pwc-archive/papers-with-abstracts huggingface dataset:
Nonparametric Decentralized Detection and Sparse Sensor Selection via Multi-Sensor Online Kernel Scalar Quantization
Authors: Jing Guo, Raghu G. Raj, David J. Love, Christopher G. Brinton
Date: 2022-05-21 00:00:00
Tasks: Classification, Quantization

Signal classification problems arise in a wide variety of applications, and their demand is only expected to grow. In this paper, we focus on the wireless sensor network signal classification setting, where each sensor forwards quantized signals to a fusion center to be classified. Our primary goal is to train a decision function and quantizers across the sensors to maximize the classification performance in an online manner. Moreover, we are interested in sparse sensor selection using a marginalized weighted kernel approach to improve network resource efficiency by disabling less reliable sensors with minimal effect on classification performance.To achieve our goals, we develop a multi-sensor online kernel scalar quantization (MSOKSQ) learning strategy that operates on the sensor outputs at the fusion center. Our theoretical analysis reveals how the proposed algorithm affects the quantizers across the sensors. Additionally, we provide a convergence analysis of our online learning approach by studying its relationship to batch learning. We conduct numerical studies under different classification and sensor network settings which demonstrate the accuracy gains from optimizing different components of MSOKSQ and robustness to reduction in the number of sensors selected.
A Collaborative Approach Using Neural Networks for BLE-RSS Lateration-Based Indoor Positioning
Authors: Pavel Pascacio, Joaquín Torres-Sospedra, Sven Casteleyn, Elena Simona Lohan
Date: 2022-05-21 00:00:00
Tasks: Indoor Localization, Position, Specificity

In daily life, mobile and wearable devices with high computing power, together with anchors deployed in indoor environments, form a common solution for the increasing demands for indoor location-based services. Within the technologies and methods currently in use for indoor localization, the approaches that rely on Bluetooth Low Energy (BLE) anchors, Received Signal Strength (RSS), and lateration are among the most popular, mainly because of their cheap and easy deployment and accessible infrastructure by a variety of devices. Nevertheless, such BLE- and RSS-based indoor positioning systems are prone to inaccuracies, mostly due to signal fluctuations, poor quantity of anchors deployed in the environment, and/or inappropriate anchor distributions, as well as mobile device hardware variability. In this paper, we address these issues by using a collaborative indoor positioning approach, which exploits neighboring devices as additional anchors in an extended positioning network. The collaborating devices' information (i.e., estimated positions and BLE-RSS) is processed using a multilayer perceptron (MLP) neural network by taking into account the device specificity in order to estimate the relative distances. After this, the lateration is applied to collaboratively estimate the device position. Finally, the stand-alone and collaborative position estimates are combined, providing the final position estimate for each device. The experimental results demonstrate that the proposed collaborative approach outperforms the stand-alone lateration method in terms of positioning accuracy.
A structural model of genome-wide association studies
Authors: Christopher Salahub
Date: 2022-05-20 00:00:00
Tasks: model

A structural genetic model incorporating a modern understanding of the genome and common practice in genome-wide association studies is derived mathematically. The model shows the Haldane map distance as a direct consequence of the structure of the genome. An expression for genetic correlation is derived under the model and compared to data resulting from the BSB mouse cross. A correlation test plot is introduced for this comparison and shows the close agreement of the model and empirical results. Noteworthy departures in this plot indicate regions which warrant further investigation.
Spatial Transcriptomics Dimensionality Reduction using Wavelet Bases
Authors: Zhuoyan Xu, Kris Sankaran
Date: 2022-05-19 00:00:00
Tasks: Dimensionality Reduction

Spatially resolved transcriptomics (ST) measures gene expression along with the spatial coordinates of the measurements. The analysis of ST data involves significant computation complexity. In this work, we propose gene expression dimensionality reduction algorithm that retains spatial structure. We combine the wavelet transformation with matrix factorization to select spatially-varying genes. We extract a low-dimensional representation of these genes. We consider Empirical Bayes setting, imposing regularization through the prior distribution of factor genes. Additionally, We provide visualization of extracted representation genes capturing the global spatial pattern. We illustrate the performance of our methods by spatial structure recovery and gene expression reconstruction in simulation. In real data experiments, our method identifies spatial structure of gene factors and outperforms regular decomposition regarding reconstruction error. We found the connection between the fluctuation of gene patterns and wavelet technique, providing smoother visualization. We develop the package and share the workflow generating reproducible quantitative results and gene visualization. The package is available at https://github.com/OliverXUZY/waveST.
Single-cell gene regulatory network analysis for mixed cell populations with applications to COVID-19 single cell data
Authors: Junjie Tang, Changhu Wang, Feiyi Xiao, Ruibin Xi
Date: 2022-05-23 00:00:00
Tasks: Variational Inference

Gene regulatory network (GRN) refers to the complex network formed by regulatory interactions between genes in living cells. In this paper, we consider inferring GRNs in single cells based on single cell RNA sequencing (scRNA-seq) data. In scRNA-seq, single cells are often profiled from mixed populations and their cell identities are unknown. A common practice for single cell GRN analysis is to first cluster the cells and infer GRNs for every cluster separately. However, this two-step procedure ignores uncertainty in the clustering step and thus could lead to inaccurate estimation of the networks. To address this problem, we propose to model scRNA-seq by the mixture multivariate Poisson log-normal (MPLN) distribution. The precision matrices of the MPLN are the GRNs of different cell types and can be jointly estimated by maximizing MPLN's lasso-penalized log-likelihood. We show that the MPLN model is identifiable and the resulting penalized log-likelihood estimator is consistent. To avoid the intractable optimization of the MPLN's log-likelihood, we develop an algorithm called VMPLN based on the variational inference method. Comprehensive simulation and real scRNA-seq data analyses reveal that VMPLN performs better than the state-of-the-art single cell GRN methods.
A mathematical model of reward-mediated learning in drug addiction
Authors: Tom Chou, Maria D'Orsogna
Date: 2022-05-22 00:00:00

Substances of abuse are known to activate and disrupt neuronal circuits in the brain reward system. We propose a simple and easily interpretable dynamical systems model to describe the neurobiology of drug addiction that incorporates the psychiatric concepts of reward prediction error (RPE), drug-induced incentive salience (IST), and opponent process theory (OPT). Drug-induced dopamine releases activate a biphasic reward response with pleasurable, positive "a-processes" (euphoria, rush) followed by unpleasant, negative "b-processes" (cravings, withdrawal). Neuroadaptive processes triggered by successive intakes enhance the negative component of the reward response, which the user compensates for by increasing drug dose and/or intake frequency. This positive feedback between physiological changes and drug self-administration leads to habituation, tolerance and eventually to full addiction. Our model gives rise to qualitatively different pathways to addiction that can represent a diverse set of user profiles (genetics, age) and drug potencies. We find that users who have, or neuroadaptively develop, a strong b-process response to drug consumption are most at risk for addiction. Finally, we include possible mechanisms to mitigate withdrawal symptoms, such as through the use of methadone or other auxiliary drugs used in detoxification.
A Flexible Bayesian Clustering of Dynamic Subpopulations in Neural Spiking Activity
Authors: Ganchao Wei, Ian H. Stevenson, XiaoJing Wang
Date: 2022-05-21 00:00:00
Tasks: Clustering, Nonparametric Clustering

With advances in neural recording techniques, neuroscientists are now able to record the spiking activity of many hundreds of neurons simultaneously, and new statistical methods are needed to understand the structure of this large-scale neural population activity. Although previous work has tried to summarize neural activity within and between known populations by extracting low-dimensional latent factors, in many cases what determines a unique population may be unclear. Neurons differ in their anatomical location, but also, in their cell types and response properties. To identify populations directly related to neural activity, we develop a clustering method based on a mixture of dynamic Poisson factor analyzers (mixDPFA) model, with the number of clusters and dimension of latent factors for each cluster treated as unknown parameters. To analyze the proposed mixDPFA model, we propose a Markov chain Monte Carlo (MCMC) algorithm to efficiently sample its posterior distribution. Validating our proposed MCMC algorithm through simulations, we find that it can accurately recover the unknown parameters and the true clustering in the model, and is insensitive to the initial cluster assignments. We then apply the proposed mixDPFA model to multi-region experimental recordings, where we find that the proposed method can identify novel, reliable clusters of neurons based on their activity, and may, thus, be a useful tool for neural data analysis.
Modular architecture facilitates noise-driven control of synchrony in neuronal networks
Authors: Hideaki Yamamoto, F. Paul Spitzner, Taiki Takemuro, Victor Buendía, Carla Morante, Tomohiro Konno, Shigeo Sato, Ayumi Hirano-Iwata, Viola Priesemann, Miguel A. Muñoz, Johannes Zierenberg, Jordi Soriano
Date: 2022-05-21 00:00:00

Brain functions require both segregated processing of information in specialized circuits, as well as integration across circuits to perform high-level information processing. One possible way to implement these seemingly opposing demands is by flexibly switching between synchronous and less synchronous states. Understanding how complex synchronization patterns are controlled by the interaction of network architecture and external perturbations is thus a central challenge in neuroscience, but the mechanisms behind such interactions remain elusive. Here, we utilise precision neuroengineering to manipulate cultured neuronal networks and show that a modular architecture facilitates desynchronization upon asynchronous stimulation, making external noise a control parameter of synchrony. Using spiking neuron models, we then demonstrate that external noise can reduce the level of available synaptic resources, which make intermodular interactions more stochastic and thereby facilitates the breakdown of synchrony. Finally, the phenomenology of stochastic intermodular interactions is formulated into a mesoscopic model that incorporates a state-dependent gating mechanism for signal propagation. Taken together, our results demonstrate a network mechanism by which asynchronous inputs tune the inherent dynamical state in structured networks of excitable units.
Cophylogeny Reconstruction Allowing for Multiple Associations Through Approximate Bayesian Computation
Authors: Blerina Sinaimeri, Laura Urbini, Marie-France Sagot, Catherine Matias
Date: 2022-05-23 00:00:00

Phylogenetic tree reconciliation is employed for the examination of coevolution between host and symbiont species. An important concern is the requirement for dependable cost values when selecting event-based parsimonious reconciliation. Although certain approaches deduce event probabilities unique to each pair of host and symbiont trees, which can subsequently be converted into cost values, a significant limitation lies in their inability to model the invasion of diverse host species by the same symbiont species (termed as a spread event), which is believed to occur in symbiotic relationships. Invasions lead to the observation of multiple associations between symbionts and their hosts (indicating that a symbiont is no longer exclusive to a single host), which are incompatible with the existing methods of coevolution. We present AmoCoala, an enhanced version of the tool Coala, that provides a more realistic estimation of cophylogeny event probabilities for a given pair of host and symbiont trees, even in the presence of spread events. We expand the classical 4-event coevolutionary model to include 2 additional spread events (vertical and horizontal spreads) that lead to multiple associations. By incorporating spread events, our reconciliation model enables a more accurate consideration of multiple associations. This improvement enhances the precision of estimated cost sets, paving the way to a more reliable reconciliation of host and symbiont trees. Our results showcase that AmoCoala produces biologically plausible reconciliation scenarios, further emphasizing its effectiveness. The software is accessible at https://github.com/sinaimeri/AmoCoala
Parameters identification for an inverse problem arising from a binary option using a Bayesian inference approach
Authors: Yasushi Ota, Yu Jiang, Daiki Maki
Date: 2022-05-23 00:00:00
Tasks: Bayesian Inference

No--arbitrage property provides a simple method for pricing financial derivatives. However, arbitrage opportunities exist among different markets in various fields, even for a very short time. By knowing that an arbitrage property exists, we can adopt a financial trading strategy. This paper investigates the inverse option problems (IOP) in the extended Black--Scholes model. We identify the model coefficients from the measured data and attempt to find arbitrage opportunities in different financial markets using a Bayesian inference approach, which is presented as an IOP solution. The posterior probability density function of the parameters is computed from the measured data.The statistics of the unknown parameters are estimated by a Markov Chain Monte Carlo (MCMC) algorithm, which exploits the posterior state space. The efficient sampling strategy of the MCMC algorithm enables us to solve inverse problems by the Bayesian inference technique. Our numerical results indicate that the Bayesian inference approach can simultaneously estimate the unknown trend and volatility coefficients from the measured data.
Sparse modeling approach to the arbitrage-free interpolation of plain-vanilla option prices and implied volatilities
Authors: Daniel Guterding
Date: 2022-05-22 00:00:00

We present a method for the arbitrage-free interpolation of plain-vanilla option prices and implied volatilities, which is based on a system of integral equations that relates terminal density and option prices. Using a discretization of the terminal density, we write these integral equations as a system of linear equations. We show that the kernel matrix of this system is in general ill-conditioned, so that it can not be solved for the discretized density using a naive approach. Instead, we construct a sparse model for the kernel matrix using the singular value decomposition (SVD), which allows us not only to systematically improve the condition number of the kernel matrix, but also determines the computational effort and accuracy of our method. In order to allow for the treatment of realistic inputs that may contain arbitrage, we reformulate the system of linear equations as an optimization problem, in which the SVD-transformed density minimizes the error between the input prices and the arbitrage-free prices generated by our method. To further stabilize the method in the presence of noisy input prices or arbitrage, we apply an $L_1$-regularization to the SVD-transformed density. Our approach, which is inspired by recent progress in theoretical physics, offers a flexible and efficient framework for the arbitrage-free interpolation of plain-vanilla option prices and implied volatilities, without the need to explicitly specify a stochastic process, expansion basis functions or any other kind of model. We demonstrate the capabilities of our method on a number of artificial and realistic test cases.
On the skew and curvature of implied and local volatilities
Authors: Elisa Alòs, David García-Lorite, Makar Pravosud
Date: 2022-05-23 00:00:00

In this paper, we study the relationship between the short-end of the local and the implied volatility surfaces. Our results, based on Malliavin calculus techniques, recover the recent $\frac{1}{H+3/2}$ rule (where $H$ denotes the Hurst parameter of the volatility process) for rough volatilitites (see Bourgey, De Marco, Friz, and Pigato (2022)), that states that the short-time skew slope of the at-the-money implied volatility is $\frac{1}{H+3/2}$ the corresponding slope for local volatilities. Moreover, we see that the at-the-money short-end curvature of the implied volatility can be written in terms of the short-end skew and curvature of the local volatility and viceversa, and that this relationship depends on $H$.
European Power Option Pricing with Extended Vasicěk Interest Rate and Exponential Ornstein-Uhlenbeck Asset Process under Different Market Assumptions
Authors: Jingwei Liu
Date: 2022-05-21 00:00:00

We propose a general framework of European power option pricing under two different market assumptions about extended Vasic\v{e}k interest rate process and exponential Ornstein-Uhlenbeck asset process with continuous dividend as underlying, in which the Brownian motions involved in Vasic\v{e}k interest rate and exponential Ornstein-Uhlenbeck process are time-dependent correlated in equivalent martingale measure probability space or real-world probability space respectively. We first develop European power option pricing in two types of payoffs with martingale method under the market assumption that Vasic\v{e}k interest rate and exponential Ornstein-Uhlenbeck process are correlated in equivalent martingale measure probability space. Then, we solve the European power option pricing under the market assumption that Vasic\v{e}k interest rate and exponential Ornstein-Uhlenbeck process are correlated in real-world probability by constructing a Girsannov transform to map real-world probability to risk-neutral equivalent martingale measure. Finally, the European power option pricing formulae are derived with numeraire change and T-forward measure under the above two market assumptions in a uniform theoretical framework and close formulae expression.
Optimizing Returns Using the Hurst Exponent and Q Learning on Momentum and Mean Reversion Strategies
Authors: Y. Chang, C. Lizardi, R. Shah
Date: 2022-05-23 00:00:00
Tasks: Q-Learning, Time Series, Time Series Analysis

Momentum and mean reversion trading strategies have opposite characteristics. The former is generally better with trending assets, and the latter is generally better with mean reverting assets. Using the Hurst exponent, which classifies time series as trending or mean reverting, we attempt to trade with each strategy when it is advantageous to generate higher returns on average. We ultimately find that trading with the Hurst exponent can achieve higher returns, but it also comes at a higher risk. Finally, we consider limitations of our study and propose a method using Q-learning to improve our strategy and implementation of individual algorithms.
Exploring the stimulative effect on following drivers in a consecutive lane-change using microscopic vehicle trajectory data
Authors: Ruifeng Gu
Date: 2022-05-18 00:00:00

Improper lane-changing behaviors may result in breakdown of traffic flow and the occurrence of various types of collisions. This study investigates lane-changing behaviors of multiple vehicles and the stimulative effect on following drivers in a consecutive lane-changing scenario. The microscopic trajectory data from the dataset are used for driving behavior analysis.Two discretionary lane-changing vehicle groups constitute a consecutive lane-changing scenario, and not only distance- and speed-related factors but also driving behaviors are taken into account to examine the impacts on the utility of following lane-changing vehicles.A random parameters logit model is developed to capture the driver psychological heterogeneity in the consecutive lane-changing situation.Furthermore, a lane-changing utility prediction model is established based on three supervised learning algorithms to detect the improper lane-changing decision. Results indicate that (1) the consecutive lane-changing behaviors have a significant negative effect on the following lane-changing vehicles after lane-change; (2) the stimulative effect exists in a consecutive lane-change situation and its influence is heterogeneous due to different psychological activities of drivers; and (3) the utility prediction model can be used to detect an improper lane-changing decision.
Using Wordle for Learning to Design and Compare Strategies
Authors: Chao-Lin Liu
Date: 2022-04-30 00:00:00

Wordle is a very popular word game that is owned by the New York Times. We can design parameterized strategies for solving Wordle, based on probabilistic, statistical, and information-theoretical information about the games. The strategies can handle a reasonably large family of Wordle-like games both systematically and dynamically, meaning that we do not rely on precomputations that may work for non-fixed games. More specifically, the answer set can be arbitrary, not confining to the current 2315 words. The answer words may include any specific number of letters (does not have to be five), and the set of symbols that form the words does not have to be limited to only the English alphabet. Exploring possible strategies for solving the Wordle-like games offers an attractive learning challenges for students who are learning to design computer games. This paper will provide the results of using two families of parameterized strategies to solve the current Wordle, using the simulator that abides by the hard-mode rules as the baseline. The baseline simulator used an average of 4.078 guesses to find the 2315 answers, and needed more than six trials to solve the game 1.77% of the time. The best performing strategy of ours used an average of 3.674 guesses to find the 2315 answers, and failed 0.65% of the time.
DagSim: Combining DAG-based model structure with unconstrained data types and relations for flexible, transparent, and modularized data simulation
Authors: Ghadi S. Al Hajj, Johan Pensar, Geir Kjetil Sandve
Date: 2022-05-06 00:00:00
Tasks: BIG-bench Machine Learning, Causal Inference

Data simulation is fundamental for machine learning and causal inference, as it allows exploration of scenarios and assessment of methods in settings with full control of ground truth. Directed acyclic graphs (DAGs) are well established for encoding the dependence structure over a collection of variables in both inference and simulation settings. However, while modern machine learning is applied to data of an increasingly complex nature, DAG-based simulation frameworks are still confined to settings with relatively simple variable types and functional forms. We here present DagSim, a Python-based framework for DAG-based data simulation without any constraints on variable types or functional relations. A succinct YAML format for defining the simulation model structure promotes transparency, while separate user-provided functions for generating each variable based on its parents ensure simulation code modularization. We illustrate the capabilities of DagSim through use cases where metadata variables control shapes in an image and patterns in bio-sequences.