pwc-archive/papers-with-abstracts huggingface dataset:
基于功率控制的D2D通信能效优化研究
Authors: 缑丽莎
Date: 2021-04-05 00:00:00
Proceeding: https://kns.cnki.net/kcms/detail/detail.aspx?dbcode=CMFD&dbname=CMFD202201&filename=1021132792.nh&uniplatform=NZKPT&v=v-LmEPhJvK4pjoIvoXlApqMvYEx0RF4BMhJDalyK6ADM97rmQwgM9DPNY9Iwkj92 2021 4

本文主要研究 D2D 通信用户共享蜂窝网络频谱资源时两个不同场景的能效优化问
题。两个场景的研究目标是一致的，均为基于控制 D2D 用户的发射功率，协调通信用
户之间的干扰，达到最大化 D2D 能效的目的。两个场景研究内容分别如下。
一对一场景下基于功率控制的能效优化：首先本场景引入全双工通信技术，使其与
D2D 通信用户相结合，构造全双工 D2D 通信链路；其次详细分析全双工 D2D 用户与蜂
窝用户之间产生的干扰，并给出相应链路之间的传输速率及能量效率表达式；进一步，
提出一种分解的方法求解全双工 D2D 通信链路的能效，通过最优功率限制区域和序列
二次规划算法求解 D2D 用户最优发射功率，通过改进的自适应禁忌搜索算法求解最优
资源链路分配，得到最优能效解决方案；最后通过仿真验证本文算法能提升全双工 D2D
通信链路能效。
多对一场景下基于功率控制的能效优化：建立 D2D 用户复用蜂窝网络系统博弈模
型，多个 D2D 用户可以随机复用蜂窝网络内用户的链路资源，将该能效优化问题建模
为合作博弈问题。首先在用户服务质量要求的约束下，综合分析通信用户之间的干扰，
并结合效用权重因子和终端电池能量构建效用函数；其次综合考虑选取合适的效用权重
因子；进一步，在阈值的限制条件下，将提出的功率控制策略多次重复迭代得到使 D2D
通信网络能效最大时最优发射功率；最后通过和其他算法对比仿真，验证本文算法能实
现更高的网络效用。
Exploring the Expansion History of the Universe
Authors: Eric V. Linder
Date: 2002-08-28 00:00:00

Exploring the recent expansion history of the universe promises insights into the cosmological model, the nature of dark energy, and potentially clues to high energy physics theories and gravitation. We examine the extent to which precision distance-redshift observations can map out the history, including the acceleration-deceleration transition, and the components and equations of state of the energy density. We consider the ability to distinguish between various dynamical scalar field models for the dark energy, as well as higher dimension and alternate gravity theories. Finally, we present a new, advantageous parametrization for the study of dark energy.
A MIP Model for Risk Constrained Switch Placement in Distribution Networks
Authors: Milad Izadi, Student Member, IEEE and Amir Safdarian, Member, IEEE
Date: 2019-03-04 00:00:00
Proceeding: IEEE 2019 3

Abstract—The great share of interruptions in distribution networks motivates distribution decision makers to establish various
reliability enhancement strategies. Amongst, deploying remote
controlled switch (RCS) can make a crucial contribution to the
reduction of interruption costs. Nevertheless, the stochastic nature
of contingencies affects the RCS worth and imposes substantial
financial risk to RCS deployment projects. This paper proposes
a mathematical model to consider the risk in the optimal RCS
deployment problem. The model determines the number and
location of RCSs such that the expected profit is maximized
while financial risk is minimized. The risk is modeled through
conditional value-at-risk (CVaR) as one of the most applied risk
indices. The risk preference of distribution companies (DisCos) is
taken into account via integrating the weighted risk index and the
expected profit in the objective function. The model is formulated
in the format of mixed integer programming (MIP), which enables
solving the problem in an effective runtime. The model is applied
to the RBTS-Bus4 and a real distribution network. Various studies
and sensitivity analyses reveal the importance of considering
financial risk in the problem and the performance of the proposed
model.
Deep Sequence Modeling for Pressure Controlled Mechanical Ventilation
Authors: Abdelghani Belgaid
Date: 2022-03-04 00:00:00
Proceeding: medRxiv 2022 3
Tasks: Time Series, Time Series Analysis

This paper presents a deep neural network approach to simulate the pressure of a mechanical ventilator. The traditional mechanical ventilator has a control pressure monitored by a medical practitioner, which could behave inaccurately by missing the proper pressure. This paper exploits recent studies and provides a simulator based on a deep sequence model to predict the airway pressure in the respiratory circuit during the inspiratory phase of a breath given a time series of control parameters and lung attributes. This approach demonstrates the effectiveness of neural network-based controllers in tracking pressure waveforms significantly better than the current industry standard and provides insights to build effective and robust pressure-controlled mechanical ventilators.
MMFN: Multi-Modal Fusion Net for End-to-End Autonomous Driving
Authors: Qingwen Zhang, Mingkai Tang, Ruoyu Geng, Feiyi Chen, Ren Xin, Lujia Wang
Date: 2022-03-01 00:00:00
Proceeding: IROS In submission 2022 3
Tasks: Autonomous Driving, CARLA MAP Leaderboard

Under review
Pylot: A Modular Platform for Exploring Latency-Accuracy Tradeoffs in Autonomous Vehicles
Authors: Ionel Gog*, Sukrit Kalra*, Peter Schafhalter*, Matthew A. Wright Joseph E. Gonzalez Ion Stoica
Date: 2021-05-30 00:00:00
Proceeding: ICRA 2021 5
Tasks: Autonomous Driving, Autonomous Vehicles, CARLA MAP Leaderboard

We present Pylot, a platform for autonomous vehicle (AV) research and development, built with the goal to allow researchers to study the effects of the latency and accuracy of their models and algorithms on the end-to-end driving behavior of an AV. This is achieved through a modular structure enabled by our high-performance dataflow system that represents AV software pipeline components (object detectors, motion planners, etc.) as a dataflow graph of operators which communicate on data streams using timestamped messages. Pylot readily interfaces with popular AV simulators like CARLA, and is easily deployable to real-world vehicles with minimal code changes.To reduce the burden of developing an entire pipeline for evaluating a single component, Pylot provides several state-of-the-art reference implementations for the various components of an AV pipeline. Using these reference implementations, a Pylot-based AV pipeline is able to drive a real vehicle, and attains a high score on the CARLA Autonomous Driving Challenge. We also present several case studies enabled by Pylot, including evidence of a need for context-dependent components, and per-component time allocation. Pylot is open source, with the code available at https://github.com/erdos-project/pylot.
An integrative approach for modeling and simulation of Heterocyst pattern formation in Cyanobacteria strands
Authors: Alejandro Torres-Sanchez, Jesus Gomez-Gardenes, Fernando Falo
Date: 2014-08-05 00:00:00

A comprehensive approach to cellular differentiation in cyanobacteria is developed. To this aim, the process of heterocyst cell formation is studied under a systems biology point of view. By relying on statistical physics techniques, we translate the essential ingredients and mechanisms of the genetic circuit into a set of differential equations that describes the continuous time evolution of combined nitrogen, PatS, HetR and NtcA concentrations. The detailed analysis of these equations gives insight into the single cell dynamics. On the other hand, the inclusion of diffusion and noisy conditions allows simulating the formation of heterocysts patterns in cyanobacteria strains. The time evolution of relevant component concentrations are calculated allowing for a comparison with experiments. Finally, we discuss the validity and the possible improvements of the model.
Pseudo-Stereo for Monocular 3D Object Detection in Autonomous Driving
Authors: Yi-Nan Chen, Hang Dai, Yong Ding
Date: 2022-03-04 00:00:00
Proceeding: CVPR 2022 1
Tasks: 3D Object Detection, Autonomous Driving, Depth Estimation, Image Generation, Monocular 3D Object Detection, object-detection, Object Detection

Pseudo-LiDAR 3D detectors have made remarkable progress in monocular 3D detection by enhancing the capability of perceiving depth with depth estimation networks, and using LiDAR-based 3D detection architectures. The advanced stereo 3D detectors can also accurately localize 3D objects. The gap in image-to-image generation for stereo views is much smaller than that in image-to-LiDAR generation. Motivated by this, we propose a Pseudo-Stereo 3D detection framework with three novel virtual view generation methods, including image-level generation, feature-level generation, and feature-clone, for detecting 3D objects from a single image. Our analysis of depth-aware learning shows that the depth loss is effective in only feature-level virtual view generation and the estimated depth map is effective in both image-level and feature-level in our framework. We propose a disparity-wise dynamic convolution with dynamic kernels sampled from the disparity feature map to filter the features adaptively from a single image for generating virtual image features, which eases the feature degradation caused by the depth estimation errors. Till submission (November 18, 2021), our Pseudo-Stereo 3D detection framework ranks 1st on car, pedestrian, and cyclist among the monocular 3D detectors with publications on the KITTI-3D benchmark. The code is released at https://github.com/revisitq/Pseudo-Stereo-3D.
E-FCNN for tiny facial expression recognition
Authors: Jie Shao, Qiyu Cheng
Date: 2020-08-20 00:00:00
Proceeding: Applied Intelligence 2020 8
Tasks: Facial Expression Recognition, Facial Expression Recognition (FER), Image Super-Resolution, Super-Resolution

As a hot issue in recent years, facial expression recognition(FER) has been widely applied in many fields, but it still
faces great challenges in tiny facial expression recognition. Currently, most of the FER networks only consider images of
ideal sizes. Their recognition accuracy would significantly decrease as the image resolution decreases. However, images
captured by surveillance cameras often contain tiny faces with low-resolution. This paper proposes an edge-aware feedback
convolutional neural network(E-FCNN) for tiny FER, which associates image super-resolution and facial expression
recognition together. To effectively leverage the texture information of faces, we design a novel three-stream super-resolution network, which is embedded with an edge-enhancement block as one branch. The other two branches are the up-sampling branch and SR(Super-Resolution) primary branch. Specifically, visual features are extracted from tiny images based on a hierarchical strategy, and then put into a feedback block with fused results of the three branches. Experiments are performed on down-sampled images in four facial expression datasets: CK+, FER2013, BU-3DFE, RAF-DB. The results demonstrate the favorable performance of our network.
BEATS: An Open-Source, High-Precision, Multi-Channel EEG Acquisition Tool System
Authors: Bing Zou, Yubo Zheng, Mu Shen, Yingying Luo, Lei LI, Lin Zhang
Date: 2022-03-04 00:00:00
Tasks: 4k, Brain Computer Interface, EEG, Electroencephalogram (EEG)

Stable and accurate electroencephalogram (EEG) signal acquisition is fundamental in non-invasive brain-computer interface (BCI) technology. Commonly used EEG acquisition system's hardware and software are usually closed-source. Its inability to flexible expansion and secondary development is a major obstacle to real-time BCI research. This paper presents the Beijing University of Posts and Telecommunications EEG Acquisition Tool System named BEATS. It implements a comprehensive system from hardware to software, composed of the analog front-end, microprocessor, and software platform. BEATS is capable of collecting 32-channel EEG signals at a guaranteed sampling rate of 4k Hz with wireless transmission. Compared to state-of-the-art systems used in many EEG fields, it displays a better sampling rate. Using techniques including direct memory access, first in first out, and timer, the precision and stability of the acquisition are ensured at the microsecond level. An evaluation is conducted during 24 hours of continuous acquisitions. The data loss is 0 packets and the average maximum delay is only 0.07 s/h. Moreover, as an open source system, BEATS provides detailed design files, and adopts a plug-in structure and easy-to-access materials, which makes it can be quickly reproduced. Schematics, source code, and other materials of BEATS are available at https://github.com/buptantEEG/BEATS.
Semantic-Aware Representation Blending for Multi-Label Image Recognition with Partial Labels
Authors: Tao Pu, Tianshui Chen, Hefeng Wu, Liang Lin
Date: 2022-03-04 00:00:00
Tasks: Multi-Label Image Recognition, Multi-label Image Recognition with Partial Labels

Training the multi-label image recognition models with partial labels, in which merely some labels are known while others are unknown for each image, is a considerably challenging and practical task. To address this task, current algorithms mainly depend on pre-training classification or similarity models to generate pseudo labels for the unknown labels. However, these algorithms depend on sufficient multi-label annotations to train the models, leading to poor performance especially with low known label proportion. In this work, we propose to blend category-specific representation across different images to transfer information of known labels to complement unknown labels, which can get rid of pre-training models and thus does not depend on sufficient annotations. To this end, we design a unified semantic-aware representation blending (SARB) framework that exploits instance-level and prototype-level semantic representation to complement unknown labels by two complementary modules: 1) an instance-level representation blending (ILRB) module blends the representations of the known labels in an image to the representations of the unknown labels in another image to complement these unknown labels. 2) a prototype-level representation blending (PLRB) module learns more stable representation prototypes for each category and blends the representation of unknown labels with the prototypes of corresponding labels to complement these labels. Extensive experiments on the MS-COCO, Visual Genome, Pascal VOC 2007 datasets show that the proposed SARB framework obtains superior performance over current leading competitors on all known label proportion settings, i.e., with the mAP improvement of 4.6%, 4.%, 2.2% on these three datasets when the known label proportion is 10%. Codes are available at https://github.com/HCPLab-SYSU/HCP-MLR-PL.
Learning Category-Level Generalizable Object Manipulation Policy via Generative Adversarial Self-Imitation Learning from Demonstrations
Authors: Hao Shen, Weikang Wan, He Wang
Date: 2022-03-04 00:00:00
Tasks: Imitation Learning

Generalizable object manipulation skills are critical for intelligent and multi-functional robots to work in real-world complex scenes. Despite the recent progress in reinforcement learning, it is still very challenging to learn a generalizable manipulation policy that can handle a category of geometrically diverse articulated objects. In this work, we tackle this category-level object manipulation policy learning problem via imitation learning in a task-agnostic manner, where we assume no handcrafted dense rewards but only a terminal reward. Given this novel and challenging generalizable policy learning problem, we identify several key issues that can fail the previous imitation learning algorithms and hinder the generalization to unseen instances. We then propose several general but critical techniques, including generative adversarial self-imitation learning from demonstrations, progressive growing of discriminator, and instance-balancing for expert buffer, that accurately pinpoints and tackles these issues and can benefit category-level manipulation policy learning regardless of the tasks. Our experiments on ManiSkill benchmarks demonstrate a remarkable improvement on all tasks and our ablation studies further validate the contribution of each proposed technique.
HyperTransformer: A Textural and Spectral Feature Fusion Transformer for Pansharpening
Authors: Wele Gedara Chaminda Bandara, Vishal M. Patel
Date: 2022-03-04 00:00:00
Proceeding: CVPR 2022 1
Tasks: Pansharpening, Super-Resolution

Pansharpening aims to fuse a registered high-resolution panchromatic image (PAN) with a low-resolution hyperspectral image (LR-HSI) to generate an enhanced HSI with high spectral and spatial resolution. Existing pansharpening approaches neglect using an attention mechanism to transfer HR texture features from PAN to LR-HSI features, resulting in spatial and spectral distortions. In this paper, we present a novel attention mechanism for pansharpening called HyperTransformer, in which features of LR-HSI and PAN are formulated as queries and keys in a transformer, respectively. HyperTransformer consists of three main modules, namely two separate feature extractors for PAN and HSI, a multi-head feature soft attention module, and a spatial-spectral feature fusion module. Such a network improves both spatial and spectral quality measures of the pansharpened HSI by learning cross-feature space dependencies and long-range details of PAN and LR-HSI. Furthermore, HyperTransformer can be utilized across multiple spatial scales at the backbone for obtaining improved performance. Extensive experiments conducted on three widely used datasets demonstrate that HyperTransformer achieves significant improvement over the state-of-the-art methods on both spatial and spectral quality measures. Implementation code and pre-trained weights can be accessed at https://github.com/wgcban/HyperTransformer.
No More Than 6ft Apart: Robust K-Means via Radius Upper Bounds
Authors: Ahmed Imtiaz Humayun, Randall Balestriero, Anastasios Kyrillidis, Richard Baraniuk
Date: 2022-03-04 00:00:00
Tasks: Clustering

Centroid based clustering methods such as k-means, k-medoids and k-centers are heavily applied as a go-to tool in exploratory data analysis. In many cases, those methods are used to obtain representative centroids of the data manifold for visualization or summarization of a dataset. Real world datasets often contain inherent abnormalities, e.g., repeated samples and sampling bias, that manifest imbalanced clustering. We propose to remedy such a scenario by introducing a maximal radius constraint $r$ on the clusters formed by the centroids, i.e., samples from the same cluster should not be more than $2r$ apart in terms of $\ell_2$ distance. We achieve this constraint by solving a semi-definite program, followed by a linear assignment problem with quadratic constraints. Through qualitative results, we show that our proposed method is robust towards dataset imbalances and sampling artifacts. To the best of our knowledge, ours is the first constrained k-means clustering method with hard radius constraints. Codes at https://bit.ly/kmeans-constrained
Pedestrian Stop and Go Forecasting with Hybrid Feature Fusion
Authors: Dongxu Guo, Taylor Mordan, Alexandre Alahi
Date: 2022-03-04 00:00:00
Tasks: Autonomous Driving, motion prediction, Navigate

Forecasting pedestrians' future motions is essential for autonomous driving systems to safely navigate in urban areas. However, existing prediction algorithms often overly rely on past observed trajectories and tend to fail around abrupt dynamic changes, such as when pedestrians suddenly start or stop walking. We suggest that predicting these highly non-linear transitions should form a core component to improve the robustness of motion prediction algorithms. In this paper, we introduce the new task of pedestrian stop and go forecasting. Considering the lack of suitable existing datasets for it, we release TRANS, a benchmark for explicitly studying the stop and go behaviors of pedestrians in urban traffic. We build it from several existing datasets annotated with pedestrians' walking motions, in order to have various scenarios and behaviors. We also propose a novel hybrid model that leverages pedestrian-specific and scene features from several modalities, both video sequences and high-level attributes, and gradually fuses them to integrate multiple levels of context. We evaluate our model and several baselines on TRANS, and set a new benchmark for the community to work on pedestrian stop and go forecasting.
AutoDIME: Automatic Design of Interesting Multi-Agent Environments
Authors: Ingmar Kanitscheider, Harri Edwards
Date: 2022-03-04 00:00:00
Tasks: Diagnostic, MuJoCo, Value prediction

Designing a distribution of environments in which RL agents can learn interesting and useful skills is a challenging and poorly understood task, for multi-agent environments the difficulties are only exacerbated. One approach is to train a second RL agent, called a teacher, who samples environments that are conducive for the learning of student agents. However, most previous proposals for teacher rewards do not generalize straightforwardly to the multi-agent setting. We examine a set of intrinsic teacher rewards derived from prediction problems that can be applied in multi-agent settings and evaluate them in Mujoco tasks such as multi-agent Hide and Seek as well as a diagnostic single-agent maze task. Of the intrinsic rewards considered we found value disagreement to be most consistent across tasks, leading to faster and more reliable emergence of advanced skills in Hide and Seek and the maze task. Another candidate intrinsic reward considered, value prediction error, also worked well in Hide and Seek but was susceptible to noisy-TV style distractions in stochastic environments. Policy disagreement performed well in the maze task but did not speed up learning in Hide and Seek. Our results suggest that intrinsic teacher rewards, and in particular value disagreement, are a promising approach for automating both single and multi-agent environment design.
Cooperative Task and Motion Planning for Multi-Arm Assembly Systems
Authors: Jingkai Chen, Jiaoyang Li, Yijiang Huang, Caelan Garrett, Dawei Sun, Chuchu Fan, Andreas Hofmann, Caitlin Mueller, Sven Koenig, Brian C. Williams
Date: 2022-03-04 00:00:00
Tasks: Motion Planning, Multi-Agent Path Finding, Task and Motion Planning

Multi-robot assembly systems are becoming increasingly appealing in manufacturing due to their ability to automatically, flexibly, and quickly construct desired structural designs. However, effectively planning for these systems in a manner that ensures each robot is simultaneously productive, and not idle, is challenging due to (1) the close proximity that the robots must operate in to manipulate the structure and (2) the inherent structural partial orderings on when each part can be installed. In this paper, we present a task and motion planning framework that jointly plans safe, low-makespan plans for a team of robots to assemble complex spatial structures. Our framework takes a hierarchical approach that, at the high level, uses Mixed-integer Linear Programs to compute an abstract plan comprised of an allocation of robots to tasks subject to precedence constraints and, at the low level, builds on a state-of-the-art algorithm for Multi-Agent Path Finding to plan collision-free robot motions that realize this abstract plan. Critical to our approach is the inclusion of certain collision constraints and movement durations during high-level planning, which better informs the search for abstract plans that are likely to be both feasible and low-makespan while keeping the search tractable. We demonstrate our planning system on several challenging assembly domains with several (sometimes heterogeneous) robots with grippers or suction plates for assembling structures with up to 23 objects involving Lego bricks, bars, plates, or irregularly shaped blocks.