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This paper exploits the potential of edge intelligence empowered satellite-terrestrial networks, where users' computation tasks are offloaded to the satellites or terrestrial base stations. The computation task offloading in such networks involves the edge cloud selection and bandwidth allocations for the access and backhaul links, which aims to minimize the energy consumption under the delay and satellites' energy constraints. To address it, an alternating direction method of multipliers (ADMM)-inspired algorithm is proposed to decompose the joint optimization problem into small-scale subproblems. Moreover, we develop a hybrid quantum double deep Q-learning (DDQN) approach to optimize the edge cloud selection. This novel deep reinforcement learning architecture enables that classical and quantum neural networks process information in parallel. Simulation results confirm the efficiency of the proposed algorithm, and indicate that duality gap is tiny and a larger reward can be generated from a few data points compared to the classical DDQN.

This paper studies the large-scale cell-free networks where dense distributed access points (APs) serve many users. As a promising next-generation network architecture, cell-free networks enable ultra-reliable connections and minimal fading/blockage, which are much favorable to the millimeter wave and Terahertz transmissions. However, conventional beam management with large phased arrays in a cell is very time-consuming in the higher-frequencies, and could be worsened when deploying a large number of coordinated APs in the cell-free systems. To tackle this challenge, the spatial-spectral cell-free networks with the leaky-wave antennas are established by coupling the propagation angles with frequencies. The beam training overhead in this direction can be significantly reduced through exploiting such spatial-spectral coupling effects. In the considered large-scale spatial-spectral cell-free networks, a novel subchannel allocation solution at sub-terahertz bands is proposed by leveraging the relationship between cross-entropy method and mixture model. Since initial access and AP clustering play a key role in achieving scalable large-scale cell-free networks, a hierarchical AP clustering solution is proposed to make the joint initial access and cluster formation, which is adaptive and has no need to initialize the number of AP clusters. After AP clustering, a subchannel allocation solution is devised to manage the interference between AP clusters. Numerical results are presented to confirm the efficiency of the proposed solutions and indicate that besides subchannel allocation, AP clustering can also have a big impact on the large-scale cell-free network performance at sub-terahertz bands.

A response to commenter Ke Lan's comment on our paper published in Nature Communications (2023)14:5782 by J. Yan et al

Edge computing plays an essential role in the vehicle-to-infrastructure (V2I) networks, where vehicles offload their intensive computation tasks to the road-side units for saving energy and reduce the latency. This paper designs the optimal task offloading policy to address the concerns involving processing delay, energy consumption and edge computing cost. Each computation task consisting of some interdependent sub-tasks is characterized as a directed acyclic graph (DAG). In such dynamic networks, a novel hierarchical Offloading scheme is proposed by leveraging deep reinforcement learning (DRL). The inter-dependencies among the DAGs of the computation tasks are extracted using a graph neural network with attention mechanism. A parameterized DRL algorithm is developed to deal with the hierarchical action space containing both discrete and continuous actions. Simulation results with a real-world car speed dataset demonstrate that the proposed scheme can effectively reduce the system overhead.

The evolution of software testing from manual to automated methods has significantly influenced quality assurance (QA) practices. However, challenges persist in post-execution phases, particularly in result analysis and reporting. Traditional post-execution validation phases require manual intervention for result analysis and report generation, leading to inefficiencies and potential development cycle delays. This paper introduces BugBlitz-AI, an AI-powered validation toolkit designed to enhance end-to-end test automation by automating result analysis and bug reporting processes. BugBlitz-AI leverages recent advancements in artificial intelligence to reduce the time-intensive tasks of manual result analysis and report generation, allowing QA teams to focus more on crucial aspects of product quality. By adopting BugBlitz-AI, organizations can advance automated testing practices and integrate AI into QA processes, ensuring higher product quality and faster time-to-market. The paper outlines BugBlitz-AI's architecture, discusses related work, details its quality enhancement strategies, and presents results demonstrating its effectiveness in real-world scenarios.

A novel self-sustaining mechanism is proposed for large-scale helical structures in compressible turbulent flows. The existence of two channels of subgrid-scale and viscosity terms for large-scale helicity evolution is confirmed for the first time, through selecting a physical definition of the large-scale helicity in compressible turbulence. Under the influence of the fluid element expansion, it is found that the helicity is generated at small scales via the second-channel viscosity, and the inverse cross-scale helicity transfers at inertial scales through the second-channel helicity flux. Together, they form a self-induced mechanism, which provides a physical insight into the long-period characteristic of large-scale helical structures in the evolution of compressible flow systems.

Pillars of Creation, one of the most recognized objects in the sky, are believed to be associated with the formation of young stars. However, so far, the formation and maintenance mechanism for the pillars are still not fully understood due to the complexity of the nonlinear radiation magneto-hydrodynamics (RMHD). Here, assuming laboratory laser-driven conditions, we studied the self-consistent dynamics of pillar structures in magnetic fields by means of two-dimensional (2D) and three-dimensional (3D) RMHD simulations, and these results also support our proposed experimental scheme. We find only when the magnetic pressure and ablation pressure are comparable, the magnetic field can significantly alter the plasma hydrodynamics. For medium magnetized cases ($\beta_{initial} \approx 3.5$), {the initial magnetic fields undergo compression and amplification. This amplification results in the magnetic pressure inside the pillar becoming large enough to support the sides of the pillar against radial collapse due to pressure from the surrounding hot plasma. This effect is particularly pronounced for the parallel component ($B_y$), which is consistent with observational results.} In contrast, a strong perpendicular ($B_x, B_z$) magnetic field ($\beta_{initial} < 1$) almost remains its initial distribution and significantly suppresses the expansion of blow-off gas plasma, leading to the inability to form pillar-like structures. The 3D simulations suggest that the bending at the head of `Column \uppercase\expandafter{\romannumeral1}' in pillars of creation may be due to the non-parallel magnetic fields. After similarity scaling transformation, our results can be applied to explain the formation and maintenance mechanism of the pillars, and can also provide useful information for future experimental designs.

In this paper, we prove the following inequality \begin{equation*} \|\big(\int_{\mathbb{R}^n}\frac{|f(\cdot+y)-f(\cdot)|^q}{|y|^{n+sq}}dy\big)^{\frac{1}{q}}\|_{L^{p,\infty}(\mathbb{R}^n)}\lesssim\|f\|_{\dot{L}^p_s(\mathbb{R}^n)}, \end{equation*} where $\|\cdot\|_{L^{p,\infty}(\mathbb{R}^n)}$ is the weak $L^p$ quasinorm and $\|\cdot\|_{\dot{L}^p_s(\mathbb{R}^n)}$ is the homogeneous Sobolev norm, and parameters satisfy the condition that $1

Cell-free network architecture plays a promising role in the terahertz (THz) networks since it provides better link reliability and uniformly good services for all the users compared to the co-located massive MIMO counterpart, and the spatial-spectral THz link has the advantages of lower initial access latency and fast beam operations. To this end, this work studies cell-free spatial-spectral THz networks with leaky-wave antennas, to exploit the benefits of leveraging both cell-free and spatial-spectral THz technologies. By addressing the coupling effects between propagation angles and frequencies, we propose novel frequency-dependent THz transmit antenna selection schemes to maximize the transmission rate. Numerical results confirm that the proposed antenna selection schemes can achieve much larger transmission rate than the maximal ratio transmission of using all the transmit antennas with equal subchannel bandwidth allocation in higher THz frequencies.

This paper studies multiuser immersive communications networks in which different user equipment may demand various extended reality (XR) services. In such heterogeneous networks, time-frequency resource allocation needs to be more adaptive since XR services are usually multi-modal and latency-sensitive. To this end, we develop a scalable time-frequency resource allocation method based on multi-numerology and mini-slot. To appropriately determining the discrete parameters of multi-numerology and mini-slot for multiuser immersive communications, the proposed method first presents a novel flexible time-frequency resource block configuration, then it leverages the deep reinforcement learning to maximize the total quality-of-experience (QoE) under different users' QoE constraints. The results confirm the efficiency and scalability of the proposed time-frequency resource allocation method.

In inertial confinement fusion (ICF) implosions, mixing the ablator into the fuel and the hot spot is one of the most adverse factors that lead to ignition degradation. Recent experiments in the Marble campaign at the Omega laser facility and the National Ignition Facility (NIF) demonstrate the significance of the temperature separation in heterogeneous mixing flows. In the present work we provide an approach to deal with thermally disequilibrium multi-component flows with the ultimate aim to investigate the temperature separation impact on mixing and fusion burn. The present work is two-fold, i.e., (a)we derive a model governing the multi-component flows in thermal disequilibrium with transport terms, (b)we use the derived model to study the Rayleigh-Taylor (RT) instability in thermally relaxing multi-component systems. The model is reduced from the fully disequilibrium multi-phase Baer-Nunziato model in the limit of small Knudsen number $Kn << 1$. Velocity disequilibrium is closed with the diffusion laws and only one mass weighted velocity is retained formally. Thus, the complex wave structure of the original Baer-Nunziato model is simplified to a large extent and the obtained model is much more computationally affordable. Moreover, the capability to deal with finite temperature relaxation is kept. Efficient numerical methods for solving the proposed model are aslo presented. Equipped with the proposed model and numerical methods, we further investigate the impact of the thermal relaxation on the RT instability development at the inertial confinement fusion (ICF) deceleration stage. On the basis of numerical simulations, we have found that for the RT instability at an interface between the high-density low-temperature component and the low-density high-temperature component, the thermal relaxation significantly suppress the development of the instability.

A discrete Boltzmann model (DBM) for plasma kinetics is proposed. The constructing of DBM mainly considers two aspects. The first is to build a physical model with sufficient physical functions before simulation. The second is to present schemes for extracting more valuable information from massive data after simulation. For the first aspect, the model is equivalent to a magnetohydrodynamic model plus a coarse-grained model for the most relevant TNE behaviors including the entropy production rate. A number of typical benchmark problems including Orszag-Tang (OT) vortex problem are used to verify the physical functions of DBM. For the second aspect, the DBM use non-conserved kinetic moments of (f-feq) to describe non-equilibrium state and behaviours of complex system. The OT vortex problem and the Richtmyer-Meshkov instability (RMI) are practical applications of the second aspect. For RMI with interface inverse and re-shock process, it is found that, in the case without magnetic field, the non-organized momentum flux shows the most pronounced effects near shock front, while the non-organized energy flux shows the most pronounced behaviors near perturbed interface. The influence of magnetic field on TNE effects shows stages: before the interface inverse, the TNE strength is enhanced by reducing the interface inverse speed; while after the interface inverse, the TNE strength is significantly reduced. Both the global average TNE strength and entropy production rate contributed by non-organized energy flux can be used as physical criteria to identify whether or not the magnetic field is sufficient to prevent the interface inverse., Comment: 20 pages, 15 figures

End-to-end (E2E) artificial intelligence (AI) pipelines are composed of several stages including data preprocessing, data ingestion, defining and training the model, hyperparameter optimization, deployment, inference, postprocessing, followed by downstream analyses. To obtain efficient E2E workflow, it is required to optimize almost all the stages of pipeline. Intel Xeon processors come with large memory capacities, bundled with AI acceleration (e.g., Intel Deep Learning Boost), well suited to run multiple instances of training and inference pipelines in parallel and has low total cost of ownership (TCO). To showcase the performance on Xeon processors, we applied comprehensive optimization strategies coupled with software and hardware acceleration on variety of E2E pipelines in the areas of Computer Vision, NLP, Recommendation systems, etc. We were able to achieve a performance improvement, ranging from 1.8x to 81.7x across different E2E pipelines. In this paper, we will be highlighting the optimization strategies adopted by us to achieve this performance on Intel Xeon processors with a set of eight different E2E pipelines., Comment: 10 pages, 11 figures, 3 tables

Artificial skin with the sense of touch can support robots to interact with the surrounding environment efficiently and accomplish complex tasks. Traditional multi-layered artificial skins require complex manufacturing processes which can result in high cost as well as limitations on the material and size of the skin. In this paper, we demonstrate a machine learning based approach to predict positions of point loads using the most direct response as input signal: strain distribution. Starting with the simplest problem, predicting the position of a single point load acting on a flat surface, an ML model is developed, trained, and tested. Accurate predictions are obtained from the ML model, parameters that affect the accuracy are discussed, and validation tests are performed. After that, the ML model is modified to solve multi-objective prediction problems: predicting positions and magnitudes of multiple point loads. In the end, the ML model is upgraded to a 2-step model to predict the position of a point load acting on a deformable surface. The demonstrated approach enables a normal untreated surface to feel a touch no matter what the surface is made of or how large or small the size of the surface is. Therefore, we believe this ML-based load position prediction approach could be a promising tool for applications such as flexible touch screens, smart skin for robots, and micro touch sensors.

Biological materials exhibit complex structure-property relationships which are only beginning to be elucidated. Understanding the underlying physical mechanisms of the structure-property relationships is the key to designing bioinspired materials. The eggshell is an excellent example because many design trade-offs are well balanced by its seemingly simple but highly evolved structures. The first design trade-off: to break an eggshell, the internal breaking load needed is much lower than the external breaking load. The reason is that the dome-shaped structure leads to a special stress distribution which inhibits the propagation of the first crack. The second design trade-off: the eggshell is tough during the incubation but weakened at the time of hatching. The membrane of the eggshell significantly improves the toughness by bridging the primary crack and creating more secondary cracks. Moreover, the moisture content-controlled property of the membrane makes the toughness of the eggshell tunable. The third design trade-off: the eggshell must be thick enough to withstand the external forces, but the supply of the building material calcium is limited. The proposed three-index model indicates that the driving force in the evolution of eggshell thickness could be the balance between the protection and nutrient consumption to maximize survivability. The general methodologies presented here hold great potential for the development of eggshell-inspired structural materials that can be used in sports safety applications and the packaging industry.

This paper presents a mechanically bistable mechanism of the compressed pre-curved beam. A governing equation is proposed which can be used to predict and explain the bistability of the compressed pre-curved beam. FE simulations and experimental tests are performed to validate the analytical solution. The beams unique tunable and asymmetrical potential energy landscape is demonstrated which enables the compressed pre-curved beam to switch its stable position without the need of applying an external force to it. Based on that, a coupled beam element is designed and used as a building block to develop reprogrammable metasurfaces that can be programmed to exhibit different configurations.

The rapidly developing robotics industry demands structures with novel mechanical functions. Traditional approaches, developing new materials and designing new structures, face two challenges. Highly complex force-displacement functions can hardly be realized and one fabricated structure only has one or limited function. We perform theoretical calculations and FE simulations to demonstrate that the two challenges can be solved by making the mechanical property or geometry of the structure tunable. The mechanical property/geometry tuning approaches are further validated by experimental tests and the results show that multiple different and highly complex objective functions can be achieved accurately. We expect that the proposed tuning approaches will facilitate the development of advanced forms of mechanical metamaterials and a new generation of robotic hands.

A Discrete Boltzmann Model(DBM) based on the Shakhov model for detonation is proposed. Compared with the DBM based on the Bhatnagar-Gross-Krook (BGK) model, the current model has a flexible Prandtl numbers and consequently can be applied to a much wider range of detonation phenomena. Besides the Hydrodynamic Non-Equilibrium (HNE) behaviors usually investigated by the Navier-Stokes model, the most relevant Thermodynamic Non-Equilibrium (TNE) effects can be probed by the current model. The model is validated by some well-known benchmarks,and some steady and unsteady detonation processes are investigated. As for the von Neumann peak relative to the wave front, it is found that (i) (within the range of numerical experiments) the peak heights of pressure, density and flow velocity increase exponentially with the Prandtl number, the maximum stress increases parabolically with the Prandtl number, and the maximum heat flux decreases exponentially with the Prandtl number; (ii) the peak heights of pressure, density, temperature and flow velocity and the maximum stress within the peak are parabolically increase with the Mach number, the maximum heat flux decreases exponentially with the Mach number.

Nonequilibrium kinetic effects are widespread in fluid systems and might have a significant impact on the inertial confinement fusion ignition process, and the entropy production rate is a key factor in accessing the compression process. In this work, we study the Richtmyer-Meshkov instability (RMI) and the reshock process by a two-fluid discrete Boltzmann method (DBM). Firstly, the DBM result for the perturbation amplitude evolution is in good agreement with that of experiment. Greatly different from the case of normal shocking on unperturbed plane interface between two uniform media, in the RMI case, the Thermodynamic Non-Equilibrium (TNE) quantities show complex but inspiring kinetic effects in the shocking process and behind the shock front. The kinetic effects are detected by two sets of TNE quantities. The first set are $\left |\Delta _{2}^{ {\rm{*}}}\right |$,$\left |\Delta _{3,1}^{ {\rm{*}}}\right |$, $\left | \Delta _{3}^{ {\rm{*}}}\right |$, and $\left |\Delta _{4,2}^{ {\rm{*}}}\right |$. All the four TNE measures abruptly increase in the shocking process. $\left |\Delta _{3,1}^{ {\rm{*}}}\right |$ and $\left |\Delta _{3}^{ {\rm{*}}}\right |$ show similar behaviors. They continue to increase in a much lower rate behind the shock front. $\left |\Delta _{2}^{ {\rm{*}}}\right |$ and $\left |\Delta _{4,2}^{ {\rm{*}}}\right |$ have different dimensions, but show similar behaviors. They quickly decrease to be very small behind the shock front. The second set of TNE quantities are ${\dot{S} _{NOMF}}$, ${\dot{S} _{NOEF}}$ and ${\dot{S} _{sum}}$. It is found that the mixing zone is the primary contribution region to the ${\dot{S} _{NOEF}}$, while the flow field region excluding mixing zone is the primary contribution region to the ${\dot{S} _{NOMF}}$. The light fluid has a higher entropy production rate than the heavy fluid., Comment: Accepted by Commun. Theor. Phys