pwc-archive/papers-with-abstracts huggingface dataset:
Single-cell Bayesian deconvolution
Authors: Gabriel Torregrosa, David Oriola, Vikas Trivedi, Jordi Garcia-Ojalvo
Date: 2022-02-13 00:00:00

Individual cells exhibit substantial heterogeneity in protein abundance and activity, which is frequently reflected in broad distributions of fluorescently labeled reporters. Since all cellular components are intrinsically fluorescent to some extent, the observed distributions contain background noise that masks the natural heterogeneity of cellular populations. This limits our ability to characterize cell-fate decision processes that are key for development, immune response, tissue homeostasis, and many other biological functions. It is therefore important to separate the contributions from signal and noise in single-cell measurements. Addressing this issue rigorously requires deconvolving the noise distribution from the signal, but approaches in that direction are still limited. Here we present a non-parametric Bayesian formalism that performs such a deconvolution efficiently on multidimensional measurements, in a way that allows estimating confidence intervals precisely. We use the approach to study the expression of the mesodermal transcription factor Brachyury in mouse embryonic stem cells undergoing differentiation.
Do cities have a unique magnetic pulse?
Authors: Vincent Dumont, Trevor A. Bowen, Roger Roglans, Gregory Dobler, Mohit S. Sharma, Andy Karpf, Stuart D. Bale, Arne Wickenbrock, Elena Zhivun, Tom Kornack, Jonathan S. Wurtele, Dmitry Budker
Date: 2022-02-12 00:00:00
Tasks: Sensitivity

We present a comparative analysis of urban magnetic fields between two American cities: Berkeley (California) and Brooklyn Borough of New York City (New York). Our analysis uses data taken over a four-week period during which magnetic field data were continuously recorded using a fluxgate magnetometer of 70 pT/$\sqrt{\mathrm{Hz}}$ sensitivity. We identified significant differences in the magnetic signatures. In particular, we noticed that Berkeley reaches a near-zero magnetic field activity at night whereas magnetic activity in Brooklyn continues during nighttime. We also present auxiliary measurements acquired using magnetoresistive vector magnetometers (VMR), with sensitivity of 300 pT/$\sqrt{\mathrm{Hz}}$, and demonstrate how cross-correlation, and frequency-domain analysis, combined with data filtering can be used to extract urban magnetometry signals and study local anthropogenic activities. Finally, we discuss the potential of using magnetometer networks to characterize the global magnetic field of cities and give directions for future development.
Wearable Conformal Fiber Sensor for High Fidelity Physiological Measurements
Authors: Alessio Stefani, Ivan D. Rukhlenko, Antoine F. J. Runge, Maryanne C. J. Large, Simon C. Fleming
Date: 2022-01-24 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Wearable devices are becoming increasingly important, addressing needs in both the fitness and the medical markets. In this paper, we describe a novel sensing platform based on a hollow-core polyurethane optical fiber, operating through capillary guidance, that acts as a conformal sensor of pressure or deformation. The novelty is achieved by combining a simple structure (hollow capillary) and a simple detection technique (intensity-based measurement) with unconventional material properties (extreme deformability and high optical absorbance). Used on the wrist and ankle, the sensor allows detailed features of the cardiac pulse wave to be identified with high fidelity, while on the chest it allows the simultaneous measurement of breathing rate and walking cadence. Used together, an array of such sensors (with others) could be incorporated into clothing and provide physiologically rich real-time data for health monitoring.
Flexible learning of quantum states with generative query neural networks
Authors: Yan Zhu, Ya-Dong Wu, Ge Bai, Dong-Sheng Wang, Yuexuan Wang, Giulio Chiribella
Date: 2022-02-14 00:00:00

Deep neural networks are a powerful tool for the characterization of quantum states. Existing networks are typically trained with experimental data gathered from the specific quantum state that needs to be characterized. But is it possible to train a neural network offline and to make predictions about quantum states other than the ones used for the training? Here we introduce a model of network that can be trained with classically simulated data from a fiducial set of states and measurements, and can later be used to characterize quantum states that share structural similarities with the states in the fiducial set. With little guidance of quantum physics, the network builds its own data-driven representation of quantum states, and then uses it to predict the outcome statistics of quantum measurements that have not been performed yet. The state representation produced by the network can also be used for tasks beyond the prediction of outcome statistics, including clustering of quantum states and identification of different phases of matter. Our network model provides a flexible approach that can be applied to online learning scenarios, where predictions must be generated as soon as experimental data become available, and to blind learning scenarios where the learner has only access to an encrypted description of the quantum hardware.
Equal Predictive Ability Tests Based on Panel Data with Applications to OECD and IMF Forecasts
Authors: Oguzhan Akgun, Alain Pirotte, Giovanni Urga, Zhenlin Yang
Date: 2020-03-05 00:00:00

We propose two types of equal predictive ability (EPA) tests with panels to compare the predictions made by two forecasters. The first type, namely $S$-statistics, focuses on the overall EPA hypothesis which states that the EPA holds on average over all panel units and over time. The second, called $C$-statistics, focuses on the clustered EPA hypothesis where the EPA holds jointly for a fixed number of clusters of panel units. The asymptotic properties of the proposed tests are evaluated under weak and strong cross-sectional dependence. An extensive Monte Carlo simulation shows that the proposed tests have very good finite sample properties even with little information about the cross-sectional dependence in the data. The proposed framework is applied to compare the economic growth forecasts of the OECD and the IMF, and to evaluate the performance of the consumer price inflation forecasts of the IMF.
Simple Models and Biased Forecasts
Authors: Pooya Molavi
Date: 2022-02-14 00:00:00
Tasks: State Space Models, Time Series, Time Series Analysis

This paper proposes a framework in which agents are constrained to use simple models to forecast economic variables and characterizes the resulting biases. It considers agents who can only entertain state-space models with no more than d states, where d measures the intertemporal complexity of a model. Agents are boundedly rational in that they can only consider models that are too simple to capture the true process, yet they use the best model among those considered. Using simple models adds persistence to forward-looking decisions and increases the comovement among them. This mechanism narrows the gap between business-cycle theory and data. In a new neoclassical synthesis model, the assumption that agents use simple models fits the data much better than the rational-expectations hypothesis. Moreover, simple models simultaneously resolve the Barro-King and forward guidance puzzles while improving the propagation of TFP shocks.
Order of Commitments in Bayesian Persuasion with Partial-informed Senders
Authors: Shih-Tang Su, Vijay G. Subramanian
Date: 2022-02-14 00:00:00

The commitment power of senders distinguishes Bayesian persuasion problems from other games with (strategic) communication. Persuasion games with multiple senders have largely studied simultaneous commitment and signalling settings. However, many real-world instances with multiple senders have sequential signalling. In such contexts, commitments can also be made sequentially, and then the order of commitment by the senders -- the sender signalling last committing first or last -- could significantly impact the equilibrium payoffs and strategies. For a two-sender persuasion game where the senders are partially aware of the state of the world, we find necessary and sufficient conditions to determine when different commitment orders yield different payoff profiles. In particular, for the two-sender setting, we show that different payoff profiles arise if two properties hold: 1) the two senders are willing to collaborate in persuading the receiver in some state(s); and 2) the sender signalling second can carry out a credible threat when committing first such that the other sender's room to design signals gets constrained.
Observational Learning with Competitive Prices
Authors: Zikai Xu
Date: 2022-02-13 00:00:00
Tasks: Informativeness

Will people eventually learn the value of an asset through observable information? This paper studies observational learning in a market with competitive prices. Comparing a market with public signals and a market with private signals in a sequential trading model, we find that Pairwise Informativeness (PI) is the sufficient and necessary learning condition for a market with public signals; and Avery and Zemsky Condition (AZC) is the sufficient and necessary learning condition for a market with private signals. Moreover, when the number of states is 2 or 3, PI and AZC are equivalent. And when the number of states is greater than 3, PI and Monotonic Likelihood Ratio Property (MLRP) together imply asymptotic learning in the private signal case.
Equilibrium Defaultable Corporate Debt and Investment
Authors: Hong Chen, Murray Zed Frank
Date: 2022-02-11 00:00:00

In dynamic capital structure models with an investor break-even condition, the firm's Bellman equation may not generate a contraction mapping, so the standard existence and uniqueness conditions do not apply. First, we provide an example showing the problem in a classical trade-off model. The firm can issue one-period defaultable debt, invest in capital and pay a dividend. If the firm cannot meet the required debt payment, it is liquidated. Second, we show how to use a dual to the original problem and a change of measure, such that existence and uniqueness can be proved. In the unique Markov-perfect equilibrium, firm decisions reflect state-dependent capital and debt targets. Our approach may be useful for other dynamic firm models that have an investor break-even condition.
FrAUG: A Frame Rate Based Data Augmentation Method for Depression Detection from Speech Signals
Authors: Vijay Ravi, Jinhan Wang, Jonathan Flint, Abeer Alwan
Date: 2022-02-11 00:00:00
Tasks: Data Augmentation, Depression Detection

In this paper, a data augmentation method is proposed for depression detection from speech signals. Samples for data augmentation were created by changing the frame-width and the frame-shift parameters during the feature extraction process. Unlike other data augmentation methods (such as VTLP, pitch perturbation, or speed perturbation), the proposed method does not explicitly change acoustic parameters but rather the time-frequency resolution of frame-level features. The proposed method was evaluated using two different datasets, models, and input acoustic features. For the DAIC-WOZ (English) dataset when using the DepAudioNet model and mel-Spectrograms as input, the proposed method resulted in an improvement of 5.97% (validation) and 25.13% (test) when compared to the baseline. The improvements for the CONVERGE (Mandarin) dataset when using the x-vector embeddings with CNN as the backend and MFCCs as input features were 9.32% (validation) and 12.99% (test). Baseline systems do not incorporate any data augmentation. Further, the proposed method outperformed commonly used data-augmentation methods such as noise augmentation, VTLP, Speed, and Pitch Perturbation. All improvements were statistically significant.
Sequential Doppler Shift based Optimal Localization and Synchronization with TOA
Authors: Sihao Zhao, Ningyan Guo, Xiao-Ping Zhang, Xiaowei Cui, Mingquan Lu
Date: 2022-02-14 00:00:00

Doppler shift is an important measurement for localization and synchronization (LAS), and is available in various practical systems. Existing studies on LAS techniques in a time division broadcast LAS system (TDBS) only use sequential time-of-arrival (TOA) measurements from the broadcast signals. In this paper, we develop a new optimal LAS method in the TDBS, namely LAS-SDT, by taking advantage of the sequential Doppler shift and TOA measurements. It achieves higher accuracy compared with the conventional TOA-only method for user devices (UDs) with motion and clock drift. Another two variant methods, LAS-SDT-v for the case with UD velocity aiding, and LAS-SDT-k for the case with UD clock drift aiding, are developed. We derive the Cramer-Rao lower bound (CRLB) for these different cases. We show analytically that the accuracies of the estimated UD position, clock offset, velocity and clock drift are all significantly higher than those of the conventional LAS method using TOAs only. Numerical results corroborate the theoretical analysis and show the optimal estimation performance of the LAS-SDT.
Jointly Learned Symbol Detection and Signal Reflection in RIS-Aided Multi-user MIMO Systems
Authors: Liuhang Wang, Nir Shlezinger, George C. Alexandropoulos, Haiyang Zhang, Baoyun Wang, Yonina C. Elda
Date: 2022-02-14 00:00:00

Reconfigurable Intelligent Surfaces (RISs) are regarded as a key technology for future wireless communications, enabling programmable radio propagation environments. However, the passive reflecting feature of RISs induces notable challenges on channel estimation, making coherent symbol detection a challenging task. In this paper, we consider the uplink of RIS-aided multi-user Multiple-Input Multiple-Output (MIMO) systems and propose a Machine Learning (ML) approach to jointly design the multi-antenna receiver and configure the RIS reflection coefficients, which does not require explicit full knowledge of the channel input-output relationship. Our approach devises a ML-based receiver, while the configurations of the RIS reflection patterns affecting the underlying propagation channel are treated as hyperparameters. Based on this system design formulation, we propose a Bayesian ML framework for optimizing the RIS hyperparameters, according to which the transmitted pilots are directly used to jointly tune the RIS and the multi-antenna receiver. Our simulation results demonstrate the capability of the proposed approach to provide reliable communications in non-linear channel conditions corrupted by Gaussian noise.
Modelling Underwater Acoustic Propagation using One-way Wave Equations
Authors: Indrakshi Dey
Date: 2022-02-14 00:00:00

The primary contribution of this paper is to characterize the propagation of acoustic signal carrying information through any medium and the interaction of the travelling acoustic signal with the surrounding medium. We will use the concept of damped harmonic oscillator to model the medium and Milne's oscillator technique to map the interaction of the acoustic signal with the medium. The acoustic signal itself will be modelled using the one-way wave equation formulated in terms of acoustic pressure and velocity of acoustic waves through the medium. Using the above-mentioned concepts, we calculated the effective signal strength, phase shift and time period of the communicated signal. Numerical results are generated to present the evolution of signal strength and received signal envelope in underwater environment.
Model-Based Neural Network and Its Application to Line Spectral Estimation
Authors: Yi Jiang, Tianyi Zhang, Wei zhang
Date: 2022-02-14 00:00:00
Tasks: parameter estimation

This paper presents the concept of "model-based neural network"(MNN), which is inspired by the classic artificial neural network (ANN) but for different usages. Instead of being used as a data-driven classifier, a MNN serves as a modeling tool with artfully defined inputs, outputs, and activation functions which have explicit physical meanings. Owing to the same layered form as an ANN, a MNN can also be optimized using the back-propagation (BP) algorithm. As an interesting application, the classic problem of line spectral estimation can be modeled by a MNN. We propose to first initialize the MNN by the fast Fourier transform (FFT) based spectral estimation, and then optimize the MNN by the BP algorithm, which automatically yields the maximum likelihood (ML) parameter estimation of the frequency spectrum. We also design a method of merging and pruning the hidden-layer nodes of the MNN, which can be used for model-order selection, i.e., to estimate the number of sinusoids. Numerical simulations verify the effectiveness of the proposed method.
Ultrasonic Backscatter Communication for Implantable Medical Devices
Authors: Qianqian Wang, Quansheng Guan, Julian Cheng, Yuankun Tang
Date: 2022-02-14 00:00:00
Tasks: TAG

This paper proposes an ultrasonic backscatter communication (UsBC) system for passive implantable medical devices (IMDs) that can operate without batteries, enabling versatile revolutionary applications for future healthcare. The proposed UsBC system consists of a reader and a tag. The reader sends interrogation pulses to the tag. The tag backscatters the pulses based on the piezoelectric effect of a piezo transducer. We present several basic modulation schemes for UsBC by impedance matching of the piezo transducer. To mitigate the interference of other scatters in the human body, the tag transmits information bits by codeword mapping, and the reader performs codeword matching before energy detection in the reader. We further derive the theoretical bit-error rate (BER) expression. Monte Carlo simulations verify the theoretical analysis and show that passive UsBC can achieve low BER and low complexity, which is desirable for size- and energy-constrained IMDs.
Robust Time-Varying Graph Signal Recovery for Dynamic Physical Sensor Network Data
Authors: Eisuke Yamagata, Kazuki Naganuma, Shunsuke Ono
Date: 2022-02-13 00:00:00
Tasks: Graph Learning, Missing Values

We propose a time-varying graph signal recovery method for estimating the true time-varying graph signal from corrupted observations by leveraging dynamic graphs. Most of the conventional methods for time-varying graph signal recovery have been proposed under the assumption that the underlying graph that houses the signals is static. However, in light of rapid advances in sensor technology, the assumption that sensor networks are time-varying like the signals is becoming a very practical problem setting. In this paper, we focus on such cases and formulate dynamic graph signal recovery as a constrained convex optimization problem that simultaneously estimates both time-varying graph signals and sparsely modeled outliers. In our formulation, we use two types of regularizations, time-varying graph Laplacian-based and temporal differencebased, and also separately modeled missing values with known positions and unknown outliers to achieve robust estimations from highly degraded data. In addition, an algorithm is developed to efficiently solve the optimization problem based on a primaldual splitting method. Extensive experiments on simulated drone remote sensing data and real-world sea surface temperature data demonstrate the advantages of the proposed method over existing methods.
Significant Low-dimensional Spectral-temporal Features for Seizure Detection
Authors: Xucun Yan, Dongping Yang, Zihuai Lin, Branka Vucetic
Date: 2022-02-13 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Onset Detection, Seizure Detection

Seizure onset detection in electroencephalography (EEG) signals is a challenging task due to the non-stereotyped seizure activities as well as their stochastic and non-stationary characteristics in nature. Joint spectral-temporal features are believed to contain sufficient and powerful feature information for absence seizure detection. However, the resulting high-dimensional features involve redundant information and require heavy computational load. Here, we discover significant low-dimensional spectral-temporal features in terms of mean-standard deviation of wavelet transform coefficient (MS-WTC), based on which a novel absence seizure detection framework is developed. The EEG signals are transformed into the spectral-temporal domain, with their low-dimensional features fed into a convolutional neural network. Superior detection performance is achieved on the widely-used benchmark dataset as well as a clinical dataset from the Chinese 301 Hospital. For the former, seven classification tasks were evaluated with the accuracy from 99.8% to 100.0%, while for the latter, the method achieved a mean accuracy of 94.7%, overwhelming other methods with low-dimensional temporal and spectral features. Experimental results on two seizure datasets demonstrate reliability, efficiency and stability of our proposed MS-WTC method, validating the significance of the extracted low-dimensional spectral-temporal features.
Individual-based and continuum models of phenotypically heterogeneous growing cell populations
Authors: Fiona R Macfarlane, Xinran Ruan, Tommaso Lorenzi
Date: 2022-02-14 00:00:00

Existing studies comparing individual-based models of growing cell populations and their continuum counterparts have mainly focused on homogeneous populations, in which all cells have the same phenotypic characteristics. However, significant intercellular phenotypic variability is commonly observed in cellular systems. Therefore, we develop here an individual-based model for the growth of phenotypically heterogeneous cell populations. In this model, the phenotypic state of each cell is described by a structuring variable that captures intercellular variability in cell proliferation and migration rates. The model tracks the spatial evolutionary dynamics of single cells, which undergo pressure-dependent proliferation, heritable phenotypic changes and directional movement in response to pressure differentials. We formally show that the continuum limit of this model comprises a non-local partial differential equation for the cell population density, which generalises earlier models of growing cell populations. Results of the individual-based model illustrate how proliferation-migration tradeoffs shaping the evolution of single cells can lead to the formation of travelling waves at the population level where highly-mobile cells locally dominate at the invasive front, while more-proliferative cells are found at the rear. We demonstrate that there is an excellent quantitative agreement between these results and the results of numerical simulations and formal travelling-wave analysis of the continuum model, when sufficiently large cell numbers are considered. We provide numerical evidence of scenarios in which the predictions of the two models may differ due to demographic stochasticity, which cannot be captured by the continuum model. This indicates the importance of integrating individual-based and continuum approaches when modelling the growth of phenotypically heterogeneous cell populations.