pwc-archive/papers-with-abstracts huggingface dataset:
Software-Defined Virtual Synchronous Condenser
Authors: Zimin Jiang, Peng Zhang, Yifan Zhou, Łukasz Kocewiak, Divya Kurthakoti Chandrashekhara, Marie-Lou Picherit, Zefan Tang, Kenneth B. Bowes, Guangya Yang
Date: 2023-11-15 00:00:00

Synchronous condensers (SCs) play important roles in integrating wind energy into relatively weak power grids. However, the design of SCs usually depends on specific application requirements and may not be adaptive enough to the frequently-changing grid conditions caused by the transition from conventional to renewable power generation. This paper devises a software-defined virtual synchronous condenser (SDViSC) method to address the challenges. Our contributions are fourfold: 1) design of a virtual synchronous condenser (ViSC) to enable full converter wind turbines to provide built-in SC functionalities; 2) engineering SDViSCs to transfer hardware-based ViSC controllers into software services, where a Tustin transformation-based software-defined control algorithm guarantees accurate tracking of fast dynamics under limited communication bandwidth; 3) a software-defined networking-enhanced SDViSC communication scheme to allow enhanced communication reliability and reduced communication bandwidth occupation; and 4) Prototype of SDViSC on our real-time, cyber-in-the-loop digital twin of large-wind-farm in an RTDS environment. Extensive test results validate the excellent performance of SDViSC to support reliable and resilient operations of wind farms under various physical and cyber conditions.
On Distinguishability of Anomalies as Physical Faults or Actuation Cyberattacks
Authors: Tanushree Roy, Satadru Dey
Date: 2023-11-15 00:00:00

Increased automation has created an impetus to integrate infrastructure with wide-spread connectivity in order to improve efficiency, sustainability, autonomy, and security. Nonetheless, this reliance on connectivity and the inevitability of complexity in this system increases the vulnerabilities to physical faults or degradation and external cyber-threats. However, strategies to counteract faults and cyberattacks would be widely different and thus it is vital to not only detect but also to identify the nature of the anomaly that is present in these systems. In this work, we propose a mathematical framework to distinguish between physical faults and cyberattack using a sliding mode based unknown input observer. Finally, we present simulation case studies to distinguish between physical faults and cyberattacks using the proposed Distinguishability metric and criterion. The simulation results show that the proposed framework successfully distinguishes between faults and cyberattacks.
The Physical Logic of Protein Machines
Authors: John M. McBride, Tsvi Tlusty
Date: 2023-11-16 00:00:00

Proteins are intricate molecular machines whose complexity arises from the heterogeneity of the amino acid building blocks and their dynamic network of many-body interactions. These nanomachines gain function when put in the context of a whole organism through interaction with other inhabitants of the biological realm. And this functionality shapes their evolutionary histories through intertwined paths of selection and adaptation. Recent advances in machine learning have solved the decades-old problem of how protein sequence determines their structure. However, the ultimate question regarding the basic logic of protein machines remains open: How does the collective physics of proteins lead to their functionality? and how does a sequence encode the full range of dynamics and chemical interactions that facilitate function? Here, we explore these questions within a physical approach that treats proteins as mechano-chemical machines, which are adapted to function via concerted evolution of structure, motion, and chemical interactions.
Inference in Auctions with Many Bidders Using Transaction Prices
Authors: Federico A. Bugni, Yulong Wang
Date: 2023-11-16 00:00:00

This paper studies inference in first- and second-price sealed-bid auctions with many bidders, using an asymptotic framework where the number of bidders increases while the number of auctions remains fixed. Relevant applications include online, treasury, spectrum, and art auctions. Our approach enables asymptotically exact inference on key features such as the winner's expected utility, the seller's expected revenue, and the tail of the valuation distribution using only transaction price data. Our simulations demonstrate the accuracy of the methods in finite samples. We apply our methods to Hong Kong vehicle license auctions, focusing on high-priced, single-letter plates.
Estimating Functionals of the Joint Distribution of Potential Outcomes with Optimal Transport
Authors: Daniel Ober-Reynolds
Date: 2023-11-15 00:00:00

Many causal parameters depend on a moment of the joint distribution of potential outcomes. Such parameters are especially relevant in policy evaluation settings, where noncompliance is common and accommodated through the model of Imbens & Angrist (1994). This paper shows that the sharp identified set for these parameters is an interval with endpoints characterized by the value of optimal transport problems. Sample analogue estimators are proposed based on the dual problem of optimal transport. These estimators are root-n consistent and converge in distribution under mild assumptions. Inference procedures based on the bootstrap are straightforward and computationally convenient. The ideas and estimators are demonstrated in an application revisiting the National Supported Work Demonstration job training program. I find suggestive evidence that workers who would see below average earnings without treatment tend to see above average benefits from treatment.
The efficacy of the sugar-free labels is reduced by the health-sweetness tradeoff
Authors: Ksenia Panidi, Yaroslava Grebenschikova, Vasily Klucharev
Date: 2023-11-16 00:00:00

In the present study, we use an experimental setting to explore the effects of sugar-free labels on the willingness to pay for food products. In our experiment, participants placed bids for sugar-containing and analogous sugar-free products in a Becker-deGroot-Marschak auction to determine the willingness to pay. Additionally, they rated each product on the level of perceived healthiness, sweetness, tastiness and familiarity with the product. We then used structural equation modelling to estimate the direct, indirect and total effect of the label on the willingness to pay. The results suggest that sugar-free labels significantly increase the willingness to pay due to the perception of sugar-free products as healthier than sugar-containing ones. However, this positive effect is overridden by a significant decrease in perceived sweetness (and hence, tastiness) of products labelled as sugar-free compared to sugar-containing products. As in our sample, healthiness and tastiness are positively related, while healthiness and sweetness are related negatively, these results suggest that it is health-sweetness rather than health-tastiness tradeoff that decreases the efficiency of the sugar-free labelling in nudging consumers towards healthier options.
Urban economics of migration in the cities of the northeast region of Brazil
Authors: Denise Cristina Bomtempo
Date: 2023-11-15 00:00:00

The focus of this text is to discuss how geographical science can contribute to an understanding of international migration in the 21st century. To this end, in the introduction we present the central ideas, as well as the internal structure of the text. Then, we present the theoretical and methodological approach that guides the research and, in section three, we show the results through texts and cartograms based on secondary data and empirical information. Finally, in the final remarks, we summarize the text with a view to contributing to the proposed debate.
New configurations of the interface between innovation and urban spatial agglomeration: the localized industrial systems (lis) of the clothing in Fortaleza/Brazil
Authors: Edilson Pereira Junior
Date: 2023-11-15 00:00:00

The paper seeks to interpret the interface between innovation, industry and urban business agglomeration, by trying to understand how local/regional entrepreneurs and social agents use the forces of agglomeration to organize productive activities with a view to achieving greater competitiveness and strategic advantages. There are many territorial manifestations that materialize from this new reality and the text seeks to propose a reading of its characteristics based on what we call "productive spatial configurations", which represent the specific functioning of a certain innovative production process and its territorial impact. To illustrate this approach, we take as an example a case study that illustrates how productive spatial configurations manifest themselves through the revitalization of an industrial economy that incorporates innovative efforts, whether technological, process or organizational. This is the localized industrial system (LIS) of clothing and apparel in Fortaleza, Cear\'a state, Brazil, which reveals an industrial experience of resistance with significant organizational innovation in the production and distribution processes of clothing. The main objective of the proposal is to organize theoretical and empirical tools that will allow us to read the combination of economic, social and political variables in spaces where collaborative networks of companies, service centers and public institutions flourish, in the context of various industrial production processes. This could point to the progress we need to overcome the many false controversies on the subject.
Posterior-Mean Separable Costs of Information Acquisition
Authors: Jeffrey Mensch, Komal Malik
Date: 2023-11-16 00:00:00

We analyze a problem of revealed preference given state-dependent stochastic choice data in which the payoff to a decision maker (DM) only depends on their beliefs about posterior means. Often, the DM must also learn about or pay attention to the state; in applied work on this subject, a convenient assumption is that the costs of such learning are linearly dependent in the distribution over posterior means. We provide testable conditions to identify whether this assumption holds. This allows for the use of information design techniques to solve the DM's problem.
Leveraging machine learning to enhance climate models: a review
Authors: Ahmed Elsayed, Shrouk Wally, Islam Alkabbany, Asem Ali, Aly Farag
Date: 2023-11-15 00:00:00

Recent achievements in machine learning (Ml) have had a significant impact on various fields, including climate science. Climate modeling is very important and plays a crucial role in shaping the decisions of governments and individuals in mitigating the impact of climate change. Climate change poses a serious threat to humanity, however, current climate models are limited by computational costs, uncertainties, and biases, affecting their prediction accuracy. The vast amount of climate data generated by satellites, radars, and earth system models (ESMS) poses a significant challenge. ML techniques can be effectively employed to analyze this data and extract valuable insights that aid in our understanding of the earth climate. This review paper focuses on how ml has been utilized in the last 5 years to boost the current state-of-the-art climate models. We invite the ml community to join in the global effort to accurately model the earth climate by collaborating with other fields to leverage ml as a powerful tool in this endeavor.
OFDM-based Waveforms for Joint Sensing and Communications Robust to Frequency Selective IQ Imbalance
Authors: Oliver Lang, Christian Hofbauer, Moritz Tockner, Reinhard Feger, Thomas Wagner, Mario Huemer
Date: 2023-11-16 00:00:00

Orthogonal frequency-division multiplexing (OFDM) is a promising waveform candidate for future joint sensing and communication systems. It is well known that the OFDM waveform is vulnerable to in-phase and quadrature-phase (IQ) imbalance, which increases the noise floor in a range-Doppler map (RDM). A state-of-the-art method for robustifying the OFDM waveform against IQ imbalance avoids an increased noise floor, but it generates additional ghost objects in the RDM [1]. A consequence of these additional ghost objects is a reduction of the maximum unambiguous range. In this work, a novel OFDM-based waveform robust to IQ imbalance is proposed, which neither increases the noise floor nor reduces the maximum unambiguous range. The latter is achieved by shifting the ghost objects in the RDM to different velocities such that their range variations observed over several consecutive RDMs do not correspond to the observed velocity. This allows tracking algorithms to identify them as ghost objects and eliminate them for the follow-up processing steps. Moreover, we propose complete communication systems for both the proposed waveform as well as for the state-of-the-art waveform, including methods for channel estimation, synchronization, and data estimation that are specifically designed to deal with frequency selective IQ imbalance which occurs in wideband systems. The effectiveness of these communication systems is demonstrated by means of bit error ratio (BER) simulations.
Plug-In RIS: A Novel Approach to Fully Passive Reconfigurable Intelligent Surfaces
Authors: Mahmoud Raeisi, Ibrahim Yildirim, Mehmet Cagri Ilter, Majid Gerami, Ertugrul Basar
Date: 2023-11-16 00:00:00

This paper presents a promising design concept for reconfigurable intelligent surfaces (RISs), named plug-in RIS, wherein the RIS is plugged into an appropriate position in the environment, adjusted once according to the location of both base station and blocked region, and operates with fixed beams to enhance the system performance. The plug-in RIS is a novel system design, streamlining RIS-assisted millimeter-wave (mmWave) communication without requiring decoupling two parts of the end-to-end channel, traditional control signal transmission, and online RIS configuration. In plug-in RIS-aided transmission, the transmitter efficiently activates specific regions of the divided large RIS by employing hybrid beamforming techniques, each with predetermined phase adjustments tailored to reflect signals to desired user locations. This user-centric approach enhances connectivity and overall user experience by dynamically illuminating the targeted user based on location. By introducing plug-in RIS's theoretical framework, design principles, and performance evaluation, we demonstrate its potential to revolutionize mmWave communications for the limited channel state information (CSI) scenarios. Simulation results illustrate that plug-in RIS provides power/cost-efficient solutions to overcome blockage in the mmWave communication system and a striking convergence in average bit error rate and achievable rate performance with traditional full CSI-enabled RIS solutions.
Phylogenetic trees defined by at most three characters
Authors: Katharina T. Huber, Simone Linz, Vincent Moulton, Charles Semple
Date: 2023-11-15 00:00:00

In evolutionary biology, phylogenetic trees are commonly inferred from a set of characters (partitions) of a collection of biological entities (e.g., species or individuals in a population). Such characters naturally arise from molecular sequences or morphological data. Interestingly, it has been known for some time that any binary phylogenetic tree can be (convexly) defined by a set of at most four characters, and that there are binary phylogenetic trees for which three characters are not enough. Thus, it is of interest to characterise those phylogenetic trees that are defined by a set of at most three characters. In this paper, we provide such a characterisation, in particular proving that a binary phylogenetic tree $T$ is defined by a set of at most three characters precisely if $T$ has no internal subtree isomorphic to a certain tree.
Worst-Case Optimal Investment in Incomplete Markets
Authors: Sascha Desmettre, Sebastian Merkel, Annalena Mickel, Alexander Steinicke
Date: 2023-11-16 00:00:00

We study and solve the worst-case optimal portfolio problem as pioneered by Korn and Wilmott (2002) of an investor with logarithmic preferences facing the possibility of a market crash with stochastic market coefficients by enhancing the martingale approach developed by Seifried in 2010. With the help of backward stochastic differential equations (BSDEs), we are able to characterize the resulting indifference optimal strategies in a fairly general setting. We also deal with the question of existence of those indifference strategies for market models with an unbounded market price of risk. We therefore solve the corresponding BSDEs via solving their associated PDEs using a utility crash-exposure transformation. Our approach is subsequently demonstrated for Heston's stochastic volatility model, Bates' stochastic volatility model including jumps, and Kim-Omberg's model for a stochastic excess return.
A branching model for intergenerational telomere length dynamics
Authors: Athanasios Benetos, Olivier Coudray, Anne Gégout-Petit, Lionel Lenôtre, Simon Toupance, Denis Villemonais
Date: 2023-11-16 00:00:00
Tasks: model

We build and study an individual based model of the telomere length's evolution in a population across multiple generations. This model is a continuous time typed branching process, where the type of an individual includes its gamete mean telomere length and its age. We study its Malthusian's behaviour and provide numerical simulations to understand the influence of biologically relevant parameters.
Impact of a block structure on the Lotka-Volterra model
Authors: Maxime Clenet, François Massol, Jamal Najim
Date: 2023-11-16 00:00:00

The Lotka-Volterra (LV) model is a simple, robust, and versatile model used to describe large interacting systems such as food webs or microbiomes. The model consists of $n$ coupled differential equations linking the abundances of $n$ different species. We consider a large random interaction matrix with independent entries and a block variance profile. The $i$th diagonal block represents the intra-community interaction in community $i$, while the off-diagonal blocks represent the inter-community interactions. The variance remains constant within each block, but may vary across blocks. We investigate the important case of two communities of interacting species, study how interactions affect their respective equilibrium. We also describe equilibrium with feasibility (i.e., whether there exists an equilibrium with all species at non-zero abundances) and the existence of an attrition phenomenon (some species may vanish) within each community. Information about the general case of $b$ communities ($b> 2$) is provided in the appendix.
A Proposed Artificial Neural Network based Approach for Molecules Bitter Prediction
Authors: Huynh Quoc Anh Bui, Trong Hop Do, Thanh Binh Nguyen
Date: 2023-11-15 00:00:00
Tasks: Computational chemistry, Drug Discovery

In recent years, the development of Artificial Intelligence (AI) has offered the possibility to tackle many interdisciplinary problems, and the field of chemistry is not an exception. Drug analysis is crucial in drug discovery, playing an important role in human life. However, this task encounters many difficulties due to the wide range of computational chemistry methods. Drug analysis also involves a massive amount of work, including determining taste. Thus, applying deep learning to predict a molecule's bitterness is inevitable to accelerate innovation in drug analysis by reducing the time spent. This paper proposes an artificial neural network (ANN) based approach (EC-ANN) for the molecule's bitter prediction. Our approach took the SMILE (Simplified molecular-input line-entry system) string of a molecule as the input data for the prediction, and the 256-bit ECFP descriptor is the input vector for our network. It showed impressive results compared to state-of-the-art, with a higher performance on two out of three test sets according to the experiences on three popular test sets: Phyto-Dictionary, Unimi, and Bitter-new set [1]. For the Phyto-Dictionary test set, our model recorded 0.95 and 0.983 in F1-score and AUPR, respectively, depicted as the highest score in F1-score. For the Unimi test set, our model achieved 0.88 in F1-score and 0.88 in AUPR, which is roughly 12.3% higher than the peak of previous models [1, 2, 3, 4, 5].
Heteroskedasticity as a Signature of Association for Age-Related Genes
Authors: Salman Mohamadi, Donald A. Adjeroh
Date: 2023-11-15 00:00:00
Tasks: Causal Inference, Human Aging

Human aging is a process controlled by both genetics and environment. Many studies have been conducted to identify a subset of genes related to aging from the human genome. Biologists implicitly categorize age-related genes into genes that cause aging and genes that are influenced by aging, which resulted in both causal inference and inference of associations studies. While inference of association is better explored, causal inference and computational causal inference, remains less explored. In this work, we are primarily motivated to tackle the problem of identifying genes associated with aging, while having a brief look into genes with probable causal relations, both from a computational perspective. Specifically, we form a set of hypotheses and accordingly, introduce a data-tailored framework for inference. First we perform linear modeling on the expression values of age-related genes, and then examine the presence of heteroskedastic properties in the residual of the model. We evaluate this framework and our results suggest that, 1) presence of heteroskedasticity in these residuals is a potential signature of association for age-related genes, and 2) consistent heteroskedasticity along the human life span could imply some sort of causality. To our knowledge, along with identifying age-associated genes, this is the first work to propose a framework for computational causal inference on age-related genes, using a dataset of human dermal fibroblast gene expression data. Hence the results of our simple, yet effective approach can be used not only to assess future age-related genes, but also as a possible criterion to select new associative or potential causal genes with respect to aging.
A Dynamic Computational Model of Head Sway Responses in Human Upright Stance Postural Control during Support Surface Tilt
Authors: Vittorio Lippi, Christoph Maurer, Stefan Kammermeier
Date: 2023-11-16 00:00:00

Human and humanoid posture control models usually rely on single or multiple degrees of freedom inverted pendulum representation of upright stance associated with a feedback controller. In models typically focused on the action between ankles, hips, and knees, the control of head position is often neglected, and the head is considered one with the upper body. However, two of the three main contributors to the human motion sensorium reside in the head: the vestibular and the visual system. As the third contributor, the proprioceptive system is distributed throughout the body. In human neurodegenerative brain diseases of motor control, like Progressive Supranuclear Palsy PSP and Idiopathic Parkinson's Disease IPD, clinical studies have demonstrated the importance of head motion deficits. This work specifically addresses the control of the head during a perturbed upright stance. A control model for the neck is proposed following the hypothesis of a modular posture control from previous studies. Data from human experiments are used to fit the model and retrieve sets of parameters representative of the behavior obtained in different conditions. The result of the analysis is twofold: validate the model and its underlying hypothesis and provide a system to assess the differences in posture control that can be used to identify the differences between healthy subjects and patients with different conditions. Implications for clinical pathology and application in humanoid and assistive robotics are discussed.