pwc-archive/papers-with-abstracts huggingface dataset:
Comparison between Hadamard and canonical bases for in-situ wavefront correction and the effect of ordering in compressive sensing
Authors: Dennis Scheidt, Pedro A. Quinto Su
Date: 2022-10-20 00:00:00
Tasks: Compressive Sensing

In this work we compare the Canonical and Hadamard bases for in-situ wavefront correction of a focused Gaussian beam using a spatial light modulator (SLM). The beam is perturbed with a transparent optical element (sparse) or a random scatterer (both prevent focusing at a single spot). The phase corrections are implemented with different basis sizes ($N=64, 256, 1024, 4096$) and the phase contribution of each basis element is measured with 3 step interferometry. The field is reconstructed from the complete $3N$ measurements and the correction is implemented by projecting the conjugate phase at the SLM. Our experiments show that in general, the Hadamard basis measurements yield better corrections because every element spans the relevant area of the SLM, reducing the noise in the interferograms. % In contrast, the canonical basis has the fundamental limitation that the area of the elements is proportional to $1/N$, and requires dimensions that are compatible with the spatial period of the grating. In the case of the random scatterer, we were only able to get reasonable corrections with the Hadamard basis and the intensity of the corrected spot increased monotonically with $N$, which is consistent with fast random changes in phase over small spatial scales. We also explore compressive sensing with the Hadamard basis and find that the minimum compression ratio needed to achieve corrections with similar quality to those that use the complete measurements depend on the basis ordering. The best results are reached in the case of the Hadamard-Walsh and cake cutting orderings. Surprisingly, in the case of the random scatterer we find that moderate compression ratios on the order of $10-20\%$ ($N=4096$) allow to recover focused spots, although as expected, the maximum intensities increase monotonically with the number of measurements due to the non sparsity of the signal.
The Laws of Motion of the Broker Call Rate in the United States
Authors: Alex Garivaltis
Date: 2019-06-03 00:00:00

In this paper, which is the third installment of the author's trilogy on margin loan pricing, we analyze $1,367$ monthly observations of the U.S. broker call money rate, which is the interest rate at which stock brokers can borrow to fund their margin loans to retail clients. We describe the basic features and mean-reverting behavior of this series and juxtapose the empirically-derived laws of motion with the author's prior theories of margin loan pricing (Garivaltis 2019a-b). This allows us to derive stochastic differential equations that govern the evolution of the margin loan interest rate and the leverage ratios of sophisticated brokerage clients (namely, continuous time Kelly gamblers). Finally, we apply Merton's (1974) arbitrage theory of corporate liability pricing to study theoretical constraints on the risk premia that could be generated in the market for call money. Apparently, if there is no arbitrage in the U.S. financial markets, the implication is that the total volume of call loans must constitute north of $70\%$ of the value of all leveraged portfolios.
Game-Theoretic Optimal Portfolios in Continuous Time
Authors: Alex Garivaltis
Date: 2019-06-05 00:00:00

We consider a two-person trading game in continuous time whereby each player chooses a constant rebalancing rule $b$ that he must adhere to over $[0,t]$. If $V_t(b)$ denotes the final wealth of the rebalancing rule $b$, then Player 1 (the `numerator player') picks $b$ so as to maximize $\mathbb{E}[V_t(b)/V_t(c)]$, while Player 2 (the `denominator player') picks $c$ so as to minimize it. In the unique Nash equilibrium, both players use the continuous-time Kelly rule $b^*=c^*=\Sigma^{-1}(\mu-r\textbf{1})$, where $\Sigma$ is the covariance of instantaneous returns per unit time, $\mu$ is the drift vector of the stock market, and $\textbf{1}$ is a vector of ones. Thus, even over very short intervals of time $[0,t]$, the desire to perform well relative to other traders leads one to adopt the Kelly rule, which is ordinarily derived by maximizing the asymptotic exponential growth rate of wealth. Hence, we find agreement with Bell and Cover's (1988) result in discrete time.
On the Financing of Climate Change Adaptation in Developing Countries
Authors: Francis X. Diebold
Date: 2022-10-20 00:00:00

I offer reflections on adaptation to climate change, with emphasis on developing areas.
Multilinear Superhedging of Lookback Options
Authors: Alex Garivaltis
Date: 2018-10-04 00:00:00

In a pathbreaking paper, Cover and Ordentlich (1998) solved a max-min portfolio game between a trader (who picks an entire trading algorithm, $\theta(\cdot)$) and "nature," who picks the matrix $X$ of gross-returns of all stocks in all periods. Their (zero-sum) game has the payoff kernel $W_\theta(X)/D(X)$, where $W_\theta(X)$ is the trader's final wealth and $D(X)$ is the final wealth that would have accrued to a $\$1$ deposit into the best constant-rebalanced portfolio (or fixed-fraction betting scheme) determined in hindsight. The resulting "universal portfolio" compounds its money at the same asymptotic rate as the best rebalancing rule in hindsight, thereby beating the market asymptotically under extremely general conditions. Smitten with this (1998) result, the present paper solves the most general tractable version of Cover and Ordentlich's (1998) max-min game. This obtains for performance benchmarks (read: derivatives) that are separately convex and homogeneous in each period's gross-return vector. For completely arbitrary (even non-measurable) performance benchmarks, we show how the axiom of choice can be used to "find" an exact maximin strategy for the trader.
Obvious manipulations of tops-only voting rules
Authors: R. Pablo Arribillaga, Agustin G. Bonifacio
Date: 2022-10-20 00:00:00

In a voting problem with a finite set of alternatives to choose from, we study the manipulation of tops-only rules. Since all non-dictatorial (onto) voting rules are manipulable when there are more than two alternatives and all preferences are allowed, we look for rules in which manipulations are not obvious. First, we show that a rule does not have obvious manipulations if and only if when an agent vetoes an alternative it can do so with any preference that does not have such alternative in the top. Second, we focus on two classes of tops-only rules: (i) (generalized) median voter schemes, and (ii) voting by committees. For each class, we identify which rules do not have obvious manipulations on the universal domain of preferences.
Neural Network-Based Multi-Target Detection within Correlated Heavy-Tailed Clutter
Authors: Stefan Feintuch, Haim H. Permuter, Igal Bilik, Joseph Tabrikian
Date: 2022-10-21 00:00:00

This work addresses the problem of range-Doppler multiple target detection in a radar system in the presence of slow-time correlated and heavy-tailed distributed clutter. Conventional target detection algorithms assume Gaussian-distributed clutter, but their performance is significantly degraded in the presence of correlated heavy-tailed distributed clutter. Derivation of optimal detection algorithms with heavy-tailed distributed clutter is analytically intractable. Furthermore, the clutter distribution is frequently unknown. This work proposes a deep learning-based approach for multiple target detection in the range-Doppler domain. The proposed approach is based on a unified NN model to process the time-domain radar signal for a variety of signal-to-clutter-plus-noise ratios (SCNRs) and clutter distributions, simplifying the detector architecture and the neural network training procedure. The performance of the proposed approach is evaluated in various experiments using recorded radar echoes, and via simulations, it is shown that the proposed method outperforms the conventional cell-averaging constant false-alarm rate (CA-CFAR), the ordered-statistic CFAR (OS-CFAR), and the adaptive normalized matched-filter (ANMF) detectors in terms of probability of detection in the majority of tested SCNRs and clutter scenarios.
Fingerprint Based mmWave Positioning System Aided by Reconfigurable Intelligent Surface
Authors: Tuo Wu, Cunhua Pan, Yijin Pan, Hong Ren, Maged Elkashlan, Cheng-Xiang Wang
Date: 2022-10-21 00:00:00
Tasks: Position, regression

Reconfigurable intelligent surface (RIS) is a promising technique for millimeter wave (mmWave) positioning systems. In this paper, we consider multiple mobile users (MUs) positioning problem in the multiple-input multiple-output (MIMO) time-division duplex (TDD) mmWave systems aided by the RIS. We derive the expression for the space-time channel response vector (STCRV) as a novel type of fingerprint. The STCRV consists of the multipath channel characteristics, e.g., time delay and angle of arrival (AOA), which is related to the position of the MU. By using the STCRV as input, we propose a novel residual convolution network regression (RCNR) learning algorithm to output the estimated three-dimensional (3D) position of the MU. Specifically, the RCNR learninng algorithm includes a data processing block to process the input STCRV, a normal convolution block to extract the features of STCRV, four residual convolution blocks to further extract the features and protect the integrity of the features, and a regression block to estimate the 3D position. Extensive simulation results are also presented to demonstrate that the proposed RCNR learning algorithm outperforms the traditional convolution neural network (CNN).
Data-Driven Quickest Change Detection in (Hidden) Markov Models
Authors: Qi Zhang, Zhongchang Sun, Luis C. Herrera, Shaofeng Zou
Date: 2022-10-21 00:00:00
Tasks: Change Detection, Fault Detection

The paper investigates the problems of quickest change detection in Markov models and hidden Markov models (HMMs). Sequential observations are taken from a (hidden) Markov model. At some unknown time, an event occurs in the system and changes the transition kernel of the Markov model and/or the emission probability of the HMM. The objective is to detect the change quickly, while controlling the average running length (ARL) to false alarm. The data-driven setting is studied, where no knowledge of the pre-, post-change distributions is available. Kernel-based data-driven algorithms are developed, which can be applied in the setting with continuous state, can be updated in a recursive fashion, and are computationally efficient. Lower bounds on the ARL and upper bound on the worst-case average detection delay (WADD) are derived. The WADD is at most of the order of the logarithm of the ARL. The algorithms are further numerically validated on two practical problems of fault detection in DC microgrid and photovoltaic systems.
RIS-Enabled Passive Radar towards Target Localization
Authors: Ahmad Bazzi, Marwa Chafii
Date: 2022-10-21 00:00:00
Tasks: Integrated sensing and communication, ISAC

In this paper, we study a communication-centric integrated sensing and communication (ISAC) approach, where an access point (AP) communicates with users, while a passive radar (PR) is present in the environment. We investigate the deployment of a reconfigurable intelligent surface (RIS) to enable the PR to localize a target. We derive an optimization problem for updating the phase shifters of the RIS per epoch. Due to the limited information at the PR, such as unknown payload information and unknown number of targets in the scene, we propose two methods capable of performing joint angle of arrival estimation and detection of the targets. We demonstrate the superior performance of the methods onto the proposed setting through numerical simulations, in comparison to a no-RIS baseline scheme.
Characterization of Multi-Link Propagation and Bistatic Target Reflectivity for Distributed Multi-Sensor ISAC
Authors: Reiner S. Thomä, Carsten Andrich, Julia Beuster, Heraldo Cesar Alves Costa, Sebastian Giehl, Saw James Myint, Christian Schneider, Gerd Sommerkorn
Date: 2022-10-21 00:00:00
Tasks: Integrated sensing and communication, ISAC

Integrated sensing and communication (ISAC) qualifies mobile radio systems for detecting and localizing of passive objects by means of radar sensing. Advanced ISAC networks rely on meshed mobile radio access nodes (infrastructure and/or user equipment, resp.) establishing a distributed, multistatic MIMO radar system in which each target reveals itself by its bistatic backscattering. Therefore, characterization of the bistatic reflectivity of targets along their trajectories of movement is of highest importance for ISAC performance prediction. We summarize several challenges in bistatic modeling and measurement of extended, potentially time-variant radar targets. We emphasize the specific challenges arising for distributed (hence multi-link) ISAC networks and compare to the state of the art in propagation modeling for mobile communication.
Iterative Broadband Source Localization
Authors: Coleman DeLude, Rakshith Sharma, Santhosh Karnik, Christopher Hood, Mark Davenport, Justin Romberg
Date: 2022-10-21 00:00:00

In this paper we consider the problem of localizing a set of broadband sources from a finite window of measurements. In the case of narrowband sources this can be reduced to the problem of spectral line estimation, where our goal is simply to estimate the active frequencies from a weighted mixture of pure sinusoids. There exists a plethora of modern and classical methods that effectively solve this problem. However, for a wide variety of applications the underlying sources are not narrowband and can have an appreciable amount of bandwidth. In this work, we extend classical greedy algorithms for sparse recovery (e.g., orthogonal matching pursuit) to localize broadband sources. We leverage models for samples of broadband signals based on a union of Slepian subspaces, which are more aptly suited for dealing with spectral leakage and dynamic range disparities. We show that by using these models, our adapted algorithms can successfully localize broadband sources under a variety of adverse operating scenarios. Furthermore, we show that our algorithms outperform complementary methods that use more standard Fourier models. We also show that we can perform estimation from compressed measurements with little loss in fidelity as long as the number of measurements are on the order of the signal's implicit degrees of freedom. We conclude with an in-depth application of these ideas to the problem of localization in multi-sensor arrays.
A New Approach to Extract Fetal Electrocardiogram Using Affine Combination of Adaptive Filters
Authors: Yu Xuan, Xiangyu Zhang, Shuyue Stella Li, Zihan Shen, Xin Xie, Leibny Paola Garcia, Roberto Togneri
Date: 2022-10-21 00:00:00

The detection of abnormal fetal heartbeats during pregnancy is important for monitoring the health conditions of the fetus. While adult ECG has made several advances in modern medicine, noninvasive fetal electrocardiography (FECG) remains a great challenge. In this paper, we introduce a new method based on affine combinations of adaptive filters to extract FECG signals. The affine combination of multiple filters is able to precisely fit the reference signal, and thus obtain more accurate FECGs. We proposed a method to combine the Least Mean Square (LMS) and Recursive Least Squares (RLS) filters. Our approach found that the Combined Recursive Least Squares (CRLS) filter achieves the best performance among all proposed combinations. In addition, we found that CRLS is more advantageous in extracting FECG from abdominal electrocardiograms (AECG) with a small signal-to-noise ratio (SNR). Compared with the state-of-the-art MSF-ANC method, CRLS shows improved performance. The sensitivity, accuracy, and F1 scores improved by 3.58%, 2.39%, and 1.36%, respectively.
Multi-objective Design of Uniform Sparse MIMO Arrays
Authors: Suleyman Gokhun Tanyer, Paul Dent, Murtaza Ali, Curtis Davis, SenthinelKumar Rajagopal, Peter Driessen
Date: 2022-10-20 00:00:00

The problem of multi-objective design of sparse MIMO arrays for better multitarget detection capabilities is considered. A novel approach for efficient utilization of the antenna design resources; namely, the number of available array elements, and array aperture are studied for the angular beamforming performance metrics such as, beam width, the peak to sidelobe ratio (PSLR), and grating lobe limited field of view. The limiting constraints, the physical size of elements, and mutual coupling are also considered. Thinning of fully populated uniform MIMO antenna arrays to form effective uniform sparse arrays (USA), as the term proposed here, are examined as for their capability of improving usable field of view (uFOV), beamwidth (BW), and peak-to-side lobe-ratio (PSLR) using fewer physical antenna elements, simultaneously. Sparse arrays require much less array elements to outperform uniform linear (ULA) and rectangular arrays (URA) for their beamwidths. Further, a rigorious design procedure considering the physical size limitations is currently unavailable. Here, we present a practical design architecture of such a uniform sparse array under multiple contradicting objectives. Angular resolution performances of the novel uniform sparse arrays are compared with the standard ULA and URAs. It is shown that design of an array with small inter-element spacings avoiding any grating lobes is possible even when the physical size of the elements are very large. Expanding the available elements to a much larger apertures provides much better angular resolution. However, it is also shown that these advantages come with the cost of increased side lobes. Both the simulated and the measured results demonstrate the superiority of the proposed uniform sparse array design compared to classical fully populated uniform arrays.
Signal Structure of the Starlink Ku-Band Downlink
Authors: Todd E. Humphreys, Peter A. Iannucci, Zacharias Komodromos, Andrew M. Graff
Date: 2022-10-20 00:00:00

We develop a technique for blind signal identification of the Starlink downlink signal in the 10.7 to 12.7 GHz band and present a detailed picture of the signal's structure. Importantly, the signal characterization offered herein includes the exact values of synchronization sequences embedded in the signal that can be exploited to produce pseudorange measurements. Such an understanding of the signal is essential to emerging efforts that seek to dual-purpose Starlink signals for positioning, navigation, and timing, despite their being designed solely for broadband Internet provision.
Energy prediction of pv panels for demand and response system using ANN (deep learning)
Authors: Rohaib Bhatti, Ali John Naqvi, Abdullah Tauqeer
Date: 2022-10-20 00:00:00
Tasks: feature selection

Renewable sources of energy are the future due to the environmental problems caused by non-renewable sources to produce energy. The biggest issue with renewable energy sources is that the power produced by devices such as PV solar panels depend on many uncertain factors. These factors include Solar irradiation, wind speed, temperature, hours of sunlight per day, and surface temperature of solar panels. Industries and authorities can use this predicted power through ML to control power consumption. Power forecast has multiple applications that promote the usage of green energy in the future. This paper will also help to determine the dependence of PV power production on various weather/environmental factors. For this paper, we have used regressions and ANN models to predict power. In the end, the results of power prediction using regression as well as the ANN model are compared with the actual power output. Overall, ANN performs excellently compared to the other machine learning models because of its advanced feature selection techniques.
Structure of Working Memory in Children From 3 to 8 Years Old
Authors: Barbara Carretti, David Giofre, Enrico Toffalini, Cesare Cornoldi, Massimiliano Pastore, Silvia Lanfranchi
Date: 2022-10-12 00:00:00

Several models of working memory (WM) have been proposed in the literature. Most of the research on the architecture of WM is based on adults or older children, but less is known about younger children. In this study, we tested various models of WM on a sample of 739 Italian children from 3 to 8 years old. Participants were assessed with 12 WM tasks, systematically varying the modality and level of executive control required (based on the number of activities to be performed at once: retention alone, ignoring distractors, and dealing with dual tasks). We examined younger children, n = 501, Mage = 56.8 months (SD = 6.4, 48% males) and older children, n = 238, Mage = 80.0 months (SD = 9.0, 58% males) separately using multigroup confirmatory factor analyses. A Bayesian analytical approach was adopted. Our results suggested that a four-factor model distinguishing between verbal, visual, spatial-simultaneous, and spatial-sequential components of WM achieved the best fit. Overall, the WM structure was very similar in the two groups. We further explored this result with an additional model with a central executive factor loaded on high-control tasks only, and found evidence for the presence of an executive control component. The contribution of this factor in terms of explained variance was only modest, however. Our findings demonstrate that it is important to distinguish between WM components in young children.
Opening Amyloid-Windows to the Secondary Structure of Proteins: The Amyloidogenecity Increases Tenfold Inside Beta-Sheets
Authors: Kristof Takacs, Balint Varga, Viktor Farkas, Andras Perczel, Vince Grolmusz
Date: 2022-10-21 00:00:00

Methods from artificial intelligence (AI), in general, and machine learning, in particular, have kept conquering new territories in numerous areas of science. Most of the applications of these techniques are restricted to the classification of large data sets, but new scientific knowledge can seldom be inferred from these tools. Here we show that an AI-based amyloidogenecity predictor can strongly differentiate the border- and the internal hexamers of $\beta$-pleated sheets when screening all the Protein Data Bank-deposited homology-filtered protein structures. Our main result shows that more than 30\% of internal hexamers of $\beta$ sheets are predicted to be amyloidogenic, while just outside the border regions, only 3\% are predicted as such. This result may elucidate a general protection mechanism of proteins against turning into amyloids: if the borders of $\beta$-sheets were amyloidogenic, then the whole $\beta$ sheet could turn more easily into an insoluble amyloid-structure, characterized by periodically repeated parallel $\beta$-sheets. We also present that no analogous phenomenon exists on the borders of $\alpha$-helices or randomly chosen subsequences of the studied protein structures.