pwc-archive/papers-with-abstracts huggingface dataset:
Bifurcation Analysis of an Influenza A (H1N1) Model with Treatment and Vaccination
Authors: Kazi Mehedi Mohammad, Asma Akter Akhi, Md. Kamrujjaman
Date: 2024-03-17 00:00:00

This study focuses on the modeling, mathematical analysis, developing theories, and numerical simulation of Influenza virus transmission. We have proved the existence, uniqueness, positivity, and boundedness of the solutions. Also, investigate the qualitative behavior of the models and find the basic reproduction number $(\mathcal{R}_0)$ that guarantees the asymptotic stability of the disease-free and endemic equilibrium points. The local and global asymptotic stability of the disease free state and endemic equilibrium of the system is analyzed with the Lyapunov method, Routh-Hurwitz, and other criteria and presented graphically. This study helps to investigate the effectiveness of control policy and makes suggestions for alternative control policies. Bifurcation analyses are carried out to determine prevention strategies. Transcritical, Hopf, and backward bifurcation analyses are displayed analytically and numerically to show the dynamics of disease transmission in different cases. Moreover, analysis of contour plot, box plot, relative biases, phase portraits are presented to show the influential parameters to curtail the disease outbreak. We are interested in finding the nature of $\mathcal{R}_0$, which determines whether the disease dies out or persists in the population. The findings indicate that the dynamics of the model are determined by the threshold parameter $\mathcal{R}_0$.
Nuclear compensatory evolution driven by mito-nuclear incompatibilities
Authors: Débora Princepe, Marcus A. M. de Aguiar
Date: 2024-03-15 00:00:00

Mitochondrial function relies on the coordinated expression of mitochondrial and nuclear genes, exhibiting remarkable resilience regardless the susceptibility of mitochondrial DNA (mtDNA) to accumulate harmful mutations. A suggested mechanism for preserving this mito-nuclear compatibility is the nuclear compensation, where deleterious mitochondrial alleles drive compensatory changes in nuclear genes. However, prevalence and conditioning factors for this phenomenon remain debated, with empirical evidence supporting and refuting its existence. Here, we investigate how mito-nuclear incompatibilities impact nuclear and mitochondrial substitutions in a model for species radiation under selection for mito-nuclear compatibility, similar to the process of mtDNA introgression. Mating eligibility relies on genetic (nuclear DNA) and spatial proximity, with populations evolving from partially compatible mito-nuclear states. Mutations do not confer advantages nor disadvantages, with no optimal nuclear or mitochondrial types, but individual fitness decreases with increasing incompatibilities, driving the demand for mito-nuclear genetic coordination. We find that selection consistently promotes compensation on incompatible nuclear genes, resulting in more substitutions than compatible or non-interacting genes. Surprisingly, low mitochondrial mutation rates favor compensation, as do increased selective pressure or a higher number of mismatches. High mitochondrial mutation rates boost substitutions in initially compatible nuclear genes, relaxing the selection against mito-nuclear incompatibilities and mirroring the compensatory evolution. Moreover, the presence of incompatibilities accelerates species radiation, but richness at equilibrium is not directly correlated with substitutions' response, revealing the complex dynamics triggered by mitochondrial introgression and mito-nuclear co-evolution.
The Impact of Time Delay on Mutant Fixation in Evolutionary Games
Authors: Javad Mohamadichamgavi, Mark Broom
Date: 2024-03-14 00:00:00

Evolutionary game theory examines how strategies spread and persist in populations through reproduction and imitation based on their fitness. Traditionally, models assume instantaneous dynamics where fitness depends on the current population state. However, some real-world processes unfold over time, with outcomes emerging from history. This motivates incorporating time delays into evolutionary game models, where fitness relies on the past. We study the impact of time delays on mutant fixation in a Moran Birth-death process with two strategies in a well-mixed population. At each time step of the process, an individual reproduces proportionally to fitness coming from the past. We model this as an absorbing Markov chain, allowing computational calculation of the fixation probability and time. We focus on three important games: the Stag-Hunt, Snowdrift, and Prisoner's Dilemma. We will show time delays reduce the fixation probability in the Stag-Hunt and the Prisoner's Dilemma but increase it in the Snowdrift. For the Stag-Hunt and the Prisoner's Dilemma, time delays lengthen the fixation time until a critical point, then reduce it. The Snowdrift exhibits the opposite trend.
An Improved Metric and Benchmark for Assessing the Performance of Virtual Screening Models
Authors: Michael Brocidiacono, Konstantin I. Popov, Alexander Tropsha
Date: 2024-03-15 00:00:00
Tasks: Benchmarking, Drug Discovery

Structure-based virtual screening (SBVS) is a key workflow in computational drug discovery. SBVS models are assessed by measuring the enrichment of known active molecules over decoys in retrospective screens. However, the standard formula for enrichment cannot estimate model performance on very large libraries. Additionally, current screening benchmarks cannot easily be used with machine learning (ML) models due to data leakage. We propose an improved formula for calculating VS enrichment and introduce the BayesBind benchmarking set composed of protein targets that are structurally dissimilar to those in the BigBind training set. We assess current models on this benchmark and find that none perform appreciably better than a KNN baseline.
Anatomically aware simulation of patient-specific glioblastoma xenografts
Authors: Adam A. Malik, Cecilia Krona, Soumi Kundu, Philip Gerlee, Sven Nelander
Date: 2024-03-14 00:00:00
Tasks: Anatomy

Patient-derived cells (PDC) mouse xenografts are increasingly important tools in glioblastoma (GBM) research, essential to investigate case-specific growth patterns and treatment responses. Despite the central role of xenograft models in the field, few good simulation models are available to probe the dynamics of tumor growth and to support therapy design. We therefore propose a new framework for the patient-specific simulation of GBM in the mouse brain. Unlike existing methods, our simulations leverage a high-resolution map of the mouse brain anatomy to yield patient-specific results that are in good agreement with experimental observations. To facilitate the fitting of our model to histological data, we use Approximate Bayesian Computation. Because our model uses few parameters, reflecting growth, invasion and niche dependencies, it is well suited for case comparisons and for probing treatment effects. We demonstrate how our model can be used to simulate different treatment by perturbing the different model parameters. We expect in silico replicates of mouse xenograft tumors can improve the assessment of therapeutic outcomes and boost the statistical power of preclinical GBM studies.
The UK Universities Superannuation Scheme valuations 2014-2023: gilt yield dependence, self-sufficiency and metrics
Authors: Jackie Grant
Date: 2024-02-08 00:00:00
Tasks: Decision Making

This review considers the Universities Superannuation Scheme (USS) valuations from 2014 to 2023. USS is a 70-80 billion GBP Defined Benefit pension scheme with over 500,000 members who are employed (or have been employed) at around 70 UK universities. Disputes over USS have led to a decade of industrial action. New results are presented showing the high dependence of USS pension contributions on the return from UK government bonds (the gilt yield). The two conditions of the USS-specific 'self-sufficiency' (SfS) definition are examined. USS data are presented along with new analysis. It is shown that the second SfS condition of 'maintaining a high funding ratio' dominates USS modelling to amplify gilt yield dependence, inflating the SfS liabilities beyond the regulatory requirements, and leading to excessive prudence. The Red, Amber and Green status of USS metrics 'Actual' and 'Target' Reliance are also examined. It is shown that Target Reliance tethers the cost of future pensions to the SfS definition and that Actual Reliance can simultaneously be Green and Red. Implications for regulatory intervention are considered. An aim of this review is to support evidence-based decision making and consensus building.
Optimal Portfolio Choice with Cross-Impact Propagators
Authors: Eduardo Abi Jaber, Eyal Neuman, Sturmius Tuschmann
Date: 2024-03-15 00:00:00

We consider a class of optimal portfolio choice problems in continuous time where the agent's transactions create both transient cross-impact driven by a matrix-valued Volterra propagator, as well as temporary price impact. We formulate this problem as the maximization of a revenue-risk functional, where the agent also exploits available information on a progressively measurable price predicting signal. We solve the maximization problem explicitly in terms of operator resolvents, by reducing the corresponding first order condition to a coupled system of stochastic Fredholm equations of the second kind and deriving its solution. We then give sufficient conditions on the matrix-valued propagator so that the model does not permit price manipulation. We also provide an implementation of the solutions to the optimal portfolio choice problem and to the associated optimal execution problem. Our solutions yield financial insights on the influence of cross-impact on the optimal strategies and its interplay with alpha decays.
Hydrodynamics of Markets:Hidden Links Between Physics and Finance
Authors: Alexander Lipton
Date: 2024-03-14 00:00:00
Tasks: Math

An intriguing link between a wide range of problems occurring in physics and financial engineering is presented. These problems include the evolution of small perturbations of linear flows in hydrodynamics, the movements of particles in random fields described by the Kolmogorov and Klein-Kramers equations, the Ornstein-Uhlenbeck and Feller processes, and their generalizations. They are reduced to affine differential and pseudo-differential equations and solved in a unified way by using Kelvin waves and developing a comprehensive math framework for calculating transition probabilities and expectations. Kelvin waves are instrumental for studying the well-known Black-Scholes, Heston, and Stein-Stein models and more complex path-dependent volatility models, as well as the pricing of Asian options, volatility and variance swaps, bonds, and bond options. Kelvin waves help to solve several cutting-edge problems, including hedging the impermanent loss of Automated Market Makers for cryptocurrency trading. This title is also available as Open Access on Cambridge Core.
Asset management with an ESG mandate
Authors: Michele Azzone, Emilio Barucci, Davide Stocco
Date: 2024-03-18 00:00:00
Tasks: Asset Management, Management

We investigate the portfolio frontier and risk premia in equilibrium when institutional investors aim to minimize the tracking error variance under an ESG score mandate. If a negative ESG premium is priced in the market, this mandate can reduce portfolio inefficiency when the return over-performance target is limited. In equilibrium, with asset managers endowed with an ESG mandate and mean-variance investors, a negative ESG premium arises. A result that is supported by empirical data. The negative ESG premium is due to the ESG constraint imposed on institutional investors and is not associated with a risk factor.
Layer 2 be or Layer not 2 be: Scaling on Uniswap v3
Authors: Austin Adams
Date: 2024-03-14 00:00:00

This paper studies the market structure impact of cheaper and faster chains on the Uniswap v3 Protocol. The Uniswap Protocol is the largest decentralized application on Ethereum by both gas and blockspace used, and user behaviors of the protocol are very sensitive to fluctuations in gas prices and market structure due to the economic factors of the Protocol. We focus on the chains where Uniswap v3 has the most activity, giving us the best comparison to Ethereum mainnet. Because of cheaper gas and lower block times, we find evidence that the majority of swaps get better gas-adjusted execution on these chains, liquidity providers are more capital efficient, and liquidity providers have increased fee returns from more arbitrage. We also present evidence that two second block times may be too long for optimal liquidity provider returns, compared to first come, first served. We argue that many of the current drawbacks with AMMs may be due to chain dynamics and are vastly improved with cheaper and faster transactions
Process-Aware Analysis of Treatment Paths in Heart Failure Patients: A Case Study
Authors: Harry H. Beyel, Marlo Verket, Viki Peeva, Christian Rennert, Marco Pegoraro, Katharina Schütt, Wil M. P. van der Aalst, Nikolaus Marx
Date: 2024-03-11 00:00:00
Tasks: Diversity

Process mining in healthcare presents a range of challenges when working with different types of data within the healthcare domain. There is high diversity considering the variety of data collected from healthcare processes: operational processes given by claims data, a collection of events during surgery, data related to pre-operative and post-operative care, and high-level data collections based on regular ambulant visits with no apparent events. In this case study, a data set from the last category is analyzed. We apply process-mining techniques on sparse patient heart failure data and investigate whether an information gain towards several research questions is achievable. Here, available data are transformed into an event log format, and process discovery and conformance checking are applied. Additionally, patients are split into different cohorts based on comorbidities, such as diabetes and chronic kidney disease, and multiple statistics are compared between the cohorts. Conclusively, we apply decision mining to determine whether a patient will have a cardiovascular outcome and whether a patient will die.
Ensemble learning for predictive uncertainty estimation with application to the correction of satellite precipitation products
Authors: Georgia Papacharalampous, Hristos Tyralis, Nikolaos Doulamis, Anastasios Doulamis
Date: 2024-03-14 00:00:00
Tasks: Decision Making, Ensemble Learning, Feature Engineering, quantile regression, regression, Spatial Interpolation

Predictions in the form of probability distributions are crucial for effective decision-making. Quantile regression enables such predictions within spatial prediction settings that aim to create improved precipitation datasets by merging remote sensing and gauge data. However, ensemble learning of quantile regression algorithms remains unexplored in this context and, at the same time, it has not been substantially developed so far in the broader machine learning research landscape. Here, we introduce nine quantile-based ensemble learners and address the afore-mentioned gap in precipitation dataset creation by presenting the first application of these learners to large precipitation datasets. We employed a novel feature engineering strategy, reducing predictors to distance-weighted satellite precipitation at relevant locations, combined with location elevation. Our ensemble learners include six ensemble learning and three simple methods (mean, median, best combiner), combining six individual algorithms: quantile regression (QR), quantile regression forests (QRF), generalized random forests (GRF), gradient boosting machines (GBM), light gradient boosting machines (LightGBM), and quantile regression neural networks (QRNN). These algorithms serve as both base learners and combiners within different ensemble learning methods. We evaluated performance against a reference method (QR) using quantile scoring functions in a large dataset comprising 15 years of monthly gauge-measured and satellite precipitation in the contiguous United States (CONUS). Ensemble learning with QR and QRNN yielded the best results across quantile levels ranging from 0.025 to 0.975, outperforming the reference method by 3.91% to 8.95%. This demonstrates the potential of ensemble learning to improve probabilistic spatial predictions.
VISREAS: Complex Visual Reasoning with Unanswerable Questions
Authors: Syeda Nahida Akter, Sangwu Lee, Yingshan Chang, Yonatan Bisk, Eric Nyberg
Date: 2024-02-23 00:00:00
Tasks: Question Answering, Visual Question Answering, Visual Reasoning

Verifying a question's validity before answering is crucial in real-world applications, where users may provide imperfect instructions. In this scenario, an ideal model should address the discrepancies in the query and convey them to the users rather than generating the best possible answer. Addressing this requirement, we introduce a new compositional visual question-answering dataset, VISREAS, that consists of answerable and unanswerable visual queries formulated by traversing and perturbing commonalities and differences among objects, attributes, and relations. VISREAS contains 2.07M semantically diverse queries generated automatically using Visual Genome scene graphs. The unique feature of this task, validating question answerability with respect to an image before answering, and the poor performance of state-of-the-art models inspired the design of a new modular baseline, LOGIC2VISION that reasons by producing and executing pseudocode without any external modules to generate the answer. LOGIC2VISION outperforms generative models in VISREAS (+4.82% over LLaVA-1.5; +12.23% over InstructBLIP) and achieves a significant gain in performance against the classification models.
Autoregressive Queries for Adaptive Tracking with Spatio-TemporalTransformers
Authors: Jinxia Xie, Bineng Zhong, Zhiyi Mo, Shengping Zhang, Liangtao Shi, Shuxiang Song, Rongrong Ji
Date: 2024-03-15 00:00:00
Tasks: Visual Object Tracking, Visual Tracking

The rich spatio-temporal information is crucial to capture the complicated target appearance variations in visual tracking. However, most top-performing tracking algorithms rely on many hand-crafted components for spatio-temporal information aggregation. Consequently, the spatio-temporal information is far away from being fully explored. To alleviate this issue, we propose an adaptive tracker with spatio-temporal transformers (named AQATrack), which adopts simple autoregressive queries to effectively learn spatio-temporal information without many hand-designed components. Firstly, we introduce a set of learnable and autoregressive queries to capture the instantaneous target appearance changes in a sliding window fashion. Then, we design a novel attention mechanism for the interaction of existing queries to generate a new query in current frame. Finally, based on the initial target template and learnt autoregressive queries, a spatio-temporal information fusion module (STM) is designed for spatiotemporal formation aggregation to locate a target object. Benefiting from the STM, we can effectively combine the static appearance and instantaneous changes to guide robust tracking. Extensive experiments show that our method significantly improves the tracker's performance on six popular tracking benchmarks: LaSOT, LaSOText, TrackingNet, GOT-10k, TNL2K, and UAV123.
LSKNet: A Foundation Lightweight Backbone for Remote Sensing
Authors: YuXuan Li, Xiang Li, Yimian Dai, Qibin Hou, Li Liu, Yongxiang Liu, Ming-Ming Cheng, Jian Yang
Date: 2024-03-18 00:00:00
Tasks: Change Detection, object-detection, Object Detection, Object Detection In Aerial Images, Semantic Segmentation

Remote sensing images pose distinct challenges for downstream tasks due to their inherent complexity. While a considerable amount of research has been dedicated to remote sensing classification, object detection and semantic segmentation, most of these studies have overlooked the valuable prior knowledge embedded within remote sensing scenarios. Such prior knowledge can be useful because remote sensing objects may be mistakenly recognized without referencing a sufficiently long-range context, which can vary for different objects. This paper considers these priors and proposes a lightweight Large Selective Kernel Network (LSKNet) backbone. LSKNet can dynamically adjust its large spatial receptive field to better model the ranging context of various objects in remote sensing scenarios. To our knowledge, large and selective kernel mechanisms have not been previously explored in remote sensing images. Without bells and whistles, our lightweight LSKNet sets new state-of-the-art scores on standard remote sensing classification, object detection and semantic segmentation benchmarks. Our comprehensive analysis further validated the significance of the identified priors and the effectiveness of LSKNet. The code is available at https://github.com/zcablii/LSKNet.
Relational Representation Learning Network for Cross-Spectral Image Patch Matching
Authors: Chuang Yu, Yunpeng Liu, Jinmiao Zhao, Dou Quan, Zelin Shi, Xiangyu Yue
Date: 2024-03-18 00:00:00
Tasks: Patch Matching, Representation Learning

Recently, feature relation learning has drawn widespread attention in cross-spectral image patch matching. However, existing related research focuses on extracting diverse relations between image patch features and ignores sufficient intrinsic feature representations of individual image patches. Therefore, we propose an innovative relational representation learning idea that simultaneously focuses on sufficiently mining the intrinsic features of individual image patches and the relations between image patch features. Based on this, we construct a Relational Representation Learning Network (RRL-Net). Specifically, we innovatively construct an autoencoder to fully characterize the individual intrinsic features, and introduce a feature interaction learning (FIL) module to extract deep-level feature relations. To further fully mine individual intrinsic features, a lightweight multi-dimensional global-to-local attention (MGLA) module is constructed to enhance the global feature extraction of individual image patches and capture local dependencies within global features. By combining the MGLA module, we further explore the feature extraction network and construct an attention-based lightweight feature extraction (ALFE) network. In addition, we propose a multi-loss post-pruning (MLPP) optimization strategy, which greatly promotes network optimization while avoiding increases in parameters and inference time. Extensive experiments demonstrate that our RRL-Net achieves state-of-the-art (SOTA) performance on multiple public datasets. Our code are available at https://github.com/YuChuang1205/RRL-Net.