pwc-archive/papers-with-abstracts huggingface dataset:
Lp Quasi-norm Minimization: Algorithm and Applications
Authors: Omar M. Sleem, M. E. Ashour, N. S. Aybat, Constantino M. Lagoa
Date: 2023-01-27 00:00:00
Tasks: Binary Classification

Sparsity finds applications in areas as diverse as statistics, machine learning, and signal processing. Computations over sparse structures are less complex compared to their dense counterparts, and their storage consumes less space. This paper proposes a heuristic method for retrieving sparse approximate solutions of optimization problems via minimizing the $\ell_{p}$ quasi-norm, where $0<p<1$. An iterative two-block ADMM algorithm for minimizing the $\ell_{p}$ quasi-norm subject to convex constraints is proposed. For $p=s/q<1$, $s,q \in \mathbb{Z}_{+}$, the proposed algorithm requires solving for the roots of a scalar degree $2q$ polynomial as opposed to applying a soft thresholding operator in the case of $\ell_{1}$. The merit of that algorithm relies on its ability to solve the $\ell_{p}$ quasi-norm minimization subject to any convex set of constraints. However, it suffers from low speed, due to a convex projection step in each iteration, and the lack of mathematical convergence guarantee. We then aim to vanquish these shortcomings by relaxing the assumption on the constraints set to be the set formed due to convex and differentiable, with Lipschitz continuous gradient, functions, i.e. specifically, polytope sets. Using a proximal gradient step, we mitigate the convex projection step and hence enhance the algorithm speed while proving its convergence. We then present various applications where the proposed algorithm excels, namely, matrix rank minimization, sparse signal reconstruction from noisy measurements, sparse binary classification, and system identification. The results demonstrate the significant gains obtained by the proposed algorithm compared to those via $\ell_{1}$ minimization.
Conditional generalized quantiles based on expected utility model and equivalent characterization of properties
Authors: Qinyu Wu, Fan Yang, Ping Zhang
Date: 2023-01-29 00:00:00

As a counterpart to the (static) risk measures of generalized quantiles and motivated by Bellini et al. (2018), we propose a new kind of conditional risk measure called conditional generalized quantiles. We first show their well-definedness and they can be equivalently characterised by a conditional first order condition. We also discuss their main properties, and, especially, We give the characterization of coherency/convexity. For potential applications as a dynamic risk measure, we study their time consistency properties, and establish their equivalent characterizations among conditional generalized quantiles.
The influence of AGEs and enzymatic cross-links on the mechanical properties of collagen fibrils
Authors: Julia Kamml, Chun-Yu Ke, Claire Acevedo, David Kammer
Date: 2023-01-30 00:00:00
Tasks: Friction

Cross-links are considered to be a key component of collagen fibrils as they can change the fibrillar behavior in various ways. Advanced-Glycation Endproducts (AGEs), one particular type of cross-links, have been shown to accumulate and impair the mechanical properties of collageneous tissues, whereas enzymatic cross-links (ECLs) are known for stabilizing the structure of the fibril. However, the reasons for whether a given type of cross-link improves or impairs the material properties remain unknown. Here, we use coarse-grained steered molecular models to evaluate the effect of AGEs and ECLs cross-links content on the deformation and failure properties of collagen fibrils. Our simulations show that the collagen fibrils stiffen at high strain levels when the AGEs content exceeds a critical value. In addition, the strength of the fibril increases with AGEs accumulation. By analyzing the forces within the different types of cross-links (AGEs and ECLs) as well as their failure, we demonstrate that a change of deformation mechanism is at the origin of these observations. A high AGEs content reinforces force transfer through AGEs cross-links rather than through friction between sliding tropocollagen molecules. We show that this failure mechanism, which is associated with lower energy dissipation, results in more abrupt failure of the collagen fibril. Our results provide a direct and causal link between increased AGEs content, inhibited intra-fibrillar sliding, increased stiffness, and abrupt fibril fracture. Therefore, they explain the mechanical origin of bone brittleness as commonly observed in elderly and diabetic populations. Our findings contribute to a better understanding of the mechanisms underlying impaired tissue behaviour due to elevated AGEs content and could enable targeted measures regarding the reduction of specific collagen cross-linking levels.
PhaVIP: Phage VIrion Protein classification based on chaos game representation and Vision Transformer
Authors: Jiayu Shang, Cheng Peng, Xubo Tang, Yanni Sun
Date: 2023-01-29 00:00:00
Tasks: Classification, image-classification, Image Classification

Motivation: As viruses that mainly infect bacteria, phages are key players across a wide range of ecosystems. Analyzing phage proteins is indispensable for understanding phages' functions and roles in microbiomes. High-throughput sequencing enables us to obtain phages in different microbiomes with low cost. However, compared to the fast accumulation of newly identified phages, phage protein classification remains difficult. In particular, a fundamental need is to annotate virion proteins, the structural proteins such as major tail, baseplate etc. Although there are experimental methods for virion protein identification, they are too expensive or time-consuming, leaving a large number of proteins unclassified. Thus, there is a great demand to develop a computational method for fast and accurate phage virion protein classification. Results: In this work, we adapted the state-of-the-art image classification model, Vision Transformer, to conduct virion protein classification. By encoding protein sequences into unique images using chaos gaming representation, we can leverage Vision Transformer to learn both local and global features from sequence ``images''. Our method, PhaVIP, has two main functions: classifying PVP and non-PVP sequences and annotating the types of PVP, such as capsid and tail. We tested PhaVIP on several datasets with increasing difficulty and benchmarked it against alternative tools. The experimental results show that PhaVIP has superior performance. After validating the performance of PhaVIP, we investigated two applications that can use the output of PhaVIP: phage taxonomy classification and phage host prediction. The results show the benefit of using classified proteins rather than all proteins.
Graph rules for recurrent neural network dynamics: extended version
Authors: Carina Curto, Katherine Morrison
Date: 2023-01-30 00:00:00
Tasks: Survey

This is an extended version of our survey article, "Graph rules for recurrent neural network dynamics," to appear in the April 2023 edition of the Notices of the AMS. It includes additional results, derivations, figures, references, and a set of open questions.
Contrasting dynamics of seed banks and standing vegetation of annuals and perennials along a rainfall gradient
Authors: Niv DeMalach, Jaime Kigel, Marcelo Sternberg
Date: 2023-01-30 00:00:00

The soil seed bank is a major component of plant communities. However, long-term analyses of the dynamics of the seed bank and the ensuing vegetation are rare. Here, we studied the dynamics in plant communities with high dominance of annuals in Mediterranean, semiarid, and arid ecosystems for nine consecutive years. For annuals, we hypothesized that the density of the seed bank would be more stable than the density of the standing herbaceous vegetation. Moreover, we predicted that differences in temporal variability between the seed bank and the vegetation would increase with aridity, where year-to-year rainfall variability is higher. We found that the temporal variability at the population level (assessed as the standard deviation of the loge-transformed density) of the nine dominant annuals in each site did not differ between the seed bank and the ensuing vegetation in any of the sites. For the total density of annuals, patterns depended on aridity. In the Mediterranean site, the temporal variability was similar in the seed bank and the vegetation (0.40 vs. 0.40). Still, in the semiarid and arid sites, variability in the seed bank was lower than in the vegetation (0.49 vs. 1.01 and 0.63 vs. 1.38, respectively). This difference between the population-level patterns and the total density of annuals can be related to the lower population synchrony in their seed bank. In contrast, for the herbaceous perennials (all species combined), the seed bank variability was higher than in the vegetation. Overall, our results highlight the role of the seed bank in buffering the annual vegetation density with increasing climatic uncertainty typical in aridity gradients. This role is crucial under the increasing uncertainty imposed by climatic change in the region.
Sustainable Diversity of Phage-Bacteria Systems
Authors: Namiko Mitarai, Anastasios Marantos, Kim Sneppen
Date: 2023-01-29 00:00:00
Tasks: Diversity

Bacteriophages are central to microbial ecosystems for balancing bacterial populations and promoting evolution by applying strong selection pressure. Here we review some of the known aspects that modulate phage-bacteria interaction in a way that naturally promotes their coexistence. We focus on the modulations that arise from structural, physical, or physiological constraints. We argue they should play roles in many phage-bacteria systems providing sustainable diversity.
Optimizing a Bayesian method for estimating the Hurst exponent in behavioral sciences
Authors: Madhur Mangalam, Taylor Wilson, Joel Sommerfeld, Aaron D Likens
Date: 2023-01-28 00:00:00
Tasks: Computational Efficiency, Time Series, Time Series Analysis

The Bayesian Hurst-Kolmogorov (HK) method estimates the Hurst exponent of a time series more accurately than the age-old detrended fluctuation analysis (DFA), especially when the time series is short. However, this advantage comes at the cost of computation time. The computation time increases exponentially with $N$, easily exceeding several hours for $N = 1024$, limiting the utility of the HK method in real-time paradigms, such as biofeedback and brain-computer interfaces. To address this issue, we have provided data on the estimation accuracy of $H$ for synthetic time series as a function of \textit{a priori} known values of $H$, the time series length, and the simulated sample size from the posterior distribution -- a critical step in the Bayesian estimation method. The simulated sample from the posterior distribution as small as $n = 25$ suffices to estimate $H$ with reasonable accuracy for a time series as short as $256$ measurements. Using a larger simulated sample from the posterior distribution -- i.e., $n > 50$ -- provides only marginal gain in accuracy, which might not be worth trading off with computational efficiency. We suggest balancing the simulated sample size from the posterior distribution of $H$ with the computational resources available to the user, preferring a minimum of $n = 50$ and opting for larger sample sizes based on time and resource constraints
Validation of machine learning based scenario generators
Authors: Gero Junike, Solveig Flaig, Ralf Werner
Date: 2023-01-30 00:00:00
Tasks: Memorization, Model Optimization

Machine learning (ML) methods are becoming increasingly important in the design economic scenario generators for internal models. Validation of data-driven models differs from classical theory-based models. We discuss two novel aspects of such a validation: first, checking dependencies between risk factors and second, detecting unwanted memorization effects. The first task becomes necessary since in ML-based methods dependencies are no longer derived from a financial-mathematical theory but are driven by data. The need for the latter task arises since it cannot be ruled out that ML-based models merely reproduce the empirical data rather than generating new scenarios. To address the first issue, we propose to use an existing test from the literature. For the second issue, we introduce and discuss a novel memorization ratio. Numerical experiments based on real market data are included and an autoencoder-based scenario generator is validated with these two methods.
A Machine Learning Approach for Player and Position Adjusted Expected Goals in Football (Soccer)
Authors: James H. Hewitt, Oktay Karakuş
Date: 2023-01-19 00:00:00
Tasks: Binary Classification, Position

Football is a very result-driven industry, with goals being rarer than in most sports, so having further parameters to judge the performance of teams and individuals is key. Expected Goals (xG) allow further insight than just a scoreline. To tackle the need for further analysis in football, this paper uses machine learning applications that are developed and applied to Football Event data. From the concept, a Binary Classification problem is created whereby a probabilistic valuation is outputted using Logistic Regression and Gradient Boosting based approaches. The model successfully predicts xGs probability values for football players based on 15,575 shots. The proposed solution utilises StatsBomb as the data provider and an industry benchmark to tune the models in the right direction. The proposed ML solution for xG is further used to tackle the age-old cliche of: 'the ball has fallen to the wrong guy there'. The development of the model is used to adjust and gain more realistic values of expected goals than the general models show. To achieve this, this paper tackles Positional Adjusted xG, splitting the training data into Forward, Midfield, and Defence with the aim of providing insight into player qualities based on their positional sub-group. Positional Adjusted xG successfully predicts and proves that more attacking players are better at accumulating xG. The highest value belonged to Forwards followed by Midfielders and Defenders. Finally, this study has further developments into Player Adjusted xG with the aim of proving that Messi is statistically at a higher efficiency level than the average footballer. This is achieved by using Messi subset samples to quantify his qualities in comparison to the average xG models finding that Messi xG performs 347 xG higher than the general model outcome.
EuclidNet: Deep Visual Reasoning for Constructible Problems in Geometry
Authors: Man Fai Wong, Xintong Qi, Chee Wei Tan
Date: 2022-12-27 00:00:00
Tasks: Automated Theorem Proving, Visual Reasoning

In this paper, we present a deep learning-based framework for solving geometric construction problems through visual reasoning, which is useful for automated geometry theorem proving. Constructible problems in geometry often ask for the sequence of straightedge-and-compass constructions to construct a given goal given some initial setup. Our EuclidNet framework leverages the neural network architecture Mask R-CNN to extract the visual features from the initial setup and goal configuration with extra points of intersection, and then generate possible construction steps as intermediary data models that are used as feedback in the training process for further refinement of the construction step sequence. This process is repeated recursively until either a solution is found, in which case we backtrack the path for a step-by-step construction guide, or the problem is identified as unsolvable. Our EuclidNet framework is validated on complex Japanese Sangaku geometry problems, demonstrating its capacity to leverage backtracking for deep visual reasoning of challenging problems.
Key Feature Replacement of In-Distribution Samples for Out-of-Distribution Detection
Authors: Jaeyoung Kim, Seo Taek Kong, Dongbin Na, Kyu-Hwan Jung
Date: 2022-12-26 00:00:00
Tasks: Out-of-Distribution Detection, Out of Distribution (OOD) Detection

Out-of-distribution (OOD) detection can be used in deep learning-based applications to reject outlier samples from being unreliably classified by deep neural networks. Learning to classify between OOD and in-distribution samples is difficult because data comprising the former is extremely diverse. It has been observed that an auxiliary OOD dataset is most effective in training a "rejection" network when its samples are semantically similar to in-distribution images. We first deduce that OOD images are perceived by a deep neural network to be semantically similar to in-distribution samples when they share a common background, as deep networks are observed to incorrectly classify such images with high confidence. We then propose a simple yet effective Key In-distribution feature Replacement BY inpainting (KIRBY) procedure that constructs a surrogate OOD dataset by replacing class-discriminative features of in-distribution samples with marginal background features. The procedure can be implemented using off-the-shelf vision algorithms, where each step within the algorithm is shown to make the surrogate data increasingly similar to in-distribution data. Design choices in each step are studied extensively, and an exhaustive comparison with state-of-the-art algorithms demonstrates KIRBY's competitiveness on various benchmarks.
RFPose-OT: RF-Based 3D Human Pose Estimation via Optimal Transport Theory
Authors: Cong Yu, Dongheng Zhang, Zhi Wu, Zhi Lu, Chunyang Xie, Yang Hu, Yan Chen
Date: 2022-12-26 00:00:00
Tasks: 3D Human Pose Estimation, Pose Estimation

This paper introduces a novel framework, i.e., RFPose-OT, to enable the 3D human pose estimation from Radio Frequency (RF) signals. Different from existing methods that predict human poses from RF signals on the signal level directly, we consider the structure difference between the RF signals and the human poses, propose to transform the RF signals to the pose domain on the feature level based on Optimal Transport (OT) theory, and generate human poses from the transformed features. To evaluate RFPose-OT, we build a radio system and a multi-view camera system to acquire the RF signal data and the ground-truth human poses. The experimental results in basic indoor environment, occlusion indoor environment, and outdoor environment, all demonstrate that RFPose-OT can predict 3D human poses with higher precision than the state-of-the-art methods.
Attribute-Guided Multi-Level Attention Network for Fine-Grained Fashion Retrieval
Authors: Ling Xiao, Toshihiko Yamasaki
Date: 2022-12-27 00:00:00
Tasks: Attribute, image-classification, Image Classification, object-detection, Object Detection, Retrieval

Fine-grained fashion retrieval searches for items that share a similar attribute with the query image. Most existing methods use a pre-trained feature extractor (e.g., ResNet 50) to capture image representations. However, a pre-trained feature backbone is typically trained for image classification and object detection, which are fundamentally different tasks from fine-grained fashion retrieval. Therefore, existing methods suffer from a feature gap problem when directly using the pre-trained backbone for fine-tuning. To solve this problem, we introduce an attribute-guided multi-level attention network (AG-MAN). Specifically, we first enhance the pre-trained feature extractor to capture multi-level image embedding, thereby enriching the low-level features within these representations. Then, we propose a classification scheme where images with the same attribute, albeit with different values, are categorized into the same class. This can further alleviate the feature gap problem by perturbing object-centric feature learning. Moreover, we propose an improved attribute-guided attention module for extracting more accurate attribute-specific representations. Our model consistently outperforms existing attention based methods when assessed on the FashionAI (62.8788% in MAP), DeepFashion (8.9804% in MAP), and Zappos50k datasets (93.32% in Prediction accuracy). Especially, ours improves the most typical ASENet_V2 model by 2.12%, 0.31%, and 0.78% points in FashionAI, DeepFashion, and Zappos50k datasets, respectively. The source code is available in https://github.com/Dr-LingXiao/AG-MAN.
Enhancing Neural Network Differential Equation Solvers
Authors: Matthew J. H. Wright
Date: 2022-12-28 00:00:00

We motivate the use of neural networks for the construction of numerical solutions to differential equations. We prove that there exists a feed-forward neural network that can arbitrarily minimise an objective function that is zero at the solution of Poisson's equation, allowing us to guarantee that neural network solution estimates can get arbitrarily close to the exact solutions. We also show how these estimates can be appreciably enhanced through various strategies, in particular through the construction of error correction networks, for which we propose a general method. We conclude by providing numerical experiments that attest to the validity of all such strategies for variants of Poisson's equation.
A deep learning approach for adaptive zoning
Authors: Massimiliano Lupo Pasini, Luka Malenica, Kwitae Chong, Stuart Slattery
Date: 2022-12-10 00:00:00
Tasks: Deep Learning

We propose a supervised deep learning (DL) approach to perform adaptive zoning on time dependent partial differential equations that model the propagation of 1D shock waves in a compressible medium. We train a neural network on a dataset composed of different static shock profiles associated with the corresponding adapted meshes computed with standard adaptive zoning techniques. We show that the trained DL model learns how to capture the presence of shocks in the domain and generates at each time step an adapted non-uniform mesh that relocates the grid nodes to improve the accuracy of Lax-Wendroff and fifth order weighted essentially non-oscillatory (WENO5) space discretization schemes. We also show that the surrogate DL model reduces the computational time to perform adaptive zoning by at least a 2x factor with respect to standard techniques without compromising the accuracy of the reconstruction of the physical quantities of interest.
A New Multi-objective Approach to Optimize Irrigation Using a Crop Simulation Model and Weather History
Authors: Mikhail Gasanov, Daniil Merkulov, Artyom Nikitin, Sergey Matveev, Nikita Stasenko, Anna Petrovskaia, Mariia Pukalchik & Ivan Oseledets
Date: 2021-06-09 00:00:00
Proceeding: Computational Science–ICCS 2021: 21st International Conference, Krakow, Poland, 2021 6

Optimization of water consumption in agriculture is necessary to preserve freshwater reserves and reduce the environment’s burden. Finding optimal irrigation and water resources for crops is necessary to increase the efficiency of water usage. Many optimization approaches maximize crop yield or profit but do not consider the impact on the environment. We propose a machine learning approach based on the crop simulation model WOFOST to assess the crop yield and water use efficiency. In our research, we use weather history to evaluate various weather scenarios. The application of multi-criteria optimization based on the non-dominated sorting genetic algorithm-II (NSGA-II) allows users to find the dates and volume of water for irrigation, maximizing the yield and reducing the total water consumption. In the study case, we compared the effectiveness of NSGA-II with Monte Carlo search and a real farmer’s strategy. We showed a decrease in water consumption simultaneously with increased sugar-beet yield using the NSGA-II algorithm. Our approach yielded a higher potato crop than a farmer with a similar level of water consumption. The NSGA-II algorithm received an increase in yield for potato crops, but water use efficiency remained at the farmer’s level. NSGA-II used water resources more efficiently than the Monte Carlo search and reduced water losses to the lower soil horizons.