pwc-archive/papers-with-abstracts huggingface dataset:
Mathematical model of subthalamic nucleus neuron: characteristic activity patterns and bifurcation analysis
Authors: Choongseok Park, Leonid L. Rubchinsky, Sungwoo Ahn
Date: 2021-10-19 00:00:00
Tasks: Rhythm

The subthalamic nucleus (STN) has an important role in the pathophysiology of the basal ganglia in Parkinson's disease. The ability of STN cells to generate bursting rhythms under either transient or sustained hyperpolarization may underlie the excessively synchronous beta rhythms observed in Parkinson's disease. In this study, we developed a conductance-based single compartment model of an STN neuron, which is able to generate characteristic activity patterns observed in experiments including hyperpolarization-induced bursts and post-inhibitory rebound bursts. This study focused on the role of three currents in rhythm generation: T-type calcium (CaT) current, L-type calcium (CaL) current, and hyperpolarization-activated cyclic nucleotide-gated (HCN) current. To investigate the effects of these currents in rhythm generation, we performed a bifurcation analysis using slow variables in these currents. Bifurcation analysis showed that the HCN current promotes single-spike activity patterns rather than bursting in agreement with experimental results. It also showed that the CaT current is necessary for characteristic bursting activity patterns. In particular, the CaT current enables STN neurons to generate these activity patterns under hyperpolarizing stimuli. The CaL current enriches and reinforces these characteristic activity patterns. In hyperpolarization-induced bursts or post-inhibitory rebound bursts, the CaL current allows STN neurons to generate long bursting patterns. Thus, bifurcation analysis explained the synergistic interaction of the CaT and CaL currents, which enables STN neurons to respond to hyperpolarizing stimuli in a salient way. The results of this study implicate the importance of CaT and CaL currents in the pathophysiology of the basal ganglia in Parkinson's disease.
Attention Overload
Authors: Matias D. Cattaneo, Paul Cheung, Xinwei Ma, Yusufcan Masatlioglu
Date: 2021-10-20 00:00:00

We introduce an Attention Overload Model that captures the idea that alternatives compete for the decision maker's attention, and hence the attention that each alternative receives decreases as the choice problem becomes larger. Using this nonparametric restriction on the random attention formation, we show that a fruitful revealed preference theory can be developed and provide testable implications on the observed choice behavior that can be used to (point or partially) identify the decision maker's preference and attention frequency. We then enhance our attention overload model to accommodate heterogeneous preferences. Due to the nonparametric nature of our identifying assumption, we must discipline the amount of heterogeneity in the choice model: we propose the idea of List-based Attention Overload, where alternatives are presented to the decision makers as a list that correlates with both heterogeneous preferences and random attention. We show that preference and attention frequencies are (point or partially) identifiable under nonparametric assumptions on the list and attention formation mechanisms, even when the true underlying list is unknown to the researcher. Building on our identification results, for both preference and attention frequencies, we develop econometric methods for estimation and inference that are valid in settings with a large number of alternatives and choice problems, a distinctive feature of the economic environment we consider. We provide a software package in R implementing our empirical methods, and illustrate them in a simulation study.
One Instrument to Rule Them All: The Bias and Coverage of Just-ID IV
Authors: Joshua Angrist, Michal Kolesár
Date: 2021-10-20 00:00:00
Tasks: All

We revisit the finite-sample behavior of single-variable just-identified instrumental variables (just-ID IV) estimators, arguing that in most microeconometric applications, the usual inference strategies are likely reliable. Three widely-cited applications are used to explain why this is so. We then consider pretesting strategies of the form $t_{1}>c$, where $t_{1}$ is the first-stage $t$-statistic, and the first-stage sign is given. Although pervasive in empirical practice, pretesting on the first-stage $F$-statistic exacerbates bias and distorts inference. We show, however, that median bias is both minimized and roughly halved by setting $c=0$, that is by screening on the sign of the \textit{estimated} first stage. This bias reduction is a free lunch: conventional confidence interval coverage is unchanged by screening on the estimated first-stage sign. To the extent that IV analysts sign-screen already, these results strengthen the case for a sanguine view of the finite-sample behavior of just-ID IV.
Bi-integrative analysis of two-dimensional heterogeneous panel data model
Authors: Wei Wang, Xiaodong Yan, Yanyan Ren, Zhijie Xiao
Date: 2021-10-20 00:00:00
Tasks: Econometrics, Vocal Bursts Valence Prediction

Heterogeneous panel data models that allow the coefficients to vary across individuals and/or change over time have received increasingly more attention in statistics and econometrics. This paper proposes a two-dimensional heterogeneous panel regression model that incorporate a group structure of individual heterogeneous effects with cohort formation for their time-variations, which allows common coefficients between nonadjacent time points. A bi-integrative procedure that detects the information regarding group and cohort patterns simultaneously via a doubly penalized least square with concave fused penalties is introduced. We use an alternating direction method of multipliers (ADMM) algorithm that automatically bi-integrates the two-dimensional heterogeneous panel data model pertaining to a common one. Consistency and asymptotic normality for the proposed estimators are developed. We show that the resulting estimators exhibit oracle properties, i.e., the proposed estimator is asymptotically equivalent to the oracle estimator obtained using the known group and cohort structures. Furthermore, the simulation studies provide supportive evidence that the proposed method has good finite sample performance. A real data empirical application has been provided to highlight the proposed method.
Difference-in-Differences with Geocoded Microdata
Authors: Kyle Butts
Date: 2021-10-19 00:00:00

This paper formalizes a common approach for estimating effects of treatment at a specific location using geocoded microdata. This estimator compares units immediately next to treatment (an inner-ring) to units just slightly further away (an outer-ring). I introduce intuitive assumptions needed to identify the average treatment effect among the affected units and illustrates pitfalls that occur when these assumptions fail. Since one of these assumptions requires knowledge of exactly how far treatment effects are experienced, I propose a new method that relaxes this assumption and allows for nonparametric estimation using partitioning-based least squares developed in Cattaneo et. al. (2019). Since treatment effects typically decay/change over distance, this estimator improves analysis by estimating a treatment effect curve as a function of distance from treatment. This is contrast to the traditional method which, at best, identifies the average effect of treatment. To illustrate the advantages of this method, I show that Linden and Rockoff (2008) under estimate the effects of increased crime risk on home values closest to the treatment and overestimate how far the effects extend by selecting a treatment ring that is too wide.
Marital Stability With Committed Couples: A Revealed Preference Analysis
Authors: Mikhail Freer, Khushboo Surana
Date: 2021-10-20 00:00:00

We present a revealed preference characterization of marital stability where some couples are committed. A couple is committed if they can divorce only with mutual consent. We provide theoretical insights into the potential of the characterization for identifying intrahousehold consumption patterns. We demonstrate that without price variation for private goods among potential couples, intrahousehold resource allocations can only be identified for non-committed couples. We conduct simulations using Dutch household data to support our theoretical findings. Our results show that with price variation, the empirical implications of marital stability allow for the identification of household consumption allocations for both committed and non-committed couples.
Optimally Targeting Interventions in Networks during a Pandemic: Theory and Evidence from the Networks of Nursing Homes in the United States
Authors: Roland Pongou, Guy Tchuente, Jean-Baptiste Tondji
Date: 2021-10-19 00:00:00

This study develops an economic model for a social planner who prioritizes health over short-term wealth accumulation during a pandemic. Agents are connected through a weighted undirected network of contacts, and the planner's objective is to determine the policy that contains the spread of infection below a tolerable incidence level, and that maximizes the present discounted value of real income, in that order of priority. The optimal unique policy depends both on the configuration of the contact network and the tolerable infection incidence. Comparative statics analyses are conducted: (i) they reveal the tradeoff between the economic cost of the pandemic and the infection incidence allowed; and (ii) they suggest a correlation between different measures of network centrality and individual lockdown probability with the correlation increasing with the tolerable infection incidence level. Using unique data on the networks of nursing and long-term homes in the U.S., we calibrate our model at the state level and estimate the tolerable COVID-19 infection incidence level. We find that laissez-faire (more tolerance to the virus spread) pandemic policy is associated with an increased number of deaths in nursing homes and higher state GDP growth. In terms of the death count, laissez-faire is more harmful to nursing homes than more peripheral in the networks, those located in deprived counties, and those who work for a profit. We also find that U.S. states with a Republican governor have a higher level of tolerable incidence, but policies tend to converge with high death count.
Water Quality of Lake Mjøsa through Satellite Images: A Preliminary Study
Authors: Anders Gjesdal Oliversen, Hilda Deborah
Date: 2021-10-20 00:00:00

Since around 2010, the water quality in lake Mj{\o}sa has begun to decline after an earlier successful effort to improve the water quality (\textit{Mj{\o}saksjonen}). In this study, we investigate the possibility of using satellite imagery to monitor the water quality of lake Mj{\o}sa, and also compare the use of such an approach to current methods for monitoring the lake. Using satellite images for remote sensing has layers of complexity that were not apparent to us at first, and this paper summarizes some of our findings by discussing factors of water quality, state of the art for use of satellite imagery for water quality and some of our own initial results. While our results where not too accurate for this preliminary study, it gives an introduction to the field and some of the methods that may bring better results for further research. Satellite images can play an important role in monitoring changing waters. The field is in development and many factors of water quality may be analysed with increasing levels of accuracy.
One-Bit ADCs/DACs based MIMO Radar: Performance Analysis and Joint Design
Authors: Minglong Deng, Ziyang Cheng, Linlong Wu, Bhavani Shankar, Zishu He
Date: 2021-10-21 00:00:00

Extremely low-resolution (e.g. one-bit) analog-to-digital converters (ADCs) and digital-to-analog converters (DACs) can substantially reduce hardware cost and power consumption for MIMO radar especially with large scale antennas. In this paper, we focus on the detection performance analysis and joint design for the MIMO radar with one-bit ADCs and DACs. Specifically, under the assumption of low signal-to-noise ratio (SNR) and interference-to-noise ratio (INR), we derive the expressions of probability of detection ($\mathcal{P}_d$) and probability of false alarm ($\mathcal{P}_f$) for one-bit MIMO radar and also the theoretical performance gap to infinite-bit MIMO radars for the noise-only case. We further find that for a fixed $\mathcal{P}_f$, $\mathcal{P}_d$ depends on the defined quantized signal-to-interference-plus-noise ratio (QSINR), which is a function of the transmit waveform and receive filter. Thus, an optimization problem arises naturally to maximize the QSINR by joint designing the waveform and filter. For the formulated problem, we propose an alternatin\emph{g} wavefo\emph{r}m and filt\emph{e}r d\emph{e}sign for QSINR maximiza\emph{t}ion (GREET). At each iteration of GREET, the receive filter is upadted via the minimum variance distortionless response (MVDR) method, and the one-bit waveform is optimized based on the alternating direction method of multipliers (ADMM) algorithm where the closed-form solutions are obtained for both the primary and slack variables. Numerical simulations are consistent to the theoretical performance analysis and demonstrate the effectiveness of the proposed design algorithm.
The First Airborne Experiment of Sparse Microwave Imaging: Prototype System Design and Result Analysis
Authors: Zhe Zhang, Bingchen Zhang, Chenglong Jiang, Xingdong Liang, Longyong Chen, Wen Hong, Yirong Wu
Date: 2021-10-20 00:00:00

In this paper we report the first airborne experiments of sparse microwave imaging, conducted in September 2013 and May 2014, using our prototype sparse microwave imaging radar system. This is the first reported imaging radar system and airborne experiment that specially designed for sparse microwave imaging. Sparse microwave imaging is a novel concept of radar imaging, it is mainly the combination of traditional radar imaging technology and newly developed sparse signal processing theory, achieving benefits in both improving the imaging quality of current microwave imaging systems and designing optimized sparse microwave imaging radar system to reduce system sampling rate towards the sparse target scenes. During recent years, many researchers focus on related topics of sparse microwave imaging, but rarely few paid attention to prototype system design and experiment. We introduce our prototype sparse microwave imaging radar system, including its system design, hardware considerations and signal processing methods. Several design principles should be considered during the system designing, including the sampling scheme, antenna, SNR, waveform, resolution, etc. We select jittered sampling in azimuth and uniform sampling in range to balance the system complexity and performance. The imaging algorithm is accelerated $\ell_q$ regularization algorithm. To test the prototype radar system and verify the effectiveness of sparse microwave imaging framework, airborne experiments are carried out using our prototype system and we achieve the first sparse microwave image successfully. We analyze the imaging performance of prototype sparse microwave radar system with different sparsities, sampling rates, SNRs and sampling schemes, using three-dimensional phase transit diagram as the evaluation tool.
Vehicular Blockage Modelling and Performance Analysis for mmWave V2V Communications
Authors: Kai Dong, Marouan Mizmizi, Dario Tagliaferri, Umberto Spagnolini
Date: 2021-10-20 00:00:00
Tasks: Scheduling

Vehicle-to-Everything (V2X) communications are revolutionizing the connectivity of transportation systems supporting safe and efficient road mobility. To meet the growing bandwidth eagerness of V2X services, millimeter-wave (e.g., 5G new radio over spectrum 26.50 - 48.20 GHz) and sub-THz (e.g., 120 GHz) frequencies are being investigated for the large available spectrum. Communication at these frequencies requires beam-type connectivity as a solution for the severe path loss attenuation. However, beams can be blocked, with negative consequences for communication reliability. Blockage prediction is necessary and challenging when the blocker is dynamic in high mobility scenarios such as Vehicle-to-Vehicle (V2V). This paper presents an analytical model to derive the unconditional probability of blockage in a highway multi-lane scenario. The proposed model accounts for the traffic density, the 3D dimensions of the vehicles, and the position of the antennas. Moreover, by setting the communication parameters and a target quality of service, it is possible to predict the signal-to-noise ratio distribution and the service probability, which can be used for resource scheduling. Exhaustive numerical results confirm the validity of the proposed model.
RSS-based Multiple Sources Localization with Unknown Log-normal Shadow Fading
Authors: Yueyan Chu, Wenbin Guo, Kangyong You, Lei Zhao, Tao Peng, Wenbo Wang
Date: 2021-10-20 00:00:00

Multi-source localization based on received signal strength (RSS) has drawn great interest in wireless sensor networks. However, the shadow fading term caused by obstacles cannot be separated from the received signal, which leads to severe error in location estimate. In this paper, we approximate the log-normal sum distribution through Fenton-Wilkinson method to formulate a non-convex maximum likelihood (ML) estimator with unknown shadow fading factor. In order to overcome the difficulty in solving the non-convex problem, we propose a novel algorithm to estimate the locations of sources. Specifically, the region is divided into $N$ grids firstly, and the multi-source localization is converted into a sparse recovery problem so that we can obtain the sparse solution. Then we utilize the K-means clustering method to obtain the rough locations of the off-grid sources as the initial feasible point of the ML estimator. Finally, an iterative refinement of the estimated locations is proposed by dynamic updating of the localization dictionary. The proposed algorithm can efficiently approach a superior local optimal solution of the ML estimator. It is shown from the simulation results that the proposed method has a promising localization performance and improves the robustness for multi-source localization in unknown shadow fading environments. Moreover, the proposed method provides a better computational complexity from $O(K^3N^3)$ to $O(N^3)$.
Monolithic Integrated Multiband Acoustic Devices on Heterogeneous Substrate for Sub-6 GHz RF-FEMs
Authors: Shibin Zhang, Hongyan Zhou, Pengcheng Zheng, Jinbo Wu, Liping Zhang, Zhongxu Li, Kai Huang, Xin Ou, Xi Wang
Date: 2021-10-20 00:00:00

Monolithic integration of multiband (1.4~ 6.0 GHz) RF acoustic devices were successfully demonstrated within the same process flow by using the lithium niobate (LN) thin film on silicon carbide (LNOSiC) substrate. A novel surface mode with sinking energy distribution was proposed, exhibiting reduced propagation loss. Surface wave and Lamb wave resonators with suppressed transverse modes and leaky modes were demonstrated, showing scalable resonances from 1.4 to 5.7 GHz, electromechanical coupling coefficients (k2) between 7.9% and 29.3%, and maximum Bode-Q (Qmax) larger than 3200. Arrayed filters with a small footprint (4.0 x 2.5 mm2) but diverse center frequencies (fc) and 3-dB fractional bandwidths (FBW) were achieved, showing fc from 1.4 to 6.0 GHz, FBW between 3.3% and 13.3%, and insertion loss (IL) between 0.59 and 2.10 dB. These results may promote the progress of hundred-filter sub-6 GHz RF front-end modules (RF-FEMs).
A Geometry-Based Stochastic Model for Truck Communication Channels in Freeway Scenarios
Authors: Chen Huang, Rui Wang, Cheng-Xiang Wang, Pan Tang, Andreas F. Molisch
Date: 2021-10-20 00:00:00
Tasks: Collision Avoidance

Vehicle-to-vehicle (V2V) wireless communication systems are fundamental in many intelligent transportation applications, e.g., traffic load control, driverless vehicle, and collision avoidance. Hence, developing appropriate V2V communication systems and standardization require realistic V2V propagation channel models. However, most existing V2V channel modeling studies focus on car-to-car channels; only a few investigate truck-to-car (T2C) or truck-to-truck (T2T) channels. In this paper, a hybrid geometry-based stochastic model (GBSM) is proposed for T2X (T2C or T2T) channels in freeway environments. Next, we parameterize this GBSM from the extensive channel measurements. We extract the multipath components (MPCs) by using a joint maximum likelihood estimation (RiMAX) and then cluster the MPCs based on their evolution patterns.We classify the determined clusters as line-of-sight, multiple-bounce reflections from static interaction objects (IOs), multiple-bounce reflections from mobile IOs, multiple-bounce reflections, and diffuse scattering. Specifically, we model multiple-bounce reflections as double clusters following the COST 273/COST2100 method. This article presents the complete parameterization of the channel model. We validate this model by contrasting the root-mean-square delay spread and the angular spreads of departure/arrival derived from the channel model with the outcomes directly derived from the measurements.
Continuous Authentication Using Mouse Movements, Machine Learning, and Minecraft
Authors: Nyle Siddiqui, Rushit Dave, Naeem Seliya
Date: 2021-10-15 00:00:00
Tasks: BIG-bench Machine Learning, Minecraft

Mouse dynamics has grown in popularity as a novel irreproducible behavioral biometric. Datasets which contain general unrestricted mouse movements from users are sparse in the current literature. The Balabit mouse dynamics dataset produced in 2016 was made for a data science competition and despite some of its shortcomings, is considered to be the first publicly available mouse dynamics dataset. Collecting mouse movements in a dull administrative manner as Balabit does may unintentionally homogenize data and is also not representative of realworld application scenarios. This paper presents a novel mouse dynamics dataset that has been collected while 10 users play the video game Minecraft on a desktop computer. Binary Random Forest (RF) classifiers are created for each user to detect differences between a specific users movements and an imposters movements. Two evaluation scenarios are proposed to evaluate the performance of these classifiers; one scenario outperformed previous works in all evaluation metrics, reaching average accuracy rates of 92%, while the other scenario successfully reported reduced instances of false authentications of imposters.
Computing the Probability of a Financial Market Failure: A New Measure of Systemic Risk
Authors: Robert Jarrow, Philip Protter, Alejandra Quintos
Date: 2021-10-21 00:00:00

This paper characterizes the probability of a market failure defined as the default of two or more globally systemically important banks (G-SIBs) in a small interval of time. The default probabilities of the G-SIBs are correlated through the possible existence of a market-wide stress event. The characterization employs a multivariate Cox process across the G-SIBs, which allows us to relate our work to the existing literature on intensity-based models. Various theorems related to market failure probabilities are derived, including the probability of a market failure due to two banks defaulting over the next infinitesimal interval, the probability of a catastrophic market failure, the impact of increasing the number of G-SIBs in an economy, and the impact of changing the initial conditions of the economy's state variables. We also show that if there are too many G-SIBs, a market failure is inevitable, i.e., the probability of a market failure tends to 1.