pwc-archive/papers-with-abstracts huggingface dataset:
BS-RIS-User Association and Beamforming Designs for RIS-aided Cellular Networks
Authors: Sifan Liu, Pengfei Ni, Rang Liu, Yang Liu, Ming Li, Qian Liu
Date: 2021-06-27 00:00:00

Reconfigurable intelligent surface (RIS) has been regarded as a revolutionary and promising technology owing to its powerful feature of adaptively shaping wireless propagation environment. However, as a frequency-selective device, the RIS can only effectively provide tunable phase-shifts for signals within a certain frequency band. Thus, base-station (BS)-RIS-user association is an important issue to maximize the efficiency and ability of the RIS in cellular networks. In this paper, we consider a RIS-aided cellular network and aim to maximize the sum-rate of downlink transmissions by designing BS-RIS-user association as well as the active and passive beamforming of BSs and RIS, respectively. A dynamically successive access algorithm is developed to design the user association. During the dynamical access process, an iterative algorithm is proposed to alternatively obtain the active and passive beamforming. Finally, the optimal BS-RIS association is obtained by an exhaustive search method. Simulation results illustrate the significant performance improvement of the proposed BS-RIS-user association and beamforming design algorithm.
Reduced Training Overhead for WLAN MU-MIMO Channel Feedback with Compressed Sensing
Authors: Prasanna Sethuraman
Date: 2021-06-26 00:00:00
Tasks: compressed sensing

The WLAN packet format has a short training field (STF) for synchronization followed by a long training field (LTF) for channel estimation. To enable MIMO channel estimation, the LTF is repeated as many times as the number of spatial streams. For MU-MIMO, the CSI feedback in the 802.11ac/ax requires the access point (AP) to send a null data packet (NDP) where the HT/VHT/HE LTF is repeated as many times as the number of transmit antennas $N_{t}$. With each LTF being 4$\mu$s long in case of VHT and 12$\mu$s to 16$\mu$s long in case of High Efficiency WLAN (HEW), the length of NDP grows linearly with increasing $N_{t}$. Furthermore, the station (STA) with $N_{r}$ receive antennas needs to expend significant processing power to compute SVD per tone for the $N_{r}\times N_{t}$ channel matrix for generating the feedback bits, which again increases linearly with $N_{t}\cdot N_{r}$. To reduce the training and feedback overhead, this paper proposes a scheme based on Compressed Sensing that allows only a subset of tones per LTF to be transmitted in NDP, which can be used by STA to compute channel estimates that are then sent back without any further processing. Since AP knows the measurement matrix, the full dimension time domain channel estimates can be recovered by running the L1 minimization algorithms (OMP, CoSAMP). AP can further process the time domain channel estimates to generate the SVD precoding matrix.
A Chirp Spread Spectrum Modulation Scheme for Robust Power Line Communication
Authors: Stephen Robson, Abderrahmane Haddad
Date: 2021-06-26 00:00:00

This paper proposes the use of a LoRa like chirp spread spectrum physical layer as the basis for a new Power Line Communication modulation scheme suited for low-bandwidth communication. It is shown that robust communication can be established even in channels exhibiting both extreme multipath interference and low SNR (-40dB), with synchronisation requirements significantly reduced compared to conventional LoRa. ATP-EMTP simulations using frequency dependent line and transformer models, and simulations using artificial Rayleigh channels demonstrate the effectiveness of the new scheme in providing load data from LV feeders back to the MV primary substation. We further present experimental results based on a Field Programmable Gate Array hardware implementation of the proposed scheme.
On Stochastic Partial Differential Equations and their applications to Derivative Pricing through a conditional Feynman-Kac formula
Authors: Kaustav Das, Ivan Guo, Grégoire Loeper
Date: 2021-06-28 00:00:00

In a multi-dimensional diffusion framework, the price of a financial derivative can be expressed as an iterated conditional expectation, where the inner conditional expectation conditions on the future of an auxiliary process that enters into the dynamics for the spot. Inspired by results from non-linear filtering theory, we show that this inner conditional expectation solves a backward SPDE (a so-called 'conditional Feynman-Kac formula'), thereby establishing a connection between SPDE and derivative pricing theory. Unlike situations considered previously in the literature, the problem at hand requires conditioning on a backward filtration generated by the noise of the auxiliary process and enlarged by its terminal value, leading us to search for a backward Brownian motion in this filtration. This adds an additional source of irregularity to the associated SPDE which must be tackled with new techniques. Moreover, through the conditional Feynman-Kac formula, we establish an alternative class of so-called mixed Monte-Carlo PDE numerical methods for pricing financial derivatives. Finally, we provide a simple demonstration of this method by pricing a European put option.
Aldosterone and Dexamethasone Activate African Lungfish Mineralocorticoid Receptor: Increased Activation After Removal of the Amino-Terminal Domain
Authors: Yoshinao Katsu, Shin Oana, Xiaozhi Lin, Susumu Hyodo, Michael E. Baker
Date: 2021-06-27 00:00:00

Aldosterone, the main physiological mineralocorticoid in humans and other terrestrial vertebrates, first appears in lungfish, which are lobe-finned fish that are forerunners of terrestrial vertebrates. Aldosterone activation of the MR regulates internal homeostasis of water, sodium and potassium, which was critical in the conquest of land by vertebrates. We studied transcriptional activation of the slender African lungfish MR by aldosterone, other corticosteroids and progesterone and find that aldosterone, 11-deoxycorticosterone, 11-deoxycortisol and progesterone have half-maximal responses (EC50s) below 1 nM and are potential physiological mineralocorticoids. In contrast, EC50s for corticosterone and cortisol were 23 nM and 66 nM, respectively. Unexpectedly, truncated lungfish MR, consisting of the DNA-binding, hinge and steroid-binding domains, had a stronger response to corticosteroids and progesterone than full-length lungfish MR, indicating that the N-terminal domain represses steroid activation of lungfish MR, unlike human MR in which the N-terminal domain contains an activation function. BLAST searches of GenBank did not retrieve a GR ortholog, leading us to test dexamethasone and triamcinolone for activation of lungfish MR. At 10 nM, both synthetic glucocorticoids are about 4-fold stronger than 10 nM aldosterone in activating full-length lungfish MR, leading us to propose that lungfish MR also functions as a GR.
Blastocoel morphogenesis: a biophysics perspective
Authors: Mathieu Le-Verge-Serandour, Hervé Turlier
Date: 2021-06-28 00:00:00
Tasks: Diversity

The blastocoel is a fluid-filled cavity characteristic of animal embryos at the blastula stage. Its emergence is commonly described as the result of cleavage patterning, but this historical view conceals a large diversity of mechanisms and overlooks many unsolved questions from a biophysics perspective. In this review, we describe generic mechanisms for blastocoel morphogenesis, rooted in biological literature and simple physical principles. We propose novel directions of study and emphasize the importance to study blastocoel morphogenesis as an evolutionary and physical continuum.
Cell types and ontologies of the Human Cell Atlas
Authors: David Osumi-Sutherland, Chuan Xu, Maria Keays, Peter V. Kharchenko, Aviv Regev, Ed Lein, Sarah A. Teichmann
Date: 2021-06-28 00:00:00

Massive single-cell profiling efforts have accelerated our discovery of the cellular composition of the human body, while at the same time raising the need to formalise this new knowledge. Here, we review current cell ontology efforts to harmonise and integrate different sources of annotations of cell types and states. We illustrate with examples how a unified ontology can consolidate and advance our understanding of cell types across scientific communities and biological domains.
Analysis of a model of the Calvin cycle with diffusion of ATP
Authors: Burcu Gürbüz, Alan D. Rendall
Date: 2021-06-28 00:00:00

The dynamics of a mathematical model of the Calvin cycle, which is part of photosynthesis, is analysed. Since diffusion of ATP is included in the model a system of reaction-diffusion equations is obtained. It is proved that for a suitable choice of parameters there exist spatially inhomogeneous positive steady states, in fact infinitely many of them. It is also shown that all positive steady states, homogeneous and inhomogeneous, are nonlinearly unstable. The only smooth steady state which could be stable is a trivial one, where all concentrations except that of ATP are zero. It is found that in the spatially homogeneous case there are steady states with the property that the linearization about that state has eigenvalues which are not real, indicating the presence of oscillations. Numerical simulations exhibit solutions for which the concentrations are not monotone functions of time.
Complex network modelling of EEG band coupling in dyslexia: an exploratory analysis of auditory processing and diagnosis
Authors: Nicolás J. Gallego-Molina, Andrés Ortiz, Francisco J. Martínez-Murcia, Marco Formoso, Almudena Giménez
Date: 2021-06-28 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

Complex network analysis has an increasing relevance in the study of neurological disorders, enhancing the knowledge of brain's structural and functional organization. Network structure and efficiency reveal different brain states along with different ways of processing the information. This work is structured around the exploratory analysis of the brain processes involved in low-level auditory processing. A complex network analysis was performed on the basis of brain coupling obtained from Electroencephalography (EEG) data, while different auditory stimuli were presented to the subjects. This coupling is inferred from the Phase-Amplitude coupling (PAC) from different EEG electrodes to explore differences between controls and dyslexic subjects. Coupling data allows the construction of a graph, and then, graph theory is used to study the characteristics of the complex networks throughout time for controls and dyslexics. This results in a set of metrics including clustering coefficient, path length and small-worldness. From this, different characteristics linked to the temporal evolution of networks and coupling are pointed out for dyslexics. Our study revealed patterns related to Dyslexia as losing the small-world topology. Finally, these graph-based features are used to classify between controls and dyslexic subjects by means of a Support Vector Machine (SVM).
Photoacoustic Silk Scaffolds for Neural stimulation and Regeneration
Authors: Nan Zheng, Vincent Fitzpatrick, Ran Cheng, Linli Shi, David L. Kaplan, Chen Yang
Date: 2021-06-28 00:00:00

Neural interfaces using biocompatible scaffolds provide crucial properties for the functional repair of nerve injuries and neurodegenerative diseases, including cell adhesion, structural support, and mass transport. Neural stimulation has also been found to be effective in promoting neural regeneration. This work provides a new strategy to integrate photoacoustic (PA) neural stimulation into hydrogel scaffolds using a nanocomposite hydrogel approach. Specifically, polyethylene glycol (PEG)-functionalized carbon nanotubes (CNT), highly efficient photoacoustic agents, are embedded into silk fibroin to form biocompatible and soft photoacoustic materials. We show that these photoacoustic functional scaffolds enable non-genetic activation of neurons with a spatial precision defined by the area of light illumination, promoting neuron regeneration. These CNT/silk scaffolds offered reliable and repeatable photoacoustic neural stimulation. 94% of photoacoustic stimulated neurons exhibit a fluorescence change larger than 10% in calcium imaging in the light illuminated area. The on-demand photoacoustic stimulation increased neurite outgrowth by 1.74-fold in a dorsal root ganglion model, when compared to the unstimulated group. We also confirmed that photoacoustic neural stimulation promoted neurite outgrowth by impacting the brain-derived neurotrophic factor (BDNF) pathway. As a multifunctional neural scaffold, CNT/silk scaffolds demonstrated non-genetic PA neural stimulation functions and promoted neurite outgrowth, providing a new method for non-pharmacological neural regeneration.
A Theory of Language Learning
Authors: Robert Worden
Date: 2021-06-06 00:00:00
Tasks: Language Acquisition

A theory of language learning is described, which uses Bayesian induction of feature structures (scripts) and script functions. Each word sense in a language is mentally represented by an m-script, a script function which embodies all the syntax and semantics of the word. M-scripts form a fully-lexicalised unification grammar, which can support adult language. Each word m-script can be learnt robustly from about six learning examples. The theory has been implemented as a computer model, which can bootstrap-learn a language from zero vocabulary. The Bayesian learning mechanism is (1) Capable: to learn arbitrarily complex meanings and syntactic structures; (2) Fast: learning these structures from a few examples each; (3) Robust: learning in the presence of much irrelevant noise, and (4) Self-repairing: able to acquire implicit negative evidence, using it to learn exceptions. Children learning language are clearly all of (1) - (4), whereas connectionist theories fail on (1) and (2), and symbolic theories fail on (3) and (4). The theory is in good agreement with many key facts of language acquisition, including facts which are problematic for other theories. It is compared with over 100 key cross-linguistic findings about acquisition of the lexicon, phrase structure, morphology, complementation and control, auxiliaries, verb argument structures, gaps and movement - in nearly all cases giving unforced agreement without extra assumptions.
Stochastic clonal dynamics and genetic turnover in exponentially growing populations
Authors: Arman Angaji, Christoph Velling, Johannes Berg
Date: 2021-06-27 00:00:00

We consider an exponentially growing population of cells undergoing mutations and ask about the effect of reproductive fluctuations (genetic drift) on its long-term evolution. We combine first step analysis with the stochastic dynamics of a birth-death process to analytically calculate the probability that the parent of a given genotype will go extinct. We compare the results with numerical simulations and show how this turnover of genetic clones can be used to infer the rates underlying the population dynamics. Our work is motivated by growing populations of tumour cells, the epidemic spread of viruses, and bacterial growth.
UNISWAP: Impermanent Loss and Risk Profile of a Liquidity Provider
Authors: Andreas A. Aigner, Gurvinder Dhaliwal
Date: 2021-06-28 00:00:00

Uniswap is a decentralized exchange (DEX) and was first launched on November 2, 2018 on the Ethereum mainnet [1] and is part of an Ecosystem of products in Decentralized Finance (DeFi). It replaces a traditional order book type of trading common on centralized exchanges (CEX) with a deterministic model that swaps currencies (or tokens/assets) along a fixed price function determined by the amount of currencies supplied by the liquidity providers. Liquidity providers can be regarded as investors in the decentralized exchange and earn fixed commissions per trade. They lock up funds in liquidity pools for distinct pairs of currencies allowing market participants to swap them using the fixed price function. Liquidity providers take on market risk as a liquidity provider in exchange for earning commissions on each trade. Here we analyze the risk profile of a liquidity provider and the so called impermanent (unrealized) loss in particular. We provide an improved version of the commonly denoted impermanent loss function for Uniswap v2 on the semi-infinite domain. The differences between Uniswap v2 and v3 are also discussed.
Optimal investment and proportional reinsurance in a regime-switching market model under forward preferences
Authors: Katia Colaneri, Alessandra Cretarola, Benedetta Salterini
Date: 2021-06-25 00:00:00
Tasks: Sensitivity

In this paper we study the optimal investment and reinsurance problem of an insurance company whose investment preferences are described via a forward dynamic exponential utility in a regime-switching market model. Financial and actuarial frameworks are dependent since stock prices and insurance claims vary according to a common factor given by a continuous time finite state Markov chain. We construct the value function and we prove that it is a forward dynamic utility. Then, we characterize the investment strategy and the optimal proportional level of reinsurance. We also perform numerical experiments and provide sensitivity analyses with respect to some model parameters.
Risk contributions of lambda quantiles
Authors: Akif Ince, Ilaria Peri, Silvana Pesenti
Date: 2021-06-28 00:00:00

Risk contributions of portfolios form an indispensable part of risk adjusted performance measurement. The risk contribution of a portfolio, e.g., in the Euler or Aumann-Shapley framework, is given by the partial derivatives of a risk measure applied to the portfolio profit and loss in direction of the asset units. For risk measures that are not positively homogeneous of degree 1, however, known capital allocation principles do not apply. We study the class of lambda quantile risk measures that includes the well-known Value-at-Risk as a special case but for which no known allocation rule is applicable. We prove differentiability and derive explicit formulae of the derivatives of lambda quantiles with respect to their portfolio composition, that is their risk contribution. For this purpose, we define lambda quantiles on the space of portfolio compositions and consider generic (also non-linear) portfolio operators. We further derive the Euler decomposition of lambda quantiles for generic portfolios and show that lambda quantiles are homogeneous in the space of portfolio compositions, with a homogeneity degree that depends on the portfolio composition and the lambda function. This result is in stark contrast to the positive homogeneity properties of risk measures defined on the space of random variables which admit a constant homogeneity degree. We introduce a generalised version of Euler contributions and Euler allocation rule, which are compatible with risk measures of any homogeneity degree and non-linear but homogeneous portfolios. These concepts are illustrated by a non-linear portfolio using financial market data.
Hierarchical contagions in the interdependent financial network
Authors: William A. Barnett, Xue Wang, Hai-Chuan Xu, Wei-Xing Zhou
Date: 2021-06-27 00:00:00
Tasks: Management

We derive the default cascade model and the fire-sale spillover model in a unified interdependent framework. The interactions among banks include not only direct cross-holding, but also indirect dependency by holding mutual assets outside the banking system. Using data extracted from the European Banking Authority, we present the interdependency network composed of 48 banks and 21 asset classes. For the robustness, we employ three methods, called $\textit{Anan}$, $\textit{Ha\l{}a}$ and $\textit{Maxe}$, to reconstruct the asset/liability cross-holding network. Then we combine the external portfolio holdings of each bank to compute the interdependency matrix. The interdependency network is much denser than the direct cross-holding network, showing the complex latent interaction among banks. Finally, we perform macroprudential stress tests for the European banking system, using the adverse scenario in EBA stress test as the initial shock. For different reconstructed networks, we illustrate the hierarchical cascades and show that the failure hierarchies are roughly the same except for a few banks, reflecting the overlapping portfolio holding accounts for the majority of defaults. We also calculate systemic vulnerability and individual vulnerability, which provide important information for supervision and relevant management actions.
Pre-treatment of outliers and anomalies in plant data: Methodology and case study of a Vacuum Distillation Unit
Authors: Kamil Oster, Stefan Güttel, Jonathan L. Shapiro, Lu Chen, Megan Jobson
Date: 2021-06-17 00:00:00
Tasks: Time Series Analysis

Data pre-treatment plays a significant role in improving data quality, thus allowing extraction of accurate information from raw data. One of the data pre-treatment techniques commonly used is outliers detection. The so-called 3${\sigma}$ method is a common practice to identify the outliers. As shown in the manuscript, it does not identify all outliers, resulting in possible distortion of the overall statistics of the data. This problem can have a significant impact on further data analysis and can lead to reduction in the accuracy of predictive models. There is a plethora of various techniques for outliers detection, however, aside from theoretical work, they all require case study work. Two types of outliers were considered: short-term (erroneous data, noise) and long-term outliers (e.g. malfunctioning for longer periods). The data used were taken from the vacuum distillation unit (VDU) of an Asian refinery and included 40 physical sensors (temperature, pressure and flow rate). We used a modified method for 3${\sigma}$ thresholds to identify the short-term outliers, i.e. ensors data are divided into chunks determined by change points and 3${\sigma}$ thresholds are calculated within each chunk representing near-normal distribution. We have shown that piecewise 3${\sigma}$ method offers a better approach to short-term outliers detection than 3${\sigma}$ method applied to the entire time series. Nevertheless, this does not perform well for long-term outliers (which can represent another state in the data). In this case, we used principal component analysis (PCA) with Hotelling's $T^2$ statistics to identify the long-term outliers. The results obtained with PCA were subject to DBSCAN clustering method. The outliers (which were visually obvious and correctly detected by the PCA method) were also correctly identified by DBSCAN which supported the consistency and accuracy of the PCA method.