pwc-archive/papers-with-abstracts huggingface dataset:
mmAlert: mmWave Link Blockage Prediction via Passive Sensing
Authors: Chao Yu, Yifei Sun, Yan Luo, Rui Wang
Date: 2023-02-22 00:00:00
Tasks: Prediction

In this letter, the mmAlert system, predicting millimeter wave (mmWave) link blockage during data communication, is elaborated and demonstrated. The passive sensing method is adopted for mobile blocker detection, where two receive beams with separated radio frequency (RF) chains are equipped at the data communication receiver. One receive beam is aligned to the direction of line-of-sight (LoS) path, and the other one periodically sweeps the region close to the LoS path. By comparing the signals received by the above two beams, the Doppler frequencies of the signal scattered from the mobile blocker can be detected. Furthermore, by tracking the Doppler frequencies and the angle-of-arrivals (AoAs) of the scattered signals, the trajectory of the mobile blocker can be estimated, such that the potential link blockage can be predicted by assuming consistent mobile velocity. It is demonstrated via experiments that the mmAlert system can always detect the motions of the walking person close to the LoS path, and predict 90\% of the LoS blockage with sensing time of 1.4 seconds.
Cyclic Delay-Doppler Shift: A Simple Transmit Diversity Technique for Delay-Doppler Waveforms in Doubly Selective Channels
Authors: Haoran Yin, Jiaojiao Xiong, Yu Zhou, Chi Zhang, Di Zhang, Xizhang Wei, Yanqun Tang
Date: 2023-02-22 00:00:00
Tasks: Diversity

Delay-Doppler waveform design has been considered as a promising solution to achieve reliable communication under high-mobility channels for the space-air-ground-integrated networks (SAGIN). In this paper, we introduce the cyclic delay-Doppler shift (CDDS) technique for delay-Doppler waveforms to extract transmit diversity in doubly selective channels. Two simple CDDS schemes, named time-domain CDDS (TD-CDDS) and modulation-domain CDDS (MD-CDDS), are proposed in the setting of multiple-input multiple-output (MIMO). We demonstrate the applications of CDDS on two representative delay-Doppler waveforms, namely orthogonal time frequency space (OTFS) and affine frequency division multiplexing (AFDM), by deriving their corresponding CDDS matrices. Furthermore, we prove theoretically and experimentally that CDDS can provide OTFS and AFDM with full transmit diversity gain on most occasions.
Minorization-based Low-Complexity Design for IRS-Aided ISAC Systems
Authors: Yi-Kai Li, Athina Petropulu
Date: 2023-02-22 00:00:00
Tasks: Integrated sensing and communication, ISAC

A low-complexity design is proposed for an integrated sensing and communication (ISAC) system aided by an intelligent reflecting surface (IRS). The radar precoder and IRS parameter are computed alternatingly to maximize the weighted sum signal-to-noise ratio (SNR) at the radar and communication receivers. The IRS design problem has an objective function of fourth order in the IRS parameter matrix, and is subject to highly non-convex unit modulus constraints. To address this challenging problem and obtain a low-complexity solution, we employ a minorization technique twice; the original fourth order objective is first surrogated with a quadratic one via minorization, and is then minorized again to a linear one. This leads to a closed form solution for the IRS parameter in each iteration, thus reducing the IRS design complexity. Numerical results are presented to show the effectiveness of the proposed method.
Secure Antenna Selection and Beamforming in MIMO Systems
Authors: Zhenqiao Cheng, Nanxi Li, Ruizhe Long, Jianchi Zhu, Chongjun Ouyang, Peng Chen
Date: 2023-02-22 00:00:00

This work proposes a novel joint design for multiuser multiple-input multiple-output wiretap channels. The base station exploits a switching network to connect a subset of its antennas to the available radio frequency chains. The switching network and transmit beamformers are jointly designed to maximize the weighted secrecy sum-rate for this setting. The principal design problem reduces to an NP-hard mixed-integer non-linear programming. We invoke the fractional programming technique and the penalty dual decomposition method to develop a tractable iterative algorithm that effectively approximates the optimal design. Our numerical investigations validate the effectiveness of the proposed algorithm and its superior performance compared with the benchmark.
On distributional graph signals
Authors: Feng Ji, Xingchao Jian, Wee Peng Tay
Date: 2023-02-22 00:00:00

Graph signal processing (GSP) studies graph-structured data, where the central concept is the vector space of graph signals. To study a vector space, we have many useful tools up our sleeves. However, uncertainty is omnipresent in practice, and using a vector to model a real signal can be erroneous in some situations. In this paper, we want to use the Wasserstein space as a replacement for the vector space of graph signals, to account for signal stochasticity. The Wasserstein is strictly more general in which the classical graph signal space embeds isometrically. An element in the Wasserstein space is called a distributional graph signal. On the other hand, signal processing for a probability space of graphs has been proposed in the literature. In this work, we propose a unified framework that also encompasses existing theories regarding graph uncertainty. We develop signal processing tools to study the new notion of distributional graph signals. We also demonstrate how the theory can be applied by using real datasets.
Travelling waves in a coarse-grained model of volume-filling cell invasion: Simulations and comparisons
Authors: Rebecca M. Crossley, Philip K. Maini, Tommaso Lorenzi, Ruth E. Baker
Date: 2023-02-22 00:00:00

Many reaction-diffusion models produce travelling wave solutions that can be interpreted as waves of invasion in biological scenarios such as wound healing or tumour growth. These partial differential equation models have since been adapted to describe the interactions between cells and extracellular matrix (ECM), using a variety of different underlying assumptions. In this work, we derive a system of reaction-diffusion equations, with cross-species density-dependent diffusion, by coarse-graining an agent-based, volume-filling model of cell invasion into ECM. We study the resulting travelling wave solutions both numerically and analytically across various parameter regimes. Subsequently, we perform a systematic comparison between the behaviours observed in this model and those predicted by simpler models in the literature that do not take into account volume-filling effects in the same way. Our study justifies the use of some of these simpler, more analytically tractable models in reproducing the qualitative properties of the solutions in some parameter regimes, but it also reveals some interesting properties arising from the introduction of cell and ECM volume-filling effects, where standard model simplifications might not be appropriate.
Data-Efficient Protein 3D Geometric Pretraining via Refinement of Diffused Protein Structure Decoy
Authors: Yufei Huang, Lirong Wu, Haitao Lin, Jiangbin Zheng, Ge Wang, Stan Z. Li
Date: 2023-02-05 00:00:00
Tasks: Diversity, Drug Design

Learning meaningful protein representation is important for a variety of biological downstream tasks such as structure-based drug design. Having witnessed the success of protein sequence pretraining, pretraining for structural data which is more informative has become a promising research topic. However, there are three major challenges facing protein structure pretraining: insufficient sample diversity, physically unrealistic modeling, and the lack of protein-specific pretext tasks. To try to address these challenges, we present the 3D Geometric Pretraining. In this paper, we propose a unified framework for protein pretraining and a 3D geometric-based, data-efficient, and protein-specific pretext task: RefineDiff (Refine the Diffused Protein Structure Decoy). After pretraining our geometric-aware model with this task on limited data(less than 1% of SOTA models), we obtained informative protein representations that can achieve comparable performance for various downstream tasks.
Quantifying the common genetic variability of bacterial traits
Authors: T. Tien Mai, Gerry Tonkin-Hill, John A. Lees, Jukka Corander
Date: 2023-02-22 00:00:00

The study of common heritability, or co-heritability, among multiple traits has been widely established in quantitative and molecular genetics. However, in bacteria, genome-based estimation of heritability has only been considered very recently and no methods are currently available for considering co-heritability. Here we introduce such a method and demonstrate its usefulness by multi-trait analyses of the three major human pathogens \textit{Escherichia coli}, \textit{Neisseria gonorrhoeae} and \textit{Streprococcus pneumoniae}. We anticipate that the increased availability of high-throughput genomic and phenotypic screens of bacterial populations will spawn ample future opportunities to understand the common molecular basis of different traits in bacteria.
On the scope of applicability of the models of Darwinian dynamics
Authors: Georgiy Karev
Date: 2023-02-22 00:00:00

In their well-known textbook (Vincent & Brown, 2005), Vincent and Brown suggested an attractive approach for studying evolutionary dynamics of populations that are heterogeneous with respect to some strategy that affects the fitness of individuals in the population. The authors developed a theory, whose goal was to expand the applicability of mathematical models of population dynamics by including dynamics of an evolving heritable phenotype trait subject to natural selection. The authors studied both the case of evolution of individual traits and of mean traits in the population (or species) and the dynamics of total population size. The authors consider the developed approach as (more or less) universally applicable to models with any fitness function and any initial distribution of strategies, which is symmetric and has small variance. Here it was shown that the scope of the approach proposed by Vincent & Brown is unfortunately much more limited. I show that the approach gives exact results only if the population dynamics linearly depends on the trait; examples where the approach is incorrect are given.
Diffusion Models in Bioinformatics: A New Wave of Deep Learning Revolution in Action
Authors: Zhiye Guo, Jian Liu, Yanli Wang, Mengrui Chen, Duolin Wang, Dong Xu, Jianlin Cheng
Date: 2023-02-13 00:00:00
Tasks: Denoising, Protein Design

Denoising diffusion models have emerged as one of the most powerful generative models in recent years. They have achieved remarkable success in many fields, such as computer vision, natural language processing (NLP), and bioinformatics. Although there are a few excellent reviews on diffusion models and their applications in computer vision and NLP, there is a lack of an overview of their applications in bioinformatics. This review aims to provide a rather thorough overview of the applications of diffusion models in bioinformatics to aid their further development in bioinformatics and computational biology. We start with an introduction of the key concepts and theoretical foundations of three cornerstone diffusion modeling frameworks (denoising diffusion probabilistic models, noise-conditioned scoring networks, and stochastic differential equations), followed by a comprehensive description of diffusion models employed in the different domains of bioinformatics, including cryo-EM data enhancement, single-cell data analysis, protein design and generation, drug and small molecule design, and protein-ligand interaction. The review is concluded with a summary of the potential new development and applications of diffusion models in bioinformatics.
Machine Learning Techniques for Predicting the Short-Term Outcome of Resective Surgery in Lesional-Drug Resistance Epilepsy
Authors: Zahra Jourahmad, Jafar Mehvari Habibabadi, Houshang Moein, Reza Basiratnia, Ali Rahmani Geranqayeh, Saeed Shiry Ghidary, Seyed-Ali Sadegh-Zadeh
Date: 2023-02-10 00:00:00

In this study, we developed and tested machine learning models to predict epilepsy surgical outcome using noninvasive clinical and demographic data from patients. Methods: Seven dif-ferent categorization algorithms were used to analyze the data. The techniques are also evaluated using the Leave-One-Out method. For precise evaluation of the results, the parameters accuracy, precision, recall and, F1-score are calculated. Results: Our findings revealed that a machine learning-based presurgical model of patients' clinical features may accurately predict the outcome of epilepsy surgery in patients with drug-resistant lesional epilepsy. The support vector machine (SVM) with the linear kernel yielded 76.1% in terms of accuracy could predict results in 96.7% of temporal lobe epilepsy (TLE) patients and 79.5% of extratemporal lobe epilepsy (ETLE) cases using ten clinical features. Significance: To predict the outcome of epilepsy surgery, this study recommends the use of a machine learning strategy based on supervised classification and se-lection of feature subsets data mining. Progress in the development of machine learning-based prediction models offers optimism for personalised medicine access.
FTX's downfall and Binance's consolidation: The fragility of centralised digital finance
Authors: David Vidal-Tomás, Antonio Briola, Tomaso Aste
Date: 2023-02-22 00:00:00

This paper investigates the causes of the FTX digital currency exchange's failure in November 2022. We identify the collapse of the Terra-Luna ecosystem as the pivotal event that triggered a significant decrease in the exchange's liquidity. Analysing on-chain data, we report that FTX heavily relied on leveraging and misusing its native token, FTT, and we show how this behaviour exacerbated the company's fragile financial situation. To gain further insights into the downfall, we study evolutionary dependency structures of 199 cryptocurrencies on an hourly basis, and we investigate public trades at the time of the events. Results suggest that the collapse was actively accelerated by Binance tweets causing a systemic reaction in the cryptocurrency market. Finally, identifying the actors who mostly benefited from the FTX's collapse and highlighting a generalised trend toward centralisation in the crypto space, we emphasise the importance of genuinely decentralised finance for a transparent, future digital economy.
The inverse Cox-Ingersoll-Ross process for parsimonious financial price modeling
Authors: Li Lin, Didier Sornette
Date: 2023-02-22 00:00:00

We propose a formulation to construct new classes of financial price processes based on the insight that the key variable driving prices $P$ is the earning-over-price ratio $\gamma \simeq 1/P$, which we refer to as the earning yield and is analogous to the yield-to-maturity of an equivalent perpetual bond. This modeling strategy is illustrated with the choice for real-time $\gamma$ in the form of the Cox-Ingersoll-Ross (CIR) process, which allows us to derive analytically many stylised facts of financial prices and returns, such as the power law distribution of returns, transient super-exponential bubble behavior, and the fat-tailed distribution of prices before bubbles burst. Our model sheds new light on rationalizing the excess volatility and the equity premium puzzles. The model is calibrated to five well-known historical bubbles in the US and China stock markets via a quasi-maximum likelihood method with the L-BFGS-B optimization algorithm. Using $\phi$-divergence statistics adapted to models prescribed in terms of stochastic differential equations, we show the superiority of the CIR process for $\gamma_t$ against three alternative models.
The configurable tree graph (CT-graph): measurable problems in partially observable and distal reward environments for lifelong reinforcement learning
Authors: Andrea Soltoggio, Eseoghene Ben-Iwhiwhu, Christos Peridis, Pawel Ladosz, Jeffery Dick, Praveen K. Pilly, Soheil Kolouri
Date: 2023-01-21 00:00:00
Tasks: Lifelong learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This paper introduces a set of formally defined and transparent problems for reinforcement learning algorithms with the following characteristics: (1) variable degrees of observability (non-Markov observations), (2) distal and sparse rewards, (3) variable and hierarchical reward structure, (4) multiple-task generation, (5) variable problem complexity. The environment provides 1D or 2D categorical observations, and takes actions as input. The core structure of the CT-graph is a multi-branch tree graph with arbitrary branching factor, depth, and observation sets that can be varied to increase the dimensions of the problem in a controllable and measurable way. Two main categories of states, decision states and wait states, are devised to create a hierarchy of importance among observations, typical of real-world problems. A large observation set can produce a vast set of histories that impairs memory-augmented agents. Variable reward functions allow for the easy creation of multiple tasks and the ability of an agent to efficiently adapt in dynamic scenarios where tasks with controllable degrees of similarities are presented. Challenging complexity levels can be easily achieved due to the exponential growth of the graph. The problem formulation and accompanying code provide a fast, transparent, and mathematically defined set of configurable tests to compare the performance of reinforcement learning algorithms, in particular in lifelong learning settings.
An Asymmetric Loss with Anomaly Detection LSTM Framework for Power Consumption Prediction
Authors: Jihan Ghanim, Maha Issa, Mariette Awad
Date: 2023-02-05 00:00:00
Tasks: Anomaly Detection, Load Forecasting

Building an accurate load forecasting model with minimal underpredictions is vital to prevent any undesired power outages due to underproduction of electricity. However, the power consumption patterns of the residential sector contain fluctuations and anomalies making them challenging to predict. In this paper, we propose multiple Long Short-Term Memory (LSTM) frameworks with different asymmetric loss functions to impose a higher penalty on underpredictions. We also apply a density-based spatial clustering of applications with noise (DBSCAN) anomaly detection approach, prior to the load forecasting task, to remove any present oultiers. Considering the effect of weather and social factors, seasonality splitting is performed on the three considered datasets from France, Germany, and Hungary containing hourly power consumption, weather, and calendar features. Root-mean-square error (RMSE) results show that removing the anomalies efficiently reduces the underestimation and overestimation errors in all the seasonal datasets. Additionally, asymmetric loss functions and seasonality splitting effectively minimize underestimations despite increasing the overestimation error to some degree. Reducing underpredictions of electricity consumption is essential to prevent power outages that can be damaging to the community.
An Implicit GNN Solver for Poisson-like problems
Authors: Matthieu Nastorg, Michele Alessandro Bucci, Thibault Faney, Jean-Marc Gratien, Guillaume Charpiat, Marc Schoenauer
Date: 2023-02-06 00:00:00
Tasks: Graph Neural Network

This paper presents $\Psi$-GNN, a novel Graph Neural Network (GNN) approach for solving the ubiquitous Poisson PDE problems with mixed boundary conditions. By leveraging the Implicit Layer Theory, $\Psi$-GNN models an "infinitely" deep network, thus avoiding the empirical tuning of the number of required Message Passing layers to attain the solution. Its original architecture explicitly takes into account the boundary conditions, a critical prerequisite for physical applications, and is able to adapt to any initially provided solution. $\Psi$-GNN is trained using a "physics-informed" loss, and the training process is stable by design, and insensitive to its initialization. Furthermore, the consistency of the approach is theoretically proven, and its flexibility and generalization efficiency are experimentally demonstrated: the same learned model can accurately handle unstructured meshes of various sizes, as well as different boundary conditions. To the best of our knowledge, $\Psi$-GNN is the first physics-informed GNN-based method that can handle various unstructured domains, boundary conditions and initial solutions while also providing convergence guarantees.
Eigen-informed NeuralODEs: Dealing with stability and convergence issues of NeuralODEs
Authors: Tobias Thummerer, Lars Mikelsons
Date: 2023-02-07 00:00:00

Using vanilla NeuralODEs to model large and/or complex systems often fails due two reasons: Stability and convergence. NeuralODEs are capable of describing stable as well as instable dynamic systems. Selecting an appropriate numerical solver is not trivial, because NeuralODE properties change during training. If the NeuralODE becomes more stiff, a suboptimal solver may need to perform very small solver steps, which significantly slows down the training process. If the NeuralODE becomes to instable, the numerical solver might not be able to solve it at all, which causes the training process to terminate. Often, this is tackled by choosing a computational expensive solver that is robust to instable and stiff ODEs, but at the cost of a significantly decreased training performance. Our method on the other hand, allows to enforce ODE properties that fit a specific solver or application-related boundary conditions. Concerning the convergence behavior, NeuralODEs often tend to run into local minima, especially if the system to be learned is highly dynamic and/or oscillating over multiple periods. Because of the vanishing gradient at a local minimum, the NeuralODE is often not capable of leaving it and converge to the right solution. We present a technique to add knowledge of ODE properties based on eigenvalues - like (partly) stability, oscillation capability, frequency, damping and/or stiffness - to the training objective of a NeuralODE. We exemplify our method at a linear as well as a nonlinear system model and show, that the presented training process is far more robust against local minima, instabilities and sparse data samples and improves training convergence and performance.
CQnet: convex-geometric interpretation and constraining neural-network trajectories
Authors: Bas Peters
Date: 2023-02-09 00:00:00

We introduce CQnet, a neural network with origins in the CQ algorithm for solving convex split-feasibility problems and forward-backward splitting. CQnet's trajectories are interpretable as particles that are tracking a changing constraint set via its point-to-set distance function while being elements of another constraint set at every layer. More than just a convex-geometric interpretation, CQnet accommodates learned and deterministic constraints that may be sample or data-specific and are satisfied by every layer and the output. Furthermore, the states in CQnet progress toward another constraint set at every layer. We provide proof of stability/nonexpansiveness with minimal assumptions. The combination of constraint handling and stability put forward CQnet as a candidate for various tasks where prior knowledge exists on the network states or output.