pwc-archive/papers-with-abstracts huggingface dataset:
Land Use Trade-offs in Decarbonization of Electricity Generation in the American West
Authors: Neha Patankar, Xiili Sarkela-Basset, Greg Schivley, Emily Leslie, Jesse Jenkins
Date: 2022-11-09 00:00:00

Land-use conflicts may constrain the unprecedented rates of renewable energy deployment required to meet the decarbonization goals of the Inflation Reduction Act (IRA). This paper employs geospatially resolved data and a detailed electricity system capacity expansion model to generate 160 affordable, zero-carbon electricity supply portfolios for the American west and evaluates the land use impacts of each portfolio. Less than 4% of all sites suitable for solar development and 17% of all wind sites appear in this set of portfolios. Of these sites, 53% of solar and 85% of wind sites exhibit higher development risk and potential for land use conflict. We thus find that clean electricity goals cannot be achieved in an affordable manner without substantial renewable development on sites with potential for land use conflict. However, this paper identifies significant flexibility across western U.S. states to site renewable energy or alter the composition of the electricity supply portfolio to ameliorate potential conflicts.
Parallel-Connected Battery Current Imbalance Dynamics
Authors: Andrew Weng, Sravan Pannala, Jason B. Siegel, Anna G. Stefanopoulou
Date: 2022-11-09 00:00:00

In this work, we derive analytical expressions governing state-of-charge and current imbalance dynamics for two parallel-connected batteries. The model, based on equivalent circuits and an affine open circuit voltage relation, describes the evolution of state-of-charge and current imbalance over the course of a complete charge and discharge cycle. Using this framework, we identify the conditions under which an aged battery will experience a higher current magnitude and state-of-charge deviation towards the end of a charge or discharge cycle. This work enables a quantitative understanding of how mismatches in battery capacities and resistances influence imbalance dynamics in parallel-connected battery systems, helping to pave a path forward for battery degradation modeling in heterogeneous battery systems.
6G Mobile-Edge Empowered Metaverse: Requirements, Technologies, Challenges and Research Directions
Authors: Jiadong Yu, Ahmad Alhilal, Pan Hui, Danny H. K. Tsang
Date: 2022-11-09 00:00:00
Tasks: Edge-computing, Semantic Communication

The Metaverse has emerged as the successor of the conventional mobile internet to change people's lifestyles. It has strict visual and physical requirements to ensure an immersive experience (i.e., high visual quality, low motion-to-photon latency, and real-time tactile and control experience). However, the current technologies fall short to satisfy these requirements. Mobile edge computing (MEC) has been indispensable to enable low latency and powerful computing. Moreover, the sixth generation (6G) networks promise to provide end users with seamless communications. In this paper, we explore and demonstrate the synergistic relationship between 6G and mobile-edge technologies in empowering the Metaverse with ubiquitous communications and computation. This includes the usage of heterogeneous radios, intelligent reflecting surfaces (IRS), non-orthogonal multiple access (NOMA), and digital twins (DTs) - assisted MEC. We also discuss emerging communication paradigms (i.e., semantic communication, holographic-type communication, and haptic communication) to further satisfy the demand for human-type communications and fulfill user preferences and immersive experiences in the Metaverse.
Energy System Digitization in the Era of AI: A Three-Layered Approach towards Carbon Neutrality
Authors: Le Xie, Tong Huang, Xiangtian Zheng, Yan Liu, Mengdi Wang, Vijay Vittal, P. R. Kumar, Srinivas Shakkottai, Yi Cui
Date: 2022-11-02 00:00:00
Tasks: Decision Making

The transition towards carbon-neutral electricity is one of the biggest game changers in addressing climate change since it addresses the dual challenges of removing carbon emissions from the two largest sectors of emitters: electricity and transportation. The transition to a carbon-neutral electric grid poses significant challenges to conventional paradigms of modern grid planning and operation. Much of the challenge arises from the scale of the decision making and the uncertainty associated with the energy supply and demand. Artificial Intelligence (AI) could potentially have a transformative impact on accelerating the speed and scale of carbon-neutral transition, as many decision making processes in the power grid can be cast as classic, though challenging, machine learning tasks. We point out that to amplify AI's impact on carbon-neutral transition of the electric energy systems, the AI algorithms originally developed for other applications should be tailored in three layers of technology, markets, and policy.
The Golden City on the Edge: Economic Geography and Jihad over Centuries
Authors: Masahiro Kubo, Shunsuke Tsuda
Date: 2022-11-09 00:00:00

This paper uncovers the evolution of cities and Islamist insurgencies, so called jihad, in the process of the reversal of fortune over the centuries. In West Africa, water access in ancient periods predicts the locations of the core cities of inland trade routes -- the trans-Saharan caravan routes -- founded up to the 1800s, when historical Islamic states played significant economic roles before European colonization. In contrast, ancient water access does not have a persistent influence on contemporary city formation and economic activities. After European colonization and the invention of modern trading technologies, along with the constant shrinking of water sources, landlocked pre-colonial core cities contracted or became extinct. Employing an instrumental variable strategy, we show that these deserted locations have today been replaced by battlefields for jihadist organizations. We argue that the power relations between Islamic states and the European military during the 19th century colonial era shaped the persistence of jihadist ideology as a legacy of colonization. Investigations into religious ideology related to jihadism, using individual-level surveys from Muslims, support this mechanism. Moreover, the concentration of jihadist violence in "past-core-and-present-periphery" areas in West Africa is consistent with a global-scale phenomenon. Finally, spillovers of violent events beyond these stylized locations are partly explained by organizational heterogeneity among competing factions (Al Qaeda and the Islamic State) over time.
Macroeconomic performance of oil price shocks in Russia
Authors: Ayaz Zeynalov, Kristina Tiron
Date: 2022-11-08 00:00:00

Oil price fluctuations severely impact the economies of both oil-exporting and importing countries. High oil prices can benefit oil exporters by increasing foreign currency inflow; however, an economy can suffer from a weakening of the manufacturing sectors and experience a significant downtrend in the country's price competitiveness as the domestic currency appreciates. We investigate the oil price fluctuations from Q1, 2004 to Q3, 2021 and their impact on the Russian macroeconomic indicators, particularly industrial production, exchange rate, inflation and interest rates. We assess whether and how much the Russian macroeconomic variables have been responsive to the oil price fluctuations in recent years. The outcomes from VAR model confirm that the monetary channel is more responsive to oil price shocks than the fiscal one. Regarding fiscal channel of the oil price impact, industrial production is strongly pro-cyclical to oil price shocks. As for the monetary channel, higher oil price volatility is pressuring the Russian ruble, inflation and interest rates are substantially counter-cyclical to oil price shocks.
PAT-CNN: Automatic Segmentation and Quantification of Pericardial Adipose Tissue from T2-Weighted Cardiac Magnetic Resonance Images
Authors: Zhuoyu Li, Camille Petri, James Howard, Graham Cole, Marta Varela
Date: 2022-11-09 00:00:00

Background: Increased pericardial adipose tissue (PAT) is associated with many types of cardiovascular disease (CVD). Although cardiac magnetic resonance images (CMRI) are often acquired in patients with CVD, there are currently no tools to automatically identify and quantify PAT from CMRI. The aim of this study was to create a neural network to segment PAT from T2-weighted CMRI and explore the correlations between PAT volumes (PATV) and CVD outcomes and mortality. Methods: We trained and tested a deep learning model, PAT-CNN, to segment PAT on T2-weighted cardiac MR images. Using the segmentations from PAT-CNN, we automatically calculated PATV on images from 391 patients. We analysed correlations between PATV and CVD diagnosis and 1-year mortality post-imaging. Results: PAT-CNN was able to accurately segment PAT with Dice score/ Hausdorff distances of 0.74 +- 0.03/27.1 +- 10.9~mm, similar to the values obtained when comparing the segmentations of two independent human observers ($0.76 +- 0.06/21.2 +- 10.3~mm$). Regression models showed that, independently of sex and body-mass index, PATV is significantly positively correlated with a diagnosis of CVD and with 1-year all cause mortality (p-value < 0.01). Conclusions: PAT-CNN can segment PAT from T2-weighted CMR images automatically and accurately. Increased PATV as measured automatically from CMRI is significantly associated with the presence of CVD and can independently predict 1-year mortality.
On the Robustness of deep learning-based MRI Reconstruction to image transformations
Authors: Jinghan Jia, Mingyi Hong, Yimeng Zhang, Mehmet Akçakaya, Sijia Liu
Date: 2022-11-09 00:00:00
Tasks: image-classification, Image Classification, Image Reconstruction, MRI Reconstruction

Although deep learning (DL) has received much attention in accelerated magnetic resonance imaging (MRI), recent studies show that tiny input perturbations may lead to instabilities of DL-based MRI reconstruction models. However, the approaches of robustifying these models are underdeveloped. Compared to image classification, it could be much more challenging to achieve a robust MRI image reconstruction network considering its regression-based learning objective, limited amount of training data, and lack of efficient robustness metrics. To circumvent the above limitations, our work revisits the problem of DL-based image reconstruction through the lens of robust machine learning. We find a new instability source of MRI image reconstruction, i.e., the lack of reconstruction robustness against spatial transformations of an input, e.g., rotation and cutout. Inspired by this new robustness metric, we develop a robustness-aware image reconstruction method that can defend against both pixel-wise adversarial perturbations as well as spatial transformations. Extensive experiments are also conducted to demonstrate the effectiveness of our proposed approaches.
Multimodal Remote Sensing Image Registration Based on Adaptive Multi-scale PIIFD
Authors: Ning li, YuXuan Li, Jichao Jiao
Date: 2022-11-09 00:00:00
Tasks: Image Registration

In recent years, due to the wide application of multi-sensor vision systems, multimodal image acquisition technology has continued to develop, and the registration problem based on multimodal images has gradually emerged. Most of the existing multimodal image registration methods are only suitable for two modalities, and cannot uniformly register multiple modal image data. Therefore, this paper proposes a multimodal remote sensing image registration method based on adaptive multi-scale PIIFD(AM-PIIFD). This method extracts KAZE features, which can effectively retain edge feature information while filtering noise. Then adaptive multi-scale PIIFD is calculated for matching. Finally, the mismatch is removed through the consistency of the feature main direction, and the image alignment transformation is realized. The qualitative and quantitative comparisons with other three advanced methods shows that our method can achieve excellent performance in multimodal remote sensing image registration.
LGSQE: Lightweight Generated Sample Quality Evaluatoin
Authors: Ganning Zhao, Vasileios Magoulianitis, Suya You, C. -C. Jay Kuo
Date: 2022-11-08 00:00:00

Despite prolific work on evaluating generative models, little research has been done on the quality evaluation of an individual generated sample. To address this problem, a lightweight generated sample quality evaluation (LGSQE) method is proposed in this work. In the training stage of LGSQE, a binary classifier is trained on real and synthetic samples, where real and synthetic data are labeled by 0 and 1, respectively. In the inference stage, the classifier assigns soft labels (ranging from 0 to 1) to each generated sample. The value of soft label indicates the quality level; namely, the quality is better if its soft label is closer to 0. LGSQE can serve as a post-processing module for quality control. Furthermore, LGSQE can be used to evaluate the performance of generative models, such as accuracy, AUC, precision and recall, by aggregating sample-level quality. Experiments are conducted on CIFAR-10 and MNIST to demonstrate that LGSQE can preserve the same performance rank order as that predicted by the Frechet Inception Distance (FID) but with significantly lower complexity.
Particle Flow Gaussian Sum Particle Filter
Authors: Karthik Comandur, Yunpeng Li, Santosh Nannuru
Date: 2022-11-09 00:00:00

Particle flow Gaussian particle flow (PFGPF) uses an invertible particle flow to generate a proposal density. It approximates the predictive and posterior distributions as Gaussian densities. In this paper, we use bank of PFGPF filters to construct a Particle flow Gaussian sum particle filter (PFGSPF), which approximates the predictive and posterior as Gaussian mixture model. This approximation is useful in complex estimation problems where a single Gaussian approximation is not sufficient. We compare the performance of this proposed filter with PFGPF and others in challenging numerical simulations.
An Exponentially-Tight Approximate Factorization of the Joint PDF of Statistical Dependent Measurements in Wireless Sensor Networks
Authors: Juan Augusto Maya, Leonardo Rey Vega, Andrea M. Tonello
Date: 2022-11-09 00:00:00

We consider the distributed detection problem of a temporally correlated random radio source signal using a wireless sensor network capable of measuring the energy of the received signals. It is well-known that optimal tests in the Neyman-Pearson setting are based on likelihood ratio tests (LRT), which, in this set-up, evaluate the quotient between the probability density functions (PDF) of the measurements when the source signal is present and absent. When the source is present, the computation of the joint PDF of the energy measurements at the nodes is a challenging problem. This is due to the statistical dependence introduced to the received signals by the radio source propagated through fading channels. We deal with this problem using the characteristic function of the (intractable) joint PDF, and proposing an approximation to it. We derive bounds for the approximation error in two wireless propagation scenarios, slow and fast fading, and show that the proposed approximation is exponentially tight with the number of nodes when the time-bandwidth product is sufficiently high. The approximation is used as a substitute of the exact joint PDF for building an approximate LRT, which performs better than other well-known detectors, as verified by Monte Carlo simulations.
Conditional divergence risk measures
Authors: Giulio Principi, Fabio Maccheroni
Date: 2022-11-08 00:00:00

Our paper contributes to the theory of conditional risk measures and conditional certainty equivalents. We adopt a random modular approach which proved to be effective in the study of modular convex analysis and conditional risk measures. In particular, we study the conditional counterpart of optimized certainty equivalents. In the process, we provide representation results for niveloids in the conditional $L^{\infty}$-space. By employing such representation results we retrieve a conditional version of the variational formula for optimized certainty equivalents. In conclusion, we apply this formula to provide a variational representation of the conditional entropic risk measure.
DNA Methylation in hypoxia in Mycobacterium tuberculosis
Authors: Nayada Pandee, Prasert Auewarakul, Chanati Jantrachotechatchawan
Date: 2022-11-09 00:00:00
Tasks: Articles

Tuberculosis is one of the most lethal contagious diseases caused by Mycobacterium tuberculosis (MTB), in many cases, the infected did not show any symptoms, because the bacilli entered the dormant stage in granulomas. The dormant stage of MTB is also associated with higher resistance to drugs and the immune system. Among multiple epigenetic regulations critical to MTB stress responses, DNA methylation is necessary for the survival of MTB in hypoxic conditions, which is a common stress event during granuloma formation. This review gathers previous findings and demonstrates a meta-analysis by collecting hypoxia gene expression data from several articles and perform association analysis between those genes and methylation site profiles across whole genomes of representative strains pf lineage 2 and 4. While more data is required for more conclusive support, our results suggest that methylation sites in the possible promoter regions may induce differential gene regulation in hypoxia.
Evolution with recombination as a Metropolis-Hastings sampling procedure
Authors: Jenny M. Poulton, Lee Altenberg, Chris Watkins
Date: 2022-11-09 00:00:00

This work presents a population genetic model of evolution, which includes haploid selection, mutation, recombination, and drift. The mutation-selection equilibrium can be expressed exactly in closed form for arbitrary fitness functions without resorting to diffusion approximations. Tractability is achieved by generating new offspring using n-parent rather than 2-parent recombination. While this enforces linkage equilibrium among offspring, it allows analysis of the whole population under linkage disequilibrium. We derive a general and exact relationship between fitness fluctuations and response to selection. Our assumptions allow analytical calculation of the stationary distribution of the model for a variety of non-trivial fitness functions. These results allow us to speak to genetic architecture, i.e., what stationary distributions result from different fitness functions. This paper presents methods for exactly deriving stationary states for finite and infinite populations. This method can be applied to many fitness functions, and we give exact calculations for four of these. These results allow us to investigate metastability, tradeoffs between fitness functions, and even consider error-correcting codes
SPRINT: A fast, new software tool for reconstructing the evolutionary past of polyploid datasets
Authors: Liam J. Maher, Taoyang Wu, Katharina T. Huber
Date: 2022-11-09 00:00:00

Polyploidization is an important evolutionary process which affects organisms ranging from plants to fish and fungi. The signal left behind by it is in the form of a species' ploidy level (number of complete chromosome sets found in a cell) which is inherently non-treelike. Currently available tools for reconstructing the evolutionary past of a polyploid dataset generally start with a multi-labelled tree obtained for a dataset of interest and then derive a (phylogenetic) network from that tree in some way that reflects that past by interpreting the networks's vertices of indegree at least two as polyploidization events. Since obtaining such a tree can be computationally expensive it is paramount to have alternative approaches available that allow one to shed light into the reticulate evolutionary past of a polyploid dataset. SPRINT aims to reconstruct the evolutionary past of a polyploid dataset in terms of a binary network which realises the dataset's ploidy profile (vector of ploidy levels of the dataset's taxa) and requires the fewest number of polyploidization events. It does this by representing the ploidy level of a species x in terms of the number of directed paths from the root of the network to the leaf of the network labelled by x. SPRINT is distributed on GitHub: https://github.com/lmaher1/SPRINT.
Inferring the ecology of north-Pacific albacore tuna from catch-and-effort data
Authors: Hirotaka Ijima, Carolina Minte-Vera, Yi-Jay Chang, Daisuke Ochi, Yuichi Tsuda, Marko Jusup
Date: 2022-11-09 00:00:00
Tasks: Management

Catch-and-effort data are among the primary sources of information for assessing the status of terrestrial wildlife and fish. In fishery science, elaborate stock-assessment models are fitted to such data in order to estimate fish-population sizes and guide management decisions. Given the importance of catch-and-effort data, we scoured a comprehensive dataset pertaining to albacore tuna (Thunnus alalunga) in the north Pacific ocean for novel ecological information content about this commercially valuable species. Specifically, we used unsupervised learning based on finite mixture modelling to reveal that the north Pacific albacore-tuna stock can be divided into four pseudo-cohorts ranging in age from approximately 3 to 12 years old. We discovered that smaller size pseudo-cohorts inhabit relatively high -- subtropical to temperate -- latitudes, with hotspots off the coast of Japan. Larger size pseudo-cohorts inhabit lower -- tropical to subtropical -- latitudes, with hotspots in the western and central north Pacific. These results offer evidence that albacore tuna prefer different habitats depending on their size and age, and point to long-term migratory routes for the species that the current tagging technology is unlikely to capture in full. We discuss the implications of the results for data-driven modelling of albacore tuna in the north Pacific, as well as the management of the north Pacific albacore-tuna fishery.
Randomization of Short-Rate Models, Analytic Pricing and Flexibility in Controlling Implied Volatilities
Authors: Lech A. Grzelak
Date: 2022-11-09 00:00:00

We focus on extending existing short-rate models, enabling control of the generated implied volatility while preserving analyticity. We achieve this goal by applying the Randomized Affine Diffusion (RAnD) method to the class of short-rate processes under the Heath-Jarrow-Morton framework. Under arbitrage-free conditions, the model parameters can be exogenously stochastic, thus facilitating additional degrees of freedom that enhance the calibration procedure. We show that with the randomized short-rate models, the shapes of implied volatility can be controlled and significantly improve the quality of the model calibration, even for standard 1D variants. In particular, we illustrate that randomization applied to the Hull-White model leads to dynamics of the local volatility type, with the prices for standard volatility-sensitive derivatives explicitly available. The randomized Hull-White (rHW) model offers an almost perfect calibration fit to the swaption implied volatilities.