Statistical CSIT Aided User Scheduling for Broadcast MU-MISO System

Abstract: Recent studies show that the statistical channel state information (SCSI) helps to largely increase the capacity of communication systems when the instantaneous perfect CSI (IPCSI) is unavailable. In this paper, we consider multi-user multipleinput- single-output (MU-MISO) broadcast channels where the transmitter has the knowledge of SCSI. The major issue concerned in our work is to improve the average group-rate of the whole system by scheduling users over different time slots. With SCSI at the transmitter side, we are able to precode signals and hence compute the theoretical achievable group-rate of arbitrary user groups. Based on the group-rates, we propose tier-2 Munkres user scheduling algorithm (T2-MUSA) which leads to higher average group-rate than existing algorithms with generally better fairness. The optimality of the proposed algorithm in energy-fair user scheduling space is proved and we derive a lower bound of a special case to verify the validity of our simulations. In addition, many conventional user scheduling algorithms maintain queue stability by solving a weighted sum-rate (WSR) problem, using queue lengths to represent weight coefficients. Inspired by T2-MUSA we propose a QoS-based Munkres user scheduling algorithm (QB-MUSA) aimed at stabilizing queue lengths and maximizing throughput. In results, we show that QB-MUSA exhibits higher throughput than the conventional weighted sumrate (WSR) based algorithm

Representation Disparity-aware Distillation for 3D Object Detection

In this paper, we focus on developing knowledge distillation (KD) for compact 3D detectors. We observe that off-the-shelf KD methods manifest their efficacy only when the teacher model and student counterpart share similar intermediate feature representations. This might explain why they are less effective in building extreme-compact 3D detectors where significant representation disparity arises due primarily to the intrinsic sparsity and irregularity in 3D point clouds. This paper presents a novel representation disparity-aware distillation (RDD) method to address the representation disparity issue and reduce performance gap between compact students and over-parameterized teachers. This is accomplished by building our RDD from an innovative perspective of information bottleneck (IB), which can effectively minimize the disparity of proposal region pairs from student and teacher in features and logits. Extensive experiments are performed to demonstrate the superiority of our RDD over existing KD methods. For example, our RDD increases mAP of CP-Voxel-S to 57.1% on nuScenes dataset, which even surpasses teacher performance while taking up only 42% FLOPs.Comment: Accepted by ICCV2023. arXiv admin note: text overlap with arXiv:2205.15156 by other author

Anisotropic and high thermal conductivity in monolayer quasi-hexagonal fullerene: A comparative study against bulk phase fullerene

Recently a novel two-dimensional (2D) C$_{60}$ based crystal called quasi-hexagonal-phase fullerene (QHPF) has been fabricated and demonstrated to be a promising candidate for 2D electronic devices [Hou et al. Nature 606, 507-510 (2022)]. We construct an accurate and transferable machine-learned potential to study heat transport and related properties of this material, with a comparison to the face-centered-cubic bulk-phase fullerene (BPF). Using the homogeneous nonequilibrium molecular dynamics and the related spectral decomposition methods, we show that the thermal conductivity in QHPF is anisotropic, which is 137(7) W/mK at 300 K in the direction parallel to the cycloaddition bonds and 102(3) W/mK in the perpendicular in-plane direction. By contrast, the thermal conductivity in BPF is isotropic and is only 0.45(5) W/mK. We show that the inter-molecular covalent bonding in QHPF plays a crucial role in enhancing the thermal conductivity in QHPF as compared to that in BPF. The heat transport properties as characterized in this work will be useful for the application of QHPF as novel 2D electronic devices.Comment: 11 pages, 12 figure

DCP-NAS: Discrepant Child-Parent Neural Architecture Search for 1-bit CNNs

Neural architecture search (NAS) proves to be among the effective approaches for many tasks by generating an application-adaptive neural architecture, which is still challenged by high computational cost and memory consumption. At the same time, 1-bit convolutional neural networks (CNNs) with binary weights and activations show their potential for resource-limited embedded devices. One natural approach is to use 1-bit CNNs to reduce the computation and memory cost of NAS by taking advantage of the strengths of each in a unified framework, while searching the 1-bit CNNs is more challenging due to the more complicated processes involved. In this paper, we introduce Discrepant Child-Parent Neural Architecture Search (DCP-NAS) to efficiently search 1-bit CNNs, based on a new framework of searching the 1-bit model (Child) under the supervision of a real-valued model (Parent). Particularly, we first utilize a Parent model to calculate a tangent direction, based on which the tangent propagation method is introduced to search the optimized 1-bit Child. We further observe a coupling relationship between the weights and architecture parameters existing in such differentiable frameworks. To address the issue, we propose a decoupled optimization method to search an optimized architecture. Extensive experiments demonstrate that our DCP-NAS achieves much better results than prior arts on both CIFAR-10 and ImageNet datasets. In particular, the backbones achieved by our DCP-NAS achieve strong generalization performance on person re-identification and object detection.Comment: Accepted by International Journal of Computer Visio

IDa-Det: An Information Discrepancy-aware Distillation for 1-bit Detectors

Knowledge distillation (KD) has been proven to be useful for training compact object detection models. However, we observe that KD is often effective when the teacher model and student counterpart share similar proposal information. This explains why existing KD methods are less effective for 1-bit detectors, caused by a significant information discrepancy between the real-valued teacher and the 1-bit student. This paper presents an Information Discrepancy-aware strategy (IDa-Det) to distill 1-bit detectors that can effectively eliminate information discrepancies and significantly reduce the performance gap between a 1-bit detector and its real-valued counterpart. We formulate the distillation process as a bi-level optimization formulation. At the inner level, we select the representative proposals with maximum information discrepancy. We then introduce a novel entropy distillation loss to reduce the disparity based on the selected proposals. Extensive experiments demonstrate IDa-Det's superiority over state-of-the-art 1-bit detectors and KD methods on both PASCAL VOC and COCO datasets. IDa-Det achieves a 76.9% mAP for a 1-bit Faster-RCNN with ResNet-18 backbone. Our code is open-sourced on https://github.com/SteveTsui/IDa-Det

Memory-enhancing effect of Rhodiola rosea L extract on aged mice

Purpose: The memory-enhancing effects of Rhodiola rosea L. extract (RRLE) on normal aged mice were assessed.Methods: In the open-field test, the effect of RRLE (150 and 300 mg/kg) on mouse locomotive activities was evaluated by investigating the extract’s influence on CAT and AchE activities in the brain tissue of mice.Results: Compared with aged group, high dose of RRLE reduced the total distance (3212.4 ± 123.1 cm, p < 0.05) significantly, increased catalase (CAT) activity (101.4 ± 12.2 U/mg pro, p < 0.05), and inhibited acetyl cholinesterase (AChE) activity (0.94 ± 0.12 U/mg pro, p < 0.05) in the brain tissue of aged mice.Conclusion: The results show that RRLE improves the memory functions of aged mice probably by increasing CAT activity while decreasing AChE activity.Keywords: Rhodiola rosea, Memory function, Catalase, Acetyl cholinesterase, Open-field tes

Sum Rate Maximization of D2D Communications in Cognitive Radio Network Using Cheating Strategy

This paper focuses on the cheating algorithm for device-to-device (D2D) pairs that reuse the uplink channels of cellular users. We are concerned about the way how D2D pairs are matched with cellular users (CUs) to maximize their sum rate. In contrast with Munkres’ algorithm which gives the optimal matching in terms of the maximum throughput, Gale-Shapley algorithm ensures the stability of the system on the same time and achieves a men-optimal stable matching. In our system, D2D pairs play the role of “men,” so that each D2D pair could be matched to the CU that ranks as high as possible in the D2D pair’s preference list. It is found by previous studies that, by unilaterally falsifying preference lists in a particular way, some men can get better partners, while no men get worse off. We utilize this theory to exploit the best cheating strategy for D2D pairs. We find out that to acquire such a cheating strategy, we need to seek as many and as large cabals as possible. To this end, we develop a cabal finding algorithm named RHSTLC, and also we prove that it reaches the Pareto optimality. In comparison with other algorithms proposed by related works, the results show that our algorithm can considerably improve the sum rate of D2D pairs

Stability of inactive components of cathode laminates for lithium ion batteries at high potential

The stability of inactive components in LIB (lithium ion batteries) electrodes upon exposure to high potentials can affect cell performance. A series of Li/ inactive component cells with aluminum, conductive carbon, and graphite as the inactive component were prepared and stored at high potential for one week. Electrochemical measurements and ex-situ surface analysis, including TEM (transmission electron microscopy), XPS (X-ray photoelectron spectroscopy), and FTIR (Fourier transform infrared spectroscopy), were conducted to investigate the stability of inactive components in the presence of LiPF6 in 3:7 ethylene carbonate (EC) and ethyl methyl carbonate (EMC) electrolyte at different potentials. The results show that all components are stable upon storage at 4.3 V. Storage at 4.6 or 4.9 V results in no aluminum corrosion, but limited decomposition on conductive carbon and greater decomposition on graphite. Storage at 5.3 V results in significant electrolyte oxidation to generate poly(ethylene carbonate) on the surface of all inactive electrodes and aluminum corrosion