pwc-archive/papers-with-abstracts huggingface dataset:
Physical Confinement and Cell Proximity Increase Cell Migration Rates and Invasiveness: A Mathematical Model of Cancer Cell Invasion through Flexible Channels
Authors: Qiyao Peng, Fred J Vermolen, Daphne Weihs
Date: 2022-08-23 00:00:00

Cancer cell migration between different body parts is the driving force behind cancer metastasis, which is the main cause of mortality of patients. Migration of cancer cells often proceeds by penetration through narrow cavities in locally stiff, yet flexible tissues. In our previous work, we developed a model for cell geometry evolution during invasion, which we extend here to investigate whether leader and follower (cancer) cells that only interact mechanically can benefit from sequential transmigration through narrow micro-channels and cavities. We consider two cases of cells sequentially migrating through a flexible channel: leader and follower cells being closely adjacent or distant. Using Wilcoxon's signed-rank test on the data collected from Monte Carlo simulations, we conclude that the modelled transmigration speed for the follower cell is significantly larger than for the leader cell when cells are distant, i.e. follower cells transmigrate after the leader has completed the crossing. Furthermore, it appears that there exists an optimum with respect to the width of the channel such that cell moves fastest. On the other hand, in the case of closely adjacent cells, effectively performing collective migration, the leader cell moves $12\%$ faster since the follower cell pushes it. This work shows that mechanical interactions between cells can increase the net transmigration speed of cancer cells, resulting in increased invasiveness. In other words, interaction between cancer cells can accelerate metastatic invasion.
Parameter Sensitivity of Deep-Feature based Evaluation Metrics for Audio Textures
Authors: Chitralekha Gupta, Yize Wei, Zequn Gong, Purnima Kamath, Zhuoyao Li, Lonce Wyse
Date: 2022-08-23 00:00:00
Tasks: Sensitivity, Texture Synthesis

Standard evaluation metrics such as the Inception score and Fr\'echet Audio Distance provide a general audio quality distance metric between the synthesized audio and reference clean audio. However, the sensitivity of these metrics to variations in the statistical parameters that define an audio texture is not well studied. In this work, we provide a systematic study of the sensitivity of some of the existing audio quality evaluation metrics to parameter variations in audio textures. Furthermore, we also study three more potentially parameter-sensitive metrics for audio texture synthesis, (a) a Gram matrix based distance, (b) an Accumulated Gram metric using a summarized version of the Gram matrices, and (c) a cochlear-model based statistical features metric. These metrics use deep features that summarize the statistics of any given audio texture, thus being inherently sensitive to variations in the statistical parameters that define an audio texture. We study and evaluate the sensitivity of existing standard metrics as well as Gram matrix and cochlear-model based metrics to control-parameter variations in audio textures across a wide range of texture and parameter types, and validate with subjective evaluation. We find that each of the metrics is sensitive to different sets of texture-parameter types. This is the first step towards investigating objective metrics for assessing parameter sensitivity in audio textures.
A Compact Quasi-Yagi Antenna for FMCW Radar-on-Chip based Through-Wall Imaging
Authors: Anand Kumar, Easha, Debdeep Sarkar, Gaurab Banerjee
Date: 2022-08-23 00:00:00

A compact quasi-Yagi antenna with a modified ground plane is designed for a through-wall radar (TWR) on-chip. A slot-based ground plane modification in the proposed antenna results in significant miniaturization with an increase in the impedance bandwidth by 44.62%. The antenna has a high directivity of 9.02 dBi and a front-to-back ratio of 25.76 dB at 2.4 GHz. Based on experiments in real-world deployment scenarios, the performance of the proposed quasi-Yagi antenna is found to be comparable to that of a Vivaldi antenna and a commercial-off-the-shelf (COTS) horn antenna. Spectrogram-based signatures of a moving person behind a wooden partition and a 40 cm thick masonry wall are successfully obtained using the designed antenna, demonstrating the suitability of the quasi-Yagi antenna for portable applications using a radar-on-chip.
Handling Disjunctions in Signal Temporal Logic Based Control Through Nonsmooth Barrier Functions
Authors: Adrian Wiltz, Dimos V. Dimarogonas
Date: 2022-08-23 00:00:00

For a class of spatio-temporal tasks defined by a fragment of Signal Temporal Logic (STL), we construct a nonsmooth time-varying control barrier function (CBF) and develop a controller based on a set of simple optimization problems. Each of the optimization problems invokes constraints that allow to exploit the piece-wise smoothness of the CBF for optimization additionally to the common gradient constraint in the context of CBFs. In this way, the conservativeness of the control approach is reduced in those points where the CBF is nonsmooth. Thereby, nonsmooth CBFs become applicable to time-varying control tasks. Moreover, we overcome the problem of vanishing gradients for the considered class of constraints which allows us to consider more complex tasks including disjunctions compared to approaches based on smooth CBFs. As a well-established and systematic method to encode spatiotemporal constraints, we define the class of tasks under consideration as an STL-fragment. The results are demonstrated in a relevant simulation example.
Features and Potentialities of Static Passive EM Skins for NLOS Specular Wireless Links
Authors: Giacomo Oliveri, Marco Salucci, Andrea Massa
Date: 2022-08-23 00:00:00

The ability of passive flat patterned electromagnetic skins (EMSs) to overcome the asymptotic limit of the total path attenuation (TPA) of flat metallic reflectors of arbitrary size in non-line-of-sight (NLOS) specular wireless links is assessed. Closed-form expressions for the achievable TPA in EMS-powered NLOS links as well as the condition on the panel size of EMS-screens to improve the performance of flat passive conductive screens (PCSs) with the same aperture are derived and numerically validated by considering different incidence angles, screen apertures, transmitter/receiver distances, antenna gains, meta-atom geometries, and carrier frequencies.
Feeder Microgrid Management on an Active Distribution System during a Severe Outage
Authors: Valliappan Muthukaruppan, Ashwin Shirsat, Rongxing Hu, Victor Paduani, Bei Xu, Yiyan Li, Mesut Baran, Ning Lu, David Lubkeman, Wenyuan Tang
Date: 2022-08-23 00:00:00
Tasks: energy management, Management

Forming a microgrid on a distribution system with large scale outage after a severe weather event is emerging as a viable solution to improve resiliency at the distribution level. This option becomes more attractive when the distribution system has high levels of distributed PV. The management of such feeder-level microgrid has however many challenges, such as limited resources that can be deployed on the feeder quickly, and the limited real-time monitoring and control on the distribution system. Effective use of the distributed PV is also challenging as they are not monitored and controlled. To handle these challenges, the paper proposes a 2-stage hierarchical energy management scheme to securely operate these feeder level micorgrids. The first stage of the scheme solves a sequential rolling optimization problem to optimally schedule the main resources (such as a mobile diesel generator and battery storage unit). The second stage adopts a dispatching scheme for the main resources to adjust the stage-1 set-points closer to real- time. The proposed scheme has unique features to assure that the scheme is robust under highly varying operating conditions with limited system observability: (i) an innovative PV forecast error adjustment and a dynamic reserve adjustment scheme to handle the extreme uncertainty on PV power output, and (ii) an intelligent fuel management scheme to assure that the resources are utilized optimally over the multiple days of the restoration period. The proposed algorithm is tested on sample system with real-time data. The results show that the proposed scheme performs well in maximizing service to loads by effective use of all the resources and by properly taking into account the challenging operating conditions.
Performance Analysis of Semi-Persistent Scheduling Throughput in 5G NR-V2X: A MAC Perspective
Authors: Ran Wei
Date: 2022-08-23 00:00:00
Tasks: Scheduling

The packet throughput in 5th Generation (5G) New Radio (NR) Vehicle-to-Everything (V2X) is highly dependent on the Medium Access Control (MAC) based scheduling algorithm with no base station participation. In particular, the Semi-Persistent Scheduling (SPS) algorithm has been standardized by the 3rd Generation Partnership Project (3GPP) for V2X resource scheduling in the out-of-coverage scenario. This paper analyzes the NR-V2X SPS throughput from the MAC perspective, where the packet reception ratio (PRR) and half-duplex (HD) effect dominate. We first investigate the average throughput in the fully connected vehicular network, in which all the vehicles share the same throughput. Subsequently, the average throughput as a function of distance in the partially connected vehicular network is analyzed. The Monte Carlo simulation results show that increasing the resource keeping probability can improve the average throughput. Meanwhile, in the partially connected network, the lower resource keeping probability is prone to obtaining the higher throughput gain by increasing the number of subchannels.
Fault Current-Constrained Optimal Power Flow on Unbalanced Distribution Networks
Authors: Jose E. Tabarez, Arthur K. Barnes, Adam Mate, Russell W. Bent
Date: 2022-08-22 00:00:00

With the proliferation of distributed generation into distribution networks, the need to consider fault currents in the dispatch problem becomes increasingly relevant. This paper introduces a method for adding fault current constraints into optimal power flow in order to reduce fault currents while minimizing generation cost. The optimal power flow problem is formulated as a single optimization problem with sub-networks representing the faults of interest. Having a single optimization problem allows the decision variables to be coupled across the optimal power flow and the fault current studies without having to iterate over possible solutions. The proposed method is applicable to unbalanced distribution networks, including those with transformers that introduce phase-shifts.
Learning to Solve Optimization Problems with Hard Linear Constraints
Authors: Meiyi Li, Soheil Kolouri, Javad Mohammadi
Date: 2022-08-22 00:00:00
Tasks: Decision Making, Image Registration

Constrained optimization problems appear in a wide variety of challenging real-world problems, where constraints often capture the physics of the underlying system. Classic methods for solving these problems rely on iterative algorithms that explore the feasible domain in the search for the best solution. These iterative methods are often the computational bottleneck in decision-making and adversely impact time-sensitive applications. Recently, neural approximators have shown promise as a replacement for the iterative solvers that can output the optimal solution in a single feed-forward providing rapid solutions to optimization problems. However, enforcing constraints through neural networks remains an open challenge. This paper develops a neural approximator that maps the inputs to an optimization problem with hard linear constraints to a feasible solution that is nearly optimal. Our proposed approach consists of four main steps: 1) reducing the original problem to optimization on a set of independent variables, 2) finding a gauge function that maps the infty-norm unit ball to the feasible set of the reduced problem, 3)learning a neural approximator that maps the optimization's inputs to an optimal point in the infty-norm unit ball, and 4) find the values of the dependent variables from the independent variable and recover the solution to the original problem. We can guarantee hard feasibility through this sequence of steps. Unlike the current learning-assisted solutions, our method is free of parameter-tuning and removes iterations altogether. We demonstrate the performance of our proposed method in quadratic programming in the context of the optimal power dispatch (critical to the resiliency of our electric grid) and a constrained non-convex optimization in the context of image registration problems.
Laminar model for the joint development of ocular dominance columns and CO blobs in V1
Authors: Andrew M. Oster
Date: 2022-08-22 00:00:00

In this paper, we present a multi--layer, activity--dependent model for the joint development of ocular dominance (OD) columns and cytochrome oxidase (CO) blobs in primate primary visual cortex (V1). For simplicity, we focus on layers 4C and 2/3 with both layers receiving direct thalamic inputs and layer 4C sending vertical projections to layer 2/3. Both the thalamic and the vertical connections are taken to be modifiable by activity. Using a correlation--based Hebbian learning rule with subtractive normalization, we show how the formation of an OD map in layer 4C is inherited by layer 2/3 via the vertical projections. Competition between these feedforward projections and the direct thalamic input to layer 2/3 then results in the formation of CO blobs superimposed upon the ocular dominance map. The spacing of the OD columns is determined by the spatial profile of the intralaminar connections within layer 4, while the spacing of CO blobs depends both on the width of the OD columns inherited from layer 4 and the spatial distribution of intralaminar connections within the superficial layer. The resulting CO blob distribution is shown to be consistent with experimental data. In addition, we numerically simulate monocular deprivation and find that while the CO blob distribution is unaltered, the OD pattern undergoes modification. The OD stripes of the deprived eye narrow, whereas the OD stripes for the remaining open eye widen.
High Dimensional Latent Panel Quantile Regression with an Application to Asset Pricing
Authors: Alexandre Belloni, Mingli Chen, Oscar Hernan Madrid Padilla, Zixuan, Wang
Date: 2019-12-04 00:00:00
Tasks: quantile regression, regression

We propose a generalization of the linear panel quantile regression model to accommodate both \textit{sparse} and \textit{dense} parts: sparse means while the number of covariates available is large, potentially only a much smaller number of them have a nonzero impact on each conditional quantile of the response variable; while the dense part is represent by a low-rank matrix that can be approximated by latent factors and their loadings. Such a structure poses problems for traditional sparse estimators, such as the $\ell_1$-penalised Quantile Regression, and for traditional latent factor estimator, such as PCA. We propose a new estimation procedure, based on the ADMM algorithm, consists of combining the quantile loss function with $\ell_1$ \textit{and} nuclear norm regularization. We show, under general conditions, that our estimator can consistently estimate both the nonzero coefficients of the covariates and the latent low-rank matrix. Our proposed model has a "Characteristics + Latent Factors" Asset Pricing Model interpretation: we apply our model and estimator with a large-dimensional panel of financial data and find that (i) characteristics have sparser predictive power once latent factors were controlled (ii) the factors and coefficients at upper and lower quantiles are different from the median.
Beta-Sorted Portfolios
Authors: Matias D. Cattaneo, Richard K. Crump, Weining Wang
Date: 2022-08-23 00:00:00
Tasks: Uncertainty Quantification

Beta-sorted portfolios -- portfolios comprised of assets with similar covariation to selected risk factors -- are a popular tool in empirical finance to analyze models of (conditional) expected returns. Despite their widespread use, little is known of their statistical properties in contrast to comparable procedures such as two-pass regressions. We formally investigate the properties of beta-sorted portfolio returns by casting the procedure as a two-step nonparametric estimator with a nonparametric first step and a beta-adaptive portfolios construction. Our framework rationalize the well-known estimation algorithm with precise economic and statistical assumptions on the general data generating process and characterize its key features. We study beta-sorted portfolios for both a single cross-section as well as for aggregation over time (e.g., the grand mean), offering conditions that ensure consistency and asymptotic normality along with new uniform inference procedures allowing for uncertainty quantification and testing of various relevant hypotheses in financial applications. We also highlight some limitations of current empirical practices and discuss what inferences can and cannot be drawn from returns to beta-sorted portfolios for either a single cross-section or across the whole sample. Finally, we illustrate the functionality of our new procedures in an empirical application.
Sorting and Grading
Authors: Jacopo Bizzotto, Adrien Vigier
Date: 2022-08-23 00:00:00

We propose a framework to assess how to optimally sort and grade students of heterogenous ability. Potential employers face uncertainty regarding an individual's productive value. Knowing which school an individual went to is useful for two reasons: firstly, average student ability may differ across schools; secondly, different schools may use different grading rules and thus provide varying incentives to exert effort. An optimal school system exhibits coarse stratification with respect to ability, and more lenient grading at the top-tier schools than at the bottom-tier schools. Our paper contributes to the ongoing policy debate on tracking in secondary schools.
Equilibrium selection: a geometric approach
Authors: Andrea Loi, Stefano Matta, Daria Uccheddu
Date: 2022-08-23 00:00:00

In this paper we propose a geometric approach to the selection of the equi- librium price. After a perturbation of the parameters, the new price is selected thorough the composition of two maps: the projection on the linearization of the equilibrium man- ifold, a method that underlies econometric modeling, and the exponential map, that associates a tangent vector with a geodesic on the manifold. As a corollary of our main result, we prove the equivalence between zero curvature and uniqueness of equilibrium in the case of an arbitrary number of goods and two consumers, thus extending the previous result by [6].
Limit Orders and Knightian Uncertainty
Authors: Michael Greinecker, Christoph Kuzmics
Date: 2022-08-23 00:00:00

A range of empirical puzzles in finance has been explained as a consequence of traders being averse to ambiguity. Ambiguity averse traders can behave in financial portfolio problems in ways that cannot be rationalized as maximizing subjective expected utility. However, this paper shows that when traders have access to limit orders, all investment behavior of an ambiguity-averse decision-maker is observationally equivalent to the behavior of a subjective expected utility maximizer with the same risk preferences; ambiguity aversion has no additional explanatory power.
Multi-Resolution Subspace-Based Optimization Method for the Retrieval of 2D Perfect Electric Conductors
Authors: Xiuzhu Ye, Francesco Zardi, Marco Salucci, Andrea Massa
Date: 2022-08-23 00:00:00
Tasks: Computational Efficiency, Retrieval

Perfect Electric Conductors (PECs) are imaged integrating the subspace-based optimizationmethod (SOM) within the iterative multi-scaling scheme (IMSA). Without a-priori information on the number or/and the locations of the scatterers and modelling their EM scattering interactions with a (known) probing source in terms of surface electric field integral equations, a segment-based representation of PECs is retrieved from the scattered field samples. The proposed IMSA-SOM inversion method is validated against both synthetic and experimental data by assessing the reconstruction accuracy, the robustness to the noise, and the computational efficiency with some comparisons, as well.
A Simulation Method for MMW Radar Sensing in Traffic Intersection Based on BART Algorithm
Authors: Ming Zong, Zhanyu Zhu
Date: 2022-08-23 00:00:00
Tasks: Object Recognition

Millimeter-wave (mmw) radar is indispensable for Intelligent Transportation Systems (ITS), which can monitor traffic conditions in all weathers. An end-to-end simulation method for mmw radar monitoring and identification at traffic intersections is proposed in this paper. In this method, a virtual intersection scenario model is constructed, and the scattering coefficient of the target is calculated using the Bidirectional Analytical Ray Tracing (BART) algorithm. Combined with the generation of time-domain waveforms, the operation of frequency-domain convolution is simplified by inverse Fourier transform, and the echo signals received by the sparse array are simulated. After raw signal processing, point cloud images containing target position information and Range-Doppler Map (RDM) containing target state feature are obtained. The performance of mmw radar in detecting the specific location information of the target is evaluated by analyzing point cloud images. In addition, a self-defined convolutional neural network is introduced in this paper to evaluate the object recognition performance of the RDM. After the training of the neural network, the classification accuracy of this method for four types of vehicle targets can reach 92%.
Data-driven Signal Decomposition Approaches: A Comparative Analysis
Authors: Thomas Eriksen, Naveed Ur Rehman
Date: 2022-08-23 00:00:00

Signal decomposition (SD) approaches aim to decompose non-stationary signals into their constituent amplitude- and frequency-modulated components. This represents an important preprocessing step in many practical signal processing pipelines, providing useful knowledge and insight into the data and relevant underlying system(s) while also facilitating tasks such as noise or artefact removal and feature extraction. The popular SD methods are mostly data-driven, striving to obtain inherent well-behaved signal components without making many prior assumptions on input data. Among those methods include empirical mode decomposition (EMD) and variants, variational mode decomposition (VMD) and variants, synchrosqueezed transform (SST) and variants and sliding singular spectrum analysis (SSA). With the increasing popularity and utility of these methods in wide-ranging application, it is imperative to gain a better understanding and insight into the operation of these algorithms, evaluate their accuracy with and without noise in input data and gauge their sensitivity against algorithmic parameter changes. In this work, we achieve those tasks through extensive experiments involving carefully designed synthetic and real-life signals. Based on our experimental observations, we comment on the pros and cons of the considered SD algorithms as well as highlighting the best practices, in terms of parameter selection, for the their successful operation. The SD algorithms for both single- and multi-channel (multivariate) data fall within the scope of our work. For multivariate signals, we evaluate the performance of the popular algorithms in terms of fulfilling the mode-alignment property, especially in the presence of noise.