pwc-archive/papers-with-abstracts huggingface dataset:
A Strong Sustainability Paradigm Based Analytical Hierarchy Process (SSP-AHP) Method to Evaluate Sustainable Healthcare Systems
Authors: Jarosław Wątróbski, Aleksandra Bączkiewicz, Iga Rudawska
Date: 2023-05-13 00:00:00
Tasks: Benchmarking

The recent studies signify the growing concern of researchers towards monitoring and measuring sustainability performance at various levels and in many fields, including healthcare. However, there is no agreed approach to assessing the sustainability of health systems. Moreover, social indicators are less developed and less succinct. Therefore, the authors seek to map sustainable reference values in healthcare and propose a conceptual and structured framework that can guide the measurement of the social sustainability-oriented health systems. Based on a new multi-criteria method called Strong Sustainability Paradigm based Analytical Hierarchy Process, (SSP-AHP), the presented approach opens the availability for systems' comparison and benchmarking. The Strong Sustainability Paradigm incorporated into the multi-criteria evaluation method prevents the exchangeability of criteria by promoting alternatives that achieve good performance values on all criteria, implying sustainability. The research results offer insights into the core domains, sub-domains, and indicators supporting a more comprehensive assessment of the social sustainability of health systems. The framework constructed in this study consists of five major areas: equity, quality, responsiveness, financial coverage, and adaptability. The proposed set of indicators can also serve as a reference instrument, providing transparency about core aspects of performance to be measured and reported, as well as supporting policy-makers in decisions regarding sectoral strategies in healthcare. Our findings suggest that the most socially sustainable systems are Nordic countries. They offer a high level of social and financial protection, achieving very good health outcomes. On the other hand, the most unsustainable systems located in central and eastern European countries.
Frame-wise and overlap-robust speaker embeddings for meeting diarization
Authors: Tobias Cord-Landwehr, Christoph Boeddeker, Cătălin Zorilă, Rama Doddipatla, Reinhold Haeb-Umbach
Date: 2023-06-01 00:00:00

Using a Teacher-Student training approach we developed a speaker embedding extraction system that outputs embeddings at frame rate. Given this high temporal resolution and the fact that the student produces sensible speaker embeddings even for segments with speech overlap, the frame-wise embeddings serve as an appropriate representation of the input speech signal for an end-to-end neural meeting diarization (EEND) system. We show in experiments that this representation helps mitigate a well-known problem of EEND systems: when increasing the number of speakers the diarization performance drop is significantly reduced. We also introduce block-wise processing to be able to diarize arbitrarily long meetings.
Speaker-independent Speech Inversion for Estimation of Nasalance
Authors: Yashish M. Siriwardena, Carol Espy-Wilson, Suzanne Boyce, Mark K. Tiede, Liran Oren
Date: 2023-05-31 00:00:00

The velopharyngeal (VP) valve regulates the opening between the nasal and oral cavities. This valve opens and closes through a coordinated motion of the velum and pharyngeal walls. Nasalance is an objective measure derived from the oral and nasal acoustic signals that correlate with nasality. In this work, we evaluate the degree to which the nasalance measure reflects fine-grained patterns of VP movement by comparison with simultaneously collected direct measures of VP opening using high-speed nasopharyngoscopy (HSN). We show that nasalance is significantly correlated with the HSN signal, and that both match expected patterns of nasality. We then train a temporal convolution-based speech inversion system in a speaker-independent fashion to estimate VP movement for nasality, using nasalance as the ground truth. In further experiments, we also show the importance of incorporating source features (from glottal activity) to improve nasality prediction.
An algebraic theory to discriminate qualia in the brain
Authors: Yoshiyuki Ohmura, Wataru Shimaya, Yasuo Kuniyoshi
Date: 2023-05-31 00:00:00
Tasks: Representation Learning

The mind-brain problem is to bridge relations between in higher-level mental events and in lower-level neural events. To address this, some mathematical models have been proposed to explain how the brain can represent the discriminative structure of qualia, but they remain unresolved due to a lack of validation methods. To understand the qualia discrimination mechanism, we need to ask how the brain autonomously develops such a mathematical structure using the constructive approach. In unsupervised representation learning, independence between axes is generally used to constrain the latent vector but independence between axes cannot explain qualia type discrimination because independent axes cannot distinguish between inter-qualia type independence (e.g., vision and touch) and intra-qualia type independence (e.g., green and red). We hypothesised that inter-axis independence must be weakened in order to discriminate qualia types. To solve the problem, we formulate an algebraic independence to link it to the other-qualia-type invariant transformations, whose transformation value is a vector space rather than a scalar. In addition, we show that a brain model that learns to satisfy the algebraic independence between neural networks separates the latent space into multiple metric spaces corresponding to qualia types, suggesting that our theory can contribute to the further development of the mathematical theory of consciousness.
Detecting Heart Disease from Multi-View Ultrasound Images via Supervised Attention Multiple Instance Learning
Authors: Zhe Huang, Benjamin S. Wessler, Michael C. Hughes
Date: 2023-05-25 00:00:00
Tasks: Contrastive Learning, Multiple Instance Learning

Aortic stenosis (AS) is a degenerative valve condition that causes substantial morbidity and mortality. This condition is under-diagnosed and under-treated. In clinical practice, AS is diagnosed with expert review of transthoracic echocardiography, which produces dozens of ultrasound images of the heart. Only some of these views show the aortic valve. To automate screening for AS, deep networks must learn to mimic a human expert's ability to identify views of the aortic valve then aggregate across these relevant images to produce a study-level diagnosis. We find previous approaches to AS detection yield insufficient accuracy due to relying on inflexible averages across images. We further find that off-the-shelf attention-based multiple instance learning (MIL) performs poorly. We contribute a new end-to-end MIL approach with two key methodological innovations. First, a supervised attention technique guides the learned attention mechanism to favor relevant views. Second, a novel self-supervised pretraining strategy applies contrastive learning on the representation of the whole study instead of individual images as commonly done in prior literature. Experiments on an open-access dataset and an external validation set show that our approach yields higher accuracy while reducing model size.
Coherent FDA Receiver and Joint Range-Space-Time Processing
Authors: Wenkai Jia, Andreas Jakobsson, Wen-Qin Wang
Date: 2023-06-01 00:00:00

When a target is masked by mainlobe clutter with the same Doppler frequency, it is difficult for conventional airborne radars to determine whether a target is present in a given observation using regular space-time adaptive processing techniques. Different from phased-array and multiple-input multiple-output (MIMO) arrays, frequency diverse arrays (FDAs) employ frequency offsets across the array elements, delivering additional range-controllable degrees of freedom, potentially enabling suppression for this kind of clutter. However, the reception of coherent FDA systems employing small frequency offsets and achieving high transmit gain can be further improved. To this end, this work proposes an coherent airborne FDA radar receiver that explores the orthogonality of echo signals in the Doppler domain, allowing a joint space-time processing module to be deployed to separate the aliased returns. The resulting range-space-time adaptive processing allows for a preferable detection performance for coherent airborne FDA radars as compared to current alternative techniques.
Low-Cost GNSS Simulators with Wireless Clock Synchronization for Indoor Positioning
Authors: Woohyun Kim, Jiwon Seo
Date: 2023-06-01 00:00:00
Tasks: Outdoor Positioning, Position

In regions where global navigation satellite systems (GNSS) signals are unavailable, such as underground areas and tunnels, GNSS simulators can be deployed for transmitting simulated GNSS signals. Then, a GNSS receiver in the simulator coverage outputs the position based on the received GNSS signals (e.g., Global Positioning System (GPS) L1 signals in this study) transmitted by the corresponding simulator. This approach provides periodic position updates to GNSS users while deploying a small number of simulators without modifying the hardware and software of user receivers. However, the simulator clock should be synchronized to the GNSS satellite clock to generate almost identical signals to the live-sky GNSS signals, which is necessary for seamless indoor and outdoor positioning handover. The conventional clock synchronization method based on the wired connection between each simulator and an outdoor GNSS antenna causes practical difficulty and increases the cost of deploying the simulators. This study proposes a wireless clock synchronization method based on a private time server and time delay calibration. Additionally, we derived the constraints for determining the optimal simulator coverage and separation between adjacent simulators. The positioning performance of the proposed GPS simulator-based indoor positioning system was demonstrated in the underground testbed for a driving vehicle with a GPS receiver and a pedestrian with a smartphone. The average position errors were 3.7 m for the vehicle and 9.6 m for the pedestrian during the field tests with successful indoor and outdoor positioning handovers. Since those errors are within the coverage of each deployed simulator, it is confirmed that the proposed system with wireless clock synchronization can effectively provide periodic position updates to users where live-sky GNSS signals are unavailable.
CRAP: Clutter Removal with Acquisitions Under Phase Noise
Authors: Marcus Henninger, Silvio Mandelli, Artjom Grudnitsky, Thorsten Wild, Stephan ten Brink
Date: 2023-06-01 00:00:00
Tasks: Integrated sensing and communication, ISAC

The emergence of Integrated Sensing and Communication (ISAC) in future 6G networks comes with a variety of challenges to be solved. One of those is clutter removal, which should be applied to remove the influence of unwanted components, scattered by the environment, in the acquired sensing signal. While legacy radar systems already implement different clutter removal algorithms, ISAC requires techniques that are tailored to the envisioned use cases and the specific challenges that communications deployments bring along, like phase noise due to clock errors between transmitter and receiver. To that end, in this work we introduce Clutter Removal with Acquisitions Under Phase Noise (CRAP). We propose to vectorize the time-frequency channel acquired in a radio frame in a high-dimensional space. In an offline clutter acquisition step, singular value decomposition is used to determine the major clutter components. At runtime, the clutter is then estimated and removed by a subspace projection of the acquired radio frame onto the clutter components. Simulation results prove that CRAP offers benefits over prior art techniques robust to phase noise. In particular, our proposal does not suppress zero Doppler information, thereby enabling the detection of slow targets. Moreover, we show CRAP's real-time applicability in a millimeter-wave ISAC proof of concept, where a pedestrian is tracked in a cluttered lab environment.
Space-Time Phase Coupling in STMM-based Wireless Communications
Authors: Marouan Mizmizi, Dario Tagliaferri, Marco Di Renzo, Umberto Spagnolini
Date: 2023-06-01 00:00:00

Space-time modulated metasurfaces (STMMs) are a recently proposed generalization of reconfigurable intelligent surfaces, which include a proper time-varying phase at the metasurface elements, enabling higher flexibility and control of the reflected signals. The spatial component can be designed to control the direction of reflection, while the temporal one can be adjusted to change the frequency of the reflected signal or to convey information. However, the coupling between the spatial and temporal phases at the STMM can adversely affect its performance. Therefore, this paper analyzes the system parameters that affect the space-time coupling. Furthermore, two methods for space-time decoupling are investigated. Numerical results highlight the effectiveness of the proposed decoupling methods and reveal that the space-time phase coupling increases with the bandwidth of the temporal phase, the size of the STMM, and with grazing angles of incidence onto the STMM.
Fast Variational Block-Sparse Bayesian Learning
Authors: Jakob Möderl, Erik Leitinger, Bernard H. Fleury, Franz Pernkopf, Klaus Witrisal
Date: 2023-06-01 00:00:00
Tasks: Scheduling

We present a variational Bayesian (VB) implementation of block-sparse Bayesian learning (BSBL), which approximates the posterior probability density function (PDF) of the latent variables a factorzied proxy PDFs. The prior distribution of the BSBL hyperparameters is selected to be the generalized inverse Gaussian distribution. This choice unifies commonly used hyperpriors, e.g. the Gamma distribution, inverse Gamma distribution, and Jeffrey's improper prior. The resulting variational BSBL (VA-BSBL) algorithm updates in an iterative manner the proxi PDFs of the weights and hyperparameters. The proxy PDF of the weights only depends on the proxy PDFs of the hyperparameters through the means of the latter PDFs. We adopt a scheduling of the iterations in which the proxi PDF of a single hyperparameter and the proxi PDF of the weights are cyclically updated ad infinitum. The resulting sequence of means of the hyperparameter proxi PDFs computed in this way can be expressed as a nonlinear first-order recurrence relation. Hence, the fixed points of the recurrence relation can be used for single-step check for convergence of this sequence. Including this check procedure in the VA-BSBL, we obtain a fast version of the algorithm, coined fast-BSBL (F-BSBL), which shows a two-order-of-magnitude faster runtime. Additionally, we analyse a necessary condition for the existence of fixed points and show that only certain improper generalized inverse Gaussian hyperpriors induce a sparse estimate of the weights. Finally, we generalize this equivalence to BSBL. Specifically, we show that the presented VA-BSBL is equivalent to expectation-maximization (EM)-based Type-II-BSBL. As a result, the fast versions of them, i.e. FBSBL for the former and Type-II-BSBL using coordinate ascent for the latter, are equivalent as well. This result allows for reinterpreting previous works on BSBL within our framework.
A 3-step Low-latency Low-Power Multichannel Time-to-Digital Converter based on Time Residual Amplifier
Authors: Florent Bouyjou
Date: 2023-06-01 00:00:00

This paper proposes and evaluates a novel architecture for a low-power Time-to-Digital Converter with high resolution, optimized for both integration in multichannel chips and high rate operation (40 Mconversion/s/channel). This converter is based on a three-step architecture. The first step uses a counter whereas the following ones are based on two kinds of Delay Line structures. A programmable time amplifier is used between the second and third steps to reach the final resolution of 24.4 ps in the standard mode of operation. The system makes use of common continuously stabilized master blocks that control trimmable slave blocks, in each channel, against the effects of global PVT variations. Thanks to this structure, the power consumption of a channel is considerably reduced when it does not process a hit, and limited to 2.2 mW when it processes a hit. In the 130 nm CMOS technology used for the prototype, the area of a TDC channel is only 0.051 mm2. This compactness combined with low power consumption is a key advantage for integration in multi-channel front-end chips. The performance of this new structure has been evaluated on prototype chips. Measurements show excellent timing performance over a wide range of operating temperatures (-40{\deg}C to 60{\deg}C) in agreement with our expectations. For example, the measured timing integral nonlinearity is better than 1 LSB (25 ps) and the overall timing precision is better than 21 ps RMS.
Stochastic Analysis of LMS Algorithm with Delayed Block Coefficient Adaptation
Authors: Mohd. Tasleem Khan, Oscar Gustafsson
Date: 2023-05-31 00:00:00

In high sample-rate applications of the least-mean-square (LMS) adaptive filtering algorithm, pipelining or/and block processing is required. As opposed to earlier work, pipelining and block processing are jointly considered to obtain what we refer to as the delayed block LMS (DBLMS) algorithm. Different stochastic analyses for the steady and transient states to estimate the step-size bound, adaptation accuracy, and adaptation speed based on the recursive relation of delayed block excess mean square error (MSE) are presented. The effect of different amounts of pipelining delays and block sizes on the adaptation accuracy and speed of the adaptive filter with different filter lengths and speed-ups are studied. It is concluded that for a constant speed-up, a large delay and small block size lead to a slower convergence rate compared to a small delay and large block size with almost the same steady-state MSE. Monte Carlo simulations indicate a good agreement with the proposed estimates for Gaussian inputs.
Efficient Stochastic Approximation of Minimax Excess Risk Optimization
Authors: Lijun Zhang, Haomin Bai, Wei-Wei Tu, Ping Yang, Yao Hu
Date: 2023-05-31 00:00:00

While traditional distributionally robust optimization (DRO) aims to minimize the maximal risk over a set of distributions, Agarwal and Zhang (2022) recently proposed a variant that replaces risk with excess risk. Compared to DRO, the new formulation$\unicode{x2013}$minimax excess risk optimization (MERO) has the advantage of suppressing the effect of heterogeneous noise in different distributions. However, the choice of excess risk leads to a very challenging minimax optimization problem, and currently there exists only an inefficient algorithm for empirical MERO. In this paper, we develop efficient stochastic approximation approaches which directly target MERO. Specifically, we leverage techniques from stochastic convex optimization to estimate the minimal risk of every distribution, and solve MERO as a stochastic convex-concave optimization (SCCO) problem with biased gradients. The presence of bias makes existing theoretical guarantees of SCCO inapplicable, and fortunately, we demonstrate that the bias, caused by the estimation error of the minimal risk, is under-control. Thus, MERO can still be optimized with a nearly optimal convergence rate. Moreover, we investigate a practical scenario where the quantity of samples drawn from each distribution may differ, and propose a stochastic approach that delivers distribution-dependent convergence rates.
Muller's ratchet in a near-critical regime: tournament versus fitness proportional selection
Authors: Jan Lukas Igelbrink, Adrián González Casanova, Charline Smadi, Anton Wakolbinger
Date: 2023-06-01 00:00:00

Muller's ratchet, in its prototype version, models a haploid, asexual population whose size~$N$ is constant over the generations. Slightly deleterious mutations are acquired along the lineages at a constant rate, and individuals carrying less mutations have a selective advantage. The classical variant considers {\it fitness proportional} selection, but other fitness schemes are conceivable as well. Inspired by the work of Etheridge et al. ([EPW09]) we propose a parameter scaling which fits well to the ``near-critical'' regime that was in the focus of [EPW09] (and in which the mutation-selection ratio diverges logarithmically as $N\to \infty$). Using a Moran model, we investigate the``rule of thumb'' given in [EPW09] for the click rate of the ``classical ratchet'' by putting it into the context of new results on the long-time evolution of the size of the best class of the ratchet with (binary) tournament selection, which (other than that of the classical ratchet) follows an autonomous dynamics up to the time of its extinction. In [GSW23] it was discovered that the tournament ratchet has a hierarchy of dual processes which can be constructed on top of an Ancestral Selection graph with a Poisson decoration. For a regime in which the mutation/selection-ratio remains bounded away from 1, this was used in [GSW23] to reveal the asymptotics of the click rates as well as that of the type frequency profile between clicks. We will describe how these ideas can be extended to the near-critical regime in which the mutation-selection ratio of the tournament ratchet converges to 1 as $N\to \infty$.
Oscillations in p-adic diffusion processes and simulation of the conformational dynamics of protein
Authors: A. Kh. Bikulov, A. P. Zubarev
Date: 2023-04-27 00:00:00

Logarithmic oscillations superimposed on a power-law trend appear in the behavior of various complex hierarchical systems. In this paper, we study the logarithmic oscillations of relaxation curves in p-adic diffusion models that are used to describe the conformational dynamics of protein. We consider the case of a purely p-adic diffusion, as well as the case of p-adic diffusion with a reaction sink. We show that, relaxation curves for large times in these two cases are described by a power law on which logarithmic oscillations are superimposed whose period and amplitude are determined by the parameters of the model. We also provide a physical explanation of the emergence of oscillations in relaxation curves and discuss the relation of the results to the experiments on relaxation dynamics of protein.
Cell motility modes are selected by the interplay of mechanosensitive adhesion and membrane tension
Authors: Yuzhu Chen, David Saintillan, Padmini Rangamani
Date: 2023-05-31 00:00:00

The initiation of directional cell motion requires symmetry breaking that can happen both with or without external stimuli. During cell crawling, forces generated by the cytoskeleton and their transmission through mechanosensitive adhesions to the extracellular substrate play a crucial role. In a recently proposed 1D model (Sens, PNAS 2020), a mechanical feedback loop between force-sensitive adhesions and cell tension was shown to be sufficient to explain spontaneous symmetry breaking and multiple motility patterns through stick-slip dynamics, without the need to account for signaling networks or active polar gels. We extended this model to 2D to study the interplay between cell shape and mechanics during crawling. Through a local force balance along a deformable boundary, we show that the membrane tension coupled with shape change can regulate the spatiotemporal evolution of the stochastic binding of mechanosensitive adhesions. Linear stability analysis identified the unstable parameter regimes where spontaneous symmetry breaking can take place. sing simulations to solve the fully coupled nonlinear system of equations, we show that starting from a randomly perturbed circular shape, this instability can lead to keratocyte-like shapes. Simulations predict that different adhesion kinetics and membrane tension can result in different cell motility modes including gliding, zigzag, rotating, and sometimes chaotic movements. Thus, using a minimal model of cell motility, we identify that the interplay between adhesions and tension can select emergent motility modes.