pwc-archive/papers-with-abstracts huggingface dataset:
Lightweight UAV-based Measurement System for Air-to-Ground Channels at 28 GHz
Authors: Vasilii Semkin, Seongjoon Kang, Jaakko Haarla, William Xia, Ismo Huhtinen, Giovanni Geraci, Angel Lozano, Giuseppe Loianno, Marco Mezzavilla, Sundeep Rangan
Date: 2021-03-31 00:00:00

Wireless communication at millimeter wave frequencies has attracted considerable attention for the delivery of high-bit-rate connectivity to unmanned aerial vehicles (UAVs). However, conducting the channel measurements necessary to assess communication at these frequencies has been challenging due to the severe payload and power restrictions in commercial UAVs. This work presents a novel lightweight (approximately 1.3 kg) channel measurement system at 28 GHz installed on a commercially available UAV. A ground transmitter equipped with a horn antenna conveys sounding signals to a UAV equipped with a lightweight spectrum analyzer. We demonstrate that the measurements can be highly influenced by the antenna pattern as shaped by the UAV's frame. A calibration procedure is presented to correct for the resulting angular variations in antenna gain. The measurement setup is then validated on real flights from an airstrip at distances in excess of 300 m.
Low-dimensional Denoising Embedding Transformer for ECG Classification
Authors: Jian Guan, Wenbo Wang, Pengming Feng, Xinxin Wang, Wenwu Wang
Date: 2021-03-31 00:00:00
Tasks: Classification, Denoising, ECG Classification, General Classification

The transformer based model (e.g., FusingTF) has been employed recently for Electrocardiogram (ECG) signal classification. However, the high-dimensional embedding obtained via 1-D convolution and positional encoding can lead to the loss of the signal's own temporal information and a large amount of training parameters. In this paper, we propose a new method for ECG classification, called low-dimensional denoising embedding transformer (LDTF), which contains two components, i.e., low-dimensional denoising embedding (LDE) and transformer learning. In the LDE component, a low-dimensional representation of the signal is obtained in the time-frequency domain while preserving its own temporal information. And with the low dimensional embedding, the transformer learning is then used to obtain a deeper and narrower structure with fewer training parameters than that of the FusingTF. Experiments conducted on the MIT-BIH dataset demonstrates the effectiveness and the superior performance of our proposed method, as compared with state-of-the-art methods.
Green IoT using UAVs in B5G Networks: A Review of Applications and Strategies
Authors: S. H. Alsamhi, Fatemeh Afghah, Radhya Sahal, Ammar Hawbani, A. A. Al-qaness, B. Lee, Mohsen Guizani
Date: 2021-03-31 00:00:00
Tasks: Management, Survey

Unmanned Aerial Vehicles (UAVs) present a promising advanced technology that can enhance people life quality and smartness of cities dramatically and increase overall economic efficiency. UAVs have attained a significant interest in supporting many applications such as surveillance, agriculture, communication, transportation, pollution monitoring, disaster management, public safety, healthcare, and environmental preservation. Industry 4.0 applications are conceived of intelligent things that can automatically and collaboratively improve beyond 5G (B5G). Therefore, the Internet of Things (IoT) is required to ensure collaboration between the vast multitude of things efficiently anywhere in real-world applications that are monitored in real-time. However, many IoT devices consume a significant amount of energy when transmitting the collected data from surrounding environments. Due to a drone's capability to fly closer to IoT, UAV technology plays a vital role in greening IoT by transmitting collected data to achieve a sustainable, reliable, eco-friendly Industry 4.0. This survey presents an overview of the techniques and strategies proposed recently to achieve green IoT using UAVs infrastructure for a reliable and sustainable smart world. This survey is different from other attempts in terms of concept, focus, and discussion. Finally, various use cases, challenges, and opportunities regarding green IoT using UAVs are presented.
Weighted SPICE Algorithms for Range-Doppler Imaging Using One-Bit Automotive Radar
Authors: Xiaolei Shang, Jian Li, Petre Stoica
Date: 2021-03-31 00:00:00
Tasks: parameter estimation

We consider the problem of range-Doppler imaging using one-bit automotive LFMCW1 or PMCW radar that utilizes one-bit ADC sampling with time-varying thresholds at the receiver. The one-bit sampling technique can significantly reduce the cost as well as the power consumption of automotive radar systems. We formulate the one-bit LFMCW/PMCW radar rangeDoppler imaging problem as one-bit sparse parameter estimation. The recently proposed hyperparameter-free (and hence user friendly) weighted SPICE algorithms, including SPICE, LIKES, SLIM and IAA, achieve excellent parameter estimation performance for data sampled with high precision. However, these algorithms cannot be used directly for one-bit data. In this paper we first present a regularized minimization algorithm, referred to as 1bSLIM, for accurate range-Doppler imaging using onebit radar systems. Then, we describe how to extend the SPICE, LIKES and IAA algorithms to the one-bit data case, and refer to these extensions as 1bSPICE, 1bLIKES and 1bIAA. These onebit hyperparameter-free algorithms are unified within the one-bit weighted SPICE framework. Moreover, efficient implementations of the aforementioned algorithms are investigated that rely heavily on the use of FFTs. Finally, both simulated and experimental examples are provided to demonstrate the effectiveness of the proposed algorithms for range-Doppler imaging using one-bit automotive radar systems.
Delay Analysis of Wireless Federated Learning Based on Saddle Point Approximation and Large Deviation Theory
Authors: Lintao Li, Longwei Yang, Xin Guo, Yuanming Shi, Haiming Wang, Wei Chen, Khaled B. Letaief
Date: 2021-03-31 00:00:00
Tasks: Federated Learning

Federated learning (FL) is a collaborative machine learning paradigm, which enables deep learning model training over a large volume of decentralized data residing in mobile devices without accessing clients' private data. Driven by the ever increasing demand for model training of mobile applications or devices, a vast majority of FL tasks are implemented over wireless fading channels. Due to the time-varying nature of wireless channels, however, random delay occurs in both the uplink and downlink transmissions of FL. How to analyze the overall time consumption of a wireless FL task, or more specifically, a FL's delay distribution, becomes a challenging but important open problem, especially for delay-sensitive model training. In this paper, we present a unified framework to calculate the approximate delay distributions of FL over arbitrary fading channels. Specifically, saddle point approximation, extreme value theory (EVT), and large deviation theory (LDT) are jointly exploited to find the approximate delay distribution along with its tail distribution, which characterizes the quality-of-service of a wireless FL system. Simulation results will demonstrate that our approximation method achieves a small approximation error, which vanishes with the increase of training accuracy.
UAV Deployment Optimization for Secure Precise Wireless Transmission
Authors: Tong Shen, Guiyang Xia, Jingjing Ye, Lichuan Gu, Xiaobo Zhou, Feng Shu
Date: 2021-03-31 00:00:00
Tasks: Position

This paper develops an unmanned aerial vehicle (UAV) deployment scheme in the context of the directional modulation-based secure precise wireless transmissions (SPWTs), where the optimal UAV position for the SPWT is derived to maximum the secrecy rate (SR) without injecting any artificial noise (AN) signaling. To be specific, the proposed scheme reveals that the optimal position of UAV for maximizing the SR performance has to be placed at the null space of Eves channel, which impels the received energy of the confidential message at the unintended receiver deteriorating to zero whilst benefits the one at the intended receiver achieving its maximum value. Finally, simulation results verify the optimality of our proposed scheme in terms of the achievable SR performance.
Joint Channel Estimation and Synchronization Techniques for Time Interleaved Block Windowed Burst OFDM
Authors: João Martins, Filipe Conceição, Marco Gomes, Vitor Silva, Rui Dinis
Date: 2021-03-30 00:00:00

From a conceptual perspective, 5G technology promises to deliver low latency, high data rate and more reliable connections for the next generations of communication systems. To face these demands, modulation schemes based on Orthogonal Frequency Domain Multiplexing (OFDM) can accommodate these requirements for wireless systems. On the other hand, several hybrid OFDM-based systems such as the Time-Interleaved Block Windowed Burst Orthogonal Frequency Division Multiplexing (TIBWB-OFDM) are capable of achieving even better spectral confinement and power efficiency. This paper addresses to the implementation of the TIBWB-OFDM system in a more realistic and practical wireless link scenarios by addressing the challenges of proper and reliable channel estimation and frame synchronization. We propose to incorporate a preamble formed by optimum correlation training sequences, such as the Zadoff-Chu (ZC) sequences. The added ZC preamble sequence is used to jointly estimate the frame beginning, through signal correlation strategies and a threshold decision device, and acquire the channel state information (CSI), by employing estimators based on the preamble sequence and the transmitted data. The employed receiver estimators show that it is possible to detect the TIBWB-OFDM frame beginning and provide a close BER performance comparatively to the one where the perfect channel is known.
Functional and spatial rewiring jointly generate convergent-divergent units in self-organizing networks
Authors: Jia Li, Ilias Rentzeperis, Cees van Leeuwen
Date: 2021-03-30 00:00:00
Tasks: Sensitivity

Self-organization through adaptive rewiring of random neural networks generates brain-like topologies comprising modular small-world structures with rich club effects, merely as the product of optimizing the network topology. In the nervous system, spatial organization is optimized no less by rewiring, through minimizing wiring distance and maximizing spatially aligned wiring layouts. We show that such spatial organization principles interact constructively with adaptive rewiring, contributing to establish the networks' connectedness and modular structures. We use an evolving neural network model with weighted and directed connections, in which neural traffic flow is based on consensus and advection dynamics, to show that wiring cost minimization supports adaptive rewiring in creating convergent-divergent unit structures. Convergent-divergent units consist of a convergent input-hub, connected to a divergent output-hub via subnetworks of intermediate nodes, which may function as the computational core of the unit. The prominence of minimizing wiring distance in the dynamic evolution of the network determines the extent to which the core is encapsulated from the rest of the network, i.e., the context-sensitivity of its computations. This corresponds to the central role convergent-divergent units play in establishing context-sensitivity in neuronal information processing.
Does Double Biofeedback Affect Functional Hemispheric Asymmetry and Activity? A Pilot Study
Authors: Valeriia Demareva, Elena Mukhina, Tatiana Bobro
Date: 2021-03-30 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

In the current pilot study, we attempt to find out how double neurofeedback influences functional hemispheric asymmetry and activity. We examined 30 healthy participants (8 males; 22 females, mean age = 29; SD = 8). To measure functional hemispheric asymmetry and activity, we used computer laterometry in the "two-source" lead-lag dichotic paradigm. Double biofeedback included 8 min of EEG oscillation recording with five minutes of basic mode. During the basic mode, the current amplitude of the EEG oscillator gets transformed into feedback sounds while the current amplitude of alpha EEG oscillator is used to modulate the intensity of light signals. Double neurofeedback did not directly influence the asymmetry itself but accelerated individual sound perception characteristics during dichotic listening in the preceding effect paradigm. Further research is needed to investigate the effect of double neurofeedback training on functional brain activity and asymmetry, taking into account participants' age, gender, and motivation.
The Sixth Law of Stupidity: A Biophysical Interpretation of Carlo Cipolla's Stupidity Laws
Authors: Ilaria Perissi, Ugo Bardi
Date: 2021-03-31 00:00:00

Carlo Cipolla's stupidity quadrant and his five laws of stupidity were proposed for the first time in 1976. Exposed in a humorous mood by the author, these concepts nevertheless describe very serious features of the interactions among human beings. Here, we propose a new interpretation of Cipolla's ideas in a biophysical framework, using the well-known predator-prey or "Lotka-Volterra" model. We find that there is indeed a correspondence between Cipolla's approach, based on economics, and biophysical economics. On the basis of this examination, we propose a sixth law of stupidity, additional to the five proposed by Cipolla. The law states that humans are the stupidest species in the ecosystem.
A survey of electricity spot and futures price models for risk management applications
Authors: Thomas Deschatre, Olivier Féron, Pierre Gruet
Date: 2021-03-31 00:00:00
Tasks: Management

This review presents the set of electricity price models proposed in the literature since the opening of power markets. We focus on price models applied to financial pricing and risk management. We classify these models according to their ability to represent the random behavior of prices and some of their characteristics. In particular, this classification helps users to choose among the most suitable models for their risk management problems.
Optimal Retirement Time and Consumption with the Variation in Habitual Persistence
Authors: Lin He, Zongxia Liang, Yilun Song, Qi Ye
Date: 2021-03-31 00:00:00
Tasks: Sensitivity

In this paper,we study the individual's optimal retirement time and optimal consumption under habitual persistence. Because the individual feels equally satisfied with a lower habitual level and is more reluctant to change the habitual level after retirement, we assume that both the level and the sensitivity of the habitual consumption decline at the time of retirement. We establish the concise form of the habitual evolutions, and obtain the optimal retirement time and consumption policy based on martingale and duality methods. The optimal consumption experiences a sharp decline at retirement, but the excess consumption raises because of the reduced sensitivity of the habitual level. This result contributes to explain the "retirement consumption puzzle". Particularly, the optimal retirement and consumption policies are balanced between the wealth effect and the habitual effect. Larger wealth increases consumption, and larger growth inertia (sensitivity) of the habitual level decreases consumption and brings forward the retirement time.
GAN-based Anomaly Detection in Imbalance Problems
Authors: Junbong Kim, Kwanghee Jeong, Hyomin Choi, and Kisung Seo
Date: 2020-08-28 00:00:00
Tasks: Anomaly Detection, Defect Detection, Generative Adversarial Network, object-detection, Object Detection

Imbalance problems in object detection are one of the key
issues that affect the performance greatly. Our focus in this work is to
address an imbalance problem arising from defect detection in industrial inspections, including the different number of defect and non-defect
dataset, the gap of distribution among defect classes, and various sizes
of defects. To this end, we adopt the anomaly detection method that is
to identify unusual patterns to address such challenging problems. Especially generative adversarial network (GAN) and autoencoder-based
approaches have shown to be effective in this field. In this work, 1) we
propose a novel GAN-based anomaly detection model which consists of
an autoencoder as the generator and two separate discriminators for
each of normal and anomaly input; and 2) we also explore a way to effectively optimize our model by proposing new loss functions: Patch loss and
Anomaly adversarial loss, and further combining them to jointly train
the model. In our experiment, we evaluate our model on conventional
benchmark datasets such as MNIST, Fashion MNIST, CIFAR 10/100
data as well as on real-world industrial dataset – smartphone case defects. Finally, experimental results demonstrate the effectiveness of our
approach by showing the results of outperforming the current State-OfThe-Art approaches in terms of the average area under the ROC curve
(AUROC).
Learnable MIMO Detection Networks Based on Inexact ADMM
Authors: Minsik Kim, Daeyoung Park
Date: 2020-10-01 00:00:00
Tasks: Decoder

In this article, we present a new iterative MIMO detection algorithm based on inexact alternating direction method of multipliers. Each iteration is considered as a neural network layer with learnable parameters, which are optimized by the stochastic gradient descent algorithm with a training data set of the received vectors and the ground truth transmitted signals. Numerical results show that the proposed algorithm outperforms the existing learnable detection network and it achieves near-optimal performance close to the sphere decoder in the case of a large number of receive antennas.
PiCIE: Unsupervised Semantic Segmentation using Invariance and Equivariance in Clustering
Authors: Jang Hyun Cho, Utkarsh Mall, Kavita Bala, Bharath Hariharan
Date: 2021-03-30 00:00:00
Proceeding: CVPR 2021 1
Tasks: Clustering, Inductive Bias, Semantic Segmentation, Unsupervised Semantic Segmentation

We present a new framework for semantic segmentation without annotations via clustering. Off-the-shelf clustering methods are limited to curated, single-label, and object-centric images yet real-world data are dominantly uncurated, multi-label, and scene-centric. We extend clustering from images to pixels and assign separate cluster membership to different instances within each image. However, solely relying on pixel-wise feature similarity fails to learn high-level semantic concepts and overfits to low-level visual cues. We propose a method to incorporate geometric consistency as an inductive bias to learn invariance and equivariance for photometric and geometric variations. With our novel learning objective, our framework can learn high-level semantic concepts. Our method, PiCIE (Pixel-level feature Clustering using Invariance and Equivariance), is the first method capable of segmenting both things and stuff categories without any hyperparameter tuning or task-specific pre-processing. Our method largely outperforms existing baselines on COCO and Cityscapes with +17.5 Acc. and +4.5 mIoU. We show that PiCIE gives a better initialization for standard supervised training. The code is available at https://github.com/janghyuncho/PiCIE.
Dense Relation Distillation with Context-aware Aggregation for Few-Shot Object Detection
Authors: Hanzhe Hu, Shuai Bai, Aoxue Li, Jinshi Cui, LiWei Wang
Date: 2021-03-30 00:00:00
Proceeding: CVPR 2021 1
Tasks: Few-Shot Object Detection, Meta-Learning, Object, object-detection, Object Detection, Relation

Conventional deep learning based methods for object detection require a large amount of bounding box annotations for training, which is expensive to obtain such high quality annotated data. Few-shot object detection, which learns to adapt to novel classes with only a few annotated examples, is very challenging since the fine-grained feature of novel object can be easily overlooked with only a few data available. In this work, aiming to fully exploit features of annotated novel object and capture fine-grained features of query object, we propose Dense Relation Distillation with Context-aware Aggregation (DCNet) to tackle the few-shot detection problem. Built on the meta-learning based framework, Dense Relation Distillation module targets at fully exploiting support features, where support features and query feature are densely matched, covering all spatial locations in a feed-forward fashion. The abundant usage of the guidance information endows model the capability to handle common challenges such as appearance changes and occlusions. Moreover, to better capture scale-aware features, Context-aware Aggregation module adaptively harnesses features from different scales for a more comprehensive feature representation. Extensive experiments illustrate that our proposed approach achieves state-of-the-art results on PASCAL VOC and MS COCO datasets. Code will be made available at https://github.com/hzhupku/DCNet.
Bayes' Bluff: Opponent Modelling in Poker
Authors: Finnegan Southey, Michael P. Bowling, Bryce Larson, Carmelo Piccione, Neil Burch, Darse Billings, Chris Rayner
Date: 2012-07-04 00:00:00

Poker is a challenging problem for artificial intelligence, with non-deterministic dynamics, partial observability, and the added difficulty of unknown adversaries. Modelling all of the uncertainties in this domain is not an easy task. In this paper we present a Bayesian probabilistic model for a broad class of poker games, separating the uncertainty in the game dynamics from the uncertainty of the opponent's strategy. We then describe approaches to two key subproblems: (i) inferring a posterior over opponent strategies given a prior distribution and observations of their play, and (ii) playing an appropriate response to that distribution. We demonstrate the overall approach on a reduced version of poker using Dirichlet priors and then on the full game of Texas hold'em using a more informed prior. We demonstrate methods for playing effective responses to the opponent, based on the posterior.
Active Learning of Inverse Models with Intrinsically Motivated Goal Exploration in Robots
Authors: Adrien Baranes, Pierre-Yves Oudeyer
Date: 2013-01-21 00:00:00
Tasks: Active Learning

We introduce the Self-Adaptive Goal Generation - Robust Intelligent Adaptive Curiosity (SAGG-RIAC) architecture as an intrinsi- cally motivated goal exploration mechanism which allows active learning of inverse models in high-dimensional redundant robots. This allows a robot to efficiently and actively learn distributions of parameterized motor skills/policies that solve a corresponding distribution of parameterized tasks/goals. The architecture makes the robot sample actively novel parameterized tasks in the task space, based on a measure of competence progress, each of which triggers low-level goal-directed learning of the motor policy pa- rameters that allow to solve it. For both learning and generalization, the system leverages regression techniques which allow to infer the motor policy parameters corresponding to a given novel parameterized task, and based on the previously learnt correspondences between policy and task parameters. We present experiments with high-dimensional continuous sensorimotor spaces in three different robotic setups: 1) learning the inverse kinematics in a highly-redundant robotic arm, 2) learning omnidirectional locomotion with motor primitives in a quadruped robot, 3) an arm learning to control a fishing rod with a flexible wire. We show that 1) exploration in the task space can be a lot faster than exploration in the actuator space for learning inverse models in redundant robots; 2) selecting goals maximizing competence progress creates developmental trajectories driving the robot to progressively focus on tasks of increasing complexity and is statistically significantly more efficient than selecting tasks randomly, as well as more efficient than different standard active motor babbling methods; 3) this architecture allows the robot to actively discover which parts of its task space it can learn to reach and which part it cannot.