pwc-archive/papers-with-abstracts huggingface dataset:
Portuguese Households Savings in Times of Pandemic: A Way to Better Resist the Escalating Inflation?
Authors: Ana Lucia Luis, Natalia Teixeira, Rui Braz
Date: 2023-04-05 00:00:00

March 2020 confinement has shot Portuguese savings to historic levels, reaching 13.4% of gross disposable income in early 2021 (INE, 2023). To find similar savings figures we need to go back to 1999. With consumption reduced to a bare minimum, the Portuguese were forced to save. Households reduced spending more because of a lack of alternatives to consumption than for any other reason. The relationship between consumption, savings, and income has occupied an important role in economic thought [(Keynes, 1936; 1937); (Friedman, 1957)]. Traditionally, high levels of savings have been associated with benefits to the economy, since financing capacity is enhanced (Singh, 2010). However, the effects here can be twofold. On the one hand, it seems that Portugal faced the so-called Savings Paradox (Keynes, 1936). If consumers decide to save a considerable part of their income, there will be less demand for the goods produced. Lower demand will lead to lower supply, production, income, and, paradoxically, fewer savings. On the other hand, after having accumulated savings at the peak of the pandemic, the Portuguese are now using them to carry out postponed consumption and, hopefully, to better resist the escalating inflation. This study aims to examine Portuguese households' savings evolution during the most critical period of the pandemic, between March 2020 and April 2022. The methodology analyses the correlation between savings, consumption, and GDP as well as GDP's decomposition into its various components and concluded that these suddenly forced savings do not fit traditional economic theories of savings.
A Comparative Study of Inter-Regional Intra-Industry Disparity
Authors: Samidh Pal
Date: 2023-04-05 00:00:00

This paper investigates the inter-regional intra-industry disparity within selected Indian manufacturing industries and industrial states. The study uses three measures - the Output-Capital Ratio, the Capital-Labor Ratio, and the Output-Labor Ratio - to critically evaluate the level of disparity in average efficiency of labor and capital, as well as capital intensity. Additionally, the paper compares the rate of disparity of per capita income between six major industrial states. The study finds that underutilization of capacity is driven by an unequal distribution of high-skilled labor supply and upgraded technologies. To address these disparities, the paper suggests that policymakers campaign for labor training and technology promotion schemes throughout all regions of India. By doing so, the study argues, the country can reduce regional inequality and improve economic outcomes for all.
Synthetic Controls with Multiple Outcomes
Authors: Wei Tian, Seojeong Lee, Valentyn Panchenko
Date: 2023-04-05 00:00:00

We generalize the synthetic control (SC) method to a multiple-outcome framework, where the conventional pre-treatment time dimension is supplemented with the extra dimension of related outcomes in computing the SC weights. This generalization improves the reliability of treatment effect estimation, and can be particularly useful for evaluating the effect of a treatment on multiple outcomes or when only a small number of pre-treatment periods are available. To illustrate our method, we provide a new perspective on the classic SC application to the 1990 German reunification.
Covert learning and disclosure
Authors: Matteo Escudé
Date: 2023-04-06 00:00:00

I study a model of information acquisition and transmission in which the sender's ability to misreport her findings is limited. In equilibrium, the sender only influences the receiver by choosing to remain selectively ignorant, rather than by deceiving her about the discoveries. Although deception does not occur, I highlight how deception possibilities determine what information the sender chooses to acquire and transmit. I then turn to comparative statics, characterizing in which sense the sender benefits from her claims being more verifiable, showing this is akin to increasing her commitment power. Finally, I characterize sender- and receiver-optimal falsification environments.
Causes of Excess Capacity
Authors: Samidh Pal
Date: 2023-04-04 00:00:00

This study delves into the origins of excess capacity by examining the reactions of capital, labor, and capital intensity. To achieve this, we have employed a novel three-layered production function model, estimating the elasticity of substitution between capital and labor as a nested layer, alongside capital intensity, for all industry groups. We have then selectively analyzed a few industry groups for comparative purposes, taking into account the current government policies and manufacturing plant realities. Ultimately, we recommend that policymakers address the issue of excess capacity by stimulating the expansion of manufacturing plants with cutting-edge machinery. Our findings and recommendations are intended to appeal to academics and policymakers alike.
Classification of Skin Disease Using Transfer Learning in Convolutional Neural Networks
Authors: Jessica S. Velasco, Jomer V. Catipon, Edmund G. Monilar, Villamor M. Amon, Glenn C. Virrey, Lean Karlo S. Tolentino
Date: 2023-04-06 00:00:00
Tasks: Classification, Transfer Learning

Automatic classification of skin disease plays an important role in healthcare especially in dermatology. Dermatologists can determine different skin diseases with the help of an android device and with the use of Artificial Intelligence. Deep learning requires a lot of time to train due to the number of sequential layers and input data involved. Powerful computer involving a Graphic Processing Unit is an ideal approach to the training process due to its parallel processing capability. This study gathered images of 7 types of skin disease prevalent in the Philippines for a skin disease classification system. There are 3400 images composed of different skin diseases like chicken pox, acne, eczema, Pityriasis rosea, psoriasis, Tinea corporis and vitiligo that was used for training and testing of different convolutional network models. This study used transfer learning to skin disease classification using pre-trained weights from different convolutional neural network models such as VGG16, VGG19, MobileNet, ResNet50, InceptionV3, Inception-ResNetV2, Xception, DenseNet121, DenseNet169, DenseNet201 and NASNet mobile. The MobileNet model achieved the highest accuracy, 94.1% and the VGG16 model achieved the lowest accuracy, 44.1%.
Deep Learning Estimation of Modified Zernike Coefficients and Recovery of Point Spread Functions in Turbulence
Authors: Abu Bucker Siddik, Steven Sandoval, David Voelz, Laura E Boucheron, Luis Varela
Date: 2023-04-05 00:00:00
Tasks: Object

Recovering the turbulence-degraded point spread function from a single intensity image is important for a variety of imaging applications. Here, a deep learning model based on a convolutional neural network is applied to intensity images to predict a modified set of Zernike polynomial coefficients corresponding to wavefront aberrations in the pupil due to turbulence. The modified set assigns an absolute value to coefficients of even radial orders due to a sign ambiguity associated with this problem and is shown to be sufficient for specifying the intensity point spread function. Simulated image data of a point object and simple extended objects over a range of turbulence and detection noise levels are created for the learning model. The MSE results for the learning model show that the best prediction is found when observing a point object, but it is possible to recover a useful set of modified Zernike coefficients from an extended object image that is subject to detection noise and turbulence.
Rate Splitting for 6G Optical Wireless Networks
Authors: Khulood D. Alazwary, Ahmad Adnan Qidan, T. E. H. El-Gorashi, Jaafar M. H. Elmirghani
Date: 2023-04-06 00:00:00
Tasks: Management

This paper evaluates the performance of rate splitting (RS), a robust interference management scheme, in an optical wireless communication (OWC) network that uses infrared lasers referred to as vertical-cavity surface-emitting lasers (VCSELs) as optical transmitters. In 6G OWC, providing high spectral and energy efficiency requires advanced multiple access schemes that can serve multiple users simultaneously in a non-orthogonal fashion. In this context, RS has the potential to manage multi-user interference at high data rates compared to orthogonal transmission schemes. Simulation results show the high performance of RS compared to baseline approaches.
Lidar-Assisted Acquisition of Mobile Airborne FSO Terminals in a GPS-Denied Environment
Authors: Heyou Liu, Muhammad Salman Bashir, Mohamed-Slim Alouini
Date: 2023-04-06 00:00:00

For acquisition of narrow-beam free-space optical (FSO) terminals, a Global Positioning System (GPS) is typically required for coarse localization of the terminal. However, the GPS signal may be shadowed, or may not be present at all, especially in rough or unnameable terrains. In this study, we propose a lidar-assisted acquisition of an unmanned aerial vehicle (UAV) for FSO communications in a poor GPS environment. Such an acquisition system consists of a lidar subsystem and an FSO acquisition subsystem: The lidar subsystem is used for coarse acquisition of the UAV, whereas, the FSO subsystem is utilized for fine acquisition to obtain the UAV's accurate position. This study investigates the optimal allocation of energy between the lidar and FSO subsystems to minimize the acquisition time. Here, we minimize the average acquisition time, and maximize the cumulative distribution function of acquisition time for a fixed threshold. We learn that an optimal value of the energy allocation factor exists that provides the best performance for the proposed acquisition system.
Multi-Spectrally Constrained Low-PAPR Waveform Optimization for MIMO Radar Space-Time Adaptive Processing
Authors: Da Li, Bo Tang, Lei Xue
Date: 2023-04-05 00:00:00

This paper focuses on the joint design of transmit waveforms and receive filters for airborne multiple-input-multiple-output (MIMO) radar systems in spectrally crowded environments. The purpose is to maximize the output signal-to-interference-plus-noise-ratio (SINR) in the presence of signal-dependent clutter. To improve the practicability of the radar waveforms, both a multi-spectral constraint and a peak-to-average-power ratio (PAPR) constraint are imposed. A cyclic method is derived to iteratively optimize the transmit waveforms and receive filters. In particular, to tackle the encountered non-convex constrained fractional programming in designing the waveforms (for fixed filters), we resort to the Dinkelbach's transform, minorization-maximization (MM), and leverage the alternating direction method of multipliers (ADMM). We highlight that the proposed algorithm can iterate from an infeasible initial point and the waveforms at convergence not only satisfy the stringent constraints, but also attain superior performance.
Performance Analysis of ML-based MTC Traffic Pattern Predictors
Authors: David E. Ruiz-Guirola, Onel L. A. Lopez, Samuel Montejo-Sanchez, Richard Demo Souza, Mehdi Bennis
Date: 2023-04-04 00:00:00

Prolonging the lifetime of massive machine-type communication (MTC) networks is key to realizing a sustainable digitized society. Great energy savings can be achieved by accurately predicting MTC traffic followed by properly designed resource allocation mechanisms. However, selecting the proper MTC traffic predictor is not straightforward and depends on accuracy/complexity trade-offs and the specific MTC applications and network characteristics. Remarkably, the related state-of-the-art literature still lacks such debates. Herein, we assess the performance of several machine learning (ML) methods to predict Poisson and quasi-periodic MTC traffic in terms of accuracy and computational cost. Results show that the temporal convolutional network (TCN) outperforms the long-short term memory (LSTM), the gated recurrent units (GRU), and the recurrent neural network (RNN), in that order. For Poisson traffic, the accuracy gap between the predictors is larger than under quasi-periodic traffic. Finally, we show that running a TCN predictor is around three times more costly than other methods, while the training/inference time is the greatest/least.
A New Channel Subspace Characterization for Channel Estimation in RIS-Aided Communications
Authors: Mehdi Haghshenas, Parisa Ramezani, Maurizio Magarini, Emil Björnson
Date: 2023-04-04 00:00:00

A reconfigurable intelligent surface (RIS) is a holographic MIMO surface composed of a large number of passive elements that can induce adjustable phase shifts to the impinging waves. By creating virtual line-of-sight (LOS) paths between the transmitter and the receiver, RIS can be a game changer for millimeter-wave (mmWave) communication systems that typically suffer from severe signal attenuation. Reaping the benefits of RIS, however, relies on the accuracy of the channel estimation, which is a challenging task due to the large number of RIS elements. Specifically, conventional channel estimators require a pilot overhead equal to the number of RIS elements, which is impractical. Herein, we propose a novel way to approximately represent the RIS channels in a lower-dimensional subspace and derive the basis vectors for the identified subspace. We use this channel structure to only send pilots in this subspace, thereby vastly saving on the pilot overhead. Numerical results demonstrate that when the RIS has an element spacing of a quarter of the wavelength, our method reduces the pilot overhead by 80% with retained or even improved performance.
Bifunctional enzyme provides absolute concentration robustness in multisite covalent modification networks
Authors: Badal Joshi, Tung D. Nguyen
Date: 2023-04-05 00:00:00

Biochemical covalent modification networks exhibit a remarkable suite of steady state and dynamical properties such as multistationarity, oscillations, ultrasensitivity and absolute concentration robustness. This paper focuses on conditions required for a network to have a species with absolute concentration robustness. We find that the robustness in a substrate is endowed by its interaction with a bifunctional enzyme, which is an enzyme that has different roles when isolated versus when bound as a substrate-enzyme complex. When isolated, the bifunctional enzyme promotes production of more molecules of the robust species while when bound, the same enzyme facilitates degradation of the robust species. These dual actions produce robustness in the large class of covalent modification networks. For each network of this type, we find the network conditions for the presence of robustness, the species that has robustness, and its robustness value. The unified approach of simultaneously analyzing a large class of networks for a single property, i.e. absolute concentration robustness, reveals the underlying mechanism of the action of bifunctional enzyme while simultaneously providing a precise mathematical description of bifunctionality.
IHCV: Discovery of Hidden Time-Dependent Control Variables in Non-Linear Dynamical Systems
Authors: Juan Munoz, Subash Balsamy, Juan P. Bernal-Tamayo, Ali Balubaid, Alberto Maillo Ruiz de Infante, Vincenzo Lagani, David Gomez-Cabrero, Narsis A. Kiani, Jesper Tegner
Date: 2023-04-05 00:00:00
Tasks: Benchmarking

Discovering non-linear dynamical models from data is at the core of science. Recent progress hinges upon sparse regression of observables using extensive libraries of candidate functions. However, it remains challenging to model hidden non-observable control variables governing switching between different dynamical regimes. Here we develop a data-efficient derivative-free method, IHCV, for the Identification of Hidden Control Variables. First, the performance and robustness of IHCV against noise are evaluated by benchmarking the IHCV method using well-known bifurcation models (saddle-node, transcritical, pitchfork, Hopf). Next, we demonstrate that IHCV discovers hidden driver variables in the Lorenz, van der Pol, Hodgkin-Huxley, and Fitzhugh-Nagumo models. Finally, IHCV generalizes to the case when only partial observational is given, as demonstrated using the toggle switch model, the genetic repressilator oscillator, and a Waddington landscape model. Our proof-of-principle illustrates that utilizing normal forms could facilitate the data-efficient and scalable discovery of hidden variables controlling transitions between different dynamical regimes and non-linear models.
Hedging Valuation Adjustment for Callable Claims
Authors: Cyril Bénézet, Stéphane Crépey, Dounia Essaket
Date: 2023-04-04 00:00:00

In this work, we extend to callable assets the model risk approach of B{\'e}n{\'e}zet and Cr{\'e}pey (2024), itself leveraging on the notion of hedging valuation adjustment initially introduced for dealing with transaction costs in Burnett (2021) \&amp; Burnett and Williams (2021). The classical way to deal with model risk is to reserve the differences between the valuations in reference models and in the local models used by traders. However, while traders' prices are thus corrected, their hedging strategies and their exercise decisions are still wrong, which necessitates a risk-adjusted reserve. We illustrate our approach on a stylized callable range accrual representative of huge amounts of structured products on the market. We showthat a model risk reserve adjusted for the risk of wrong exercise decisions may largely exceed a basic reserve only accounting for valuation differences.
Improving the Hole Picture: Towards a Consensus on the Mechanism of Nuclear Transport
Authors: David Cowburn, Michael Rout
Date: 2023-04-06 00:00:00

Nuclear pore complexes (NPCs) mediate the exchange of materials between the nucleoplasm and cytoplasm, playing a key role in the separation of nucleic acids and proteins into their required compartments. The static structure of the NPC is relatively well defined by recent cryo EM and other studies. The functional roles of dynamic components in the pore of the NPC, phenylalanyl-glycyl (FG) repeat rich nucleoporins, is less clear because of our limited understanding of highly dynamic protein systems. These proteins form a restrained concentrate which interacts with and concentrates nuclear transport factors (NTRs) to provide facilitated nucleocytoplasmic transport of cargoes. Very rapid exchange among FG repeats and NTRs supports extremely fast facilitated transport, close to the rate of macromolecular diffusion in cytoplasm, while complexes without specific interactions are entropically excluded, though details on several aspects of the transport mechanism and FG repeat behaviors remain to be resolved. However, as discussed here, new technical approaches combined with more advanced modeling methods will likely provide an improved dynamic description of NPC transport, potentially at the atomic level in the near future. Such advances are likely to be of major benefit in comprehending the roles the malfunctioning NPC plays in cancer, aging, viral diseases, and neurodegeneration.
Complete mathematical characterization of two simple toggle-switch biological systems
Authors: Xavier Richard, Benoît Richard, Christian Mazza, Jan Roelof van der Meer
Date: 2023-04-06 00:00:00
Tasks: Vocal Bursts Valence Prediction

Prokaryotic gene expression is dynamic and noisy, and can lead to phenotypic variations. Previous work has shown that the selection of such variation can be modeled through basic reaction networks described by O.D.E. displaying a bistable behavior. While previous mathematical studies have shown that mono- or bistable behavior depends on the rate of the reactions in the system, no analytical solution of the curves delineating the actual parameter conditions that result in mono or bistability has so far been provided. In this work we provide the first explicit analytical solution for the boundary curve that separates the parameter space defining domains where double positive and double negative feedback loops become bistable.
Normative brain mapping using scalp EEG and potential clinical application
Authors: Vytene Janiukstyte, Thomas W Owen, Umair J Chaudhary, Beate Diehl, Louis Lemieux, John S Duncan, Jane de Tisi, Yujiang Wang, Peter N Taylor
Date: 2023-04-06 00:00:00
Tasks: EEG

A normative electrographic activity map could be a powerful resource to understand normal brain function and identify abnormal activity. Here, we present a normative brain map using scalp EEG in terms of relative band power. In this exploratory study we investigate its temporal stability, its similarity to other imaging modalities, and explore a potential clinical application. We constructed scalp EEG normative maps of brain dynamics from 17 healthy controls using source-localised resting-state scalp recordings. We then correlated these maps with those acquired from MEG and intracranial EEG to investigate their similarity. Lastly, we use the normative maps to lateralise abnormal regions in epilepsy. Spatial patterns of band powers were broadly consistent with previous literature and stable across recordings. Scalp EEG normative maps were most similar to other modalities in the alpha band, and relatively similar across most bands. Towards a clinical application in epilepsy, we found abnormal temporal regions ipsilateral to the epileptogenic hemisphere. Scalp EEG relative band power normative maps are spatially stable across time, in keeping with MEG and intracranial EEG results. Normative mapping is feasible and may be potentially clinically useful in epilepsy. Future studies with larger sample sizes and high-density EEG are now required for validation.
Complementary structural and functional abnormalities to localise epileptogenic tissue
Authors: Jonathan J Horsley, Rhys H Thomas, Fahmida A Chowdhury, Beate Diehl, Andrew W McEvoy, Anna Miserocchi, Jane de Tisi, Sjoerd B Vos, Matthew C Walker, Gavin P Winston, John S Duncan, Yujiang Wang, Peter N Taylor
Date: 2023-04-06 00:00:00
Tasks: EEG

When investigating suitability for surgery, people with drug-refractory focal epilepsy may have intracranial EEG (iEEG) electrodes implanted to localise seizure onset. Diffusion-weighted magnetic resonance imaging (dMRI) may be acquired to identify key white matter tracts for surgical avoidance. Here, we investigate whether structural connectivity abnormalities, inferred from dMRI, may be used in conjunction with functional iEEG abnormalities to aid localisation and resection of the epileptogenic zone (EZ), and improve surgical outcomes in epilepsy. We retrospectively investigated data from 43 patients with epilepsy who had surgery following iEEG. Twenty five patients (58%) were free from disabling seizures (ILAE 1 or 2) at one year. For all patients, T1-weighted and diffusion-weighted MRIs were acquired prior to iEEG implantation. Interictal iEEG functional, and dMRI structural connectivity abnormalities were quantified by comparison to a normative map and healthy controls respectively. First, we explored whether the resection of maximal (dMRI and iEEG) abnormalities related to improved surgical outcomes. Second, we investigated whether the modalities provided complementary information for improved prediction of surgical outcome. Third, we suggest how dMRI abnormalities may be useful to inform the placement of iEEG electrodes as part of the pre-surgical evaluation using a patient case study. Seizure freedom was 15 times more likely in those patients with resection of maximal dMRI and iEEG abnormalities (p=0.008). Both modalities were separately able to distinguish patient outcome groups and when combined, a decision tree correctly separated 36 out of 43 (84%) patients based on surgical outcome. Structural dMRI could be used in pre-surgical evaluations, particularly when localisation of the EZ is uncertain, to inform personalised iEEG implantation and resection.