pwc-archive/papers-with-abstracts huggingface dataset:
Achieving Efficient and Realistic Full-Radar Simulations and Automatic Data Annotation by exploiting Ray Meta Data of a Radar Ray Tracing Simulator
Authors: Christian Schüßler, Marcel Hoffmann, Vanessa Wirth, Björn Eskofier, Tim Weyrich, Marc Stamminger, Martin Vossiek
Date: 2023-05-23 00:00:00
Tasks: Object

In this work a novel radar simulation concept is introduced that allows to simulate realistic radar data for Range, Doppler, and for arbitrary antenna positions in an efficient way. Further, it makes it possible to automatically annotate the simulated radar signal by allowing to decompose it into different parts. This approach allows not only almost perfect annotations possible, but also allows the annotation of exotic effects, such as multi-path effects or to label signal parts originating from different parts of an object. This is possible by adapting the computation process of a Monte Carlo shooting and bouncing rays (SBR) simulator. By considering the hits of each simulated ray, various meta data can be stored such as hit position, mesh pointer, object IDs, and many more. This collected meta data can then be utilized to predict the change of path lengths introduced by object motion to obtain Doppler information or to apply specific ray filter rules in order obtain radar signals that only fulfil specific conditions, such as multiple bounces or containing specific object IDs. Using this approach, perfect and otherwise almost impossible annotations schemes can be realized.
A Deep Matched Filter For R-Peak Detection in Ear-ECG
Authors: Harry J. Davies, Ghena Hammour, Marek Zylinski, Amir Nassibi, Danilo P. Mandic
Date: 2023-05-23 00:00:00

The Ear-ECG provides a continuous Lead I electrocardiogram (ECG) by measuring the potential difference related to heart activity using electrodes that can be embedded within earphones. The significant increase in wearability and comfort afforded by Ear-ECG is often accompanied by a corresponding degradation in signal quality - a common obstacle that is shared by most wearable technologies. We aim to resolve this issue by introducing a Deep Matched Filter (Deep-MF) for the highly accurate detection of R-peaks in wearable ECG, thus enhancing the utility of Ear-ECG in real-world scenarios. The Deep-MF consists of an encoder stage (trained as part of an encoder-decoder module to reproduce ground truth ECG), and an R-peak classifier stage. Through its operation as a Matched Filter, the encoder searches for matches with an ECG template pattern in the input signal, prior to filtering the matches with the subsequent convolutional layers and selecting peaks corresponding to true ECG matches. The so condensed latent representation of R-peak information is then fed into a simple R-peak classifier, of which the output provides precise R-peak locations. The proposed Deep Matched Filter is evaluated using leave-one-subject-out cross validation over 36 subjects with an age range of 18-75, with the Deep-MF outperforming existing algorithms for R-peak detection in noisy ECG. The Deep-MF achieves a median R-peak recall of 94.9\%, a median precision of 91.2\% and an (AUC) value of 0.97. Furthermore, we demonstrate that the Deep Matched Filter algorithm not only retains the initialised ECG kernel structure during the training process, but also amplifies portions of the ECG which it deems most valuable. Overall, the Deep Matched Filter serves as a valuable step forward for the real-world functionality of Ear-ECG and, through its explainable operation, the acceptance of deep learning models in e-health.
Experimental Assessment of Misalignment Effects in Terahertz Communications
Authors: Hasan Nayir, Erhan Karakoca, Güneş Karabulut Kurt, Ali Görçin
Date: 2023-05-23 00:00:00

Terahertz (THz) frequencies are important for next generation wireless systems due to the advantages in terms of large available bandwidths. On the other hand, the limited range due to high attenuation in these frequencies can be overcome via densely installed heterogeneous networks also utilizing UAVs in a three-dimensional hyperspace. Yet, THz communications rely on precise beam alignment, if not handled properly results in low signal strength at the receiver which impacts THz signals more than conventional ones. This work focuses on the importance of precise alignment in THz communication systems and the significant effect of proper alignment is validated through comprehensive measurements conducted through a state-of-the-art measurement setup, which enables accurate data collection between 240 GHz to 300 GHz at varying angles and distances in an anechoic chamber eliminating reflections. By analyzing the channel frequency and impulse responses of these extensive and particular measurements, this study provides the first quantifiable results in terms of measuring the effects of beam misalignment in THz frequencies.
Analog Implementation of the Softmax Function
Authors: Jacob Sillman
Date: 2023-05-23 00:00:00

An analog implementation of the Softmax activation function is presented. A modular design is proposed, scaling linearly with the number of inputs and outputs. The circuit behaves similarly using both a BJT and NMOS design scheme. Experimental results extracted from a BJT breadboard prototype presents computational accuracy within 4.2% margin of error. Simulation data presents accuracy within 1.3% margin of error for BJT and 0.9% margin of error for NMOS design schemes.
Digital-SC: Digital Semantic Communication with Adaptive Network Split and Learned Non-Linear Quantization
Authors: Lei Guo, Wei Chen, Yuxuan Sun, Bo Ai
Date: 2023-05-22 00:00:00
Tasks: image-classification, Image Classification, Intelligent Communication, Quantization, Semantic Communication

Semantic communication, an intelligent communication paradigm that aims to transmit useful information in the semantic domain, is facilitated by deep learning techniques. Robust semantic features can be learned and transmitted in an analog fashion, but it poses new challenges to hardware, protocol, and encryption. In this paper, we propose a digital semantic communication system, which consists of an encoding network deployed on a resource-limited device and a decoding network deployed at the edge. To acquire better semantic representation for digital transmission, a novel non-linear quantization module is proposed to efficiently quantize semantic features with trainable quantization levels. Additionally, structured pruning is incorporated to reduce the dimension of the transmitted features. We also introduce a semantic learning loss (SLL) function to reduce semantic error. To adapt to various channel conditions and inputs under constraints of communication and computing resources, a policy network is designed to adaptively choose the split point and the dimension of the transmitted semantic features. Experiments using the CIFAR-10 and ImageNet dataset for image classification are employed to evaluate the proposed digital semantic communication network, and ablation studies are conducted to assess the proposed quantization module, structured pruning and SLL.
Learning to Estimate: A Real-Time Online Learning Framework for MIMO-OFDM Channel Estimation
Authors: Lianjun Li, Sai Sree Rayala, Jiarui Xu, Lizhong Zheng, Lingjia Liu
Date: 2023-05-22 00:00:00
Tasks: Binary Classification

In this paper we introduce StructNet-CE, a novel real-time online learning framework for MIMO-OFDM channel estimation, which only utilizes over-the-air (OTA) pilot symbols for online training and converges within one OFDM subframe. The design of StructNet-CE leverages the structure information in the MIMO-OFDM system, including the repetitive structure of modulation constellation and the invariant property of symbol classification to inter-stream interference. The embedded structure information enables StructNet-CE to conduct channel estimation with a binary classification task and accurately learn channel coefficients with as few as two pilot OFDM symbols. Experiments show that the channel estimation performance is significantly improved with the incorporation of structure knowledge. StructNet-CE is compatible and readily applicable to current and future wireless networks, demonstrating the effectiveness and importance of combining machine learning techniques with domain knowledge for wireless communication systems.
Toeplitz Inverse Eigenvalue Problem: Application to the Uniform Linear Antenna Array Calibration
Authors: Yuri Abramovich, Tanit Pongsiri
Date: 2023-05-22 00:00:00

The inverse Toeplitz eigenvalue problem (ToIEP) concerns finding a vector that specifies the real-valued symmetric Toeplitz matrix with the prescribed set of eigenvalues. Since phase "calibration" errors in uniform linear antenna arrays (ULAs) do not change the covariance matrix eigenvalues and the moduli of the covariance matrix elements, we formulate a number of the new ToIEP problems of the Hermitian Toeplitz matrix reconstruction, given the moduli of the matrix elements and the matrix eigenvalues. We demonstrate that for the real-valued case, only two solutions to this problem exist, with the "non-physical" one that in most practical cases could be easily disregarded. The computational algorithm for the real-valued case is quite simple. For the complex-valued case, we demonstrate that the family of solutions is broader and includes solutions inappropriate for calibration. For this reason, we modified this ToIEP problem to match the covariance matrix of the uncalibrated ULA. We investigate the statistical convergence of the ad-hoc algorithm with the sample matrices instead of the true ones. The proposed ad-hoc algorithms require the so-called "strong" or "argumental" convergence, which means a large enough required sample volume that reduces the errors in the estimated covariance matrix elements. Along with the ULA arrays, we also considered the fully augmentable minimum redundancy arrays that generate the same (full) set of covariance lags as the uniform linear arrays, and we specified the conditions when the ULA Toeplitz covariance matrix may be reconstructed given the M-variate MRA covariance matrix.
On the role of eigenvalue disparity and coordinate transformations in the reduction of the linear noise approximation
Authors: Justin Eilertsen, Wylie Stroberg
Date: 2023-05-22 00:00:00

Eigenvalue disparity, also known as timescale separation, permits the systematic reduction of deterministic models of enzyme kinetics. Geometric singular perturbation theory, of which eigenvalue disparity is central, provides a coordinate-free framework for deriving reduced mass action models in the deterministic realm. Moreover, homologous deterministic reductions are often employed in stochastic models to reduce the computational complexity required to simulate reactions with the Gillespie algorithm. Interestingly, several detailed studies indicate that timescale separation does not always guarantee the accuracy of reduced stochastic models. In this work, we examine the roles of timescale separation and coordinate transformations in the reduction of the Linear Noise Approximation (LNA) and, unlike previous studies, we do not require the system to be comprised of distinct fast and slow variables. Instead, we adopt a coordinate-free approach. We demonstrate that eigenvalue disparity does not guarantee the accuracy of the reduced LNA, known as the slow scale LNA (ssLNA). However, the inaccuracy of the ssLNA can often be eliminated with a proper coordinate transformation. For planar systems in separated (standard) form, we prove that the error between the variances of the slow variable generated by the LNA and the ssLNA is $\mathcal{O}(\varepsilon)$. We also address a nilpotent Jacobian scenario and use the blow-up method to construct a reduced equation that is accurate near the singular limit in the deterministic regime. However, this reduction in the stochastic regime is far less accurate, which illustrates that eigenvalue disparity plays a central role in stochastic model reduction.
Strong attentional modulation of V1/V2 activity implements a robust, contrast-invariant control mechanism for selective information processing
Authors: Lukas-Paul Rausch, Maik Schünemann, Eric Drebitz, Daniel Harnack, Udo A. Ernst, Andreas K. Kreiter
Date: 2023-05-23 00:00:00

When selective attention is devoted to one of multiple stimuli within receptive fields of neurons in visual area V4, cells respond as if only the attended stimulus was present. The underlying neural mechanisms are still debated, but computational studies suggest that a small rate advantage for neural populations passing the attended signal to V4 suffices to establish such selective processing. We challenged this theory by pairing stimuli with different luminance contrasts, such that attention on a weak target stimulus would have to overcome a large activation difference to a strong distracter. In this situation we found unexpectedly large attentional target facilitation in macaque V1/V2 which far surpasses known magnitudes of attentional modulation. Target facilitation scales with contrast difference and combines with distracter suppression to achieve the required rate advantage. These effects can be explained by a contrast-independent attentional control mechanism with excitatory centre and suppressive surround targeting divisive normalization units.
Routing by spontaneous synchronization
Authors: Maik Schünemann, Udo Ernst
Date: 2023-05-23 00:00:00
Tasks: Open-Ended Question Answering

Selective attention allows to process stimuli which are behaviorally relevant, while attenuating distracting information. However, it is an open question what mechanisms implement selective routing, and how they are engaged in dependence on behavioral need. Here we introduce a novel framework for selective processing by spontaneous synchronization. Input signals become organized into 'avalanches' of synchronized spikes which propagate to target populations. Selective attention enhances spontaneous synchronization and boosts signal transfer by a simple disinhibition of a control population, without requiring changes in synaptic weights. Our framework is fully analytically tractable and provides a complete understanding of all stages of the routing mechanism, yielding closed-form expressions for input-output correlations. Interestingly, although gamma oscillations can naturally occur through a recurrent dynamics, we can formally show that the routing mechanism itself does not require such oscillatory activity and works equally well if synchronous events would be randomly shuffled over time. Our framework explains a large range of physiological findings in a unified framework and makes specific predictions about putative control mechanisms and their effects on neural dynamics.
Signatures of hierarchical temporal processing in the mouse visual system
Authors: Lucas Rudelt, Daniel González Marx, F. Paul Spitzner, Benjamin Cramer, Johannes Zierenberg, Viola Priesemann
Date: 2023-05-22 00:00:00

A core challenge for the brain is to process information across various timescales. This could be achieved by a hierarchical organization of temporal processing through intrinsic mechanisms (e.g., recurrent coupling or adaptation), but recent evidence from spike recordings of the rodent visual system seems to conflict with this hypothesis. Here, we used an optimized information-theoretic and classical autocorrelation analysis to show that information- and intrinsic timescales of spiking activity increase along the anatomical hierarchy of the mouse visual system, while information-theoretic predictability decreases. Moreover, the timescale hierarchy was invariant to the stimulus condition, whereas the decrease in predictability was strongest under natural movie stimulation. We could reproduce this effect in a basic recurrent network model with correlated sensory input. Our findings suggest that the rodent visual system indeed employs intrinsic mechanisms to achieve longer integration for higher cortical areas, while simultaneously reducing predictability for an efficient neural code.
An Analytically Solvable Model of Firing Rate Heterogeneity in Balanced State Networks
Authors: Alexander Schmidt, Peter Hiemeyer, Fred Wolf
Date: 2023-05-22 00:00:00

Distributions of neuronal activity within cortical circuits are often found to display highly skewed shapes with many neurons emitting action potentials at low or vanishing rates, while some are active at high rates. Theoretical studies were able to reproduce such distributions, but come with a lack of mathematical tractability, preventing a deeper understanding of the impact of model parameters. In this study, using the Gauss-Rice neuron model, we present a balanced-state cortical circuit model for which the firing rate distribution can be exactly calculated. It offers selfconsistent solutions to recurrent neuronal networks and allows for the combination of multiple neuronal populations, with single or multiple synaptic receptors (e.g. AMPA and NMDA in excitatory populations), paving the way for a deeper understanding of how firing rate distributions are impacted by single neuron or synaptic properties.
A new sulfur bioconversion process development for energy- and space-efficient secondary wastewater treatment
Authors: Chu-Kuan Jiang, Yang-Fan Deng, Hongxiao Guo, Guang-Hao Chen, Di wu
Date: 2023-05-22 00:00:00

Harvesting organic matter from wastewater is widely applied to maximize energy recovery; however, it limits the applicability of secondary treatment for acceptable effluent discharge into surface water bodies. To turn this bottleneck issue into an opportunity, this study developed oxygen-induced thiosulfatE production duRing sulfATe reductiOn (EARTO) to provide an efficient electron donor for wastewater treatment. Typical pretreated wastewater was synthesized with chemical oxygen demand of 110 mg/L, sulfate of 50 mg S/L, and varying dissolved oxygen (DO) and was fed into a moving-bed biofilm reactor (MBBR). The MBBR was operated continuously with a short hydraulic retention time of 40 min for 349 days. The formation rate of thiosulfate reached 0.12-0.18 g S/(m2.d) with a high produced thiosulfate-S/TdS-S ratio of 38-73% when influent DO was 2.7-3.6 mg/L. The sludge yield was 0.23-0.29 gVSS/gCOD, much lower than it was in conventional activated sludge processes. Then, batch tests and metabolism analysis were conducted to confirm the oxygen effect on thiosulfate formation, characterize the roles of sulfate and microbial activities, and explore the mechanism of oxygen-induced thiosulfate formation in ERATO. Results examined that oxygen supply promoted the thiosulfate-Sproduced/TdS-Sproduced ratio from 4% to 24-26%, demonstrated that sulfate and microbial activities were critical for thiosulfate production, and indicated that oxygen induces thiosulfate formation through two pathways: 1) direct sulfide oxidation, and 2) indirect sulfide oxidation, sulfide is first oxidized to S0 (dominant) which then reacts with sulfite derived from oxygen-regulated biological sulfate reduction. The proposed compact ERATO process, featuring high thiosulfate production and low sludge production, supports space- and energy-efficient secondary wastewater treatment.
A Markov chain model to investigate the spread of antibiotic-resistant bacteria in hospitals
Authors: Fabio A. C. C. Chalub, Antonio Gómez-Corral, Martín López-García, Fátima Palacios-Rodríguez
Date: 2023-05-23 00:00:00
Tasks: Epidemiology

Ordinary differential equation (ODE) models used in mathematical epidemiology assume explicitly or implicitly large populations. For the study of infections in a hospital this is an extremely restrictive assumption as typically a hospital ward has a few dozen, or even fewer, patients. This work reframes a well-known model used in the study of the spread of antibiotic-resistant bacteria in hospitals, to consider the pathogen transmission dynamics in small populations. In this vein, this paper proposes a Markov chain model to describe the spread of a single bacterial species in a hospital ward where patients may be free of bacteria or may carry bacterial strains that are either sensitive or resistant to antimicrobial agents. We determine the probability law of the \emph{exact} reproduction number ${\cal R}_{exact,0}$, which is here defined as the random number of secondary infections generated by those patients who are accommodated in a predetermined bed before a patient who is free of bacteria is accommodated in this bed for the first time. Specifically, we decompose the exact reproduction number ${\cal R}_{exact,0}$ into two contributions allowing us to distinguish between infections due to the sensitive and the resistant bacterial strains. Our methodology is mainly based on structured Markov chains and the use of related matrix-analytic methods. This guarantees the compatibility of the new, finite-population model, with large population models present in the literature and takes full advantage, in its mathematical analysis, of the intrinsic stochasticity.
Upper extremity kinematics: Development of a quantitative measure of impairment severity and dissimilarity after stroke
Authors: Khadija F. Zaidi, Michelle Harris-Love
Date: 2023-05-22 00:00:00

Strokes are a leading cause of disability, with many experiencing difficulty in recovering arm movement, particularly hand function and grasping ability. There is currently no objective measure of movement quality, and without it, rehabilitative interventions remain at best estimations of the underlying neural structures response to produce movement. In this paper, we utilize a novel modification to Procrustean distance to quantify curve dissimilarity and propose the Reach Severity and Dissimilarity Index (RSDI) as an objective measure of motor deficits. All experiments took place at the Medstar National Rehabilitation Hospital; persons with stroke were recruited from the hospital patient population. Using Fugl-Meyer (FM) scores and reach capacities, stroke survivors were placed in mild or severe impairment groups. Individuals completed sets of reach-to-target tasks to extrapolate kinematic metrics describing motor performance. The Procrustes method of statistical shape analysis was modified to identify reaching sub-movements that were congruous to able-bodied sub-movements. Movement initiation proceeds comparably to the reference curve in two- and three-dimensional representations of mild impairment movement. There were significant effects of the location of congruent segments between subject and reference curves, mean velocities, peak roll angle, and target error. These metrics were used to calculate a preliminary RSDI score with severity and dissimilarity sub-scores, and subjects were reclassified in terms of rehabilitation goals as Speed Emphasis, Strength Emphasis, and Combined Emphasis. The Modified Procrustes method shows promise in identifying disruptions in movement and monitoring recovery without adding to patient burden. The proposed RSDI score, while limited in scope, can be adapted and expanded to other functional movements and used as an objective clinical tool.
The Quadratic Local Variance Gamma Model: an arbitrage-free interpolation of class C3 for option prices
Authors: Fabien Le Floc'h
Date: 2023-05-23 00:00:00

This paper generalizes the local variance gamma model of Carr and Nadtochiy, to a piecewise quadratic local variance function. The formulation encompasses the piecewise linear Bachelier and piecewise linear Black local variance gamma models. The quadratic local variance function results in an arbitrage-free interpolation of class C3. The increased smoothness over the piecewise-constant and piecewise-linear representation allows to reduce the number of knots when interpolating raw market quotes, thus providing an interesting alternative to regularization while reducing the computational cost.