pwc-archive/papers-with-abstracts huggingface dataset:
Neuromorphic-P2M: Processing-in-Pixel-in-Memory Paradigm for Neuromorphic Image Sensors
Authors: Md Abdullah-Al Kaiser, Gourav Datta, Zixu Wang, Ajey P. Jacob, Peter A. Beerel, Akhilesh R. Jaiswal
Date: 2023-01-22 00:00:00

Edge devices equipped with computer vision must deal with vast amounts of sensory data with limited computing resources. Hence, researchers have been exploring different energy-efficient solutions such as near-sensor processing, in-sensor processing, and in-pixel processing, bringing the computation closer to the sensor. In particular, in-pixel processing embeds the computation capabilities inside the pixel array and achieves high energy efficiency by generating low-level features instead of the raw data stream from CMOS image sensors. Many different in-pixel processing techniques and approaches have been demonstrated on conventional frame-based CMOS imagers, however, the processing-in-pixel approach for neuromorphic vision sensors has not been explored so far. In this work, we for the first time, propose an asynchronous non-von-Neumann analog processing-in-pixel paradigm to perform convolution operations by integrating in-situ multi-bit multi-channel convolution inside the pixel array performing analog multiply and accumulate (MAC) operations that consume significantly less energy than their digital MAC alternative. To make this approach viable, we incorporate the circuit's non-ideality, leakage, and process variations into a novel hardware-algorithm co-design framework that leverages extensive HSpice simulations of our proposed circuit using the GF22nm FD-SOI technology node. We verified our framework on state-of-the-art neuromorphic vision sensor datasets and show that our solution consumes ~2x lower backend-processor energy while maintaining almost similar front-end (sensor) energy on the IBM DVS128-Gesture dataset than the state-of-the-art while maintaining a high test accuracy of 88.36%.
A Hardware Prototype of Wideband High-Dynamic Range ADC
Authors: Satish Mulleti, Eliya Reznitskiy, Shlomi Savariego, Moshe Namer, Nimrod Glazer, Yonina C. Eldar
Date: 2023-01-23 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Key parameters of analog-to-digital converters (ADCs) are their sampling rate and dynamic range. Power consumption and cost of an ADC are directly proportional to the sampling rate; hence, it is desirable to keep it as low as possible. The dynamic range of an ADC also plays an important role, and ideally, it should be greater than the signal's; otherwise, the signal will be clipped. To avoid clipping, modulo folding can be used before sampling, followed by an unfolding algorithm to recover the true signal. In this paper, we present a modulo hardware prototype that can be used before sampling to avoid clipping. Our modulo hardware operates prior to the sampling mechanism and can fold higher frequency signals compared to existing hardware. We present a detailed design of the hardware and also address key issues that arise during implementation. In terms of applications, we show the reconstruction of finite-rate-of-innovation signals which are beyond the dynamic range of the ADC. Our system operates at six times below the Nyquist rate of the signal and can accommodate eight-times larger signals than the ADC's dynamic range.
Target-Mounted Intelligent Reflecting Surface for Joint Location and Orientation Estimation
Authors: Peilan Wang, Weidong Mei, Jun Fang, Rui Zhang
Date: 2023-01-23 00:00:00

Intelligent reflecting surface (IRS) has been widely recognized as an efficient technique to reconfigure the electromagnetic environment in favor of wireless communication performance. In this paper, we propose a new application of IRS for device-free target sensing via joint location and orientation estimation. In particular, different from the existing works that use IRS as an additional anchor node for localization/sensing, we consider mounting IRS on the sensing target, whereby estimating the IRS's location and orientation as that of the target by leveraging IRS's controllable signal reflection. To this end, we first propose a tensor-based method to acquire essential angle information between the IRS and the sensing transmitter as well as a set of distributed sensing receivers. Next, based on the estimated angle information, we formulate two optimization problems to estimate the location and orientation of the IRS/target, respectively, and obtain the locally optimal solutions to them by invoking two iterative algorithms, namely, gradient descent method and manifold optimization. In particular, we show that the orientation estimation problem admits a closed-form solution in a special case that usually holds in practice. Furthermore, theoretical analysis is conducted to draw essential insights into the proposed sensing system design and performance. Simulation results verify our theoretical analysis and demonstrate that the proposed methods can achieve high estimation accuracy which is close to the theoretical bound.
Monitoring of the heart movements using a FMCW radar and correlation with an ECG
Authors: Rémi Grisot, Pierre Laurent, Claire Migliaccio, Jean-Yves Dauvignac, Mélanie Brulc, Camille Chiquet, Jean-Paul Caruana
Date: 2023-01-21 00:00:00

Monitoring the activity of the heart is important for diagnosing and preventing cardiovascular diseases. The electrocardiogram (ECG) is the gold standard for diagnosing such diseases. It monitors the heart's electrical activity, and while this is highly correlated with the cardiac mechanical activity, it does not provide all the information. Other sensors such as the echocardiograph are able to monitor the heart's movements, but such tools are expensive and hard to operate. Therefore, contactless monitoring of the heart using RF sensing has gained interest in recent years. In this paper, we describe a process to extract the movements of the heart from millimeter wave radar with high accuracy, and thus we provide a noninvasive and affordable way to monitor cardiac movements. We then demonstrate the correlation between the observed movements and the ECG. Furthermore, we propose an algorithm to synchronize the ECG signal and the processed signal from the radar sensor. The results we obtained provide insights on the mechanical activity of the heart, which could assist cardiologists in their diagnoses.
Vision Aided Environment Semantics Extraction and Its Application in mmWave Beam Selection
Authors: Feiyang Wen, Weihua Xu, Feifei Gao, Chengkang Pan, Guangyi Liu
Date: 2023-01-21 00:00:00
Tasks: Keypoint Detection

In this letter, we propose a novel mmWave beam selection method based on the environment semantics extracted from user-side camera images. Specifically, we first define the environment semantics as the spatial distribution of the scatterers that affect the wireless propagation channels and utilize the keypoint detection technique to extract them from the input images. Then, we design a deep neural network with the environment semantics as the input that can output the optimal beam pairs at the mobile station (MS) and the base station (BS). Compared with the existing beam selection approaches that directly use images as the input, the proposed semantic-based method can explicitly obtain the environmental features that account for the propagation of wireless signals, thus reducing the storage and computational burden. Simulation results show that the proposed method can precisely estimate the location of the scatterers and outperform the existing works based on computer vision or light detection and ranging (LIDAR).
How Potent are Evasion Attacks for Poisoning Federated Learning-Based Signal Classifiers?
Authors: Su Wang, Rajeev Sahay, Christopher G. Brinton
Date: 2023-01-21 00:00:00
Tasks: Federated Learning, Model Poisoning

There has been recent interest in leveraging federated learning (FL) for radio signal classification tasks. In FL, model parameters are periodically communicated from participating devices, training on their own local datasets, to a central server which aggregates them into a global model. While FL has privacy/security advantages due to raw data not leaving the devices, it is still susceptible to several adversarial attacks. In this work, we reveal the susceptibility of FL-based signal classifiers to model poisoning attacks, which compromise the training process despite not observing data transmissions. In this capacity, we develop an attack framework in which compromised FL devices perturb their local datasets using adversarial evasion attacks. As a result, the training process of the global model significantly degrades on in-distribution signals (i.e., signals received over channels with identical distributions at each edge device). We compare our work to previously proposed FL attacks and reveal that as few as one adversarial device operating with a low-powered perturbation under our attack framework can induce the potent model poisoning attack to the global classifier. Moreover, we find that more devices partaking in adversarial poisoning will proportionally degrade the classification performance.
High-density magnetomyography is superior to high-density surface electromyography for motor unit decomposition: a simulation study
Authors: Thomas Klotz, Lena Lehmann, Francesco Negro, Oliver Röhrle
Date: 2023-01-23 00:00:00
Tasks: Anatomy

Objective: Studying motor units (MUs) is essential for understanding motor control, the detection of neuromuscular disorders and the control of human-machine interfaces. Individual motor unit firings are currently identified in vivo by decomposing electromyographic (EMG) signals. Due to our body's properties and anatomy, individual motor units can only be separated to a limited extent with surface EMG. Unlike electrical signals, magnetic fields do not interact with human tissues. This physical property and the emerging technology of quantum sensors make magnetomyography (MMG) a highly promising methodology. However, the full potential of MMG to study neuromuscular physiology has not yet been explored. Approach: In this work, we perform in silico trials that combine a biophysical model of EMG and MMG with state-of-the-art algorithms for the decomposition of motor units. This allows the prediction of an upper-bound for the motor unit decomposition accuracy. Main results: It is shown that non-invasive high-density MMG data is superior over comparable high-density surface EMG data for the robust identification of the discharge patterns of individual motor units. Decomposing MMG instead of EMG increased the number of identifiable motor units by 76%. Notably, MMG exhibits a less pronounced bias to detect superficial motor units. Significance: The presented simulations provide insights into methods to study the neuromuscular system non-invasively and in vivo that would not be easily feasible by other means. Hence, this study provides guidance for the development of novel biomedical technologies.
Are physiological oscillations 'physiological'?
Authors: Lingyun Xiong, Alan Garfinkel
Date: 2023-01-21 00:00:00

Despite widespread and striking examples of physiological oscillations, their functional role is often unclear. Even glycolysis, the paradigm example of oscillatory biochemistry, has seen questions about its oscillatory function. Here, we take a systems approach to summarize evidence that oscillations play critical physiological roles. Oscillatory behavior enables systems to avoid desensitization, to avoid chronically high and therefore toxic levels of chemicals, and to become more resistant to noise. Oscillation also enables complex physiological systems to reconcile incompatible conditions such as oxidation and reduction, by cycling between them, and to synchronize the oscillations of many small units into one large effect. In pancreatic beta cells, glycolytic oscillations are in synchrony with calcium and mitochondrial oscillations to drive pulsatile insulin release, which is pivotal for the liver to regulate blood glucose dynamics. In addition, oscillation can keep biological time, essential for embryonic development in promoting cell diversity and pattern formation. The functional importance of oscillatory processes requires a rethinking of the traditional doctrine of homeostasis, holding that physiological quantities are maintained at constant equilibrium values, a view that has largely failed us in the clinic. A more dynamic approach will enable us to view health and disease through a new light and initiate a paradigm shift in treating diseases, including depression and cancer. This modern synthesis also takes a deeper look into the mechanisms that create, sustain and abolish oscillatory processes, which requires the language of nonlinear dynamics, well beyond the linearization techniques of equilibrium control theory.
RawHash: Enabling Fast and Accurate Real-Time Analysis of Raw Nanopore Signals for Large Genomes
Authors: Can Firtina, Nika Mansouri Ghiasi, Joel Lindegger, Gagandeep Singh, Meryem Banu Cavlak, Haiyu Mao, Onur Mutlu
Date: 2023-01-22 00:00:00
Tasks: Quantization

Nanopore sequencers generate electrical raw signals in real-time while sequencing long genomic strands. These raw signals can be analyzed as they are generated, providing an opportunity for real-time genome analysis. An important feature of nanopore sequencing, Read Until, can eject strands from sequencers without fully sequencing them, which provides opportunities to computationally reduce the sequencing time and cost. However, existing works utilizing Read Until either 1) require powerful computational resources that may not be available for portable sequencers or 2) lack scalability for large genomes, rendering them inaccurate or ineffective. We propose RawHash, the first mechanism that can accurately and efficiently perform real-time analysis of nanopore raw signals for large genomes using a hash-based similarity search. To enable this, RawHash ensures the signals corresponding to the same DNA content lead to the same hash value, regardless of the slight variations in these signals. RawHash achieves an accurate hash-based similarity search via an effective quantization of the raw signals such that signals corresponding to the same DNA content have the same quantized value and, subsequently, the same hash value. We evaluate RawHash on three applications: 1) read mapping, 2) relative abundance estimation, and 3) contamination analysis. Our evaluations show that RawHash is the only tool that can provide high accuracy and high throughput for analyzing large genomes in real-time. When compared to the state-of-the-art techniques, UNCALLED and Sigmap, RawHash provides 1) 25.8x and 3.4x better average throughput and 2) significantly better accuracy for large genomes, respectively. Source code is available at https://github.com/CMU-SAFARI/RawHash.
ntLink: a toolkit for de novo genome assembly scaffolding and mapping using long reads
Authors: Lauren Coombe, René L. Warren, Johnathan Wong, Vladimir Nikolic, Inanc Birol
Date: 2023-01-20 00:00:00
Tasks: Computational Efficiency

With the increasing affordability and accessibility of genome sequencing data, de novo genome assembly is an important first step to a wide variety of downstream studies and analyses. Therefore, bioinformatics tools that enable the generation of high-quality genome assemblies in a computationally efficient manner are essential. Recent developments in long-read sequencing technologies have greatly benefited genome assembly work, including scaffolding, by providing long-range evidence that can aid in resolving the challenging repetitive regions of complex genomes. ntLink is a flexible and resource-efficient genome scaffolding tool that utilizes long-read sequencing data to improve upon draft genome assemblies built from any sequencing technologies, including the same long reads. Instead of using read alignments to identify candidate joins, ntLink utilizes minimizer-based mappings to infer how input sequences should be ordered and oriented into scaffolds. Recent improvements to ntLink have added important features such as overlap detection, gap-filling and in-code scaffolding iterations. Here, we present three basic protocols demonstrating how to use each of these new features to yield highly contiguous genome assemblies, while still maintaining ntLink's proven computational efficiency. Further, as we illustrate in the alternate protocols, the lightweight minimizer-based mappings that enable ntLink scaffolding can also be utilized for other downstream applications, such as misassembly detection. With its modularity and multiple modes of execution, ntLink has broad benefit to the genomics community, from genome scaffolding and beyond. ntLink is an open-source project and is freely available from https://github.com/bcgsc/ntLink.
Collective Intelligence and Neurodynamics: Functional Homologies
Authors: William Sulis
Date: 2023-01-23 00:00:00

A deep understanding of the dynamics of the human nervous system requires the simultaneous study of multiple spatiotemporal scales from the level of neurotransmitters up to the level of human cultures. This is likely impossible for technical and ethical reasons. Piecemeal analysis provides some understanding of the dynamics at single levels, but this does not illuminate the interactions between levels which are, at the very least, of great importance clinically. It would be useful to have an accessible biological system which could serve as a proxy for the nervous system and from which useful insights might be obtained. Functional homologies between the nervous system and collective intelligence systems, in particular social insect colonies, are described. It is proposed that social insects colonies could serve as functional proxies for nervous systems. Thus a multiscale study of social insect colonies may provide insights into the dynamics of nervous systems.
A Quantum-Classical Model of Brain Dynamics
Authors: Alessandro Sergi, Antonino Messina, Carmelo M. Vicario, Gabriella Martino
Date: 2023-01-17 00:00:00
Tasks: model

The study of the human psyche has elucidated a bipartite structure of cognition reflecting the quantum-classical nature of any process that generates knowledge and learning governed by brain activity. Acknowledging the importance of such a finding for modelization, we posit an approach to study brain by means of the quantum-classical dynamics of a Mixed Weyl symbol. The Mixed Weyl symbol is used to describe brain processes at the microscopic level and provides a link to the results of measurements made at the mesoscopic scale. Within this approach, quantum variables (such as,for example, nuclear and electron spins, dipole momenta of particles or molecules, tunneling degrees of freedom, etc may be represented by spinors while the electromagnetic fields and phonon modes involved in the processes are treated either classically or semi-classically, by also considering quantum zero-point fluctuations. Zero-point quantum effects can be incorporated into numerical simulations by controlling the temperature of each field mode via coupling to a dedicated Nos\`e-Hoover chain thermostat. The temperature of each thermostat is chosen in order to reproduce quantum statistics in the canonical ensemble. In this first paper, we introduce a quantum-classical model of brain dynamics, clarifying its mathematical strucure and focusing the discussion on its predictive value. Analytical consequences of the model are not reported in this paper, since they are left for future work. Our treatment incorporates compatible features of three well-known quantum approaches to brain dynamics - namely the electromagnetic field theory approach, the orchestrated objective reduction theory, and the dissipative quantum model of the brain - and hints at convincing arguments that sustain the existence of quantum-classical processes in the brain activity. All three models are reviewed.
Understanding Physical Processes in Describing a State of Consciousness: A Review
Authors: Charles Johnstone, Prashant S. Alegaonkar
Date: 2023-01-12 00:00:00

The way we view the reality of nature, including ourselves, depend on consciousness.It also defines the identity of the person, since we know people in terms of their experiences. In general, consciousness defines human existence in this universe. Furthermore, consciousness is associated with the most debated problems in physics such as the notion of observation, observer,in the measurement problem. However,its nature, occurrence mechanism in the brain and the definite universal locality of the consciousness are not clearly known. Due to this consciousness is considered asan essential unresolved scientific problem of the current era.Here, we review the physical processes which are associated in tackling these challenges. Firstly, we discuss the association of consciousness with transmission of signals in the brain, chain of events, quantum phenomena process and integrated information. We also highlight the roles of structure of matter,field, and the concept of universality towards understanding consciousness. Finally, we propose further studies for achieving better understanding of consciousness.
Unifying Consciousness and Time to Enhance Artificial Intelligence
Authors: Mahendra Samarawickrama
Date: 2023-01-10 00:00:00
Tasks: Ethics, Self-Learning

Consciousness is a sequential process of awareness which can focus on one piece of information at a time. This process of awareness experiences causation which underpins the notion of time while it interplays with matter and energy, forming reality. The study of Consciousness, time and reality is complex and evolving fast in many fields, including metaphysics and fundamental physics. Reality composes patterns in human Consciousness in response to the regularities in nature. These regularities could be physical (e.g., astronomical, environmental), biological, chemical, mental, social, etc. The patterns that emerged in Consciousness were correlated to the environment, life and social behaviours followed by constructed frameworks, systems and structures. The complex constructs evolved as cultures, customs, norms and values, which created a diverse society. In the evolution of responsible AI, it is important to be attuned to the evolved cultural, ethical and moral values through Consciousness. This requires the advocated design of self-learning AI aware of time perception and human ethics.