pwc-archive/papers-with-abstracts huggingface dataset:
Challenges in Gaussian Processes for Non Intrusive Load Monitoring
Authors: Aadesh Desai, Gautam Vashishtha, Zeel B Patel, Nipun Batra
Date: 2022-11-18 00:00:00
Tasks: Gaussian Processes, Non-Intrusive Load Monitoring

Non-intrusive load monitoring (NILM) or energy disaggregation aims to break down total household energy consumption into constituent appliances. Prior work has shown that providing an energy breakdown can help people save up to 15\% of energy. In recent years, deep neural networks (deep NNs) have made remarkable progress in the domain of NILM. In this paper, we demonstrate the performance of Gaussian Processes (GPs) for NILM. We choose GPs due to three main reasons: i) GPs inherently model uncertainty; ii) equivalence between infinite NNs and GPs; iii) by appropriately designing the kernel we can incorporate domain expertise. We explore and present the challenges of applying our GP approaches to NILM.
Step Counting with Attention-based LSTM
Authors: Shehroz S. Khan, Ali Abedi
Date: 2022-11-18 00:00:00
Tasks: Time Series Analysis

Physical activity is recognized as an essential component of overall health. One measure of physical activity, the step count, is well known as a predictor of long-term morbidity and mortality. Step Counting (SC) is the automated counting of the number of steps an individual takes over a specified period of time and space. Due to the ubiquity of smartphones and smartwatches, most current SC approaches rely on the built-in accelerometer sensors on these devices. The sensor signals are analyzed as multivariate time series, and the number of steps is calculated through a variety of approaches, such as time-domain, frequency-domain, machine-learning, and deep-learning approaches. Most of the existing approaches rely on dividing the input signal into windows, detecting steps in each window, and summing the detected steps. However, these approaches require the determination of multiple parameters, including the window size. Furthermore, most of the existing deep-learning SC approaches require ground-truth labels for every single step, which can be arduous and time-consuming to annotate. To circumvent these requirements, we present a novel SC approach utilizing many-to-one attention-based LSTM. With the proposed LSTM network, SC is solved as a regression problem, taking the entire sensor signal as input and the step count as the output. The analysis shows that the attention-based LSTM automatically learned the pattern of steps even in the absence of ground-truth labels. The experimental results on three publicly available SC datasets demonstrate that the proposed method successfully counts the number of steps with low values of mean absolute error and high values of SC accuracy.
The Role of Cytonemes and Diffusive Transport in the Establishment of Morphogen Gradients
Authors: Jay A. Stotsky, Hans G. Othmer
Date: 2022-11-22 00:00:00

Spatial distributions of morphogens provide positional information in developing systems, but how the distributions are established and maintained remains an open problem. Transport by diffusion has been the traditional mechanism, but recent experimental work has shown that cells can also communicate by filopodia-like structures called cytonemes that make direct cell-to-cell contacts. Here we investigate the roles each may play individually in a complex tissue and how they can jointly establish a reliable spatial distribution of a morphogen.
Probabilistic representations as building blocks for higher-level vision
Authors: Andrey Chetverikov, Árni Kristjánsson
Date: 2022-11-22 00:00:00

Current theories of perception suggest that the brain represents features of the world as probability distributions, but can such uncertain foundations provide the basis for everyday vision? Perceiving objects and scenes requires knowing not just how features (e.g., colors) are distributed but also where they are and which other features they are combined with. Using a Bayesian computational model, we recovered probabilistic representations used by human observers to search for odd stimuli among distractors. Importantly, we found that the brain integrates information between feature dimensions and spatial locations, leading to more precise representations compared to when information integration is not possible. We also uncovered representational asymmetries and biases, showing their spatial organization and explain how this structure argues against "summary statistics" accounts of visual representations. Our results confirm that probabilistically encoded visual features are bound with other features and to particular locations, providing a powerful demonstration of how probabilistic representations can be a foundation for higher-level vision.
Challenges and perspectives in computational deconvolution of genomics data
Authors: Lana X. Garmire, Yijun Li, Qianhui Huang, Chuan Xu, Sarah Teichmann, Naftali Kaminski, Matteo Pellegrini, Quan Nguyen, Andrew E. Teschendorff
Date: 2022-11-21 00:00:00
Tasks: Benchmarking

Deciphering cell type heterogeneity is crucial for systematically understanding tissue homeostasis and its dysregulation in diseases. Computational deconvolution is an efficient approach estimating cell type abundances from a variety of omics data. Despite significant methodological progress in computational deconvolution in recent years, challenges are still outstanding. Here we enlist four significant challenges related to computational deconvolution, from the quality of the reference data, generation of ground truth data, limitations of computational methodologies, and benchmarking design and implementation. Finally, we make recommendations on reference data generation, new directions of computational methodologies and strategies to promote rigorous benchmarking.
Feasibility and stability in large Lotka Volterra systems with interaction structure
Authors: Xiaoyuan Liu, George W. A. Constable, Jonathan W. Pitchford
Date: 2022-11-23 00:00:00

Complex system stability can be studied via linear stability analysis using Random Matrix Theory (RMT) or via feasibility (requiring positive equilibrium abundances). Both approaches highlight the importance of interaction structure. Here we show, analytically and numerically, how RMT and feasibility approaches can be complementary. In generalised Lotka-Volterra (GLV) models with random interaction matrices, feasibility increases when predator-prey interactions increase; increasing competition/mutualism has the opposite effect. These changes have crucial impact on the stability of the GLV model.
Experimentos sobre la Hibridación en Plantas
Authors: Gregor Mendel
Date: 2022-11-23 00:00:00

Mendel performed his experiments from 1856 and 1863, presented his results in two meetings of the Natural Science Society in Brunn in February and March of 1865, and finally published them in the iconical paper of 1866, Versuche uber Plflanzenhybriden. Two main translations to Spanish are available: the one done by Prevosti in 1977, and a more recent one by Gomez-Graciani in 2014. Both were translated from the English version of Druery and Bateson. Here, we present the first direct translation from German to Spanish of this paper. This new version corrects some errors detected in the previous versions, uses a more agile style and includes the Darwinized terms pointed out by Abbot and Faribanks in a recent paper (Genetics, 204(2):401-405, 2016; doi: 10.1534/genetics.116.194613).
Statistical learning with phylogenetic network invariants
Authors: Travis Barton, Elizabeth Gross, Colby Long, Joseph Rusinko
Date: 2022-11-22 00:00:00

Phylogenetic networks provide a means of describing the evolutionary history of sets of species believed to have undergone hybridization or gene flow during their evolution. The mutation process for a set of such species can be modeled as a Markov process on a phylogenetic network. Previous work has shown that a site-pattern probability distributions from a Jukes-Cantor phylogenetic network model must satisfy certain algebraic invariants. As a corollary, aspects of the phylogenetic network are theoretically identifiable from site-pattern frequencies. In practice, because of the probabilistic nature of sequence evolution, the phylogenetic network invariants will rarely be satisfied, even for data generated under the model. Thus, using network invariants for inferring phylogenetic networks requires some means of interpreting the residuals, or deviations from zero, when observed site-pattern frequencies are substituted into the invariants. In this work, we propose a method of utilizing invariant residuals and support vector machines to infer 4-leaf level-one phylogenetic networks, from which larger networks can be reconstructed. Given data for a set of species, the support vector machine is first trained on model data to learn the patterns of residuals corresponding to different network structures to classify the network that produced the data. We demonstrate the performance of our method on simulated data from the specified model and primate data.
Machine learning strategies to predict late adverse effects in childhood acute lymphoblastic leukemia survivors
Authors: Nicolas Raymond, Maxime Caru, Hakima Laribi, Mehdi Mitiche, Valérie Marcil, Maja Krajinovic, Daniel Curnier, Daniel Sinnett, Martin Vallières
Date: 2022-11-23 00:00:00

Acute lymphoblastic leukemia is the most frequent pediatric cancer. Approximately two third of survivors develop one or more health complications known as late adverse effects following their treatments. The existing measures offered to patients during their follow-up visits to the hospital are rather standardized for all childhood cancer survivors and not necessarily personalized for childhood ALL survivors. As a result, late adverse effects may be underdiagnosed and, in most cases, only taken care of following their appearance. Thus, it is necessary to predict these treatment-related conditions earlier in order to prevent them and enhance the survivors' health. Multiple studies have investigated the development of late adverse effects prediction tools to offer better personalized follow-up methods. However, no solution integrated the usage of neural networks to date. In this work, we developed graph-based parameters-efficient neural networks and promoted their interpretability with multiple post-hoc analyses. We first proposed a new disease-specific VO$_2$ peak prediction model that does not require patients to participate to a physical function test (e.g., 6-minute walk test) and further created an obesity prediction model using clinical variables that are available from the end of childhood ALL treatment as well as genomic variables. Our solutions were able to achieve better performance than linear and tree-based models on small cohorts of patients ($\leq$ 223) for both tasks.
Biological Random Walks: multi-omics integration for disease gene prioritization
Authors: Michele Gentili, Leonardo Martini, Marialuisa Sponziello, Luca Becchetti
Date: 2022-11-23 00:00:00
Tasks: Retrieval

Motivation: Over the past decade, network-based approaches have proven useful in identifying disease modules within the human interactome, often providing insights into key mechanisms and guiding the quest for therapeutic targets. This is all the more important, since experimental investigation of potential gene candidates is an expensive task, thus not always a feasible option. On the other hand, many sources of biological information exist beyond the interactome and an important research direction is the design of effective techniques for their integration. Results: In this work, we introduce the Biological Random Walks (BRW) approach for disease gene prioritization in the human interactome. The proposed framework leverages multiple biological sources within an integrated framework. We perform an extensive, comparative study of BRW's performance against well-established baselines. Availability and implementation: All code is publicly available and can be downloaded at \url{https://github.com/LeoM93/BiologicalRandomWalks}. We used publicly available datasets, details on their retrieval and preprocessing are provided in the supplementary material.
Evaluation of the Antibacterial and Wound Healing Properties of a Burn Ointment Containing Curcumin, Honey, and Potassium Aluminium
Authors: Mahsa Shahbandeh, Mahsa Amin Salehi, Maryam Soltanyzadeh, Mehrnaz Mirzaei, Ali Maleki, Abdolkarim Chehregani rad, Mohammad Javad Fatemi, Reza Mirnejad, Mostafa Dahmardehei
Date: 2022-11-22 00:00:00

Burn wounds can severely trouble the health system and life quality of patients. The present study aimed to analyze the synergistic healing properties of curcumin, honey, and potassium alum substances merged in a newly-devised burn ointment on second-degree burn wounds in rats. The MIC and MBC tests on 200 clinical isolates of Pseudomonas aeruginous are compared to imipenem in vitro. Their killing time and cytotoxicity are also studied using a standard isolate of P. aeruginous, fibroblast stem cells (FSC) and mouse embryonic fibroblasts (MEF). Furthermore, histopathological and histomorphological assessments are conducted on 150 male Wistar rats whitin four experimental groups to evaluate the efficiency of the prepared burn ointment. We found a significant wound healing in both macroscopical observations and microscopical evaluations. Both curcumin and honey show strong antimicrobial effects with no cytotoxicity. Also, the histopathological results present a considerable and comparable wound re-epithelization in the a group of rats treated with both honey and curcumin after 7 days. The burn ointment containing curcumin, honey, and potassium alum show considerable efficacy in accelerating the healing of experimentally-induced burn wounds in animals. Th novel onement product is propose as a powerful alternative for the topical treatment of burn injuries.
Continuous-Time Monotone Mean-Variance Portfolio Selection in Jump-Diffusion Model
Authors: Yuchen Li, Zongxia Liang, Shunzhi Pang
Date: 2022-11-22 00:00:00

We study continuous-time portfolio selection under monotone mean-variance (MMV) preferences in a jump-diffusion model, presenting an explicit solution different from that under classical mean-variance (MV) preferences in dynamic settings for the first time. We prove that the potential measures calculating MMV preferences can be restricted to non-negative Dol\'eans-Dade exponentials. We find that MMV can resolve the non-monotonicity and free cash flow stream problems of MV when the jump size can be larger than the inverse of the market price of risk. Such result is completely comparable to the earliest result by Dybvig and Ingersoll. Economically, we show that the essence of MMV lies in the pricing operator always remaining non-negative, with a value of zero assigned when the jump exceeds a certain threshold, avoiding the issue of non-monotonicity. As a result, MMV investors behave markedly different from MV investors. Furthermore, we validate the two-fund separation and establish the monotone capital asset pricing model (monotone CAPM) for MMV investors. We also study MMV in a constrained trading model and provide three specific numerical examples to show MMV's efficiency. Our finding can serve as a crucial theoretical foundation for future empirical tests of MMV and monotone CAPM's effectiveness.
Vanna-Volga pricing for single and double barrier FX options
Authors: J. Martín Ovejero
Date: 2022-11-23 00:00:00

In this paper, we provide a unified treatment of the Vanna-Volga pricing technique. We derive the value of single and double barriers FX options, as well as closed formulas for the Delta, Vega, Vanna and Volga of those contracts.
Formation of Optimal Interbank Networks under Liquidity Shocks
Authors: Daniel E. Rigobon, Ronnie Sircar
Date: 2022-11-22 00:00:00

We study the formation of an optimal interbank network in a model where banks control both their supply of liquidity, through cash reserves, and their exposures to other banks' risky projects. The value of each bank's project may suddenly decline depending on their cash reserves and both the occurence and magnitude of liquidity shocks. In two distinct settings, we solve the system-wide optimal control problem and obtain explicit formulas for the unique optimal allocations of capital. In the first decentralized setting, banks seek only to maximize their own expected utility. Second, a central planner aims to maximize the sum of all banks' expected utilities. Both of the resulting networks exhibit a `core-periphery' structure in equilibrium. However, in the decentralized setting, banks elect to hold less cash reserves compared to the centralized setting, leading to greater susceptibility to liquidity shortages. We characterize the behavior of the planner's optimal allocation as the size of the system grows. Surprisingly, the relative welfare gap is of constant order. Finally, we derive co-investment requirements that allow the decentralized system to achieve the planner's optimal level of risk. In doing so, we find that banks in the network's core are subjected to the highest co-investment requirements -- ensuring that they are sufficiently incentivized to hold significant cash reserves. Our analysis may inform regulators' requirements on banks' liquidity reserves, as have been debated in the wake of the 2023 regional banking crisis in the US.
A new encoding of implied volatility surfaces for their synthetic generation
Authors: Zheng Gong, Wojciech Frys, Renzo Tiranti, Carmine Ventre, John O'Hara, Yingbo Bai
Date: 2022-11-23 00:00:00
Tasks: Management

In financial terms, an implied volatility surface can be described by its term structure, its skewness and its overall volatility level. We use a PCA variational auto-encoder model to perfectly represent these descriptors into a latent space of three dimensions. Our new encoding brings significant benefits for synthetic surface generation, in that (i) scenario generation is more interpretable; (ii) volatility extrapolation achieve better accuracy; and, (iii) we propose a solution to infer implied volatility surfaces of a stock from an index to which it belongs directly by modelling their relationship on the latent space of the encoding. All these applications, and the latter in particular, have the potential to improve risk management of financial derivatives whenever data is scarce.
An Intraday GARCH Model for Discrete Price Changes and Irregularly Spaced Observations
Authors: Vladimír Holý
Date: 2022-11-22 00:00:00

We develop a novel observation-driven model for high-frequency prices. We account for irregularly spaced observations, simultaneous transactions, discreteness of prices, and market microstructure noise. The relation between trade durations and price volatility, as well as intraday patterns of trade durations and price volatility, is captured using smoothing splines. The dynamic model is based on the zero-inflated Skellam distribution with time-varying volatility in a score-driven framework. Market microstructure noise is filtered by including a moving average component. The model is estimated by the maximum likelihood method. In an empirical study of the IBM stock, we demonstrate that the model provides a good fit to the data. Besides modeling intraday volatility, it can also be used to measure daily realized volatility.
Early Warning Signals for Cryptocurrency Market States
Authors: Vishwas Kukreti
Date: 2022-11-22 00:00:00

Being archetypal complex systems, financial markets exhibit rich set of dynamics in their interactions. In this paper, we focus on the recently evolved cryptocurrency market as an example of a complex system and analyse the evolution of cross correlation structure of cryptocurrencies in the 5 year period from 2017 to 2022. We observe characteristic correlation structures in the observation time window duration and use these specific structures to cluster the cryptocurrency market in 4 market states.
Newly Developed Flexible Grid Trading Model Combined ANN and SSO algorithm
Authors: Wei-Chang Yeh, Yu-Hsin Hsieh, Chia-Ling Huang
Date: 2022-09-05 00:00:00

In modern society, the trading methods and strategies used in financial market have gradually changed from traditional on-site trading to electronic remote trading, and even online automatic trading performed by a pre-programmed computer programs because the continuous development of network and computer computing technology. The quantitative trading, which the main purpose is to automatically formulate people's investment decisions into a fixed and quantifiable operation logic that eliminates all emotional interference and the influence of subjective thoughts and applies this logic to financial market activities in order to obtain excess profits above average returns, has led a lot of attentions in financial market. The development of self-adjustment programming algorithms for automatically trading in financial market has transformed a top priority for academic research and financial practice. Thus, a new flexible grid trading model combined with the Simplified Swarm Optimization (SSO) algorithm for optimizing parameters for various market situations as input values and the fully connected neural network (FNN) and Long Short-Term Memory (LSTM) model for training a quantitative trading model to automatically calculate and adjust the optimal trading parameters for trading after inputting the existing market situation is developed and studied in this work. The proposed model provides a self-adjust model to reduce investors' effort in the trading market, obtains outperformed investment return rate and model robustness, and can properly control the balance between risk and return.
OOD-DiskANN: Efficient and Scalable Graph ANNS for Out-of-Distribution Queries
Authors: Shikhar Jaiswal, Ravishankar Krishnaswamy, Ankit Garg, Harsha Vardhan Simhadri, Sheshansh Agrawal
Date: 2022-10-22 00:00:00

State-of-the-art algorithms for Approximate Nearest Neighbor Search (ANNS) such as DiskANN, FAISS-IVF, and HNSW build data dependent indices that offer substantially better accuracy and search efficiency over data-agnostic indices by overfitting to the index data distribution. When the query data is drawn from a different distribution - e.g., when index represents image embeddings and query represents textual embeddings - such algorithms lose much of this performance advantage. On a variety of datasets, for a fixed recall target, latency is worse by an order of magnitude or more for Out-Of-Distribution (OOD) queries as compared to In-Distribution (ID) queries. The question we address in this work is whether ANNS algorithms can be made efficient for OOD queries if the index construction is given access to a small sample set of these queries. We answer positively by presenting OOD-DiskANN, which uses a sparing sample (1% of index set size) of OOD queries, and provides up to 40% improvement in mean query latency over SoTA algorithms of a similar memory footprint. OOD-DiskANN is scalable and has the efficiency of graph-based ANNS indices. Some of our contributions can improve query efficiency for ID queries as well.