pwc-archive/papers-with-abstracts huggingface dataset:
Brain Chains as Topological Signatures for Alzheimer's Disease
Authors: Christian Goodbrake, David Beers, Travis B. Thompson, Heather A. Harrington, Alain Goriely
Date: 2022-08-22 00:00:00

We propose a topological framework to study the evolution of Alzheimer's disease, the most common neurodegenerative disease. The modeling of this disease starts with the representation of the brain connectivity as a graph and the seeding of a toxic protein in a specific region represented by a vertex. Over time, the accumulation of toxic proteins at vertices and their propagation along edges are modeled by a dynamical system on this graph. These dynamics provide an order on the edges of the graph according to the damage created by high concentrations of proteins. This sequence of edges defines a filtration of the graph. We consider different filtrations given by different disease seeding locations. To study this filtration we propose a new combinatorial and topological method. A filtration defines a maximal chain in the partially ordered set of spanning subgraphs ordered by inclusion. To identify similar graphs, and define a topological signature, we quotient this poset by graph homotopy equivalence, which gives maximal chains in a smaller poset. We provide an algorithm to compute this direct quotient without computing all subgraphs and then propose bounds on the total number of graphs up to homotopy equivalence. To compare the maximal chains generated by this method, we extend Kendall's $d_K$ metric for permutations to more general graded posets and establish bounds for this metric. We then demonstrate the utility of this framework on actual brain graphs by studying the dynamics of tau proteins on the structural connectome. {We show that the proposed topological brain chain equivalence classes distinguish different simulated subtypes of Alzheimer's disease.
Violent suicide attempt history in elderly patients with bipolar disorder: The role of sex, abdominal obesity, and verbal memory: Results from the FACE-BD cohort (FondaMental Advanced center of Expertise for Bipolar Disorders)
Authors: Aiste Lengvenyte, Bruno Aouizerate, Valerie Aubin, Joséphine Loftus, Emeline Marlinge, Raoul Belzeaux, Caroline Dubertret, Sebastien Gard, Emmanuel Haffen, Raymund Schwan, Pierre-Michel Llorca, Christine Passerieux, Paul Roux, Mircea Polosan, Bruno Etain, Marion Leboyer, Philippe Courtet, Emilie Olié
Date: 2022-08-26 00:00:00

Background: Bipolar disorder (BD) is a chronic, lifelong condition, associated with increased risk of obesity, cognitive impairment, and suicidal behaviors. Abdominal obesity and a higher risk of violent suicide attempt (SA) seem to be shared correlates with older age, BD, and male sex until middle age when menopause-related female body changes occur. This study aimed at assessing the role of abdominal obesity and cognition in the violent SA burden of individuals with BD.Methods: From the well-defined nationwide cohort FACE-BD (FondaMental Advanced center of Expertise for Bipolar Disorders), we extracted data on 619 euthymic BD patients that were 50 years or older at inclusion. Cross-sectional clinical, cognitive, and metabolic assessments were performed. SA history was based on self-report.Results: Violent SA, in contrast to non-violent and no SA, was associated with higher waist circumference, abdominal obesity and poorer California Verbal Learning Test short-delay free recall (CVLT-SDFR) (ANOVA, p < .001, p = .014, and p = .006). Waist circumference and abdominal obesity were associated with violent SA history independently of sex, BD type and anxiety disorder (Exp(B) 1.02, CI 1.00-1.05, p = .018; Exp(B) 2.16, CI 1.00-4.64, p = .009, accordingly). In an exploratory model, waist circumference and CVLT-SDFR performance mediated the association between male sex and violent SA.Limitations: Cross-sectional design and retrospective reporting.Conclusions: Violent SA history was associated with abdominal obesity and poorer verbal memory in older age BD patients. These factors were interlinked and might mediate the association between male sex and violent SA.
Eating Smart: Free-ranging dogs follow an optimal foraging strategy while scavenging in groups
Authors: Rohan Sarkar, Sreelekshmi R, Abhijit Nayek, Anirban Bhowmick, Poushali Chakraborty, Rituparna Sonowal, Debsruti Dasgupta, Rounak Banerjee, Aritra Roy, Amartya Baran Mandal, Anindita Bhadra
Date: 2022-08-25 00:00:00
Tasks: Decision Making

Foraging and acquiring of food is a delicate balance between managing the costs, both energy and social, and individual preferences. Previous research on the solitary foraging of free ranging dogs showed that they prioritized the nutritionally highest valued food patch first but do not ignore other less valuable food either, displaying typical scavenger behaviour. The current experiment was carried out on groups of dogs with the same set up to see the change in foraging strategies, if any, under the influence of social cost like intra-group competition. We found multiple differences between the strategies of dogs foraging alone versus in groups with competition playing an implicit role in the decision making of dogs when foraging in groups. Dogs were able to continually assess and evaluate the available resources in a patch and adjust their behaviour accordingly. Foraging in groups also provided benefits of reduced individual vigilance. The various decisions and choices made seemed to have a basis in the optimal foraging theory wherein the dogs harvested the nutritionally richest patch possible with the least risk and cost involved but was willing to compromise if that was not possible. This underscores the cognitive, quick decision-making abilities and adaptable behaviour of these dogs.
A New Hyperelastic Lookup Table for RT-DC
Authors: Lucas Daniel Wittwer, Felix Reichel, Paul Müller, Jochen Guck, Sebastian Aland
Date: 2022-08-26 00:00:00

Real-time deformability cytometry (RT-DC) is an established method that quantifies features like size, shape, and stiffness for whole cell populations on a single-cell level in real time. To extract the cell stiffness, a lookup table (LUT) disentangles the experimentally derived steady state cell deformation and the projected area, yielding the Young's modulus. So far, two lookup tables exists, but are limited to simple linear material models and cylindrical channel geometries. Here, we present two new lookup tables for RT-DC based on a neo-Hookean hyperelastic material numerically derived by simulations based on the finite element method in square and cylindrical channel geometries. At the same time, we quantify the influence of the shear-thinning behaviour of the surrounding medium on the stationary deformation of cells in RT-DC and discuss the applicability and impact of the proposed LUTs regarding past and future RT-DC data analysis. Additionally, we provide insights about the cell strain and stresses, as well as the influence resulting from the rotational symmetric assumption on the cell deformation and volume estimation. The new lookup tables as well as the numerical cell shapes are made freely available.
Simulating how animals learn: a new modelling framework applied to the process of optimal foraging
Authors: Peter R. Thompson, Melodie Kunegel-Lion, Mark A. Lewis
Date: 2022-08-25 00:00:00
Tasks: Bayesian Inference

Animal learning has interested ecologists and psychologists for over a century. Mathematical models that explain how animals store and recall information have gained attention recently. Central to this work is statistical decision theory (SDT), which relates information uptake in animals to Bayesian inference. SDT effectively explains many learning tasks in animals, but extending this theory to predict how animals will learn in changing environments still poses a challenge for ecologists. We addressed this shortcoming with a novel implementation of Bayesian Markov Chain Monte Carlo (MCMC) sampling to simulate how animals sample environmental information and learn as a result. We applied our framework to an individual-based model simulating complex foraging tasks encountered by wild animals. Simulated ``animals" learned behavioral strategies that optimized foraging returns simply by following the principles of an MCMC sampler. In these simulations, behavioral plasticity was most conducive to efficient foraging in unpredictable and uncertain environments. Our model suggests that animals prioritize highly concentrated resources even when these resources are less available overall, in line with existing knowledge on optimal foraging and ideal free distribution theory. Our innovative computational modelling framework can be applied more widely to simulate the learning of many other tasks in animals and humans.
On Randomization of Affine Diffusion Processes with Application to Pricing of Options on VIX and S&P 500
Authors: Lech A. Grzelak
Date: 2022-08-26 00:00:00

The class of Affine (Jump) Diffusion (AD) has, due to its closed form characteristic function (ChF), gained tremendous popularity among practitioners and researchers. However, there is clear evidence that a linearity constraint is insufficient for precise and consistent option pricing. Any non-affine model must pass the strict requirement of quick calibration -- which is often challenging. We focus here on Randomized AD (RAnD) models, i.e., we allow for exogenous stochasticity of the model parameters. Randomization of a pricing model occurs outside the affine model and, therefore, forms a generalization that relaxes the affinity constraints. The method is generic and can apply to any model parameter. It relies on the existence of moments of the so-called randomizer- a random variable for the stochastic parameter. The RAnD model allows flexibility while benefiting from fast calibration and well-established, large-step Monte Carlo simulation, often available for AD processes. The article will discuss theoretical and practical aspects of the RAnD method, like derivations of the corresponding ChF, simulation, and computations of sensitivities. We will also illustrate the advantages of the randomized stochastic volatility models in the consistent pricing of options on the S&P 500 and VIX.
Regime-based Implied Stochastic Volatility Model for Crypto Option Pricing
Authors: Danial Saef, Yuanrong Wang, Tomaso Aste
Date: 2022-08-15 00:00:00
Tasks: Clustering

The increasing adoption of Digital Assets (DAs), such as Bitcoin (BTC), rises the need for accurate option pricing models. Yet, existing methodologies fail to cope with the volatile nature of the emerging DAs. Many models have been proposed to address the unorthodox market dynamics and frequent disruptions in the microstructure caused by the non-stationarity, and peculiar statistics, in DA markets. However, they are either prone to the curse of dimensionality, as additional complexity is required to employ traditional theories, or they overfit historical patterns that may never repeat. Instead, we leverage recent advances in market regime (MR) clustering with the Implied Stochastic Volatility Model (ISVM). Time-regime clustering is a temporal clustering method, that clusters the historic evolution of a market into different volatility periods accounting for non-stationarity. ISVM can incorporate investor expectations in each of the sentiment-driven periods by using implied volatility (IV) data. In this paper, we applied this integrated time-regime clustering and ISVM method (termed MR-ISVM) to high-frequency data on BTC options at the popular trading platform Deribit. We demonstrate that MR-ISVM contributes to overcome the burden of complex adaption to jumps in higher order characteristics of option pricing models. This allows us to price the market based on the expectations of its participants in an adaptive fashion.
Corporate Environmental Management Accounting Practicing and Reporting in Bangladesh
Authors: Nazrul Islam, Syed Khaled Rahman
Date: 2022-06-06 00:00:00
Tasks: Management

In the management of environment the Environmental Management Accounting (EMA) is essential for corporate or companies because corporate sectors are the main parties of environmental humiliation as they are existed in the environment and for protecting environment a branch of accounting is emerged which is called environmental management accounting. The objective of the study is to develop a compliance framework for EMA and appraise the ER practices in selected industries in Bangladesh. In conducting the study, 50 environmental sensitive industries were selected from DSE. A compliance checklist was developed on 75 aspects of EMA and ER under 13 groups. In developing the compliance index binary method is used i.e. 1= if ER practices; 0= if not practices. Further the level of EMR/ER practices have been evaluated in terms of selected independent variables of the company viz. total assets, total sales, return on equity and size of board. The study found that the environmental management accounting in the manufacturing companies is in poor level. The maximum compliance is 67% and the lowest is 20%. The TA, TS BS and SP have been considered to find out the explanatory variables. In most of the cases board size does not play significant role in the practice of EMA in the sampled firms.
Towards Robust Drone Vision in the Wild
Authors: Xiaoyu Lin
Date: 2022-08-21 00:00:00
Tasks: Image Super-Resolution, One-Shot Learning, Super-Resolution

The past few years have witnessed the burst of drone-based applications where computer vision plays an essential role. However, most public drone-based vision datasets focus on detection and tracking. On the other hand, the performance of most existing image super-resolution methods is sensitive to the dataset, specifically, the degradation model between high-resolution and low-resolution images. In this thesis, we propose the first image super-resolution dataset for drone vision. Image pairs are captured by two cameras on the drone with different focal lengths. We collect data at different altitudes and then propose pre-processing steps to align image pairs. Extensive empirical studies show domain gaps exist among images captured at different altitudes. Meanwhile, the performance of pretrained image super-resolution networks also suffers a drop on our dataset and varies among altitudes. Finally, we propose two methods to build a robust image super-resolution network at different altitudes. The first feeds altitude information into the network through altitude-aware layers. The second uses one-shot learning to quickly adapt the super-resolution model to unknown altitudes. Our results reveal that the proposed methods can efficiently improve the performance of super-resolution networks at varying altitudes.
T-Person-GAN: Text-to-Person Image Generation with Identity-Consistency and Manifold Mix-Up
Authors: Deyin Liu, Lin Yuanbo Wu, Bo Li, ZongYuan Ge
Date: 2022-08-18 00:00:00
Tasks: Image Generation, Text to Image Generation, Text-to-Image Generation

In this paper, we present an end-to-end approach to generate high-resolution person images conditioned on texts only. State-of-the-art text-to-image generation models are mainly designed for center-object generation, e.g., flowers and birds. Unlike center-placed objects with similar shapes and orientation, person image generation is a more challenging task, for which we observe the followings: 1) the generated images for the same person exhibit visual details with identity-consistency, e.g., identity-related textures/clothes/shoes across the images, and 2) those images should be discriminant for being robust against the inter-person variations caused by visual ambiguities. To address the above challenges, we develop an effective generative model to produce person images with two novel mechanisms. In particular, our first mechanism (called T-Person-GAN-ID) is to integrate the one-stream generator with an identity-preserving network such that the representations of generated data are regularized in their feature space to ensure the identity-consistency. The second mechanism (called T-Person-GAN-ID-MM) is based on the manifold mix-up to produce mixed images via the linear interpolation across generated images from different manifold identities, and we further enforce such interpolated images to be linearly classified in the feature space. This amounts to learning a linear classification boundary that can perfectly separate images from two identities. Our proposed method is empirically validated to achieve a remarkable improvement in text-to-person image generation. Our architecture is orthogonal to StackGAN++ , and focuses on person image generation, with all of them together to enrich the spectrum of GANs for the image generation task. Codes are available on \url{https://github.com/linwu-github/Person-Image-Generation.git}.
Swarm Analytics: Designing Information Markers to Characterise Swarm Systems in Shepherding Contexts
Authors: Adam Hepworth, Aya Hussein, Darryn Reid, Hussein Abbass
Date: 2022-08-26 00:00:00

Contemporary swarm indicators are often used in isolation, focused on extracting information at the individual or collective levels. Consequently, these are seldom integrated to infer a top-level operating picture of the swarm, its members, and its overall collective dynamics. The primary contribution of this paper is to organise a suite of indicators about swarms into an ontologically-arranged collection of information markers to characterise the swarm from the perspective of an external observer\textemdash, a recognition agent. Our contribution shows the foundations for a new area of research that we tile swarm analytics, whose primary concern is with the design and organisation of collections of swarm markers to understand, detect, recognise, track, and learn a particular insight about a swarm system. We present our designed framework of information markers that offer a new avenue for swarm research, especially for heterogeneous and cognitive swarms that may require more advanced capabilities to detect agencies and categorise agent influences and responses.
Need for Design Patterns: Interoperability Issues and Modelling Challenges for Observational Data
Authors: Trupti Padiya, Frank Löffler, Friederike Klan
Date: 2022-08-26 00:00:00
Tasks: Data Integration

Interoperability issues concerning observational data have gained attention in recent times. Automated data integration is important when it comes to the scientific analysis of observational data from different sources. However, it is hampered by various data interoperability issues. We focus exclusively on semantic interoperability issues for observational characteristics. We propose a use-case-driven approach to identify general classes of interoperability issues. In this paper, this is exemplarily done for the use-case of citizen science fireball observations. We derive key concepts for the identified interoperability issues that are generalizable to observational data in other fields of science. These key concepts contain several modeling challenges, and we broadly describe each modeling challenges associated with its interoperability issue. We believe, that addressing these challenges with a set of ontology design patterns will be an effective means for unified semantic modeling, paving the way for a unified approach for resolving interoperability issues in observational data. We demonstrate this with one design pattern, highlighting the importance and need for ontology design patterns for observational data, and leave the remaining patterns to future work. Our paper thus describes interoperability issues along with modeling challenges as a starting point for developing a set of extensible and reusable design patterns.
ESC-Rules: Explainable, Semantically Constrained Rule Sets
Authors: Martin Glauer, Robert West, Susan Michie, Janna Hastings
Date: 2022-08-26 00:00:00

We describe a novel approach to explainable prediction of a continuous variable based on learning fuzzy weighted rules. Our model trains a set of weighted rules to maximise prediction accuracy and minimise an ontology-based 'semantic loss' function including user-specified constraints on the rules that should be learned in order to maximise the explainability of the resulting rule set from a user perspective. This system fuses quantitative sub-symbolic learning with symbolic learning and constraints based on domain knowledge. We illustrate our system on a case study in predicting the outcomes of behavioural interventions for smoking cessation, and show that it outperforms other interpretable approaches, achieving performance close to that of a deep learning model, while offering transparent explainability that is an essential requirement for decision-makers in the health domain.
Itemset Utility Maximization with Correlation Measure
Authors: Jiahui Chen, Yixin Xu, Shicheng Wan, Wensheng Gan, Jerry Chun-Wei Lin
Date: 2022-08-26 00:00:00

As an important data mining technology, high utility itemset mining (HUIM) is used to find out interesting but hidden information (e.g., profit and risk). HUIM has been widely applied in many application scenarios, such as market analysis, medical detection, and web click stream analysis. However, most previous HUIM approaches often ignore the relationship between items in an itemset. Therefore, many irrelevant combinations (e.g., \{gold, apple\} and \{notebook, book\}) are discovered in HUIM. To address this limitation, many algorithms have been proposed to mine correlated high utility itemsets (CoHUIs). In this paper, we propose a novel algorithm called the Itemset Utility Maximization with Correlation Measure (CoIUM), which considers both a strong correlation and the profitable values of the items. Besides, the novel algorithm adopts a database projection mechanism to reduce the cost of database scanning. Moreover, two upper bounds and four pruning strategies are utilized to effectively prune the search space. And a concise array-based structure named utility-bin is used to calculate and store the adopted upper bounds in linear time and space. Finally, extensive experimental results on dense and sparse datasets demonstrate that CoIUM significantly outperforms the state-of-the-art algorithms in terms of runtime and memory consumption.
A Formal Comparison between Datalog-based Languages for Stream Reasoning (extended version)
Authors: Nicola Leone, Marco Manna, Maria Concetta Morelli, Simona Perri
Date: 2022-08-26 00:00:00

The paper investigates the relative expressiveness of two logic-based languages for reasoning over streams, namely LARS Programs -- the language of the Logic-based framework for Analytic Reasoning over Streams called LARS -- and LDSR -- the language of the recent extension of the I-DLV system for stream reasoning called I-DLV-sr. Although these two languages build over Datalog, they do differ both in syntax and semantics. To reconcile their expressive capabilities for stream reasoning, we define a comparison framework that allows us to show that, without any restrictions, the two languages are incomparable and to identify fragments of each language that can be expressed via the other one.