Thermal analysis of dual-phase-lag model in a two-dimensional plate subjected to a heat source moving along elliptical trajectories

In this paper, we focus on the study of heat transfer behavior for the dual-phase-lag heat conduction model, which describes the evolution of temperature in a two-dimensional rectangular plate caused by the activity of a point heat source moving along elliptical trajectories. At first, Green's function approach is applied to derive the analytical solution of temperature for the given model. Based on the series representation of this analytical solution, the thermal responses for the underlying heat transfer problem, including the relations between the moving heat source and the concomitant temperature peak, the influences of the pair of phase lags and the angular velocity of heat source on temperature, are then investigated, analyzed and discussed in detail for three different movement trajectories. Compared with the results revealed for the common situation that the heat source moves in a straight line with a constant speed, the present results show quite distinctive thermal behaviors for all cases, which subsequently can help us to better understand the internal mechanism of the dual-phase-lag heat transfer subjected to a moving heat source with curved trajectory.Comment: 15 pages, 41 figure

Interdependent Self-Construal as a Moderator in the Relationship Between Extrinsic Aspiration for Children and Parental Psychological Well-Being

Research on Self-Determination Theory suggests that the pursuit of extrinsic aspirations (e.g. to be rich) can have negative consequences on well-being. Yet little research has examined whether holding extrinsic aspiration for other people evidences similar relationships. The current research examined how holding extrinsic aspirations for one’s children (AFC) is related to parents’ own psychological well-being. I expected endorsing extrinsic AFC might also bear negatively predict parental psychological well-being. However, I contended that the strength of relationship might vary according to interdependent self-construal. To the extent people’s self-construal is interdependent, they might attach different meaning to extrinsic AFC (e.g. as a way to be responsible and competent parents). These additional meaning could neutralize or even reverse its negative implication over parental psychological well-being. Two studies (one within-culture and one cross-cultural) were designed to test these hypotheses. Parent participants completed individual difference measure of self-construal, extrinsic AFC and parental psychological well-being. The results generally confirmed the hypotheses

A Threshold Based Handover Triggering Scheme in Heterogeneous Wireless Networks

The widespread popularity of Wireless Local Area Network (WLAN) is recognized as an effective approach to complementing cellular networks for the high data rate and cost effective connectivity delivered to mobile users. Efficient handover and offloading schemes for integrated WLAN and cellular networks, referred to as Heterogeneous Wireless Networks, have thus attracted lots of attentions from both academia and industry. This paper proposes a novel Multiple-Threshold based Triggering (MTT) scheme for Cellular-to-WLAN handover control. Aiming at minimizing the probability of handover failures and unnecessary handovers, three thresholds are calculated based on a variety of network parameters such as system performance requirements, radius of the WLAN coverage, user mobility and handover delays. The thresholds are then compared against the predicted user residence time and estimated channel holding time inside WLAN to make vertical handover decisions (VHDs). Simulations were carried out to evaluate the effectiveness of MTT and results show that MTT minimizes handover failures and avoids unnecessary handovers in integrated cellular and WLAN networks, thus providing satisfactory Quality of Service (QoS) to users and improving system resource utilization

Strengthening mechanisms of highly textured Cu/Co and Ag/Al nanolayers with high density twins and stacking faults

Metallic nanolayers have attracted increasing attention as they provide unique opportunity to investigate the influence of layer interfaces on mechanical properties of metallic nanocomposites. High strength is often achieved at small (several nm) individual layer thickness (h). Recently, we discovered high-density stacking faults in FCC Co in highly (100) textured Cu/Co multilayers. In contrast in (111) textured Cu/Co nanolayers, Co remained its stable HCP structure at large h. The two Cu/Co systems have very different size dependent strengthening behavior. HCP Cu/Co has much greater peak strength than FCC Cu/Co. The large discrepancy in their strengthening mechanisms is discussed and compared to those of highly textured Cu/Ni multilayer systems. In another highly textured nanolayers system, Ag/Al, epitaxial interfaces were observed across various h (1‑200 nm). High-density nanotwins and stacking faults appear in both Ag and Al layers, and stacking fault density in Al increases sharply with decreasing h. At smaller h, hardness of Ag/Al nanolayers increases monotonically and no softening was observed. These studies allow us to investigate the influence of layer interfaces, stacking faults and nanotwins on strengthening mechanisms of metallic nanolayers. This research is funded by DOE–OBES

Excitation and voltage-gated modulation of single-mode dynamics in a planar nano-gap spin Hall nano-oscillator

We experimentally study the dynamical modes excited by current-induced spin-orbit torque and its electrostatic gating effect in a 3-terminal planar nano-gap spin Hall nano-oscillator (SHNO) with a moderate interfacial perpendicular magnetic anisotropy (IPMA). Both quasilinear propagating spin-wave and localized "bullet" modes are achieved and controlled by varying the applied in-plane magnetic field and driving current. The minimum linewidth shows a linear dependence on the actual temperature of the active area, confirming single-mode dynamics based on the nonlinear theory of spin-torque nano-oscillation with a single mode. The observed electrostatic gating tuning oscillation frequency arises from voltage-controlled magnetic anisotropy and threshold current of SHNO via modification of the nonlinear damping and/or the interfacial spin-orbit coupling of the magnetic multilayer. In contrast to previously observed two-mode coexistence degrading the spectral purity in Py/Pt-based SHNOs with a negligible IPMA, a single coherent spin-wave mode with a low driven current can be achieved by selecting the ferromagnet layer with a suitable IPMA because the nonlinear mode coupling can be diminished by bringing in the PMA field to compensate the easy-plane shape anisotropy. Moreover, the simulations demonstrate that the experimentally observed current and gate-voltage modulation of auto-oscillation modes are also closely associated with the nonlinear damping and mode coupling, which are determined by the ellipticity of magnetization precession. The demonstrated nonlinear mode coupling mechanism and electrical control approach of spin-wave modes could provide the clue to facilitate the implementation of the mutual synchronization map for neuromorphic computing applications in SHNO array networks.Comment: 11 pages, 10 figure

PIM-QAT: Neural Network Quantization for Processing-In-Memory (PIM) Systems

Processing-in-memory (PIM), an increasingly studied neuromorphic hardware, promises orders of energy and throughput improvements for deep learning inference. Leveraging the massively parallel and efficient analog computing inside memories, PIM circumvents the bottlenecks of data movements in conventional digital hardware. However, an extra quantization step (i.e. PIM quantization), typically with limited resolution due to hardware constraints, is required to convert the analog computing results into digital domain. Meanwhile, non-ideal effects extensively exist in PIM quantization because of the imperfect analog-to-digital interface, which further compromises the inference accuracy. In this paper, we propose a method for training quantized networks to incorporate PIM quantization, which is ubiquitous to all PIM systems. Specifically, we propose a PIM quantization aware training (PIM-QAT) algorithm, and introduce rescaling techniques during backward and forward propagation by analyzing the training dynamics to facilitate training convergence. We also propose two techniques, namely batch normalization (BN) calibration and adjusted precision training, to suppress the adverse effects of non-ideal linearity and stochastic thermal noise involved in real PIM chips. Our method is validated on three mainstream PIM decomposition schemes, and physically on a prototype chip. Comparing with directly deploying conventionally trained quantized model on PIM systems, which does not take into account this extra quantization step and thus fails, our method provides significant improvement. It also achieves comparable inference accuracy on PIM systems as that of conventionally quantized models on digital hardware, across CIFAR10 and CIFAR100 datasets using various network depths for the most popular network topology.Comment: 25 pages, 12 figures, 8 table

Understanding User Behavior in Volumetric Video Watching: Dataset, Analysis and Prediction

Volumetric video emerges as a new attractive video paradigm in recent years since it provides an immersive and interactive 3D viewing experience with six degree-of-freedom (DoF). Unlike traditional 2D or panoramic videos, volumetric videos require dense point clouds, voxels, meshes, or huge neural models to depict volumetric scenes, which results in a prohibitively high bandwidth burden for video delivery. Users' behavior analysis, especially the viewport and gaze analysis, then plays a significant role in prioritizing the content streaming within users' viewport and degrading the remaining content to maximize user QoE with limited bandwidth. Although understanding user behavior is crucial, to the best of our best knowledge, there are no available 3D volumetric video viewing datasets containing fine-grained user interactivity features, not to mention further analysis and behavior prediction. In this paper, we for the first time release a volumetric video viewing behavior dataset, with a large scale, multiple dimensions, and diverse conditions. We conduct an in-depth analysis to understand user behaviors when viewing volumetric videos. Interesting findings on user viewport, gaze, and motion preference related to different videos and users are revealed. We finally design a transformer-based viewport prediction model that fuses the features of both gaze and motion, which is able to achieve high accuracy at various conditions. Our prediction model is expected to further benefit volumetric video streaming optimization. Our dataset, along with the corresponding visualization tools is accessible at https://cuhksz-inml.github.io/user-behavior-in-vv-watching/Comment: Accepted by ACM MM'2