pwc-archive/papers-with-abstracts huggingface dataset:
Better than DFA? A Bayesian Method for Estimating the Hurst Exponent in Behavioral Sciences
Authors: Aaron D. Likens, Madhur Mangalam, Aaron Y. Wong, Anaelle C. Charles, Caitlin Mills
Date: 2023-01-26 00:00:00
Tasks: Time Series, Time Series Analysis

Detrended Fluctuation Analysis (DFA) is the most popular fractal analytical technique used to evaluate the strength of long-range correlations in empirical time series in terms of the Hurst exponent, $H$. Specifically, DFA quantifies the linear regression slope in log-log coordinates representing the relationship between the time series' variability and the number of timescales over which this variability is computed. We compared the performance of two methods of fractal analysis -- the current gold standard, DFA, and a Bayesian method that is not currently well-known in behavioral sciences: the Hurst-Kolmogorov (HK) method -- in estimating the Hurst exponent of synthetic and empirical time series. Simulations demonstrate that the HK method consistently outperforms DFA in three important ways. The HK method: (i) accurately assesses long-range correlations when the measurement time series is short, (ii) shows minimal dispersion about the central tendency, and (iii) yields a point estimate that does not depend on the length of the measurement time series or its underlying Hurst exponent. Comparing the two methods using empirical time series from multiple settings further supports these findings. We conclude that applying DFA to synthetic time series and empirical time series during brief trials is unreliable and encourage the systematic application of the HK method to assess the Hurst exponent of empirical time series in behavioral sciences.
Persistent topological Laplacian analysis of SARS-CoV-2 variants
Authors: Xiaoqi Wei, Jiahui Chen, Guo-Wei Wei
Date: 2023-01-25 00:00:00
Tasks: Topological Data Analysis

Topological data analysis (TDA) is an emerging field in mathematics and data science. Its central technique, persistent homology, has had tremendous success in many science and engineering disciplines. However, persistent homology has limitations, including its incapability of describing the homotopic shape evolution of data during filtration. Persistent topological Laplacians (PTLs), such as persistent Laplacian and persistent sheaf Laplacian, were proposed to overcome the drawback of persistent homology. In this work, we examine the modeling and analysis power of PTLs in the study of the protein structures of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike receptor binding domain (RBD) and its variants, i.e., Alpha, Beta, Gamma, BA.1, and BA.2. First, we employ PTLs to study how the RBD mutation-induced structural changes of RBD-angiotensin-converting enzyme 2 (ACE2) binding complexes are captured in the changes of spectra of the PTLs among SARS-CoV-2 variants. Additionally, we use PTLs to analyze the binding of RBD and ACE2-induced structural changes of various SARS-CoV-2 variants. Finally, we explore the impacts of computationally generated RBD structures on PTL-based machine learning, including deep learning, and predictions of deep mutational scanning datasets for the SARS-CoV-2 Omicron BA.2 variant. Our results indicate that PTLs have advantages over persistent homology in analyzing protein structural changes and provide a powerful new TDA tool for data science.
A Deep Neural Network Algorithm for Linear-Quadratic Portfolio Optimization with MGARCH and Small Transaction Costs
Authors: Andrew Papanicolaou, Hao Fu, Prashanth Krishnamurthy, Farshad Khorrami
Date: 2023-01-25 00:00:00
Tasks: Portfolio Optimization, Reinforcement Learning (RL)

We analyze a fixed-point algorithm for reinforcement learning (RL) of optimal portfolio mean-variance preferences in the setting of multivariate generalized autoregressive conditional-heteroskedasticity (MGARCH) with a small penalty on trading. A numerical solution is obtained using a neural network (NN) architecture within a recursive RL loop. A fixed-point theorem proves that NN approximation error has a big-oh bound that we can reduce by increasing the number of NN parameters. The functional form of the trading penalty has a parameter $\epsilon>0$ that controls the magnitude of transaction costs. When $\epsilon$ is small, we can implement an NN algorithm based on the expansion of the solution in powers of $\epsilon$. This expansion has a base term equal to a myopic solution with an explicit form, and a first-order correction term that we compute in the RL loop. Our expansion-based algorithm is stable, allows for fast computation, and outputs a solution that shows positive testing performance.
New developments in econophysics: Option pricing formulas
Authors: Moawia Alghalith
Date: 2023-01-25 00:00:00

We synthesize and discuss some new developments in econophysics. In doing so, we focus on option pricing. We relax the assumptions of constant volatility and interest rate. In doing so, we rely on the square root of the Brownian motion. We also provide simple, closed-form pricing formulas for the American and Bermudan options.
The Probability Conflation: A Reply
Authors: Nassim Nicholas Taleb, Ron Richman, Marcos Carreira, James Sharpe
Date: 2023-01-26 00:00:00

We respond to Tetlock et al. (2022) showing 1) how expert judgment fails to reflect tail risk, 2) the lack of compatibility between forecasting tournaments and tail risk assessment methods (such as extreme value theory). More importantly, we communicate a new result showing a greater gap between the properties of tail expectation and those of the corresponding probability.
A Review of Scene Representations for Robot Manipulators
Authors: Carter Sifferman
Date: 2022-12-22 00:00:00
Tasks: Collision Avoidance

For a robot to act intelligently, it needs to sense the world around it. Increasingly, robots build an internal representation of the world from sensor readings. This representation can then be used to inform downstream tasks, such as manipulation, collision avoidance, or human interaction. In practice, scene representations vary widely depending on the type of robot, the sensing modality, and the task that the robot is designed to do. This review provides an overview of the scene representations used for robot manipulators (robot arms). We focus primarily on representations which are built from real world sensing and are used to inform some downstream robotics task.
Using Gaze for Behavioural Biometrics
Authors: Alessandro D’Amelio, Sabrina Patania, Sathya Bursic, Vittorio Cuculo, Giuseppe Boccignone
Date: 2023-01-22 00:00:00
Proceeding: Sensors 2023 1
Tasks: Gaze Estimation

A principled approach to the analysis of eye movements for behavioural biometrics is laid down. The approach grounds in foraging theory, which provides a sound basis to capture the uniqueness of individual eye movement behaviour. We propose a composite Ornstein-Uhlenbeck process for quantifying the exploration/exploitation signature characterising the foraging eye behaviour. The relevant parameters of the composite model, inferred from eye-tracking data via Bayesian analysis, are shown to yield a suitable feature set for biometric identification; the latter is eventually accomplished via a classical classification technique. A proof of concept of the method is provided by measuring its identification performance on a publicly available dataset. Data and code for reproducing the analyses are made available. Overall, we argue that the approach offers a fresh view on either the analyses of eye-tracking data and prospective applications in this field.
Accurate and Resource-Efficient Lipreading with Efficientnetv2 and Transformers
Authors: Alexandros Koumparoulis, Gerasimos Potamianos
Date: 2022-05-23 00:00:00
Proceeding: ICASSP 2022 5
Tasks: image-classification, Image Classification, Lipreading

We present a novel resource-efficient end-to-end architecture for lipreading that achieves state-of-the-art results on a popular and challenging benchmark. In particular, we make the following contributions: First, inspired by the recent success of the EfficientNet architecture in image classification and our earlier work on resource-efficient lipreading models (MobiLipNet), we introduce Efficient-Nets to the lipreading task. Second, we show that the currently most popular in the literature 3D front-end contains a max-pool layer that prohibits networks from reaching superior performance and propose its removal. Finally, we improve our system’s back-end robustness by including a Transformer encoder. We evaluate our proposed system on the “Lipreading In-The-Wild” (LRW) corpus, a database containing short video segments from BBC TV broadcasts. The proposed network (T-variant) attains 88.53% word accuracy, a 0.17% absolute improvement over the current state-of-the-art, while being five times less computationally intensive. Further, an up-scaled version of our model (L-variant) achieves 89.52%, a new state-of-the-art result on the LRW corpus.
SOTAB: The WDC Schema.org Table Annotation Benchmark
Authors: Keti Korini, Ralph Peeters, Christian Bizer
Date: 2023-01-09 00:00:00
Proceeding: SemTab@ISWC 2023 1
Tasks: Columns Property Annotation, Column Type Annotation, Data Integration, Missing Values, Table annotation

Understanding the semantics of table elements is a prerequisite for many data integration and data discovery tasks. Table annotation is the task of labeling table elements with terms from a given vocabulary. This paper presents the WDC Schema.org Table Annotation Benchmark (SOTAB) for comparing the performance of table annotation systems. SOTAB covers the column type annotation (CTA) and columns property annotation (CPA) tasks. SOTAB provides ∼50,000 annotated tables for each of the tasks containing Schema.org data from different websites. The tables cover 17 different types of entities such as movie, event, local business, recipe, job posting, or product. The tables stem from the WDC Schema.org Table Corpus which was created by extracting Schema.org annotations from the Common Crawl. Consequently, the labels used for annotating columns in SOTAB are part of the Schema.org vocabulary. The benchmark covers 91 types for CTA and 176 properties for CPA distributed across textual, numerical and date/time columns. The tables are split into fixed training, validation and test sets. The test sets are further divided into subsets focusing on specific challenges, such as columns with missing values or different value formats, in order to allow a more fine-grained comparison of annotation systems. The evaluation of SOTAB using Doduo and TURL shows that the benchmark is difficult to solve for current state-of-the-art systems.
Computational Methods for Gravitational Lensing
Authors: Charles R. Keeton
Date: 2001-02-20 00:00:00

Modern applications of strong gravitational lensing require the ability to use precise and varied observational data to constrain complex lens models. I discuss two sets of computational methods for lensing calculations. The first is a new algorithm for solving the lens equation for general mass distributions. This algorithm makes it possible to apply arbitrarily complicated models to observed lenses. The second is an evaluation of techniques for using observational data including positions, fluxes, and time delays of point-like images, as well as maps of extended images, to constrain models of strong lenses. The techniques presented here are implemented in a flexible and user-friendly software package called gravlens, which is made available to the community.
New solar opacities, abundances, helioseismology, and neutrino fluxes
Authors: John N. Bahcall, Aldo M. Serenelli, Sarbani Basu
Date: 2004-12-16 00:00:00

We construct solar models with the newly calculated radiative opacities from the Opacity Project (OP) and recently determined (lower) heavy element abundances. We compare results from the new models with predictions of a series of models that use OPAL radiative opacities, older determinations of the surface heavy element abundances, and refinements of nuclear reaction rates. For all the variations we consider, solar models that are constructed with the newer and lower heavy element abundances advocated by Asplund et al. (2005) disagree by much more than the estimated measuring errors with helioseismological determinations of the depth of the solar convective zone, the surface helium composition, the internal sound speeds, and the density profile. Using the new OP radiative opacities, the ratio of the 8B neutrino flux calculated with the older and larger heavy element abundances (or with the newer and lower heavy element abundances) to the total neutrino flux measured by the Sudbury Neutrino Observatory is 1.09 (0.87) with a 9% experimental uncertainty and a 16% theoretical uncertainty, 1 sigma errors.
Correlation of obesity and osteoporosis: Effect of free fatty acids on bone marrow-derived mesenchymal stem cell differentiation
Authors: Shan Lv*, Lin Wu*, Pe ng Cheng, Jing Yu, Aise n Zhang, Juanmin Zha, Juan Liu, Long Wang, Wenjua n Di, Miao Hu, Hanmei Qi, Yujie Li and Guo xia n Ding
Date: 2010-06-03 00:00:00
Proceeding: Experimental and Therapeutic MemEdicine 2010 6

Studies on the relationship between obesity and bone have recently become widespread. The aim of this study was to investigate the effect of obesity on bone, utilizing a diet-induced obese mouse model, and to explore the role of free fatty acids (FFAs) in the osteogenesis/adipogenesis of mouse bone marrow-derived mesenchymal stem cells (BMSCs). An obese mouse model was established by a high-fat diet (HFD). Proximal femurs were collected at sacrifice, and bone mineral density (BMDmd) in the proximal femurs was measured by dual-energy X-ray absorptiometry. Bone histomorphometry was performed using undecalcified sections of the proximal femurs. The effect of obesity on the differentiation of mouse BMSCs was assessed by colony formation assays and gene expression analysis. In vitro, various osteogenic and adipogenic genes were determined by real-time quantitative PCR
in mouse BMSCs after exposure to conditioned medium (CMcm) from FFA-treated 3T3-L1 adipocytes. Western blotting was further performed to analyze the representative protein expression of PPARpparγ and Runx2. BMD and trabecular thickness were significantly greater in the HFD mice than in the control mice. CFU-osteo assay showed significantly increased osteogenesis of BMSCs. The mRNA level of Runx2 was significantly higher, while PPARγ and Pref-1 were significantly lower in BMSCs from the HFD mice compared to the control mice. In mouse BMSCs, the Sox9 and Runx2 genes were significantly up-regulated after exposure to CM from FFA-treated adipocytes, while PPARpparγ and CEBP-α were significantly down-regulated. Osteogenesis was significantly increased, while adipogenesis was significantly decreased. In conclusion, HFD-induced obesity may play a protective role in bone formation by concomitantly promoting osteogenic and suppressing adipogenic differentiation of BMSCs through factors secreted by FFA-treated adipocytes.
Multi-planar 3D knee MRI segmentation via UNet inspired architectures
Authors: Sandeep Singh Sengar, Christopher Meulengracht, Mikael Ploug Boesen, Anders Føhrby Overgaard, Henrik Gudbergsen, Janus Damm Nybing, Mathias Perslev, Erik Bjørnager Dam
Date: 2022-12-20 00:00:00
Proceeding: International Journal of Imaging Systems and Technology 2022 12
Tasks: MRI segmentation, Segmentation

The UNet has become the golden standard method for the segmentation of 2D medical images that any new method must be validated against. In recent years, a number of variations to the seminal UNet have been proposed with promising results in the papers introducing them. However, there is no clear consensus if any of these architectures generalize as well and the UNet currently remains the methodological golden standard. For the segmentation of 3D scans, UNet-inspired methods are also dominant, but there is a larger variety across applications. By evaluating the architectures in a different dimensionality, embedded in a different method, and for a different task, we aimed to evaluate if any of these UNet alternatives are promising as a new golden standard that generalizes even better than the UNet. The purpose of this study was to compare UNet-inspired models for generalized 3D segmentation. To efficiently segment the 3D scans, we employed each UNet variant architecture as the central 2D segmentation core in the multi-planar UNet 3D segmentation method that previously demonstrated excellent generalization in the MICCAI Segmentation Decathlon. It would strongly support a claim of generalizability, if a promising UNet-variant consistently outperforms the UNet in this quite different setting. The experimental results show that the multi-planar-based UNet2+ (MPUNet2+) method outperforms other variants including the original multi-planar UNet (MPUNet).
Covid-19 chest x-ray image generation using resnet-dcgan model
Authors: Sukonya Phukan, Jyoti Singh, Rajlakshmi Gogoi, Sandipan Dhar, and Nanda Dulal Jana
Date: 2022-05-17 00:00:00
Proceeding: Advances in Intelligent Computing and Communication: Proceedings of ICAC 2021 2022 5
Tasks: Image Generation

Detection of severe diseases like COVID-19 using deep learning (DL)
models is a very time-relevant subject to be focused on looking at the present scenario.
However, there is always a problem regarding the availability of significant data for
the training of DL-based classification models. In this work, a ResNet-DCGAN
model is proposed to generate synthesized COVID-19 chest X-ray images to tackle
the data scarcity problem. Here, some image processing techniques are applied to
the publicly available COVID-19 chest X-ray images. Thereafter, a ResNet50 Deep
Convolutional Neural Network (DCNN) model is incorporated as the discriminator
to the proposed ResNet-DCGAN model. Moreover, in this work, to train the proposed
model efficiently, the RAdam optimization algorithm is used instead of the earlier
Adam optimization algorithm. The proposed ResNet-DCGAN model had the edge
over the state-of-the-art DCGAN model.
PlanVerb: Domain-Independent Verbalization and Summary of Task Plans
Authors: Gerard Canal, Senka Krivic ́, Paul Luff, Andrew Coles
Date: 2022-06-28 00:00:00
Proceeding: Proceedings of the AAAI Conference on Artificial Intelligence, 36(9) 2022 6
Tasks: Robot Navigation

For users to trust planning algorithms, they must be able to understand the planner's outputs and the reasons for each action selection. This output does not tend to be user-friendly, often consisting of sequences of parametrised actions or task networks. And these may not be practical for non-expert users who may find it easier to read natural language descriptions. In this paper, we propose PlanVerb, a domain and planner-independent method for the verbalization of task plans. It is based on semantic tagging of actions and predicates. Our method can generate natural language descriptions of plans including causal explanations. The verbalized plans can be summarized by compressing the actions that act on the same parameters. We further extend the concept of verbalization space, previously applied to robot navigation, and apply it to planning to generate different kinds of plan descriptions for different user requirements. Our method can deal with PDDL and RDDL domains, provided that they are tagged accordingly. Our user survey evaluation shows that users can read our automatically generated plan descriptions and that the explanations help them answer questions about the plan.
KGCODE-Tab Results for SemTab 2022
Authors: Xinhe Li, Shuxin Wang, Wei Zhou, Gongrui Zhang, Chenghuan Jiang, Tianyu Hong and Peng Wang
Date: 2022-10-25 00:00:00
Proceeding: SemTab@ISWC 2022 10
Tasks: Cell Entity Annotation, Columns Property Annotation, Column Type Annotation, Graph Matching

This paper presents the results of KGCODE-Tab in the tabular data to knowledge graph matching contest SemTab 2022. As an efficient tabular data linking system, KGCODE-Tab is intended to participate in three tasks of the content: Column Type Annotation (CTA), Cell Entity Annotation (CEA), and Columns Property Annotation (CPA). The specific techniques used by KGCODE-Tab will be introduced briefly. The strengths and weaknesses of KGCODE-Tab will also be discussed.
From Heuristics to Language Models: A Journey Through the Universe of Semantic Table Interpretation with DAGOBAH
Authors: Viet-Phi Huynh, Yoan Chabot, Thomas Labbé, Jixiong Liu and Raphaël Troncy
Date: 2022-10-25 00:00:00
Proceeding: SemTab@ISWC 2022 10
Tasks: Cell Entity Annotation, Column Type Annotation

This paper presents DAGOBAH SL 2022, a semantic table interpretation system that has been continuously improved over the last four years when participating in the SemTab challenge. This year, we have improved the lookup coverage using external resources and we have integrated language models for better understanding the table headers. We have also implemented various system optimizations that lead to a reduction in execution time of about 30%. In this paper, we also show the relevance of using deep learning-based approaches for resolving certain ambiguities and we discuss the limitations of existing corpora and systems for maturing further this research field.
TransIFC: Invariant Cues-aware Feature Concentration Learning for Efficient Fine-grained Bird Image Classification
Authors: Hai Liu, Cheng Zhang, Yongjian Deng, Bochen Xie, Tingting Liu, Zhaoli Zhang, You-Fu Li
Date: 2022-12-31 00:00:00
Proceeding: TIP 2022 12
Tasks: feature selection, Fine-Grained Image Classification, image-classification, Image Classification

Fine-grained bird image classification (FBIC) is not
only meaningful for endangered bird observation and protection
but also a prevalent task for image classification in multimedia
processing and computer vision. However, FBIC suffers from
several challenges, such as bird molting, complex background, and
arbitrary bird posture. To effectively tackle these challenges, we
present a novel invariant cues-aware feature concentration
Transformer (TransIFC), which learns invariant and core
information in bird images. To this end, two novel modules are
proposed to leverage the characteristics of bird images, namely,
the hierarchy stage feature aggregation (HSFA) module and the
feature in feature abstraction (FFA) module. The HSFA module
aggregates the multiscale information of bird images by
concatenating multilayer features. The FFA module extracts the
invariant cues of birds through feature selection based on
discrimination scores. Transformer is employed as the backbone
to reveal the long-dependent semantic relationships in bird
images. Moreover, abundant visualizations are provided to prove
the interpretability of the HSFA and FFA modules in TransIFC.
Comprehensive experiments demonstrate that TransIFC can
achieve state-of-the-art performance on the CUB-200-2011 dataset
(91.0%) and the NABirds dataset (90.9%). Finally, extended
experiments have been conducted on the Stanford Cars dataset to
suggest the potential of generalizing our method on other finegrained visual classification tasks.