pwc-archive/papers-with-abstracts huggingface dataset:
OpenVVC: a Lightweight Software Decoder for the Versatile Video Coding Standard
Authors: Thomas Amestoy, Pierre-loup Cabarat, Guillaume Gautier, Wassim Hamidouche, Daniel Menard
Date: 2022-05-24 00:00:00
Tasks: Decoder

In the recent years, users requirements for higher resolution, coupled with the apparition of new multimedia applications, have created the need for a new video coding standard. The new generation video coding standard, called Versatile Video Coding (VVC), has been developed by the Joint Video Experts Team, and offers coding capability beyond the previous generation High Efficiency Video Coding (HEVC) standard. Due to the incorporation of more advanced and complex tools, the decoding complexity of VVC standard compared to HEVC has approximately doubled. This complexity increase raises new research challenges to achieve live software decoding. In this context, we developed OpenVVC, an open-source software decoder that supports a broad range of VVC functionalities. This paper presents the OpenVVC software architecture, its parallelism strategy as well as a detailed set of experimental results. By combining extensive data level parallelism with frame level parallelism, OpenVVC achieves real-time decoding of UHD video content. Moreover, the memory required by OpenVVC is remarkably low, which presents a great advantage for its integration on embedded platforms with low memory resources. The code of the OpenVVC decoder is publicly available at https://github.com/OpenVVC/OpenVVC
Towards Real-World 6G Drone Communication: Position and Camera Aided Beam Prediction
Authors: Gouranga Charan, Andrew Hredzak, Christian Stoddard, Benjamin Berrey, Madhav Seth, Hector Nunez, Ahmed Alkhateeb
Date: 2022-05-24 00:00:00
Tasks: Beam Prediction, Position

Millimeter-wave (mmWave) and terahertz (THz) communication systems typically deploy large antenna arrays to guarantee sufficient receive signal power. The beam training overhead associated with these arrays, however, make it hard for these systems to support highly-mobile applications such as drone communication. To overcome this challenge, this paper proposes a machine learning-based approach that leverages additional sensory data, such as visual and positional data, for fast and accurate mmWave/THz beam prediction. The developed framework is evaluated on a real-world multi-modal mmWave drone communication dataset comprising of co-existing camera, practical GPS, and mmWave beam training data. The proposed sensing-aided solution achieves a top-1 beam prediction accuracy of 86.32% and close to 100% top-3 and top-5 accuracies, while considerably reducing the beam training overhead. This highlights a promising solution for enabling highly mobile 6G drone communications.
A Novel LFM Waveform for Terahertz-Band Joint Radar and Communications over Inter-Satellite Links
Authors: Gizem Sümen, Güneş Karabulut Kurt, Ali Görçin
Date: 2022-05-24 00:00:00

There is no doubt that we need to keep our eyes on the sky as satellite networks aim to address the demands of 6G and beyond communications systems. On the other hand, the existence of millions of space debris pieces, large or small, poses a threat to the new space communications systems which consist of large number of small satellites, especially in the low-orbit. In this study, a dual-functioning pulsed linear frequency modulated (LFM) waveform at Terahertz (THz) bands is proposed for both wireless communications and space debris sensing over low-orbit inter-satellite links (ISLs). Initially, the ambiguity function of the proposed waveform is derived. Then, velocity and range estimation performance for the radar function and bit error rate performance for the communications function are investigated. Simulation results indicate significant performance gains in the THz-bands compared to the legacy LFM systems.
Reconfigurable Intelligent Surfaces for Energy Efficiency in Full-duplex Communication System
Authors: Yiru Wang, Pengxin Guan, Hongkang Yu, Yuping Zhao
Date: 2022-05-24 00:00:00

In this letter, we study the reconfigurable intelligent surfaces (RIS) aided full-duplex (FD) communication system. By jointly designing the active beamforming of two multi-antenna sources and passive beamforming of RIS, we aim to maximize the energy efficiency of the system, where extra self-interference cancellation power consumption in FD system is also considered. We divide the optimization problem into active and passive beamforming design subproblems, and adopt the alternative optimization framework to solve them iteratively. Dinkelbach's method is used to tackle the fractional objective function in active beamforming problem. Penalty method and successive convex approximation are exploited for passive beamforming design. Simulation results show the energy efficiency of our scheme outperforms other benchmarks.
Sharp Analysis of RLS-based Digital Precoder with Limited PAPR in Massive MIMO
Authors: Xiuxiu Ma, Abla Kammoun, Ayed M. Alrashdi, Tarig Ballal, Tareq Y. Al-Naffouri, Mohamed-Slim Alouini
Date: 2022-05-24 00:00:00

This paper focuses on the performance analysis of a class of limited peak-to-average power ratio (PAPR) precoders for downlink multi-user massive multiple-input multiple-output (MIMO) systems. Contrary to conventional precoding approaches based on simple linear precoders such as maximum ratio transmission (MRT) and regularized zero-forcing (RZF), the precoders in this paper are obtained by solving a convex optimization problem. To be specific, these precoders are designed so that the power of each precoded symbol entry is restricted, and the PAPR at each antenna is tunable. By using the Convex Gaussian Min-max Theorem (CGMT), we analytically characterize the empirical distribution of the precoded vector and the joint empirical distribution between the distortion and the intended symbol vector. This allows us to study the performance of these precoders in terms of per-antenna power, per-user distortion power, signal-to-noise and distortion ratio (SINAD), and bit error probability. We show that for this class of precoders, there is an optimal transmit per-antenna power that maximizes the system performance in terms of SINAD and bit error probability.
IRS Phase-Shift Feedback Overhead-Aware Model Based on Rank-One Tensor Approximation
Authors: Bruno Sokal, Paulo R. B. Gomes, André L. F. de Almeida, Behrooz Makki, Gabor Fodor
Date: 2022-05-24 00:00:00

In this paper, we propose a rank-one tensor modeling approach that yields a compact representation of the optimum IRS phase-shift vector for reducing the feedback overhead. The main idea consists of factorizing the IRS phase-shift vector as a Kronecker product of smaller vectors, namely factors. The proposed phase-shift model allows the network to trade-off between achievable data rate and feedback reduction by controling the factorization parameters. Our simulations show that the proposed phase-shift factorization drastically reduces the feedback overhead, while improving the data rate in some scenarios, compared to the state-of-the-art schemes.
Neural Contextual Bandits Based Dynamic Sensor Selection for Low-Power Body-Area Networks
Authors: Berken Utku Demirel, Luke Chen, Mohammad Abdullah Al Faruque
Date: 2022-05-24 00:00:00
Tasks: Anomaly Detection, Multi-Armed Bandits

Providing health monitoring devices with machine intelligence is important for enabling automatic mobile healthcare applications. However, this brings additional challenges due to the resource scarcity of these devices. This work introduces a neural contextual bandits based dynamic sensor selection methodology for high-performance and resource-efficient body-area networks to realize next generation mobile health monitoring devices. The methodology utilizes contextual bandits to select the most informative sensor combinations during runtime and ignore redundant data for decreasing transmission and computing power in a body area network (BAN). The proposed method has been validated using one of the most common health monitoring applications: cardiac activity monitoring. Solutions from our proposed method are compared against those from related works in terms of classification performance and energy while considering the communication energy consumption. Our final solutions could reach $78.8\%$ AU-PRC on the PTB-XL ECG dataset for cardiac abnormality detection while decreasing the overall energy consumption and computational energy by $3.7 \times$ and $4.3 \times$, respectively.
Vehicular Connectivity on Complex Trajectories: Roadway-Geometry Aware ISAC Beam-tracking
Authors: Xiao Meng, Fan Liu, Christos Masouros, Weijie Yuan, Qixun Zhang, Zhiyong Feng
Date: 2022-05-24 00:00:00
Tasks: Integrated sensing and communication, ISAC

In this paper, we propose sensing-assisted beamforming designs for vehicles on arbitrarily shaped roads by relying on integrated sensing and communication (ISAC) signalling.Specifically, we aim to address the limitations of conventional ISAC beam-tracking schemes that do not apply to complex road geometries. To improve the tracking accuracy and communication quality of service (QoS) in vehicle to infrastructure (V2I) networks, it is essential to model the complicated roadway geometry. To that end, we impose the curvilinear coordinate system (CCS) in an interacting multiple model extended Kalman filter (IMM-EKF) framework. By doing so, both the position and the motion of the vehicle on a complicated road can be explicitly modeled and precisely tracked attributing to the benefits from the CCS. Furthermore, an optimization problem is formulated to maximize the array gain through dynamically adjusting the array size and thereby controlling the beamwidth, which takes the performance loss caused by beam misalignment into account.Numerical simulations demonstrate that the roadway geometry-aware ISAC beamforming approach outperforms the communication-only based and ISAC kinematic-only based technique in the tracking performance. Moreover, the effectiveness of the dynamic beamwidth design is also verified by our numerical results.
Handling model risk with XVAs
Authors: Cyril Bénézet, Stéphane Crépey
Date: 2022-05-24 00:00:00
Tasks: model

In this paper we revisit Burnett (2021) \& Burnett and Williams (2021)'s notion of hedging valuation adjustment (HVA), originally intended to deal with dynamic hedging frictions such as transaction costs, in the direction of model risk. The corresponding HVA reconciles a global fair valuation model with the local models used by the different desks of the bank. Model risk and dynamic hedging frictions indeed deserve a reserve, but a risk-adjusted one, so not only an HVA, but also a contribution to the KVA of the bank. The orders of magnitude of the effects involved suggest that local models should not so much be managed via reserves, as excluded altogether.
First Report of Susceptibility Status of the Invasive Vector: Aedes albopictus to insecticides used in vector control in Morocco
Authors: Souhail Aboulfadl, Fouad Mellouki, Btissam Ameur, Chafika Faraj
Date: 2022-05-22 00:00:00
Tasks: Diagnostic, Management

Aedes albopictus has been newly recorded in Agdal district at Rabat in Morocco. The establishment of this invasive mosquito could affect the public health by causing serious epidemics despite of its high nuisance in urban and sub-urban areas. Vector control is mainly based on environmental management but chemical insecticides can be used to reduce adult mosquito densities during peak periods. However, the level of susceptibility of this mosquito to insecticides has not been studied yet in Morocco. This paper reports the results of the first study conducted to monitor the insecticide resistance of adult and larva Ae. albopictus to the insecticides currently used in the vector control. The study was carried out during May-June 2018 at Rabat from the north-west of the country. Adult susceptibility tests were performed following the WHO test procedures. One organochlorate (DDT 4%), one pyrethroids (cyfluthrin 0.15%), one carbamate (bendiocarb 0.1%) and one organophosphate (fenithrothion 1%) were tested at diagnostic doses (DD). The five-fold DD of bendiocarb were also used to yield information on the intensity of mosquito adult resistance. The results of the performed susceptibility bioassay showed that the vector is susceptible to cyfluthrin and resistant to DDT, bendiocarb and fenithrothion. Larval bioassays to temephos were conducted according to WHO standard operating protocol to establish the dose-mortality relationship and deduct the LC50 and LC90 then resistance ratios. We show that larval populations of Ae. albopictus are still sensible to this insecticide. This information could help policy-makers to plan insecticide resistance management.
Extended molt phenology models improve inferences about molt duration and timing
Authors: Philipp H. Boersch-Supan, Hugh J. Hanmer, Robert A. Robinson
Date: 2022-05-24 00:00:00
Tasks: Bayesian Inference

Molt is an essential life-history event in birds and many mammals, as maintenance of feathers and fur is critical for survival. Despite this molt remains an understudied life-history event. Non-standard statistical techniques are required to estimate the phenology of molt from observations of plumage or pelage state, and existing molt phenology models have strict sampling requirements which can be difficult to meet under real-world conditions. We present an extended modelling framework that can accommodate features of real-world molt datasets, such re-encounters of individuals, misclassified molt states, and/or molt state-dependent sampling bias. We demonstrate that such features can lead to biased inferences when using existing molt phenology models, and show that our model extensions can improve inferences about molt phenology under a wide range of sampling conditions. We hope that our novel modelling framework removes barriers for modelling molt phenology data from realworld datasets and thereby further facilitates the uptake of appropriate statistical methods for such data. Although we focus on molt, the modelling framework is applicable to other phenological processes which can be recorded using either ordered categories or approximately linear progress scores.
Resource allocation determines alternate cell fate in Bistable Genetic Switch
Authors: Priya Chakraborty, Sayantari Ghosh
Date: 2022-05-24 00:00:00

Living cells need a constant availability of certain resources to have a sustained gene expression process. Limited availability of cellular resources for gene expression, like ribosomes, along with a variation of resource affinity, significantly modifies the system dynamics. Factors like the variation in rate of binding, or variation in efficiency of the recruited resource have the potential to affect crucial dynamical phenomena like cell fate determination. In this paper, we have taken a very important motif, a bistable genetic toggle switch, and explored the effect of resource imbalance in this circuit in terms of the bifurcations taking place. We show that initial asymmetric biasing to resource via resource affinity or gene copy number, significantly modifies the cell fate transition, both in pitchfork and saddle node type bifurcation. Our study establishes that in a limited resource environment, controlled resource allocation can be an important factor for robust functioning of the synthetic or cellular genetic switches.
Influence of lung physical properties on its flow--volume curves using a detailed multi-scale mathematical model of the lung
Authors: Riccardo Di Dio, Michaël Brunengo, Benjamin Mauroy
Date: 2022-05-24 00:00:00

We develop a mathematical model of the lung that can estimate independently the air flows and pressures in the upper bronchi. It accounts for the lung multi-scale properties and for the air-tissue interactions. The model equations are solved using the Discrete Fourier Transform, which allows quasi instantaneous solving, in the limit of the model hypotheses. With this model, we explore how the air flow--volume curves are affected by airways obstruction or by change in lung compliance. Our work suggests that a fine analysis of the flow-volume curves might bring information about the inner phenomena occurring in the lung.
Static Replication of Impermanent Loss for Concentrated Liquidity Provision in Decentralised Markets
Authors: Jun Deng, Hua Zong, Yun Wang
Date: 2022-05-24 00:00:00

This article analytically characterizes the impermanent loss of concentrated liquidity provision for automatic market makers in decentralised markets such as Uniswap. We propose two static replication formulas for the impermanent loss by a combination of European calls or puts with strike prices supported on the liquidity provision price interval. It facilitates liquidity providers to hedge permanent loss by trading crypto options in more liquid centralised exchanges such as Deribit. Numerical examples illustrate the astonishing accuracy of the static replication.
Fundamental Portfolio Outperforms the Market Portfolio
Authors: Hayden Brown
Date: 2022-05-24 00:00:00

There is substantial empirical evidence showing the fundamental portfolio outperforming the market portfolio. Here a theoretical foundation is laid that supports this empirical research. Assuming stock prices revert around fundamental prices with sufficient strength and symmetry, the fundamental portfolio outperforms the market portfolio in expectation. If reversion toward the fundamental price is not sufficiently strong, then the fundamental portfolio underperforms the market portfolio in expectation.
A Contrario multi-scale anomaly detection method for industrial quality inspection
Authors: Matías Tailanian, Pablo Musé, Álvaro Pardo
Date: 2022-05-03 00:00:00
Tasks: Anomaly Detection

Anomalies can be defined as any non-random structure which deviates from normality. Anomaly detection methods reported in the literature are numerous and diverse, as what is considered anomalous usually varies depending on particular scenarios and applications. In this work we propose an a contrario framework to detect anomalies in images applying statistical analysis to feature maps obtained via convolutions. We evaluate filters learned from the image under analysis via patch PCA, Gabor filters and the feature maps obtained from a pre-trained deep neural network (Resnet). The proposed method is multi-scale and fully unsupervised and is able to detect anomalies in a wide variety of scenarios. While the end goal of this work is the detection of subtle defects in leather samples for the automotive industry, we show that the same algorithm achieves state-of-the-art results in public anomalies datasets.
Deep Low-Density Separation for Semi-Supervised Classification
Authors: Michael C. Burkhart, Kyle Shan
Date: 2022-05-22 00:00:00
Tasks: Classification

Given a small set of labeled data and a large set of unlabeled data, semi-supervised learning (SSL) attempts to leverage the location of the unlabeled datapoints in order to create a better classifier than could be obtained from supervised methods applied to the labeled training set alone. Effective SSL imposes structural assumptions on the data, e.g. that neighbors are more likely to share a classification or that the decision boundary lies in an area of low density. For complex and high-dimensional data, neural networks can learn feature embeddings to which traditional SSL methods can then be applied in what we call hybrid methods. Previously-developed hybrid methods iterate between refining a latent representation and performing graph-based SSL on this representation. In this paper, we introduce a novel hybrid method that instead applies low-density separation to the embedded features. We describe it in detail and discuss why low-density separation may be better suited for SSL on neural network-based embeddings than graph-based algorithms. We validate our method using in-house customer survey data and compare it to other state-of-the-art learning methods. Our approach effectively classifies thousands of unlabeled users from a relatively small number of hand-classified examples.
Recipe for a General, Powerful, Scalable Graph Transformer
Authors: Ladislav Rampášek, Mikhail Galkin, Vijay Prakash Dwivedi, Anh Tuan Luu, Guy Wolf, Dominique Beaini
Date: 2022-05-25 00:00:00
Tasks: Graph Classification, Graph Property Prediction, Graph Regression, Graph Representation Learning, Link Prediction, Molecular Property Prediction, Node Classification, Representation Learning

We propose a recipe on how to build a general, powerful, scalable (GPS) graph Transformer with linear complexity and state-of-the-art results on a diverse set of benchmarks. Graph Transformers (GTs) have gained popularity in the field of graph representation learning with a variety of recent publications but they lack a common foundation about what constitutes a good positional or structural encoding, and what differentiates them. In this paper, we summarize the different types of encodings with a clearer definition and categorize them as being $\textit{local}$, $\textit{global}$ or $\textit{relative}$. The prior GTs are constrained to small graphs with a few hundred nodes, here we propose the first architecture with a complexity linear in the number of nodes and edges $O(N+E)$ by decoupling the local real-edge aggregation from the fully-connected Transformer. We argue that this decoupling does not negatively affect the expressivity, with our architecture being a universal function approximator on graphs. Our GPS recipe consists of choosing 3 main ingredients: (i) positional/structural encoding, (ii) local message-passing mechanism, and (iii) global attention mechanism. We provide a modular framework $\textit{GraphGPS}$ that supports multiple types of encodings and that provides efficiency and scalability both in small and large graphs. We test our architecture on 16 benchmarks and show highly competitive results in all of them, show-casing the empirical benefits gained by the modularity and the combination of different strategies.
Generating Natural Language Proofs with Verifier-Guided Search
Authors: Kaiyu Yang, Jia Deng, Danqi Chen
Date: 2022-05-25 00:00:00
Tasks: Hallucination, valid

Reasoning over natural language is a challenging problem in NLP. In this work, we focus on proof generation: Given a hypothesis and a set of supporting facts, the model generates a proof tree indicating how to derive the hypothesis from supporting facts. Compared to generating the entire proof in one shot, stepwise generation can better exploit the compositionality and generalize to longer proofs but has achieved limited success on real-world data. Existing stepwise methods struggle to generate proof steps that are both logically valid and relevant to the hypothesis. Instead, they tend to hallucinate invalid steps given the hypothesis. In this paper, we present a novel stepwise method, NLProofS (Natural Language Proof Search), which learns to generate relevant steps conditioning on the hypothesis. At the core of our approach, we train an independent verifier to check the validity of the proof steps to prevent hallucination. Instead of generating steps greedily, we search for proofs maximizing a global proof score judged by the verifier. NLProofS achieves state-of-the-art performance on EntailmentBank and RuleTaker. Specifically, it improves the correctness of predicted proofs from 27.7% to 33.3% in the distractor setting of EntailmentBank, demonstrating the effectiveness of NLProofS in generating challenging human-authored proofs.