pwc-archive/papers-with-abstracts huggingface dataset:
Using the Empirical Attainment Function for Analyzing Single-objective Black-box Optimization Algorithms
Authors: Manuel López-Ibáñez, Diederick Vermetten, Johann Dreo, Carola Doerr
Date: 2024-04-02 00:00:00

A widely accepted way to assess the performance of iterative black-box optimizers is to analyze their empirical cumulative distribution function (ECDF) of pre-defined quality targets achieved not later than a given runtime. In this work, we consider an alternative approach, based on the empirical attainment function (EAF) and we show that the target-based ECDF is an approximation of the EAF. We argue that the EAF has several advantages over the target-based ECDF. In particular, it does not require defining a priori quality targets per function, captures performance differences more precisely, and enables the use of additional summary statistics that enrich the analysis. We also show that the average area over the convergence curves is a simpler-to-calculate, but equivalent, measure of anytime performance. To facilitate the accessibility of the EAF, we integrate a module to compute it into the IOHanalyzer platform. Finally, we illustrate the use of the EAF via synthetic examples and via the data available for the BBOB suite.
Beyond Suspension: A Two-phase Methodology for Concluding Sports Leagues
Authors: Ali Hassanzadeh, Mojtaba Hosseini, John G. Turner
Date: 2024-03-29 00:00:00
Tasks: Scheduling, Stochastic Optimization

Problem definition: Professional sports leagues may be suspended due to various reasons such as the recent COVID-19 pandemic. A critical question the league must address when re-opening is how to appropriately select a subset of the remaining games to conclude the season in a shortened time frame. Academic/practical relevance: Despite the rich literature on scheduling an entire season starting from a blank slate, concluding an existing season is quite different. Our approach attempts to achieve team rankings similar to that which would have resulted had the season been played out in full. Methodology: We propose a data-driven model which exploits predictive and prescriptive analytics to produce a schedule for the remainder of the season comprised of a subset of originally-scheduled games. Our model introduces novel rankings-based objectives within a stochastic optimization model, whose parameters are first estimated using a predictive model. We introduce a deterministic equivalent reformulation along with a tailored Frank-Wolfe algorithm to efficiently solve our problem, as well as a robust counterpart based on min-max regret. Results: We present simulation-based numerical experiments from previous National Basketball Association (NBA) seasons 2004--2019, and show that our models are computationally efficient, outperform a greedy benchmark that approximates a non-rankings-based scheduling policy, and produce interpretable results. Managerial implications: Our data-driven decision-making framework may be used to produce a shortened season with 25-50\% fewer games while still producing an end-of-season ranking similar to that of the full season, had it been played.
Fully Zeroth-Order Bilevel Programming via Gaussian Smoothing
Authors: Alireza Aghasi, Saeed Ghadimi
Date: 2024-03-29 00:00:00
Tasks: Bilevel Optimization

In this paper, we study and analyze zeroth-order stochastic approximation algorithms for solving bilvel problems, when neither the upper/lower objective values, nor their unbiased gradient estimates are available. In particular, exploiting Stein's identity, we first use Gaussian smoothing to estimate first- and second-order partial derivatives of functions with two independent block of variables. We then used these estimates in the framework of a stochastic approximation algorithm for solving bilevel optimization problems and establish its non-asymptotic convergence analysis. To the best of our knowledge, this is the first time that sample complexity bounds are established for a fully stochastic zeroth-order bilevel optimization algorithm.
Fast Orthogonal Matching Pursuit through Successive Regression
Authors: Huiyuan Yu, Jia He, Maggie Cheng
Date: 2024-03-29 00:00:00
Tasks: regression

Orthogonal Matching Pursuit (OMP) has been a powerful method in sparse signal recovery and approximation. However, OMP suffers computational issues when the signal has a large number of non-zeros. This paper advances OMP and its extension called generalized OMP (gOMP) by offering fast algorithms for the orthogonal projection of the input signal at each iteration. The proposed modifications directly reduce the computational complexity of OMP and gOMP. Experiment results verified the improvement in computation time. This paper also provides sufficient conditions for exact signal recovery. For general signals with additive noise, the approximation error is at the same order as OMP (gOMP), but is obtained within much less time.
Stochastic Optimization with Constraints: A Non-asymptotic Instance-Dependent Analysis
Authors: Koulik Khamaru
Date: 2024-03-24 00:00:00
Tasks: Stochastic Optimization

We consider the problem of stochastic convex optimization under convex constraints. We analyze the behavior of a natural variance reduced proximal gradient (VRPG) algorithm for this problem. Our main result is a non-asymptotic guarantee for VRPG algorithm. Contrary to minimax worst case guarantees, our result is instance-dependent in nature. This means that our guarantee captures the complexity of the loss function, the variability of the noise, and the geometry of the constraint set. We show that the non-asymptotic performance of the VRPG algorithm is governed by the scaled distance (scaled by $\sqrt{N}$) between the solutions of the given problem and that of a certain small perturbation of the given problem -- both solved under the given convex constraints; here, $N$ denotes the number of samples. Leveraging a well-established connection between local minimax lower bounds and solutions to perturbed problems, we show that as $N \rightarrow \infty$, the VRPG algorithm achieves the renowned local minimax lower bound by H\`{a}jek and Le Cam up to universal constants and a logarithmic factor of the sample size.
Impact of heterogeneity on infection probability: Insights from single-hit dose-response models
Authors: Francisco J. Perez-Reche
Date: 2024-03-31 00:00:00

The process of infection of a host is complex, influenced by factors such as microbial variation within and between hosts as well as differences in dose across hosts. This study uses dose-response and within-host microbial infection models to delve into the impact of these factors on infection probability. It is rigorously demonstrated that within-host heterogeneity in microbial infectivity enhances the probability of infection. The effect of infectivity and dose variation between hosts is studied in terms of the expected value of the probability of infection. General analytical findings, derived under the assumption of small infectivity, reveal that both types of heterogeneity reduce the expected infection probability. Interestingly, this trend appears consistent across specific dose-response models, suggesting a limited role for the small infectivity condition. Additionally, the vital dynamics behind heterogeneous infectivity are investigated with a within-host microbial growth model which enhances the biological significance of single-hit dose-response models. Testing these mathematical predictions inspire new and challenging laboratory experiments that could deepen our understanding of infections.
The curious case of A31P, a topology-switching mutant of the Repressor of Primer protein : A molecular dynamics study of its folding and misfolding
Authors: Olympia-Dialekti Vouzina, Alexandros Tafanidis, Nicholas M. Glykos
Date: 2024-04-01 00:00:00
Tasks: Protein Structure Prediction

The effect of mutations on protein structures is usually rather localized and minor. Finding a mutation that can single-handedly change the fold and/or topology of a protein structure is a rare exception. The A31P mutant of the homodimeric Repressor of Primer (Rop) protein is one such exception: This single mutation -and as demonstrated by two independent crystal structure determinations- can convert the canonical (left-handed/all-antiparallel) 4-alpha-helical bundle of Rop, to a new form (right-handed/mixed parallel and antiparallel bundle) displaying a previously unobserved 'bisecting U' topology. The main problem with understanding the dramatic effect of this mutation on the folding of Rop is to understand its very existence : Most computational methods appear to agree that the mutation should have had no appreciable effect, with the majority of energy minimization methods and protein structure prediction protocols indicating that this mutation is fully consistent with the native Rop structure, requiring only a local and minor change at the mutation site. Here we use two long (10 us each) molecular dynamics simulations to compare the stability and dynamics of the native Rop versus a hypothetical structure that is identical with the native Rop but is carrying this single Alanine-31 to Proline mutation. Comparative analysis of the two trajectories convincingly shows that in contrast to the indications from energy minimization -but in agreement with the experimental data-, this hypothetical native-like A31P structure is unstable, with its turn regions almost completely unfolding, even under the relatively mild 320K NpT simulations that we have used for this study. We discuss the implication of these findings for the folding of the A31P mutant, especially with respect to the proposed model of a double-funneled energy landscape.
Microbial assessment in a rare Norwegian book collection: a One Health approach to cultural heritage
Authors: Sílvia O. Sequeira, Ekaterina Pasnak, Carla Viegas, Bianca Gomes, Marta Dias, Renata Cervantes, Pedro Pena, Magdalena Twarużek, Robert Kosicki, Susana Viegas, Liliana Aranha Caetano, Maria João Penetra, Inês Santos, Ana Teresa Caldeira, Catarina Pinheiro
Date: 2024-03-29 00:00:00

Microbial contamination poses a threat to both the preservation of library and archival collections and the health of staff and users. This study investigated the microbial communities and potential health risks associated with the UNESCO-classified Norwegian Sea Trade Archive (NSTA) collection exhibiting visible microbial colonization and staff health concerns. Dust samples from book surfaces and the storage environment were analysed using culturing methods, qPCR, Next Generation Sequencing, and mycotoxin, cytotoxicity and azole resistance assays. Penicillium sp., Aspergillus sp., and Cladosporium sp. were the most common fungi identified, with some potentially toxic species like Stachybotrys sp., Toxicladosporium sp. and Aspergillus section Fumigati. Fungal resistance to azoles was not detected. Only one mycotoxin, sterigmatocystin, was found in a heavily contaminated book. Dust extracts from books exhibited moderate to high cytotoxicity on human lung cells, suggesting a potential respiratory risk. The collection had higher contamination levels compared to the storage environment, likely due to improved storage conditions. Even though, overall low contamination levels were obtained, which might be underestimated due to the presence of salt (from cod preservation) that could have interfered with the analyses. This study underlines the importance of monitoring microbial communities and implementing proper storage measures to safeguard cultural heritage and staff well-being.
Molecular Generative Adversarial Network with Multi-Property Optimization
Authors: Huidong Tang, Chen Li, Sayaka Kamei, Yoshihiro Yamanishi, Yasuhiko Morimoto
Date: 2024-03-29 00:00:00
Tasks: Drug Discovery, Generative Adversarial Network, Reinforcement Learning (RL)

Deep generative models, such as generative adversarial networks (GANs), have been employed for $de~novo$ molecular generation in drug discovery. Most prior studies have utilized reinforcement learning (RL) algorithms, particularly Monte Carlo tree search (MCTS), to handle the discrete nature of molecular representations in GANs. However, due to the inherent instability in training GANs and RL models, along with the high computational cost associated with MCTS sampling, MCTS RL-based GANs struggle to scale to large chemical databases. To tackle these challenges, this study introduces a novel GAN based on actor-critic RL with instant and global rewards, called InstGAN, to generate molecules at the token-level with multi-property optimization. Furthermore, maximized information entropy is leveraged to alleviate the mode collapse. The experimental results demonstrate that InstGAN outperforms other baselines, achieves comparable performance to state-of-the-art models, and efficiently generates molecules with multi-property optimization. The source code will be released upon acceptance of the paper.
Guide to k-mer approaches for genomics across the tree of life
Authors: Katharine M. Jenike, Lucía Campos-Domínguez, Marilou Boddé, José Cerca, Christina N. Hodson, Michael C. Schatz, Kamil S. Jaron
Date: 2024-04-01 00:00:00
Tasks: Diversity

The wide array of currently available genomes display a wonderful diversity in size, composition and structure with many more to come thanks to several global biodiversity genomics initiatives starting in recent years. However, sequencing of genomes, even with all the recent advances, can still be challenging for both technical (e.g. small physical size, contaminated samples, or access to appropriate sequencing platforms) and biological reasons (e.g. germline restricted DNA, variable ploidy levels, sex chromosomes, or very large genomes). In recent years, k-mer-based techniques have become popular to overcome some of these challenges. They are based on the simple process of dividing the analysed sequences (e.g. raw reads or genomes) into a set of sub-sequences of length k, called k-mers. Despite this apparent simplicity, k-mer-based analysis allows for a rapid and intuitive assessment of complex sequencing datasets. Here, we provide the first comprehensive review to the theoretical properties and practical applications of k-mers in biodiversity genomics, serving as a reference manual for this powerful approach.
Variational design of sensory feedback for powerstroke-recovery systems
Authors: Zhuojun Yu, Peter J. Thomas
Date: 2024-03-29 00:00:00
Tasks: Sensitivity

Although the raison d'etre of the brain is the survival of the body, there are relatively few theoretical studies of closed-loop rhythmic motor control systems. In this paper we provide a unified framework, based on variational analysis, for investigating the dual goals of performance and robustness in powerstroke-recovery systems. We augment two previously published closed-loop motor control models by equipping each model with a performance measure based on the rate of progress of the system relative to a spatially extended external substrate -- such as progress relative to the ground for a locomotor task. The sensitivity measure quantifies the ability of the system to maintain performance in response to external perturbations. Motivated by a search for optimal design principles for feedback control achieving the complementary requirements of efficiency and robustness, we discuss the performance-sensitivity patterns of the systems featuring different sensory feedback architectures. In a paradigmatic half-center oscillator (HCO)-motor system, we observe that the excitation-inhibition property of feedback mechanisms determines the sensitivity pattern while the activation-inactivation property determines the performance pattern. Moreover, we show that the nonlinearity of the sigmoid activation of feedback signals allows the existence of optimal combinations of performance and sensitivity. In a detailed hindlimb locomotor system, we find that a force-dependent feedback can simultaneously optimize both performance and robustness, while length-dependent feedback variations result in significant performance-versus-sensitivity tradeoffs. Thus, this work provides an analytical framework for studying feedback control of oscillations in nonlinear dynamical systems, leading to several insights that have the potential to inform the design of control or rehabilitation systems.
Detection of bromochloro alkanes in indoor dust using a novel CP-Seeker data integration tool
Authors: Thomas J Mcgrath, Julien Saint-Vanne, Sébastien Hutinet, Walter Vetter, Giulia Poma, Yukiko Fujii, Robin E Dodson, Boris Johnson-Restrepo, Dudsadee Muenhor, Bruno Le Bizec, Gaud Dervilly, Adrian Covaci, Ronan Cariou
Date: 2024-04-02 00:00:00
Tasks: Data Integration

Bromochloro alkanes (BCAs) have been manufactured for use as flame retardants for decades and preliminary environmental risk screening suggests they are likely to behave similarly to polychlorinated alkanes (PCAs), subclasses of which are restricted as Stockholm Convention Persistent Organic Pollutants (POPs). BCAs have rarely been studied in the environment, though some evidence suggests they may migrate from treated-consumer materials into indoor dust, resulting in human exposure via inadvertent ingestion. In this study, BCA-C14 mixture standards were synthesized and used to validate an analytical method. This method relies on chloride-enhanced liquid chromatography-electrospray ionization-Orbitrap-high resolution mass spectrometry (LC-ESI-Orbitrap-HRMS) and a novel CP-Seeker integration software package for homologue detection and integration. Dust sample preparation via ultrasonic extraction, acidified silica clean-up and fractionation on neutral silica cartridges was found to be suitable for BCAs, with absolute recovery of individual homologues averaging 66 to 78% and coefficients of variation $\le$10% in replicated spiking experiments (n=3). In addition, a total of 59 indoor dust samples from six countries including Australia (n=10), Belgium (n=10), Colombia (n=10), Japan (n=10), Thailand (n=10) and the United States of America (n=9) were analysed for BCAs. BCAs were detected in seven samples from the USA, with carbon chain lengths of C8, C10, C12, C14, C16, C18, C24 to C28, C30 and C31 observed overall, though not detected in samples from any other countries. Bromination of detected homologues in the indoor dust samples ranged from Br1-4 as well as Br7, while chlorine numbers ranged from Cl2-11. BCA-C18 were the most frequently detected, observed in each of the USA samples, while the most prevalent halogenation degrees were homologues of Br2 and Cl4-5. Broad estimations of BCA concentrations in the dust samples indicated that levels may approach those of other flame retardants in at least some instances. These findings suggest that development of quantification strategies and further investigation of environmental occurrence and health implications are needed.
Watanabe's expansion: A Solution for the convexity conundrum
Authors: David García-Lorite, Raul Merino
Date: 2024-04-01 00:00:00

In this paper, we present a new method for pricing CMS derivatives. We use Mallaivin's calculus to establish a model-free connection between the price of a CMS derivative and a quadratic payoff. Then, we apply Watanabe's expansions to quadratic payoffs case under local and stochastic local volatility. Our approximations are generic. To evaluate their accuracy, we will compare the approximations numerically under the normal SABR model against the market standards: Hagan's approximation, and a Monte Carlo simulation.
Investigating Similarities Across Decentralized Financial (DeFi) Services
Authors: Junliang Luo, Stefan Kitzler, Pietro Saggese
Date: 2024-03-23 00:00:00
Tasks: Graph Representation Learning, Representation Learning

We explore the adoption of graph representation learning (GRL) algorithms to investigate similarities across services offered by Decentralized Finance (DeFi) protocols. Following existing literature, we use Ethereum transaction data to identify the DeFi building blocks. These are sets of protocol-specific smart contracts that are utilized in combination within single transactions and encapsulate the logic to conduct specific financial services such as swapping or lending cryptoassets. We propose a method to categorize these blocks into clusters based on their smart contract attributes and the graph structure of their smart contract calls. We employ GRL to create embedding vectors from building blocks and agglomerative models for clustering them. To evaluate whether they are effectively grouped in clusters of similar functionalities, we associate them with eight financial functionality categories and use this information as the target label. We find that in the best-case scenario purity reaches .888. We use additional information to associate the building blocks with protocol-specific target labels, obtaining comparable purity (.864) but higher V-Measure (.571); we discuss plausible explanations for this difference. In summary, this method helps categorize existing financial products offered by DeFi protocols, and can effectively automatize the detection of similar DeFi services, especially within protocols.
Using Machine Learning to Forecast Market Direction with Efficient Frontier Coefficients
Authors: Nolan Alexander, William Scherer
Date: 2024-03-31 00:00:00
Tasks: Portfolio Optimization

We propose a novel method to improve estimation of asset returns for portfolio optimization. This approach first performs a monthly directional market forecast using an online decision tree. The decision tree is trained on a novel set of features engineered from portfolio theory: the efficient frontier functional coefficients. Efficient frontiers can be decomposed to their functional form, a square-root second-order polynomial, and the coefficients of this function captures the information of all the constituents that compose the market in the current time period. To make these forecasts actionable, these directional forecasts are integrated to a portfolio optimization framework using expected returns conditional on the market forecast as an estimate for the return vector. This conditional expectation is calculated using the inverse Mills ratio, and the Capital Asset Pricing Model is used to translate the market forecast to individual asset forecasts. This novel method outperforms baseline portfolios, as well as other feature sets including technical indicators and the Fama-French factors. To empirically validate the proposed model, we employ a set of market sector ETFs.
Stress index strategy enhanced with financial news sentiment analysis for the equity markets
Authors: Baptiste Lefort, Eric Benhamou, Jean-Jacques Ohana, David Saltiel, Beatrice Guez, Thomas Jacquot
Date: 2024-03-12 00:00:00
Tasks: Sentiment Analysis

This paper introduces a new risk-on risk-off strategy for the stock market, which combines a financial stress indicator with a sentiment analysis done by ChatGPT reading and interpreting Bloomberg daily market summaries. Forecasts of market stress derived from volatility and credit spreads are enhanced when combined with the financial news sentiment derived from GPT-4. As a result, the strategy shows improved performance, evidenced by higher Sharpe ratio and reduced maximum drawdowns. The improved performance is consistent across the NASDAQ, the S&P 500 and the six major equity markets, indicating that the method generalises across equities markets.