pwc-archive/papers-with-abstracts huggingface dataset:
Billion-years old proteins show the importance of N-lobe orientation in Imatinib-kinase selectivity
Authors: Zahra Shamsi, Diwakar Shukla
Date: 2023-03-27 00:00:00

The molecular origins of proteins' functions are a combinatorial search problem in the proteins' sequence space, which requires enormous resources to solve. However, evolution has already solved this optimization problem for us, leaving behind suboptimal solutions along the way. Comparing suboptimal proteins along the evolutionary pathway, or ancestors, with more optimal modern proteins can lead us to the exact molecular origins of a particular function. In this paper, we study the long-standing question of the selectivity of Imatinib, an anti-cancer kinase inhibitor drug. We study two related kinases, Src and Abl, and four of their common ancestors, to which Imatinib has significantly different affinities. Our results show that the orientation of the N-lobe with respect to the C-lobe varies between the kinases along their evolutionary pathway and is consistent with Imatinib's inhibition constants as measured experimentally. The conformation of the DFG-motif (Asp-Phe-Gly) and the structure of the P-loop also seem to have different stable conformations along the evolutionary pathway, which is aligned with Imatinib's affinity.
RNAprofiling 2.0: Enhanced cluster analysis of structural ensembles
Authors: Forrest Hurley, Christine Heitsch
Date: 2023-03-27 00:00:00

Understanding the base pairing of an RNA sequence provides insight into its molecular structure.By mining suboptimal sampling data, RNAprofiling 1.0 identifies the dominant helices in low-energy secondary structures as features, organizes them into profiles which partition the Boltzmann sample, and highlights key similarities/differences among the most informative, i.e. selected, profiles in a graphical format. Version 2.0 enhances every step of this approach. First, the featured substructures are expanded from helices to stems. Second, profile selection includes low-frequency pairings similar to featured ones. In conjunction, these updates extend the utility of the method to sequences up to length 600, as evaluated over a sizable dataset. Third, relationships are visualized in a decision tree which highlights the most important structural differences. Finally, this cluster analysis is made accessible to experimental researchers in a portable format as an interactive webpage, permitting a much greater understanding of trade-offs among different possible base pairing combinations.
PhaBOX: A web server for identifying and characterizing phage contigs in metagenomic data
Authors: Jiayu Shang, Cheng Peng, Herui Liao, Xubo Tang, Yanni Sun
Date: 2023-03-28 00:00:00

Motivation: There is accumulating evidence showing the important roles of bacteriophages (phages) in regulating the structure and functions of the microbiome. However, lacking an easy-to-use and integrated phage analysis software hampers microbiome-related research from incorporating phages in the analysis. Results: In this work, we developed a web server, PhaBOX, which can comprehensively identify and analyze phage contigs in metagenomic data. It supports integrated phage analysis, including phage contig identification from the metagenomic assembly, lifestyle prediction, taxonomic classification, and host prediction. Instead of treating the algorithms as a black box, PhaBOX also supports visualization of the essential features for making predictions. The web server is designed with a user-friendly graphical interface that enables both informatics-trained and non-specialist users to analyze phages in microbiome data with ease. Availability: The web server of PhaBOX is available via: https://phage.ee.cityu.edu.hk. The source code of PhaBOX is available at: https://github.com/KennthShang/PhaBOX Contact: yannisun@cityu.edu.hk
Habitat fragmentation affects climate adaptation in a forest herb
Authors: Frederik Van Daele, Olivier Honnay, Steven Janssens, Hanne De Kort
Date: 2023-03-28 00:00:00

Climate change and the resulting increased drought frequencies pose considerable threats to forest herb populations, especially when compounded by additional environmental challenges. Specifically, habitat fragmentation may disrupt climate adaptation and cause shifts in mating systems. To examine this, we conducted a garden experiment with Primula elatior offspring from 24 populations across a climate and landscape fragmentation gradient. We evaluated vegetative, regulatory, and reproductive traits under different soil moisture regimes, assessing local adaptation and phenotypic plasticity. We also conducted a field study in 60 populations along the same gradient to examine potential breakdown of reciprocal herkogamy. Our results showed an evolutionary shift from drought avoidance in southern populations to drought tolerance in northern populations for large, connected populations. However, fragmentation disrupted climate clines and adaptive responses to drought in key traits related to growth, biomass allocation and water regulation. Our findings also indicate the beginning of an evolutionary breakdown in reciprocal herkogamy. These disruptions resulted in significantly reduced flowering investment, especially in southern fragmented populations. These findings provide new evidence of how habitat fragmentation disrupts climate adaptation and drought tolerance in Primula elatior, emphasizing the need to account for habitat fragmentation in conservation strategies to preserve resilient forest herb populations amidst global changes.
How to handle the COS method for option pricing
Authors: Gero Junike
Date: 2023-03-28 00:00:00

The Fourier Cosine Expansion (COS) method is used to price European options numerically in a very efficient way. To apply the COS method, one has to specify two parameters: a truncation range for the density of the log-returns and a number of terms N to approximate the truncated density by a cosine series. How to choose the truncation range is already known. Here, we are able to find an explicit and useful bound for N as well for pricing and for the sensitivities, i.e., the Greeks Delta and Gamma. We further show that the COS method has an exponential order of convergence when the density is smooth and decays exponentially. However, when the density is smooth and has heavy tails, as in the Finite Moment Log Stable model, the COS method does not have exponential order of convergence. Numerical experiments confirm the theoretical results.
Mean-variance hybrid portfolio optimization with quantile-based risk measure
Authors: WeiPing Wu, Yu Lin, Jianjun Gao, Ke Zhou
Date: 2023-03-28 00:00:00
Tasks: Management, Portfolio Optimization, Position

This paper addresses the importance of incorporating various risk measures in portfolio management and proposes a dynamic hybrid portfolio optimization model that combines the spectral risk measure and the Value-at-Risk in the mean-variance formulation. By utilizing the quantile optimization technique and martingale representation, we offer a solution framework for these issues and also develop a closed-form portfolio policy when all market parameters are deterministic. Our hybrid model outperforms the classical continuous-time mean-variance portfolio policy by allocating a higher position of the risky asset in favorable market states and a less risky asset in unfavorable market states. This desirable property leads to promising numerical experiment results, including improved Sortino ratio and reduced downside risk compared to the benchmark models.
Explaining Exchange Rate Forecasts with Macroeconomic Fundamentals Using Interpretive Machine Learning
Authors: Davood Pirayesh Neghab, Mucahit Cevik, M. I. M. Wahab
Date: 2023-03-23 00:00:00

The complexity and ambiguity of financial and economic systems, along with frequent changes in the economic environment, have made it difficult to make precise predictions that are supported by theory-consistent explanations. Interpreting the prediction models used for forecasting important macroeconomic indicators is highly valuable for understanding relations among different factors, increasing trust towards the prediction models, and making predictions more actionable. In this study, we develop a fundamental-based model for the Canadian-U.S. dollar exchange rate within an interpretative framework. We propose a comprehensive approach using machine learning to predict the exchange rate and employ interpretability methods to accurately analyze the relationships among macroeconomic variables. Moreover, we implement an ablation study based on the output of the interpretations to improve the predictive accuracy of the models. Our empirical results show that crude oil, as Canada's main commodity export, is the leading factor that determines the exchange rate dynamics with time-varying effects. The changes in the sign and magnitude of the contributions of crude oil to the exchange rate are consistent with significant events in the commodity and energy markets and the evolution of the crude oil trend in Canada. Gold and the TSX stock index are found to be the second and third most important variables that influence the exchange rate. Accordingly, this analysis provides trustworthy and practical insights for policymakers and economists and accurate knowledge about the predictive model's decisions, which are supported by theoretical considerations.
Optimal Cross-Correlation Estimates from Asynchronous Tick-by-Tick Trading Data
Authors: William H. Press
Date: 2023-03-18 00:00:00
Tasks: Time Series

Given two time series, A and B, sampled asynchronously at different times {t_A_i} and {t_B_j}, termed "ticks", how can one best estimate the correlation coefficient \rho between changes in A and B? We derive a natural, minimum-variance estimator that does not use any interpolation or binning, then derive from it a fast (linear time) estimator that is demonstrably nearly as good. This "fast tickwise estimator" is compared in simulation to the usual method of interpolating changes to a regular grid. Even when the grid spacing is optimized for the particular parameters (not often possible in practice), the fast tickwise estimator has generally smaller estimation errors, often by a large factor. These results are directly applicable to tick-by-tick price data of financial assets.
Robustness of Utilizing Feedback in Embodied Visual Navigation
Authors: Jenny Zhang, Samson Yu, Jiafei Duan, Cheston Tan
Date: 2023-03-06 00:00:00
Tasks: Object, Visual Navigation

This paper presents a framework for training an agent to actively request help in object-goal navigation tasks, with feedback indicating the location of the target object in its field of view. To make the agent more robust in scenarios where a teacher may not always be available, the proposed training curriculum includes a mix of episodes with and without feedback. The results show that this approach improves the agent's performance, even in the absence of feedback.
Machine learning tools to improve nonlinear modeling parameters of RC columns
Authors: Hamid Khodadadi Koodiani, Elahe Jafari, Arsalan Majlesi, Mohammad Shahin, Adolfo Matamoros, Adel Alaeddini
Date: 2023-03-09 00:00:00
Tasks: Multi-class Classification, regression

Modeling parameters are essential to the fidelity of nonlinear models of concrete structures subjected to earthquake ground motions, especially when simulating seismic events strong enough to cause collapse. This paper addresses two of the most significant barriers to improving nonlinear modeling provisions in seismic evaluation standards using experimental data sets: identifying the most likely mode of failure of structural components, and implementing data fitting techniques capable of recognizing interdependencies between input parameters and nonlinear relationships between input parameters and model outputs. Machine learning tools in the Scikit-learn and Pytorch libraries were used to calibrate equations and black-box numerical models for nonlinear modeling parameters (MP) a and b of reinforced concrete columns defined in the ASCE 41 and ACI 369.1 standards, and to estimate their most likely mode of failure. It was found that machine learning regression models and machine learning black-boxes were more accurate than current provisions in the ACI 369.1/ASCE 41 Standards. Among the regression models, Regularized Linear Regression was the most accurate for estimating MP a, and Polynomial Regression was the most accurate for estimating MP b. The two black-box models evaluated, namely the Gaussian Process Regression and the Neural Network (NN), provided the most accurate estimates of MPs a and b. The NN model was the most accurate machine learning tool of all evaluated. A multi-class classification tool from the Scikit-learn machine learning library correctly identified column mode of failure with 79% accuracy for rectangular columns and with 81% accuracy for circular columns, a substantial improvement over the classification rules in ASCE 41-13.
Backpropagation through Back Substitution with a Backslash
Authors: Alan Edelman, Ekin Akyurek, Yuyang Wang
Date: 2023-02-23 00:00:00

We present a linear algebra formulation of backpropagation which allows the calculation of gradients by using a generically written ``backslash'' or Gaussian elimination on triangular systems of equations. Generally, the matrix elements are operators. This paper has three contributions: (i) it is of intellectual value to replace traditional treatments of automatic differentiation with a (left acting) operator theoretic, graph-based approach; (ii) operators can be readily placed in matrices in software in programming languages such as Julia as an implementation option; (iii) we introduce a novel notation, ``transpose dot'' operator ``$\{\}^{T_\bullet}$'' that allows for the reversal of operators. We further demonstrate the elegance of the operators approach in a suitable programming language consisting of generic linear algebra operators such as Julia \cite{bezanson2017julia}, and that it is possible to realize this abstraction in code. Our implementation shows how generic linear algebra can allow operators as elements of matrices. In contrast to ``operator overloading,'' where backslash would normally have to be rewritten to take advantage of operators, with ``generic programming'' there is no such need.
Enlarging Instance-specific and Class-specific Information for Open-set Action Recognition
Authors: Jun Cen, Shiwei Zhang, Xiang Wang, Yixuan Pei, Zhiwu Qing, Yingya Zhang, Qifeng Chen
Date: 2023-03-25 00:00:00
Proceeding: CVPR 2023 1
Tasks: Action Recognition, Open Set Action Recognition

Open-set action recognition is to reject unknown human action cases which are out of the distribution of the training set. Existing methods mainly focus on learning better uncertainty scores but dismiss the importance of feature representations. We find that features with richer semantic diversity can significantly improve the open-set performance under the same uncertainty scores. In this paper, we begin with analyzing the feature representation behavior in the open-set action recognition (OSAR) problem based on the information bottleneck (IB) theory, and propose to enlarge the instance-specific (IS) and class-specific (CS) information contained in the feature for better performance. To this end, a novel Prototypical Similarity Learning (PSL) framework is proposed to keep the instance variance within the same class to retain more IS information. Besides, we notice that unknown samples sharing similar appearances to known samples are easily misclassified as known classes. To alleviate this issue, video shuffling is further introduced in our PSL to learn distinct temporal information between original and shuffled samples, which we find enlarges the CS information. Extensive experiments demonstrate that the proposed PSL can significantly boost both the open-set and closed-set performance and achieves state-of-the-art results on multiple benchmarks. Code is available at https://github.com/Jun-CEN/PSL.
CAMS: CAnonicalized Manipulation Spaces for Category-Level Functional Hand-Object Manipulation Synthesis
Authors: Juntian Zheng, Qingyuan Zheng, Lixing Fang, Yun Liu, Li Yi
Date: 2023-03-25 00:00:00
Proceeding: CVPR 2023 1
Tasks: Object

In this work, we focus on a novel task of category-level functional hand-object manipulation synthesis covering both rigid and articulated object categories. Given an object geometry, an initial human hand pose as well as a sparse control sequence of object poses, our goal is to generate a physically reasonable hand-object manipulation sequence that performs like human beings. To address such a challenge, we first design CAnonicalized Manipulation Spaces (CAMS), a two-level space hierarchy that canonicalizes the hand poses in an object-centric and contact-centric view. Benefiting from the representation capability of CAMS, we then present a two-stage framework for synthesizing human-like manipulation animations. Our framework achieves state-of-the-art performance for both rigid and articulated categories with impressive visual effects. Codes and video results can be found at our project homepage: https://cams-hoi.github.io/
Learning Rotation-Equivariant Features for Visual Correspondence
Authors: Jongmin Lee, Byungjin Kim, SeungWook Kim, Minsu Cho
Date: 2023-03-25 00:00:00
Proceeding: CVPR 2023 1
Tasks: Camera Pose Estimation, Pose Estimation, Self-Supervised Learning

Extracting discriminative local features that are invariant to imaging variations is an integral part of establishing correspondences between images. In this work, we introduce a self-supervised learning framework to extract discriminative rotation-invariant descriptors using group-equivariant CNNs. Thanks to employing group-equivariant CNNs, our method effectively learns to obtain rotation-equivariant features and their orientations explicitly, without having to perform sophisticated data augmentations. The resultant features and their orientations are further processed by group aligning, a novel invariant mapping technique that shifts the group-equivariant features by their orientations along the group dimension. Our group aligning technique achieves rotation-invariance without any collapse of the group dimension and thus eschews loss of discriminability. The proposed method is trained end-to-end in a self-supervised manner, where we use an orientation alignment loss for the orientation estimation and a contrastive descriptor loss for robust local descriptors to geometric/photometric variations. Our method demonstrates state-of-the-art matching accuracy among existing rotation-invariant descriptors under varying rotation and also shows competitive results when transferred to the task of keypoint matching and camera pose estimation.
A Novel Embedding Architecture and Score Level Fusion Scheme for Occluded Image Acquisition in Ear Biometrics System
Authors: Archishman Biswas, A P Goutham, Saket Pateriya, Divyang Sureshbhai Jadav, Satish Mulleti, Vikram M. Gadre
Date: 2023-03-21 00:00:00
Proceeding: National Conference on Communications (NCC) 2023 3
Tasks: One-Shot Learning

Abstract:
Significant progress in the field of ear-based biometrics has been made in recent studies. But methods to deal with degradation caused by hair occlusions during ear image acquisition have not been addressed yet. Use of occluded ear images from both sides can give better or comparable results to that of clean image acquisition from a single side. To test this, in this work we introduce a novel embedding generation network using conventional CNNs along with parallel paths of Learnable Scattering Wavelet Networks. We have shown results of experiments for augmented training of proposed embedding network and score level fusion of both side-ear images to mitigate the effects of hair occlusions in verification and identification tasks. We also show extensive simulation results over closed and open testing sets to analyse the one-shot learning capabilities of our proposed embedding network. The reported performance metrics show the improvement achieved by using our proposed embedding network and fusing both sides of occluded ear images.
Learning end-to-end patient representations through self-supervised covariate balancing for causal treatment effect estimation
Authors: Gino Tesei, Stefanos Giampanis, Jingpu Shi, Beau Norgeot
Date: 2023-03-20 00:00:00
Proceeding: Journal of Biomedical Informatics 2023 3
Tasks: Causal Inference, Representation Learning

A causal effect can be defined as a comparison of outcomes that result from two or more alternative actions, with only one of the action-outcome pairs actually being observed. In healthcare, the gold standard for causal effect measurements is randomized controlled trials (RCTs), in which a target population is explicitly defined and each study sample is randomly assigned to either the treatment or control cohorts. The great potential to derive actionable insights from causal relationships has led to a growing body of machine-learning research applying causal effect estimators to observational data in the fields of healthcare, education, and economics. The primary difference between causal effect studies utilizing observational data and RCTs is that for observational data, the study occurs after the treatment, and therefore we do not have control over the treatment assignment mechanism. This can lead to massive differences in covariate distributions between control and treatment samples, making a comparison of causal effects confounded and unreliable. Classical approaches have sought to solve this problem piecemeal, first by predicting treatment assignment and then treatment effect separately. Recent work extended part of these approaches to a new family of representation-learning algorithms, showing that the upper bound of the expected treatment effect estimation error is determined by two factors: the outcome generalization-error of the representation and the distance between the treated and control distributions induced by the representation. To achieve minimal dissimilarity in learning such distributions, in this work we propose a specific auto-balancing, self-supervised objective. Experiments on real and benchmark datasets revealed that our approach consistently produced less biased estimates than previously published state-of-the-art methods. We demonstrate that the reduction in error can be directly attributed to the ability to learn representations that explicitly reduce such dissimilarity; further, in case of violations of the positivity assumption (frequent in observational data), we show our approach performs significantly better than the previous state of the art. Thus, by learning representations that induce similar distributions of the treated and control cohorts, we present evidence to support the error bound dissimilarity hypothesis as well as providing a new state-of-the-art model for causal effect estimation.