pwc-archive/papers-with-abstracts huggingface dataset:
A Survey on Detection, Tracking, and Classification of Aerial Threats using Radars and Communications Systems
Authors: Wahab Khawaja, Martins Ezuma, Vasilii Semkin, Fatih Erden, Ozgur Ozdemir, Ismail Guvenc
Date: 2022-11-18 00:00:00
Tasks: Classification

The use of unmanned aerial vehicles (UAVs) for different applications has increased many folds in recent years. The UAVs are expected to change the future air operations. However, there are instances where the UAVs can be used for malicious purposes. The detection, tracking, and classification of UAVs is challenging compared to manned aerial vehicles (MAVs) mainly due to small size, complex shapes, and ability to fly close to the terrain and in autonomous flight patterns in swarms. In this survey, we will discuss current and future aerial threats, and provide an overview of radar systems to counter such threats. We also study the performance parameters of radar systems for the detection, tracking, and classification of UAVs compared to MAVs. In addition to dedicated radar systems, we review the use of joint communication-radar (JCR) systems, as well as passive monitoring of changes in the common communication signals, e.g., FM, LTE, and any transmissions that may radiate from a UAV, for the detection, tracking, and classification of UAVs are provided. Finally, limitations of radar systems and comparison with other techniques that do not rely on radars for detection, tracking, and classification of aerial threats are provided.
Multiuser-MIMO Systems Using Comparator Network-Aided Receivers With 1-Bit Quantization
Authors: Ana Beatriz L. B. Fernandes, Zhichao Shao, Lukas T. N. Landau, Rodrigo C. de Lamare
Date: 2022-11-18 00:00:00
Tasks: Quantization

Low-resolution analog-to-digital converters (ADCs) are promising for reducing energy consumption and costs of multiuser multiple-input multiple-output (MIMO) systems with many antennas. We propose low-resolution multiuser MIMO receivers where the signals are simultaneously processed by 1-bit ADCs and a comparator network, which can be interpreted as additional virtual channels with binary outputs. We distinguish the proposed comparator networks in fully and partially connected. For such receivers, we develop the low-resolution aware linear minimum mean-squared error (LRA-LMMSE) channel estimator and detector according to the Bussgang theorem. We also develop a robust detector which takes into account the channel state information (CSI) mismatch statistics. By exploiting knowledge of the channel coefficients we devise a mean-square error (MSE) greedy search and a sequential signal-to-interference-plus-noise ratio (SINR) search for optimization of partially connected networks. Numerical results show that a system with extra virtual channels can outperform a system with additional receive antennas, in terms of bit error rate (BER). Furthermore, by employing the proposed channel estimation with its error statistics, we construct a lower bound on the ergodic sum rate for a linear receiver. Simulation results confirm that the proposed approach outperforms the conventional 1-bit MIMO system in terms of BER, MSE and sum rate.
RIS-Enabled SISO Localization under User Mobility and Spatial-Wideband Effects
Authors: Kamran Keykhosravi, Musa Furkan Keskin, Gonzalo Seco-Granados, Petar Popovski, Henk Wymeersch
Date: 2022-11-17 00:00:00

Reconfigurable intelligent surface (RIS) is a promising technological enabler for the 6th generation (6G) of wireless systems with applications in localization and communication. In this paper, we consider the problem of positioning a single-antenna user in 3D space based on the received signal from a single-antenna base station and reflected signal from an RIS by taking into account the mobility of the user and spatial-wideband (WB) effects. To do so, we first derive the spatial-WB channel model under the far-field assumption, for orthogonal frequency-division multiplexing signal transmission with the user having a constant velocity. We derive the Cramer Rao bounds to serve as a benchmark. Furthermore, we devise a low-complexity estimator that attains the bounds in high signal-to-noise ratios. Our estimator neglects the spatial-WB effects and deals with the user mobility by estimating the radial velocities and compensating for their effects in an iterative fashion. We show that the spatial-WB effects can degrade the localization accuracy for large RIS sizes and large signal bandwidths as the direction of arrival or departure deviate from the RIS normal. In particular, for a 64X64 RIS, the proposed estimator is resilient against the spatial-WB effects up to 140 MHz bandwidth. Regarding user mobility, our results suggest that the velocity of the user influences neither the bounds nor the accuracy of our estimator. Specifically, we observe that the state of the user with a high speed (42 m/s) can be estimated virtually with the same accuracy as a static user.
Improving ECG-based COVID-19 diagnosis and mortality predictions using pre-pandemic medical records at population-scale
Authors: Weijie Sun, Sunil Vasu Kalmady, Nariman Sepehrvand, Luan Manh Chu, Zihan Wang, Amir Salimi, Abram Hindle, Russell Greiner, Padma Kaul
Date: 2022-11-14 00:00:00
Tasks: COVID-19 Diagnosis, Diagnostic, Prediction, Transfer Learning

Pandemic outbreaks such as COVID-19 occur unexpectedly, and need immediate action due to their potential devastating consequences on global health. Point-of-care routine assessments such as electrocardiogram (ECG), can be used to develop prediction models for identifying individuals at risk. However, there is often too little clinically-annotated medical data, especially in early phases of a pandemic, to develop accurate prediction models. In such situations, historical pre-pandemic health records can be utilized to estimate a preliminary model, which can then be fine-tuned based on limited available pandemic data. This study shows this approach -- pre-train deep learning models with pre-pandemic data -- can work effectively, by demonstrating substantial performance improvement over three different COVID-19 related diagnostic and prognostic prediction tasks. Similar transfer learning strategies can be useful for developing timely artificial intelligence solutions in future pandemic outbreaks.
Heterogeneous Hidden Markov Models for Sleep Activity Recognition from Multi-Source Passively Sensed Data
Authors: Fernando Moreno-Pino, María Martínez-García, Pablo M. Olmos, Antonio Artés-Rodríguez
Date: 2022-11-08 00:00:00
Tasks: Activity Recognition

Psychiatric patients' passive activity monitoring is crucial to detect behavioural shifts in real-time, comprising a tool that helps clinicians supervise patients' evolution over time and enhance the associated treatments' outcomes. Frequently, sleep disturbances and mental health deterioration are closely related, as mental health condition worsening regularly entails shifts in the patients' circadian rhythms. Therefore, Sleep Activity Recognition constitutes a behavioural marker to portray patients' activity cycles and to detect behavioural changes among them. Moreover, mobile passively sensed data captured from smartphones, thanks to these devices' ubiquity, constitute an excellent alternative to profile patients' biorhythm. In this work, we aim to identify major sleep episodes based on passively sensed data. To do so, a Heterogeneous Hidden Markov Model is proposed to model a discrete latent variable process associated with the Sleep Activity Recognition task in a self-supervised way. We validate our results against sleep metrics reported by clinically tested wearables, proving the effectiveness of the proposed approach.
CRONOS: Colorization and Contrastive Learning for Device-Free NLoS Human Presence Detection using Wi-Fi CSI
Authors: Li-Hsiang Shen, Chia-Che Hsieh, An-Hung Hsiao, Kai-Ten Feng
Date: 2022-11-07 00:00:00
Tasks: Colorization, Contrastive Learning, Human Detection

In recent years, the demand for pervasive smart services and applications has increased rapidly. Device-free human detection through sensors or cameras has been widely adopted, but it comes with privacy issues as well as misdetection for motionless people. To address these drawbacks, channel state information (CSI) captured from commercialized Wi-Fi devices provides rich signal features for accurate detection. However, existing systems suffer from inaccurate classification under a non-line-of-sight (NLoS) and stationary scenario, such as when a person is standing still in a room corner. In this work, we propose a system called CRONOS (Colorization and Contrastive Learning Enhanced NLoS Human Presence Detection), which generates dynamic recurrence plots (RPs) and color-coded CSI ratios to distinguish mobile and stationary people from vacancy in a room, respectively. We also incorporate supervised contrastive learning to retrieve substantial representations, where consultation loss is formulated to differentiate the representative distances between dynamic and stationary cases. Furthermore, we propose a self-switched static feature enhanced classifier (S3FEC) to determine the utilization of either RPs or color-coded CSI ratios. Our comprehensive experimental results show that CRONOS outperforms existing systems that either apply machine learning or non-learning based methods, as well as non-CSI based features in open literature. CRONOS achieves the highest human presence detection accuracy in vacancy, mobility, line-of-sight (LoS), and NLoS scenarios.
Towards Fast Single-Trial Online ERP based Brain-Computer Interface using dry EEG electrodes and neural networks: a pilot study
Authors: Okba Bekhelifi, Nasr-Eddine Berrached
Date: 2022-11-04 00:00:00
Tasks: Brain Computer Interface, EEG, Electroencephalogram (EEG), ERP

Speeding up the spelling in event-related potentials (ERP) based Brain-Computer Interfaces (BCI) requires eliciting strong brain responses in a short span of time, as much as the accurate classification of such evoked potentials remains challenging and imposes hard constraints for signal processing and machine learning techniques. Recent advances in stimulus presentation and deep learning showcased a promising direction in significantly improving the efficacy of those systems, in this study we propose the combination of colored inverted face stimulation with classification using convolutional neural networks in the hard settings of dry electrodes and fast flashing single-trial ERP-based BCI. The high online accuracy achieved, with two subjects passing the 90 percent correct symbol detection bar and a transfer rate above 60 bits per minute, demonstrates the approach potential in improving the practicality of ERP based BCIs.
Large Scale Radio Frequency Wideband Signal Detection & Recognition
Authors: Luke Boegner, Garrett Vanhoy, Phillip Vallance, Manbir Gulati, Dresden Feitzinger, Bradley Comar, Robert D. Miller
Date: 2022-11-04 00:00:00
Tasks: object-detection, Object Detection

Applications of deep learning to the radio frequency (RF) domain have largely concentrated on the task of narrowband signal classification after the signals of interest have already been detected and extracted from a wideband capture. To encourage broader research with wideband operations, we introduce the WidebandSig53 (WBSig53) dataset which consists of 550 thousand synthetically-generated samples from 53 different signal classes containing approximately 2 million unique signals. We extend the TorchSig signal processing machine learning toolkit for open-source and customizable generation, augmentation, and processing of the WBSig53 dataset. We conduct experiments using state of the art (SoTA) convolutional neural networks and transformers with the WBSig53 dataset. We investigate the performance of signal detection tasks, i.e. detect the presence, time, and frequency of all signals present in the input data, as well as the performance of signal recognition tasks, where networks detect the presence, time, frequency, and modulation family of all signals present in the input data. Two main approaches to these tasks are evaluated with segmentation networks and object detection networks operating on complex input spectrograms. Finally, we conduct comparative analysis of the various approaches in terms of the networks' mean average precision, mean average recall, and the speed of inference.
Deep learning for structural health monitoring: An application to heritage structures
Authors: Fabio Carrara, Fabrizio Falchi, Maria Girardi, Nicola Messina, Cristina Padovani, Daniele Pellegrini
Date: 2022-11-04 00:00:00
Tasks: Anomaly Detection, Structural Health Monitoring, Time Series, Time Series Analysis, Time Series Forecasting, Unsupervised Anomaly Detection

Thanks to recent advancements in numerical methods, computer power, and monitoring technology, seismic ambient noise provides precious information about the structural behavior of old buildings. The measurement of the vibrations produced by anthropic and environmental sources and their use for dynamic identification and structural health monitoring of buildings initiated an emerging, cross-disciplinary field engaging seismologists, engineers, mathematicians, and computer scientists. In this work, we employ recent deep learning techniques for time-series forecasting to inspect and detect anomalies in the large dataset recorded during a long-term monitoring campaign conducted on the San Frediano bell tower in Lucca. We frame the problem as an unsupervised anomaly detection task and train a Temporal Fusion Transformer to learn the normal dynamics of the structure. We then detect the anomalies by looking at the differences between the predicted and observed frequencies.
Discriminating sensor activation in activity recognition within multi-occupancy environments based on nearby interaction
Authors: Aurora Polo-Rodriguez, Javier Medina-Quero
Date: 2022-11-03 00:00:00
Tasks: Activity Recognition

This work presents a computer model to discriminate sensor activation in multi-occupancy environments based on proximity interaction. Current proximity-based and indoor location methods allow the estimation of the positions or areas where inhabitants carry out their daily human activities. The spatial-temporal relation between location and sensor activations is described in this work to generate a sensor interaction matrix for each inhabitant. This enables the use of classical HAR models to reduce the complexity of the multi-occupancy problem. A case study deployed with UWB and binary sensors is presented.
Formation of vascular-like structures using a chemotaxis-driven multiphase model
Authors: Georgina Al-Badri, James B. Phillips, Rebecca J. Shipley, Nicholas C. Ovenden
Date: 2022-11-18 00:00:00

We propose a continuum model for pattern formation, based on the multiphase model framework, to explore in vitro cell patterning within an extracellular matrix. We demonstrate that, within this framework, chemotaxis-driven cell migration can lead to formation of cell clusters and vascular-like structures in 1D and 2D respectively. The influence on pattern formation of additional mechanisms commonly included in multiphase tissue models, including cell-matrix traction, contact inhibition, and cell-cell aggregation, are also investigated. Using sensitivity analysis, the relative impact of each model parameter on the simulation outcomes is assessed to identify the key parameters involved. Chemoattractant-matrix binding is further included, motivated by previous experimental studies, and to augment the spatial scale of patterning to within a biologically plausible range. Key findings from the in-depth parameter analysis of the 1D models, both with and without chemoattractant-matrix binding, are demonstrated to translate well to the 2D model, obtaining vascular-like cell patterning for multiple parameter regimes. Overall, we demonstrate a biologically-motivated multiphase model capable of generating long-term pattern formation on a biologically plausible spatial scale both in 1D and 2D, with applications for modelling in vitro vascular network formation.
Contextual guidance: An integrated theory for astrocytes function in brain circuits and behavior
Authors: Ciaran Murphy-Royal, ShiNung Ching, Thomas Papouin
Date: 2022-11-17 00:00:00

The participation of astrocytes in brain computation was formally hypothesized in 1992, coinciding with the discovery that these glial cells display a complex form of Ca2+ excitability. This fostered conceptual advances centered on the notion of reciprocal interactions between neurons and astrocytes, which permitted a critical leap forward in uncovering many roles of astrocytes in brain circuits, and signaled the rise of a major new force in neuroscience: that of glial biology. In the past decade, a multitude of unconventional and disparate functions of astrocytes have been documented that are not predicted by these canonical models and that are challenging to piece together into a holistic and parsimonious picture. This highlights a disconnect between the rapidly evolving field of astrocyte biology and the conceptual frameworks guiding it, and emphasizes the need for a careful reconsideration of how we theorize the functional position of astrocytes in brain circuitry. Here, we propose a unifying, highly transferable, data-driven, and computationally-relevant conceptual framework for astrocyte biology, which we coin contextual guidance. It describes astrocytes as contextual gates that decode multiple environmental factors to shape neural circuitry in an adaptive, state-dependent fashion. This paradigm is organically inclusive of all fundamental features of astrocytes, many of which have remained unaccounted for in previous theories. We find that this new concept provides an intuitive and powerful theoretical space to improve our understanding of brain function and computational models thereof across scales because it depicts astrocytes as a hub for circumstantial inputs into relevant specialized circuits that permits adaptive behaviors at the network and organism level.
High-throughput mechanophenotyping of multicellular spheroids using a microfluidic micropipette aspiration chip
Authors: R. C. Boot, A. Roscani, L. van Buren, S. Maity, G. H. Koenderink, P. E. Boukany
Date: 2022-11-18 00:00:00

Cell spheroids are in vitro multicellular model systems that mimic the crowded micro-environment of biological tissues. Their mechanical characterization can provide valuable insights in how single-cell mechanics and cell-cell interactions control tissue mechanics and self-organization. However, most measurement techniques are limited to probing one spheroid at a time, require specialized equipment and are difficult to handle. Here, we developed a microfluidic chip that follows the concept of glass capillary micropipette aspiration in order to quantify the viscoelastic behavior of spheroids in an easy- to-handle, high-throughput manner. Spheroids are loaded in parallel pockets via a gentle flow, after which spheroid tongues are aspirated into adjacent aspiration channels using hydrostatic pressure. After each experiment, the spheroids are easily removed from the chip by reversing the pressure and new spheroids can be injected. The presence of multiple pockets with a uniform aspiration pressure, combined with the ease to conduct successive experiments, allows for a high throughput of tens of spheroids per day. We demonstrate that the chip provides accurate deformation data when working at different aspiration pressures. Lastly, we measure the viscoelastic properties of spheroids made of different cell lines and show how these are consistent with previous studies using established experimental techniques. In summary, our chip provides a high-throughput way to measure the viscoelastic deformation behavior of cell spheroids, in order to mechanophenotype different tissue types and examine the link between cell-intrinsic properties and overall tissue behavior.
On the Bachelier implied volatility at extreme strikes
Authors: Fabien Le Floc'h
Date: 2022-11-18 00:00:00

What kind of implied volatility extrapolation is appropriate? Roger Lee proved that the Black-Scholes implied variance can not grow faster than linearly in log-moneyness. This paper investigates what happens in the Bachelier (or Normal) implied volatility world, making sure to cover the various aspects of vanilla option arbitrages.
Robust DNN Surrogate Models with Uncertainty Quantification via Adversarial Training
Authors: Lixiang Zhang, Jia Li
Date: 2022-11-10 00:00:00
Tasks: Computational Efficiency, Uncertainty Quantification

For computational efficiency, surrogate models have been used to emulate mathematical simulators for physical or biological processes. High-speed simulation is crucial for conducting uncertainty quantification (UQ) when the simulation is repeated over many randomly sampled input points (aka, the Monte Carlo method). In some cases, UQ is only feasible with a surrogate model. Recently, Deep Neural Network (DNN) surrogate models have gained popularity for their hard-to-match emulation accuracy. However, it is well-known that DNN is prone to errors when input data are perturbed in particular ways, the very motivation for adversarial training. In the usage scenario of surrogate models, the concern is less of a deliberate attack but more of the high sensitivity of the DNN's accuracy to input directions, an issue largely ignored by researchers using emulation models. In this paper, we show the severity of this issue through empirical studies and hypothesis testing. Furthermore, we adopt methods in adversarial training to enhance the robustness of DNN surrogate models. Experiments demonstrate that our approaches significantly improve the robustness of the surrogate models without compromising emulation accuracy.
Sample-efficient Quantum Born Machine through Coding Rate Reduction
Authors: Pengyuan Zhai
Date: 2022-11-14 00:00:00

The quantum circuit Born machine (QCBM) is a quantum physics inspired implicit generative model naturally suitable for learning binary images, with a potential advantage of modeling discrete distributions that are hard to simulate classically. As data samples are generated quantum-mechanically, QCBMs encompass a unique optimization landscape. However, pioneering works on QCBMs do not consider the practical scenario where only small batch sizes are allowed during training. QCBMs trained with a statistical two-sample test objective in the image space require large amounts of projective measurements to approximate the model distribution well, unpractical for large-scale quantum systems due to the exponential scaling of the probability space. QCBMs trained adversarially against a deep neural network discriminator are proof-of-concept models that face mode collapse. In this work we investigate practical learning of QCBMs. We use the information-theoretic \textit{Maximal Coding Rate Reduction} (MCR$^2$) metric as a second moment matching tool and study its effect on mode collapse in QCBMs. We compute the sampling based gradient of MCR$^2$ with respect to quantum circuit parameters with or without an explicit feature mapping. We experimentally show that matching up to the second moment alone is not sufficient for training the quantum generator, but when combined with the class probability estimation loss, MCR$^2$ is able to resist mode collapse. In addition, we show that adversarially trained neural network kernel for infinite moment matching is also effective against mode collapse. On the Bars and Stripes dataset, our proposed techniques alleviate mode collapse to a larger degree than previous QCBM training schemes, moving one step closer towards practicality and scalability.