pwc-archive/papers-with-abstracts huggingface dataset:
Implementing Sustainable Tourism practices in luxury resorts of Maldives: Sustainability principles & Tripple Bottomline Approach
Authors: Dr Mir Hasan Naqvi, Asnan Ahmed, Dr Asif Pervez
Date: 2023-11-30 00:00:00
Tasks: Management

The aim of the research paper is to understand the sustainability challenges faced by resorts mainly luxury in Maldives and to implement the sustainable tourism practices. The Maldives economy is dependent mostly on the fishing, boat building, boat repairing and tourism. Over recent years there is a drastic change that has took place in Maldives in tourism industry. Maldives has progressed to be the upper middle-income country and luxury resorts are the reason for increased GDP in the country. Although there are some practices associated with the luxury resorts to follow in terms of environmental concerns. Present study focuses on the triple bottom line approach and the 12 major Sustainable Tourism Principles as a framework for sustainability practices and its implementation including the challenges associated in Maldives. The paper suggests some recommendations on several paradigm of enforcing laws and regulations, waste management facilities, fostering collaboration along with promoting local agriculture. The study also contemplates on several other areas such as on the impact of sustainability initiatives, coral restoration, and the use of sustainable supply chains. The intent of the current research is to suggest methods to promote the sustainable practices in luxury resort in Maldives.
Does ESG and Digital Transformation affects Corporate Sustainability? The Moderating role of Green Innovation
Authors: Chenglin Qing, Shanyue Jin
Date: 2023-11-30 00:00:00
Tasks: Management

Recently, environmental, social, and governance (ESG) has become an important factor in companies' sustainable development. Artificial intelligence (AI) is also a core digital technology that can create innovative, sustainable, comprehensive, and resilient environments. ESG- and AI-based digital transformation is a relevant strategy for managing business value and sustainability in corporate green management operations. Therefore, this study examines how corporate sustainability relates to ESG- and AI-based digital transformation. Furthermore, it confirms the moderating effect of green innovation on the process of increasing sustainability. To achieve the purpose of this study, 359 data points collected for hypothesis testing were used for statistical analysis and for mobile business platform users. The following conclusions are drawn. (1) ESG activities have become key variables that enable sustainable corporate growth. Companies can implement eco-friendly operating processes through ESG activities. (2) This study verifies the relationship between AI-based digital transformation and corporate sustainability and confirms that digital transformation positively affects corporate sustainability. In addition, societal problems can be identified and environmental accidents prevented through technological innovation. (3) This study does not verify the positive moderating effect of green innovation; however, it emphasizes its necessity and importance. Although green innovation improves performance only in the long term, it is a key factor for companies pursuing sustainable growth. This study reveals that ESG- and AI-based digital transformation is an important tool for promoting corporate sustainability, broadening the literature in related fields and providing insights for corporate management and government policymakers to advance corporate sustainability.
The impact of sensory characteristics on the willingness to pay for honey
Authors: Julia Zaripova, Ksenia Chuprianova, Irina Polyakova, Daria Semenova, Sofya Kulikova
Date: 2023-11-30 00:00:00

Honey consumption in Russia has been actively growing in recent years due to the increasing interest in healthy and environment-friendly food products. However, it remains an open question which characteristics of honey are the most significant for consumers and, more importantly, from an economic point of view, for which of them consumers are willing to pay. The purpose of this study was to investigate the role of sensory characteristics in assessing consumers' willingness to pay for honey and to determine which properties and characteristics "natural" honey should have to encourage repeated purchases by target consumers. The study involved a behavioral experiment that included a pre-test questionnaire, blind tasting of honey samples, an in-room test to assess perceived quality, and a closed auction using the Becker-DeGroote-Marschak method. As the result, it was revealed that the correspondence of the expected sensations to the actual taste, taste intensity, duration of the aftertaste and the sensations of tickling in the throat had a positive effect on both the perceived quality of the product and the willingness to pay for it, while perception of off-flavors or added sugar had a negative impact. Using factor analysis, we have combined 21 sensory characteristics of honey into eight components that were sufficient to obtain the flavor portrait of honey by Russian consumers.
A Novel Variational Approach for Multiphoton Microscopy Image Restoration: from PSF Estimation to 3D Deconvolution
Authors: Julien Ajdenbaum, Emilie Chouzenoux, Claire Lefort, Ségolène Martin, Jean-Christophe Pesquet
Date: 2023-11-30 00:00:00
Tasks: Deblurring, Image Restoration

In multi-photon microscopy (MPM), a recent in-vivo fluorescence microscopy system, the task of image restoration can be decomposed into two interlinked inverse problems: firstly, the characterization of the Point Spread Function (PSF) and subsequently, the deconvolution (i.e., deblurring) to remove the PSF effect, and reduce noise. The acquired MPM image quality is critically affected by PSF blurring and intense noise. The PSF in MPM is highly spread in 3D and is not well characterized, presenting high variability with respect to the observed objects. This makes the restoration of MPM images challenging. Common PSF estimation methods in fluorescence microscopy, including MPM, involve capturing images of sub-resolution beads, followed by quantifying the resulting ellipsoidal 3D spot. In this work, we revisit this approach, coping with its inherent limitations in terms of accuracy and practicality. We estimate the PSF from the observation of relatively large beads (approximately 1$\mu$m in diameter). This goes through the formulation and resolution of an original non-convex minimization problem, for which we propose a proximal alternating method along with convergence guarantees. Following the PSF estimation step, we then introduce an innovative strategy to deal with the high level multiplicative noise degrading the acquisitions. We rely on a heteroscedastic noise model for which we estimate the parameters. We then solve a constrained optimization problem to restore the image, accounting for the estimated PSF and noise, while allowing a minimal hyper-parameter tuning. Theoretical guarantees are given for the restoration algorithm. These algorithmic contributions lead to an end-to-end pipeline for 3D image restoration in MPM, that we share as a publicly available Python software. We demonstrate its effectiveness through several experiments on both simulated and real data.
DiffGEPCI: 3D MRI Synthesis from mGRE Signals using 2.5D Diffusion Model
Authors: Yuyang Hu, Satya V. V. N. Kothapalli, Weijie Gan, Alexander L. Sukstanskii, Gregory F. Wu, Manu Goyal, Dmitriy A. Yablonskiy, Ulugbek S. Kamilov
Date: 2023-11-29 00:00:00

We introduce a new framework called DiffGEPCI for cross-modality generation in magnetic resonance imaging (MRI) using a 2.5D conditional diffusion model. DiffGEPCI can synthesize high-quality Fluid Attenuated Inversion Recovery (FLAIR) and Magnetization Prepared-Rapid Gradient Echo (MPRAGE) images, without acquiring corresponding measurements, by leveraging multi-Gradient-Recalled Echo (mGRE) MRI signals as conditional inputs. DiffGEPCI operates in a two-step fashion: it initially estimates a 3D volume slice-by-slice using the axial plane and subsequently applies a refinement algorithm (referred to as 2.5D) to enhance the quality of the coronal and sagittal planes. Experimental validation on real mGRE data shows that DiffGEPCI achieves excellent performance, surpassing generative adversarial networks (GANs) and traditional diffusion models.
Piecewise Linearity of Min-Norm Solution Map of a Nonconvexly Regularized Convex Sparse Model
Authors: Yi Zhang, Isao Yamada
Date: 2023-11-30 00:00:00

It is well known that the minimum $\ell_2$-norm solution of the convex LASSO model, say $\mathbf{x}_{\star}$, is a continuous piecewise linear function of the regularization parameter $\lambda$, and its signed sparsity pattern is constant within each linear piece. The current study is an extension of this classic result, proving that the aforementioned properties extend to the min-norm solution map $\mathbf{x}_{\star}(\mathbf{y},\lambda)$, where $\mathbf{y}$ is the observed signal, for a generalization of LASSO termed the scaled generalized minimax concave (sGMC) model. The sGMC model adopts a nonconvex debiased variant of the $\ell_1$-norm as sparse regularizer, but its objective function is overall-convex. Based on the geometric properties of $\mathbf{x}_{\star}(\mathbf{y},\lambda)$, we propose an extension of the least angle regression (LARS) algorithm, which iteratively computes the closed-form expression of $\mathbf{x}_{\star}(\mathbf{y},\lambda)$ in each linear zone. Under suitable conditions, the proposed algorithm provably obtains the whole solution map $\mathbf{x}_{\star}(\mathbf{y},\lambda)$ within finite iterations. Notably, our proof techniques for establishing continuity and piecewise linearity of $\mathbf{x}_{\star}(\mathbf{y},\lambda)$ are novel, and they lead to two side contributions: (a) our proofs establish continuity of the sGMC solution set as a set-valued mapping of $(\mathbf{y},\lambda)$; (b) to prove piecewise linearity and piecewise constant sparsity pattern of $\mathbf{x}_{\star}(\mathbf{y},\lambda)$, we do not require any assumption that previous work relies on (whereas to prove some additional properties of $\mathbf{x}_{\star}(\mathbf{y},\lambda)$, we use a different set of assumptions from previous work).
URLLC-Awared Resource Allocation for Heterogeneous Vehicular Edge Computing
Authors: Qiong Wu, Wenhua Wang, Pingyi Fan, Qiang Fan, Jiangzhou Wang, Khaled B. Letaief
Date: 2023-11-30 00:00:00
Tasks: Deep Reinforcement Learning, Edge-computing

Vehicular edge computing (VEC) is a promising technology to support real-time vehicular applications, where vehicles offload intensive computation tasks to the nearby VEC server for processing. However, the traditional VEC that relies on single communication technology cannot well meet the communication requirement for task offloading, thus the heterogeneous VEC integrating the advantages of dedicated short-range communications (DSRC), millimeter-wave (mmWave) and cellular-based vehicle to infrastructure (C-V2I) is introduced to enhance the communication capacity. The communication resource allocation and computation resource allocation may significantly impact on the ultra-reliable low-latency communication (URLLC) performance and the VEC system utility, in this case, how to do the resource allocations is becoming necessary. In this paper, we consider a heterogeneous VEC with multiple communication technologies and various types of tasks, and propose an effective resource allocation policy to minimize the system utility while satisfying the URLLC requirement. We first formulate an optimization problem to minimize the system utility under the URLLC constraint which modeled by the moment generating function (MGF)-based stochastic network calculus (SNC), then we present a Lyapunov-guided deep reinforcement learning (DRL) method to convert and solve the optimization problem. Extensive simulation experiments illustrate that the proposed resource allocation approach is effective.
Feasibility Analysis of In-Band Coexistence in Dense LEO Satellite Communication Systems
Authors: Eunsun Kim, Ian P. Roberts, Jeffrey G. Andrews
Date: 2023-11-30 00:00:00

This work provides a rigorous methodology for assessing the feasibility of spectrum sharing between large low-earth orbit (LEO) satellite constellations. For concreteness, we focus on the existing Starlink system and the soon-to-be-launched Kuiper system, which is prohibited from inflicting excessive interference onto the incumbent Starlink ground users. We carefully model and study the potential downlink interference between the two systems and investigate how strategic satellite selection may be used by Kuiper to serve its ground users while also protecting Starlink ground users. We then extend this notion of satellite selection to the case where Kuiper has limited knowledge of Starlink's serving satellite. Our findings reveal that there is always the potential for very high and extremely low interference, depending on which Starlink and Kuiper satellites are being used to serve their users. Consequently, we show that Kuiper can protect Starlink ground users with high probability, by strategically selecting which of its satellites are used to serve its ground users. Simultaneously, Kuiper is capable of delivering near-maximal downlink SINR to its own ground users. This highlights a feasible route to the coexistence of two dense LEO satellite systems, even in scenarios where one system has limited knowledge of the other's serving satellites.
2D Signal Estimation for Sparse Distributed Target Photon Counting Data
Authors: Matthew Hayman, Robert A. Stillwell, Josh Carnes, Grant J. Kirchhoff, Scott M. Spuler, Jeffrey P. Thayer
Date: 2023-11-29 00:00:00

In this study, we explore the utilization of maximum likelihood estimation for the analysis of sparse photon counting data obtained from distributed target lidar systems. Specifically, we adapt the Poisson Total Variation processing technique to cater to this application. By assuming a Poisson noise model for the photon count observations, our approach yields denoised estimates of backscatter photon flux and related parameters. This facilitates the processing of raw photon counting signals with exceptionally high temporal and range resolutions (demonstrated here to 50 Hz and 75 cm resolutions), including data acquired through time-correlated single photon counting, without significant sacrifice of resolution. Through examination involving both simulated and real-world 2D atmospheric data, our method consistently demonstrates superior accuracy in signal recovery compared to the conventional histogram-based approach commonly employed in distributed target lidar applications.
The two square root laws of market impact and the role of sophisticated market participants
Authors: Bruno Durin, Mathieu Rosenbaum, Grégoire Szymanski
Date: 2023-11-30 00:00:00

The goal of this paper is to disentangle the roles of volume and of participation rate in the price response of the market to a sequence of transactions. To do so, we are inspired the methodology introduced in arXiv:1402.1288, arXiv:1805.07134 where price dynamics are derived from order flow dynamics using no arbitrage assumptions. We extend this approach by taking into account a sophisticated market participant having superior abilities to analyse market dynamics. Our results lead to the recovery of two square root laws: (i) For a given participation rate, during the execution of a metaorder, the market impact evolves in a square root manner with respect to the cumulated traded volume. (ii) For a given executed volume $Q$, the market impact is proportional to $\sqrt{\gamma}$, where $\gamma$ denotes the participation rate, for $\gamma$ large enough. Smaller participation rates induce a more linear dependence of the market impact in the participation rate.
Polymer conformation: genetic and environmental factors associated with this characteristic of glutenin in wheat grain
Authors: Gérard Branlard, Angelina D'orlando, Ayesha Tahir, Marc Schmutz, Larbi Rhazi, Annie Faye, Thierry Aussenac
Date: 2023-11-30 00:00:00

Using asymmetric flow field flow fractionation, the polymer mass (Mw), gyration radius (Rw) and the polydispersity index (PI) of glutenin polymers (GPs) have been previously studied. Here, using the same multi-location trials (4 years, 11 locations and 192 cultivars), the factors associated with the conformation (Conf) of the polymers were reported. Conf, which is the slope of Log (Rw) = f [Log (Mw)], varied between 0.285 and 0.740. Conf had low broad-sense heritability (H 2 = 16.8) and was significantly influenced by temperature occurring over the last month of grain filling. Higher temperatures were found to increase Rw and the compactness and sphericity of GPs. The HMW-GS and LMW-GS alleles had a significant influence on the Conf value. Assuming a Gaussian distribution for Mw, the number of polymers present in wheat grain was computed for different kernel weights and protein concentrations: it exceeded 10 12 GPs per grain. Using atomic force microscopy and cryo-microscopy, photos of GPs were revealed for the first time. Under high temperatures, GPs became larger, less reticulated, more spherical and consequently less prone to rapid hydrolysis. Some orientations aimed at reducing the possible impact of the numerous GPs for people suffering from non-celiac gluten sensitivity (NCGS) were proposed.
FoldExplorer: Fast and Accurate Protein Structure Search with Sequence-Enhanced Graph Embedding
Authors: YuAn Liu, Hong-Bin Shen
Date: 2023-11-30 00:00:00
Tasks: Graph Attention, Graph Embedding, Protein Structure Prediction

The advent of highly accurate protein structure prediction methods has fueled an exponential expansion of the protein structure database. Consequently, there is a rising demand for rapid and precise structural homolog search. Traditional alignment-based methods are dedicated to precise comparisons between pairs, exhibiting high accuracy. However, their sluggish processing speed is no longer adequate for managing the current massive volume of data. In response to this challenge, we propose a novel deep-learning approach FoldExplorer. It harnesses the powerful capabilities of graph attention neural networks and protein large language models for protein structures and sequences data processing to generate embeddings for protein structures. The structural embeddings can be used for fast and accurate protein search. The embeddings also provide insights into the protein space. FoldExplorer demonstrates a substantial performance improvement of 5% to 8% over the current state-of-the-art algorithm on the benchmark datasets. Meanwhile, FoldExplorer does not compromise on search speed and excels particularly in searching on a large-scale dataset.
InfoFlowNet: A Multi-head Attention-based Self-supervised Learning Model with Surrogate Approach for Uncovering Brain Effective Connectivity
Authors: Chun-Hsiang Chuang, Shao-Xun Fang, Chih-Sheng Huang, Weiping Ding
Date: 2023-11-30 00:00:00
Tasks: Causal Discovery, Causal Inference, EEG, Electroencephalogram (EEG), Self-Supervised Learning, Time Series

Deciphering brain network topology can enhance the depth of neuroscientific knowledge and facilitate the development of neural engineering methods. Effective connectivity, a measure of brain network dynamics, is particularly useful for investigating the directional influences among different brain regions. In this study, we introduce a novel brain causal inference model named InfoFlowNet, which leverages the self-attention mechanism to capture associations among electroencephalogram (EEG) time series. The proposed method estimates the magnitude of directional information flow (dIF) among EEG processes by measuring the loss of model inference resulting from the shuffling of the time order of the original time series. To evaluate the feasibility of InfoFlowNet, we conducted experiments using a synthetic time series and two EEG datasets. The results demonstrate that InfoFlowNet can extract time-varying causal relationships among processes, reflected in the fluctuation of dIF values. Compared with the Granger causality model and temporal causal discovery framework, InfoFlowNet can identify more significant causal edges underlying EEG processes while maintaining an acceptable computation time. Our work demonstrates the potential of InfoFlowNet for analyzing effective connectivity in EEG data. The findings highlight the importance of effective connectivity in understanding the complex dynamics of the brain network.
A computational model of behavioral adaptation to solve the credit assignment problem
Authors: Roy E. Clymer, Sanjeev V. Namjoshi
Date: 2023-11-29 00:00:00

The adaptive fitness of an organism in its ecological niche is highly reliant upon its ability to associate an environmental or internal stimulus with a behavior response through reinforcement. This simple but powerful observation has been successfully applied in a number of contexts within computational neuroscience and reinforcement learning to model both human and animal behaviors. However, a critical challenge faced by these models is the credit assignment problem which asks how past behavior comes to be associated with a delayed reinforcement signal. In this paper we reformulate the credit assignment problem to ask how past stimuli come to be linked to adaptive behavioral responses in the context of a simple neuronal circuit. We propose a biologically plausible variant of a spiking neural network which can model a wide variety of behavioral, learning, and evolutionary phenomena. Our model suggests one fundamental mechanism, potentially in use in the brains of both simple and complex organisms, that would allow it to associate a behavior with an adaptive response. We present results that showcase the model's versatility and biological plausibility in a number of tasks related to classical and operant conditioning including behavioral chaining. We then provide further simulations to demonstrate how adaptive behaviors such as reflexes and simple category detection may have evolved using our model. Our results indicate the potential for further modifications and extensions of our model to replicate more sophisticated and biologically plausible behavioral, learning, and intelligence phenomena found throughout the animal kingdom.
Computing the Bounds of the Number of Reticulations in a Tree-Child Network That Displays a Set of Trees
Authors: Yufeng Wu, Louxin Zhang
Date: 2023-11-30 00:00:00

Phylogenetic network is an evolutionary model that uses a rooted directed acyclic graph (instead of a tree) to model an evolutionary history of species in which reticulate events (e.g., hybrid speciation or horizontal gene transfer) occurred. Tree-child network is a kind of phylogenetic network with structural constraints. Existing approaches for tree-child network reconstruction can be slow for large data. In this paper, we present several computational approaches for bounding from below the number of reticulations in a tree-child network that displays a given set of rooted binary phylogenetic trees. In addition, we also present some theoretical results on bounding from above the number of reticulations. Through simulation, we demonstrate that the new lower bounds on the reticulation number for tree-child networks can practically be computed for large tree data. The bounds can provide estimates of reticulation for relatively large data.