneously regardless of how small the noise in the system is. Moreover, for groups of infinite size, fragmentation is inevitable. We also show that the model outcomes are robust under different group growth mechanisms., Comment: 17 pages, 3 figures

We numerically investigate turbulent Rayleigh-B\'enard convection with gas bubbles attached to the hot plate, mimicking a core feature in electrolysis, catalysis, or boiling. The existence of bubbles on the plate reduces the global heat transfer due to the much lower thermal conductivity of gases as compared to liquids and changes the structure of the boundary layers. The numerical simulations are performed in 3D at Prandtl number Pr=4.38 (water) and Rayleigh number $10^7\le Ra \le10^8$. For simplicity, we assume the bubbles to be equally-sized and having pinned contact lines. We vary the total gas-covered area fraction $0.18 \le S_0 \le 0.62$, the relative bubble height $0.02\le h/H \le0.05$ (where $H$ is the height of the Rayleigh-B\'enard cell), the bubble number $40 \le n \le 144$, and their spatial distribution. In all cases, asymmetric temperature profiles are observed, which we quantitatively explain based on the heat flux conservation at each horizontal section. We further propose the idea of using an equivalent single-phase setup to mimic the system with attached bubbles. Based on this equivalence, we can calculate the heat transfer. Without introducing any free parameter, the predictions for the Nusselt number, the upper and lower thermal boundary layer thicknesses, and the mean centre temperature well agree with the numerical results. Finally, our predictions also work for the cases with much larger Pr (e.g. $400$), which indicates that our results can also be applied to predict the mass transfer in water electrolysis with bubbles attached to the electrode surface or in catalysis., Comment: 12 pages, 6 figures

The Transition Edge Sensor is extremely sensitive to the change of temperature, combined with the high-Z metal of a certain thickness, it can realize the high energy resolution measurement of particles such as X-rays. X-rays with energies below 10 keV have very weak penetrating ability, so only a few microns thick of gold or bismuth can obtain quantum efficiency higher than 70\%. Therefore, the entire structure of the TES X-ray detector in this energy range can be realized in the microfabrication process. However, for X-rays or gamma rays from 10 keV to 200 keV, sub-millimeter absorber layers are required, which cannot be realized by microfabrication process. This paper first briefly introduces a set of TES X-ray detectors and their auxiliary systems built by ShanghaiTech University, then focus on the introduction of the TES $\gamma$-ray detector, with absorber based on an sub-millimeter lead-tin alloy sphere. The detector has a quantum efficiency above 70\% near 100 keV, and an energy resolution of about 161.5eV@59.5keV., Comment: 13 pages, 12 figures

The addition of alloying elements plays an essential role in helium (He) behaviours produced by transmutation in metal alloys. Effects of solutes (Ni, Cr, Ti, P, Si, C) on the behaviours of He and He-He pair in face-centred cube (fcc) iron have been investigated using first-principles calculations based on density functional theory (DFT). For the interactions of solutes and He, we found that Ti, P, Si, and C attracts He is more potent than Ni and Cr in fcc iron. We have determined the most stable configuration for the He-He pair, which is the Hesub-Hetetra pair with a binding energy of 1.60 eV. In considering the effect of solutes on the stability of the He-He pair, we have proposed a unique definition of binding energy. By applying the definition, we suggest that Ti and P could weaken He self-trapping, and Cr and C are beneficial for He self-trapping, while Ni is similar to the matrix Fe itself. For the diffusion of He, which is the necessary process of forming the He bubble, we determined that the most stable interstitial He is in a tetrahedral site and could migrate with the energy barrier of 0.16 eV in pure fcc iron. We further found that Ti and Si can increase the barrier to 0.18 and 0.20 eV; on the contrary, Cr and P decrease the barrier to 0.10 and 0.06 eV, respectively. Summarizing the calculations, we conclude that Ti decreases while Cr increases the diffusion and self-trapping of He in fcc iron.

We numerically investigate turbulent Rayleigh-B\'enard convection within two immiscible fluid layers, aiming to understand how the layer thickness and fluid properties affect the heat transfer (characterized by the Nusselt number $Nu$) in two-layer systems. Both two- and three-dimensional simulations are performed at fixed global Rayleigh number $Ra=10^8$, Prandtl number $Pr=4.38$, and Weber number $We=5$. We vary the relative thickness of the upper layer between $0.01 \le \alpha \le 0.99$ and the thermal conductivity coefficient ratio of the two liquids between $0.1 \le \lambda_k \le 10$. Two flow regimes are observed: In the first regime at $0.04\le\alpha\le0.96$, convective flows appear in both layers and $Nu$ is not sensitive to $\alpha$. In the second regime at $\alpha\le0.02$ or $\alpha\ge0.98$, convective flow only exists in the thicker layer, while the thinner one is dominated by pure conduction. In this regime, $Nu$ is sensitive to $\alpha$. To predict $Nu$ in the system in which the two layers are separated by a unique interface, we apply the Grossmann-Lohse theory for both individual layers and impose heat flux conservation at the interface. Without introducing any free parameter, the predictions for $Nu$ and for the temperature at the interface well agree with our numerical results and previous experimental data., Comment: 13 pages, 6 figures