pwc-archive/papers-with-abstracts huggingface dataset:
Grating lobe reduction in plane wave imaging with angular compounding using subtraction of coherent signals
Authors: Zhengchang Kou, Rita J. Miller, Michael L. Oelze
Date: 2022-10-24 00:00:00

Plane wave imaging (PWI) with angular compounding has gained in popularity over recent years because it provides high frame rates and good image properties. However, most linear arrays used in clinical practice have a pitch that is equal to than the wavelength of ultrasound. Hence, the presence of grating lobes is a concern for PWI using multiple transmit angles. The presence of grating lobes produces clutter in images and reduces the ability to observe tissue contrast. Techniques to reduce or eliminate the presence of grating lobes for PWI using multiple angles will result in improved image quality. Null subtraction imaging (NSI) is a nonlinear beamforming technique that has been explored for improving the lateral resolution of ultrasonic imaging. However, the apodization scheme used in NSI also eliminates or greatly reduces the presence of grating lobes. Imaging tasks using NSI were evaluated in simulations and physical experiments involving tissue-mimicking phantoms and rat tumors in vivo. Images created with NSI were compared with images created using traditional delay and sum (DAS) with Hann apodization and images created using a generalized coherence factor (GCF). NSI was observed to greatly reduce the presence of grating lobes in ultrasonic images, compared to DAS with Hann and GCF, while maintaining spatial resolution and contrast in the images. Therefore, NSI can provide a novel means of creating images using PWI with multiple steering angles on clinically available linear arrays while reducing the adverse effects associated with grating lobes.
RIS-assisted Integrated Sensing and Communications: A Subspace Rotation Approach
Authors: Xiao Meng, Fan Liu, Shihang Lu, Sundeep Prabhakar Chepuri, Christos Masouros
Date: 2022-10-23 00:00:00
Tasks: Integrated sensing and communication, ISAC

In this paper, we propose a novel joint active and passive beamforming approach for integrated sensing and communication (ISAC) transmission with assistance of reconfigurable intelligent surfaces (RISs) to simultaneously detect a target and communicate with a communication user. We first show that the sensing and communication (S&C) performance can be jointly improved due to the capability of the RISs to control the ISAC channel. In particular, we show that RISs can favourably enhance both the channel gain and the coupling degree of S&C channels by modifying the underlying subspaces. In light of this, we develop a heuristic algorithm that expands and rotates the S&C subspaces that is able to attain significantly improved ISAC performance. To verify the effectiveness of the subspace rotation scheme, we further provide a benchmark scheme which maximizes the signal-to-noise ratio (SNR) at the sensing receiver while guaranteeing the SNR at the communication user. Finally, numerical simulations are provided to validate the proposed approaches.
Neural Augmented Kalman Filtering with Bollinger Bands for Pairs Trading
Authors: Amit Milstein, Haoran Deng, Guy Revach, Hai Morgenstern, Nir Shlezinger
Date: 2022-10-19 00:00:00

Pairs trading is a family of trading techniques that determine their policies based on monitoring the relationships between pairs of assets. A common pairs trading approach relies on describing the pair-wise relationship as a linear Space State (SS) model with Gaussian noise. This representation facilitates extracting financial indicators with low complexity and latency using a Kalman Filter (KF), that are then processed using classic policies such as Bollinger Bands (BB). However, such SS models are inherently approximated and mismatched, often degrading the revenue. In this work, we propose KalmenNet-aided Bollinger bands Pairs Trading (KBPT), a deep learning aided policy that augments the operation of KF-aided BB trading. KBPT is designed by formulating an extended SS model for pairs trading that approximates their relationship as holding partial co-integration. This SS model is utilized by a trading policy that augments KF-BB trading with a dedicated neural network based on the KalmanNet architecture. The resulting KBPT is trained in a two-stage manner which first tunes the tracking algorithm in an unsupervised manner independently of the trading task, followed by its adaptation to track the financial indicators to maximize revenue while approximating BB with a differentiable mapping. KBPT thus leverages data to overcome the approximated nature of the SS model, converting the KF-BB policy into a trainable model. We empirically demonstrate that our proposed KBPT systematically yields improved revenue compared with model-based and data-driven benchmarks over various different assets.
Study and Design of Reconfigurable Intelligent Surfaces
Authors: Apostolos Spanakis-Misirlis
Date: 2022-10-16 00:00:00

In this thesis, we introduce the fundamental equations behind the estimation of the link budget in a communications channel, highlighting the key limitations of conventional systems. Furthermore, we investigate the use of reconfigurable intelligent surfaces as a modern method of overcoming obstruction losses, while making use of numerical methods and computational electromagnetics to understand its physical mechanism and probe its theory of operation. Additionally, a preprint on computational geometry is presented, applicable to the field of computational electromagnetics, enabling the simulation of systems such as reconfigurable intelligent surfaces using open-source tools. Lastly, we provide a tool for the physical optimization of radio-frequency networks, based on mathematical programming. Such a tool may be used for the optimization of reconfigurable intelligent surfaces, ultimately improving the communication channel between a transmitter and receiver.
Deep reinforcement learning for automatic run-time adaptation of UWB PHY radio settings
Authors: Dieter Coppens, Adnan Shahid, Eli de Poorter
Date: 2022-10-13 00:00:00
Tasks: Deep Reinforcement Learning, Indoor Localization, Q-Learning, Reinforcement Learning (RL)

Ultra-wideband technology has become increasingly popular for indoor localization and location-based services. This has led recent advances to be focused on reducing the ranging errors, whilst research focusing on enabling more reliable and energy efficient communication has been largely unexplored. The IEEE 802.15.4 UWB physical layer allows for several settings to be selected that influence the energy consumption, range, and reliability. Combined with the available link state diagnostics reported by UWB devices, there is an opportunity to dynamically select PHY settings based on the environment. To address this, we propose a deep Q-learning approach for enabling reliable UWB communication, maximizing packet reception rate (PRR) and minimizing energy consumption. Deep Q-learning is a good fit for this problem, as it is an inherently adaptive algorithm that responds to the environment. Validation in a realistic office environment showed that the algorithm outperforms traditional Q-learning, linear search and using a fixed PHY layer. We found that deep Q-learning achieves a higher average PRR and reduces the ranging error while using only 14% of the energy compared to a fixed PHY setting in a dynamic office environment.
Closed-form modeling of neuronal spike train statistics using multivariate Hawkes cumulants
Authors: Nicolas Privault, Michèle Thieullen
Date: 2022-10-27 00:00:00
Tasks: Form, Sensitivity

We derive exact analytical expressions for the cumulants of any orders of neuronal membrane potentials driven by spike trains in a multivariate Hawkes process model with excitation and inhibition. Such expressions can be used for the prediction and sensitivity analysis of the statistical behavior of the model over time, and to estimate the probability densities of neuronal membrane potentials using Gram-Charlier expansions. Our results are shown to provide a better alternative to Monte Carlo estimates via stochastic simulations, and computer codes based on combinatorial recursions are included.
A parametric approach to the estimation of convex risk functionals based on Wasserstein distance
Authors: Max Nendel, Alessandro Sgarabottolo
Date: 2022-10-25 00:00:00

In this paper, we explore a static setting for the assessment of risk in the context of mathematical finance and actuarial science that takes into account model uncertainty in the distribution of a possibly infinite-dimensional risk factor. We allow for perturbations around a baseline model, measured via Wasserstein distance, and we investigate to which extent this form of probabilistic imprecision can be parametrized. The aim is to come up with a convex risk functional that incorporates a sefety margin with respect to nonparametric uncertainty and still can be approximated through parametrized models. The particular form of the parametrization allows us to develop a numerical method, based on neural networks, which gives both the value of the risk functional and the optimal perturbation of the reference measure. Moreover, we study the problem under additional constraints on the perturbations, namely, a mean and a martingale constraint. We show that, in both cases, under suitable conditions on the loss function, it is still possible to estimate the risk functional by passing to a parametric family of perturbed models, which again allows for a numerical approximation via neural networks.
Intrinsic Randomness in Epidemic Modelling Beyond Statistical Uncertainty
Authors: Matthew J. Penn, Daniel J. Laydon, Joseph Penn, Charles Whittaker, Christian Morgenstern, Oliver Ratmann, Swapnil Mishra, Mikko S. Pakkanen, Christl A. Donnelly, Samir Bhatt
Date: 2022-10-25 00:00:00

Uncertainty can be classified as either aleatoric (intrinsic randomness) or epistemic (imperfect knowledge of parameters). The majority of frameworks assessing infectious disease risk consider only epistemic uncertainty. We only ever observe a single epidemic, and therefore cannot empirically determine aleatoric uncertainty. Here, we characterise both epistemic and aleatoric uncertainty using a time-varying general branching process. Our framework explicitly decomposes aleatoric variance into mechanistic components, quantifying the contribution to uncertainty produced by each factor in the epidemic process, and how these contributions vary over time. The aleatoric variance of an outbreak is itself a renewal equation where past variance affects future variance. We find that, superspreading is not necessary for substantial uncertainty, and profound variation in outbreak size can occur even without overdispersion in the offspring distribution (i.e. the distribution of the number of secondary infections an infected person produces). Aleatoric forecasting uncertainty grows dynamically and rapidly, and so forecasting using only epistemic uncertainty is a significant underestimate. Therefore, failure to account for aleatoric uncertainty will ensure that policymakers are misled about the substantially higher true extent of potential risk. We demonstrate our method, and the extent to which potential risk is underestimated, using two historical examples.
The continuous-time pre-commitment KMM problem in incomplete markets
Authors: Guohui Guan, Zongxia Liang, Yilun Song
Date: 2022-10-25 00:00:00

This paper studies the continuous-time pre-commitment KMM problem proposed by Klibanoff, Marinacci and Mukerji (2005) in incomplete financial markets, which concerns with the portfolio selection under smooth ambiguity. The decision maker (DM) is uncertain about the dominated priors of the financial market, which are characterized by a second-order distribution (SOD). The KMM model separates risk attitudes and ambiguity attitudes apart and the aim of the DM is to maximize the two-fold utility of terminal wealth, which does not belong to the classical subjective utility maximization problem. By constructing the efficient frontier, the original KMM problem is first simplified as an one-fold expected utility problem on the second-order space. In order to solve the equivalent simplified problem, this paper imposes an assumption and introduces a new distorted Legendre transformation to establish the bipolar relation and the distorted duality theorem. Then, under a further assumption that the asymptotic elasticity of the ambiguous attitude is less than 1, the uniqueness and existence of the solution to the KMM problem are shown and we obtain the semi-explicit forms of the optimal terminal wealth and the optimal strategy. Explicit forms of optimal strategies are presented for CRRA, CARA and HARA utilities in the case of Gaussian SOD in a Black-Scholes financial market, which show that DM with higher ambiguity aversion tends to be more concerned about extreme market conditions with larger bias. In the end of this work, numerical comparisons with the DMs ignoring ambiguity are revealed to illustrate the effects of ambiguity on the optimal strategies and value functions.
Approximate Pricing of Derivatives Under Fractional Stochastic Volatility Model
Authors: Yuecai Han, Xudong Zheng
Date: 2022-10-21 00:00:00

We investigate the problem of pricing derivatives under a fractional stochastic volatility model. We obtain an approximate expression of the derivative price where the stochastic volatility can be composed of deterministic functions of time and fractional Ornstein-Uhlenbeck process. Numerical simulations are given to illustrate the feasibility and operability of the approximation, and also demonstrate the effect of long-range on derivative prices.
Modelling the Bitcoin prices and the media attention to Bitcoin via the jump-type processes
Authors: Ekaterina Morozova, Vladimir Panov
Date: 2022-10-25 00:00:00
Tasks: Time Series, Time Series Analysis

In this paper, we present a new bivariate model for the joint description of the Bitcoin prices and the media attention to Bitcoin. Our model is based on the class of the L\'evy processes and is able to realistically reproduce the jump-type dynamics of the considered time series. We focus on the low-frequency setup, which is for the L\'evy - based models essentially more difficult than the high-frequency case. We design a semiparametric estimation procedure for the statistical inference on the parameters and the L\'evy measures of the considered processes. We show that the dynamics of the market attention can be effectively modelled by the L\'evy processes with finite L\'evy measures, and propose a data-driven procedure for the description of the Bitcoin prices.
Exploring Data-Driven Chemical SMILES Tokenization Approaches to Identify Key Protein-Ligand Binding Moieties
Authors: Asu Büşra Temizer, Gökçe Uludoğan, Rıza Özçelik, Taha Koulani, Elif Ozkirimli, Kutlu O. Ulgen, Nilgün Karalı, Arzucan Özgür
Date: 2022-10-26 00:00:00
Tasks: Drug Design, Drug Discovery, Property Prediction

Machine learning models have found numerous successful applications in computational drug discovery. A large body of these models represents molecules as sequences since molecular sequences are easily available, simple, and informative. The sequence-based models often segment molecular sequences into pieces called chemical words (analogous to the words that make up sentences in human languages) and then apply advanced natural language processing techniques for tasks such as $\textit{de novo}$ drug design, property prediction, and binding affinity prediction. However, the chemical characteristics and significance of these building blocks, chemical words, remain unexplored. This study aims to investigate the chemical vocabularies generated by popular subword tokenization algorithms, namely Byte Pair Encoding (BPE), WordPiece, and Unigram, and identify key chemical words associated with protein-ligand binding. To this end, we build a language-inspired pipeline that treats high affinity ligands of protein targets as documents and selects key chemical words making up those ligands based on tf-idf weighting. Further, we conduct case studies on a number of protein families to analyze the impact of key chemical words on binding. Through our analysis, we find that these key chemical words are specific to protein targets and correspond to known pharmacophores and functional groups. Our findings will help shed light on the chemistry captured by the chemical words, and by machine learning models for drug discovery at large.
Direct pairing of homologous DNA double helices may involve the B-to-C form transition
Authors: Alexey K. Mazur, Eugene Gladyshev
Date: 2022-10-25 00:00:00
Tasks: Form

In many organisms, homologous (or repetitive) chromosomal regions can associate or/and undergo concerted epigenetic changes in the absence of DNA breakage and recombination. The direct specific pairing of DNA duplexes with similar nucleotide sequences represents an attractive mechanism for recognizing such regions. Whereas the pairing of B-DNA duplexes may involve a large energy barrier, C-DNA duplexes are expected to pair much more readily. This unique feature of C-DNA is largely due to the fact that its major groove is wide and very shallow, permitting almost perfect initial homologous contacts between two duplexes without clashing. Overall, the conjectured role of C-DNA in recombination-independent pairing should revive the efforts to understand its structure and function in the cell.
Towards the convergent therapeutic potential of GPCRs in autism spectrum disorders
Authors: Anil Annamneedi, Caroline Gora, Ana Dudas, Xavier Leray, Véronique Bozon, Pascale Crepieux, Lucie P. Pellissier
Date: 2022-10-27 00:00:00

Changes in genetic and/or environmental factors to developing neural circuits and subsequent synaptic functions are known to be a causative underlying the varied socio-emotional behavioural patterns associated with autism spectrum disorders (ASD). Seven transmembrane G protein-coupled receptors (GPCRs) comprising the largest family of cell-surface receptors, mediate the transfer of extracellular signals to downstream cellular responses. Disruption of GPCR and their signalling have been implicated as a convergent pathologic mechanism of ASD. Here, we aim to review the literature about the 23 GPCRs that are genetically associated to ASD pathology according to Simons Foundation Autism Research Initiative (SFARI) database such as oxytocin (OXTR) and vasopressin (V1A, V1B) receptors, metabotropic glutamate (mGlu5, mGlu7) and gamma-aminobutyric acid (GABAB) receptors, dopamine (D1, D2), serotoninergic (5-HT1B and additionally included the 5-HT2A, 5-HT7 receptors for their strong relevance to ASD), adrenergic ($\beta$2) and cholinergic (M3) receptors, adenosine (A2A, A3) receptors, angiotensin (AT2) receptors, cannabinoid (CB1) receptors, chemokine (CX3CR1) receptors, orphan (GPR37, GPR85) and olfactory (OR1C1, OR2M4, OR2T10, OR52M1) receptors. We discussed the genetic variants, relation to core ASD behavioural deficits and update on pharmacological compounds targeting these 23 GPCRs. Of these OTR, V1A, mGlu5, D2, 5-HT2A, CB1, and GPR37 serve as the best therapeutic targets and have potential towards core domains of ASD pathology. With a functional crosstalk between different GPCRs and converging pharmacological responses, there is an urge to develop novel therapeutic strategies based on multiple GPCRs to reduce the socioeconomic burden associated with ASD and we strongly emphasize the need to prioritize the increased clinical trials targeting the multiple GPCRs.
Mathematical definition of public language, and modeling of will and consciousness based on the public language
Authors: Hana Hebishima, Mina Arakaki, Chikako Dozono, Hanna Frolova, Shinichi Inage
Date: 2022-10-26 00:00:00

To propose a mathematical model of consciousness and will, we first simulated the inverted qualia with a toy model of a neural network. As a result, we confirmed that there can be an inverted qualia on the neural network. In other words, the qualia were individual-dependent and considered difficult as an indicator of consciousness and will. To solve that difficulty, we introduce a probability space and a random variable into a set of qualia and define a public language for events. Based on this idea of public language, consciousness and will are modeled. In this proposal, future actions are randomly selected from the comparison between "recognition of events" by external observation and past episodic memory, and the actual "recognition of actions" is regarded as the occurrence of consciousness. The basic formula is also derived. This proposal is compared with other past philosophical discussions.
Preparing fMRI Data for Statistical Analysis
Authors: Alfonso Nieto-Castanon
Date: 2022-10-24 00:00:00
Tasks: Denoising, Time Series, Time Series Analysis

This chapter describes several procedures used to prepare fMRI data for statistical analyses. It includes the description of common preprocessing steps, such as spatial realignment, coregistration, and spatial normalization, aimed at the spatial alignment of all fMRI data within- and between- subjects, as well as several denoising procedures aimed at minimizing the impact of common noise sources, including physiological and residual subject motion effects, on the BOLD signal time series. The chapter ends with a description of quality control procedures recommended for detecting potential problems in the fMRI data and evaluating its suitability for subsequent statistical analyses.
A Neural Network Based Automated IFT-20 Sensory Neuron Classifier for Caenorhabditis elegans
Authors: Arvind Seshan
Date: 2022-10-24 00:00:00

Determining neuronal identity in imaging data is an essential task in neuroscience, facilitating the comparison of neural activity across organisms. Cross-organism comparison, in turn, enables a wide variety of research including whole-brain analysis of functional networks and linking the activity of specific neurons to behavior or environmental stimuli. The recent development of three-dimensional, pan-neuronal imaging with single-cell resolution within Caenorhabditis elegans has brought neuron identification, tracking, and activity monitoring all within reach. The nematode C. elegans is often used as a model organism to study neuronal activity due to factors such as its transparency and well-understood nervous system. The principal barrier to high-accuracy neuron identification is that in adult C. elegans, the position of neuronal cell bodies is not stereotyped. Existing approaches to address this issue use genetically encoded markers as an additional identifying feature. For example, the NeuroPAL strain uses multicolored fluorescent reporters. However, this approach has limited use due to the negative effects of excessive genetic modification. In this study, I propose an alternative neuronal identification technique using only single-color fluorescent images. I designed a novel neural network based classifier that automatically labels sensory neurons using an iterative, landmark-based neuron identification process inspired by the manual annotation procedures that humans employ. This design labels sensory neurons in C. elegans with 91.61% accuracy.