pwc-archive/papers-with-abstracts huggingface dataset:
A Stochastic Particle Variational Bayesian Inference Inspired Deep-Unfolding Network for Non-Convex Parameter Estimation
Authors: Zhixiang Hu, An Liu, MinJian Zhao
Date: 2023-10-09 00:00:00
Tasks: Bayesian Inference, parameter estimation

Future wireless networks are envisioned to provide ubiquitous sensing services, which also gives rise to a substantial demand for high-dimensional non-convex parameter estimation, i.e., the associated likelihood function is non-convex and contains numerous local optima. Variational Bayesian inference (VBI) provides a powerful tool for modeling complex estimation problems and reasoning with prior information, but poses a long-standing challenge on computing intractable posteriori distributions. Most existing variational methods generally rely on assumptions about specific distribution families to derive closed-form solutions, and are difficult to apply in high-dimensional, non-convex scenarios. Given these challenges, firstly, we propose a parallel stochastic particle variational Bayesian inference (PSPVBI) algorithm. Thanks to innovations such as particle approximation, additional updates of particle positions, and parallel stochastic successive convex approximation (PSSCA), PSPVBI can flexibly drive particles to fit the posteriori distribution with acceptable complexity, yielding high-precision estimates of the target parameters. Furthermore, additional speedup can be obtained by deep-unfolding (DU) the PSPVBI algorithm. Specifically, superior hyperparameters are learned to dramatically reduce the number of algorithmic iterations. In this PSPVBI-induced Deep-Unfolding Networks, some techniques related to gradient computation, data sub-sampling, differentiable sampling, and generalization ability are also employed to facilitate the practical deployment. Finally, we apply the LPSPVBI to solve several important parameter estimation problems in wireless sensing scenarios. Simulations indicate that the LPSPVBI algorithm outperforms existing solutions.
Unsupervised deep learning framework for temperature-compensated damage assessment using ultrasonic guided waves on edge device
Authors: Pankhi Kashyap, Kajal Shivgan, Sheetal Patil, Ramana Raja B, Sagar Mahajan, Sauvik Banerjee, Siddharth Tallur
Date: 2023-10-08 00:00:00
Tasks: Cloud Computing, Structural Health Monitoring

Fueled by the rapid development of machine learning (ML) and greater access to cloud computing and graphics processing units (GPUs), various deep learning based models have been proposed for improving performance of ultrasonic guided wave structural health monitoring (GW-SHM) systems, especially to counter complexity and heterogeneity in data due to varying environmental factors (e.g., temperature) and types of damages. Such models typically comprise of millions of trainable parameters, and therefore add to cost of deployment due to requirements of cloud connectivity and processing, thus limiting the scale of deployment of GW-SHM. In this work, we propose an alternative solution that leverages TinyML framework for development of light-weight ML models that could be directly deployed on embedded edge devices. The utility of our solution is illustrated by presenting an unsupervised learning framework for damage detection in honeycomb composite sandwich structure (HCSS) with disbond and delamination type of damages, validated using data generated by finite element (FE) simulations and experiments performed at various temperatures in the range 0{\deg}C to 90{\deg}C. We demonstrate a fully-integrated solution using a Xilinx Artix-7 FPGA for data acquisition and control, and edge-inference of damage.
Decentralized Federated Learning via MIMO Over-the-Air Computation: Consensus Analysis and Performance Optimization
Authors: Zhiyuan Zhai, Xiaojun Yuan, Xin Wang
Date: 2023-10-08 00:00:00
Tasks: Distributed Optimization, Federated Learning

Decentralized federated learning (DFL), inherited from distributed optimization, is an emerging paradigm to leverage the explosively growing data from wireless devices in a fully distributed manner.DFL enables joint training of machine learning model under device to device (D2D) communication fashion without the coordination of a parameter server. However, the deployment of wireless DFL is facing some pivotal challenges. Communication is a critical bottleneck due to the required extensive message exchange between neighbor devices to share the learned model. Besides, consensus becomes increasingly difficult as the number of devices grows because there is no available central server to perform coordination. To overcome these difficulties, this paper proposes employing over-the-air computation (Aircomp) to improve communication efficiency by exploiting the superposition property of analog waveform in multi-access channels, and introduce the mixing matrix mechanism to promote consensus using the spectral property of symmetric doubly stochastic matrix. Specifically, we develop a novel multiple-input multiple-output over-the-air DFL (MIMO OA-DFL) framework to study over-the-air DFL problem over MIMO multiple access channels. We conduct a general convergence analysis to quantitatively capture the influence of aggregation weight and communication error on the MIMO OA-DFL performance in \emph{ad hoc} networks. The result shows that the communication error together with the spectral gap of mixing matrix has a significant impact on the learning performance. Based on this, a joint communication-learning optimization problem is formulated to optimize transceiver beamformers and mixing matrix. Extensive numerical experiments are performed to reveal the characteristics of different topologies and demonstrate the substantial learning performance enhancement of our proposed algorithm.
OTFS based Joint Radar and Communication: Signal Analysis using the Ambiguity Function
Authors: Shalanika Dayarathna, Peter Smith, Rajitha Senanayake, Jamie Evans
Date: 2023-10-07 00:00:00

Orthogonal time frequency space (OTFS) modulation has recently been identified as a suitable waveform for joint radar and communication systems. Focusing on the effect of data modulation on the radar sensing performance, we derive the ambiguity function (AF) of the OTFS waveform and characterize the radar global accuracy. We evaluate the behavior of the AF with respect to the distribution of the modulated data and derive an accurate approximation for the mean and variance of the AF, thus, approximating its distribution by a Rice distribution. Finally, we evaluate the global radar performance of the OTFS waveform with the OFDM waveform.
Linear Least Squares Estimation of Fiber-Longitudinal Optical Power Profile
Authors: Takeo Sasai, Minami Takahashi, Masanori Nakamura, Etsushi Yamazaki, Yoshiaki Kisaka
Date: 2023-10-07 00:00:00

This paper presents a linear least squares method for fiber-longitudinal power profile estimation (PPE), which estimates an optical signal power distribution throughout a fiber-optic link at a coherent receiver. The method finds the global optimum in least square estimation of longitudinal power profiles, thus closely matching true optical power profiles and locating loss anomalies in a link with high spatial resolution. Experimental results show that the method achieves accurate PPE with an RMS error from OTDR of 0.18 dB. Consequently, it successfully identifies a loss anomaly as small as 0.77 dB, demonstrating the potential of a coherent receiver in locating even splice and connector losses. The method is also evaluated under a WDM condition with optimal system fiber launch power, highlighting its feasibility for use in operations. Furthermore, a fundamental limit for stable estimation and spatial resolution of least-squares-based PPE is quantitatively discussed in relation to the ill-posedness of PPE by evaluating the condition number of a nonlinear perturbation matrix.
Score-based Diffusion Models With Self-supervised Learning For Accelerated 3D Multi-contrast Cardiac Magnetic Resonance Imaging
Authors: Yuanyuan Liu, Zhuo-Xu Cui, Shucong Qin, Congcong Liu, Hairong Zheng, Haifeng Wang, Yihang Zhou, Dong Liang, Yanjie Zhu
Date: 2023-10-07 00:00:00
Tasks: compressed sensing, MRI Reconstruction, Self-Supervised Learning

Long scan time significantly hinders the widespread applications of three-dimensional multi-contrast cardiac magnetic resonance (3D-MC-CMR) imaging. This study aims to accelerate 3D-MC-CMR acquisition by a novel method based on score-based diffusion models with self-supervised learning. Specifically, we first establish a mapping between the undersampled k-space measurements and the MR images, utilizing a self-supervised Bayesian reconstruction network. Secondly, we develop a joint score-based diffusion model on 3D-MC-CMR images to capture their inherent distribution. The 3D-MC-CMR images are finally reconstructed using the conditioned Langenvin Markov chain Monte Carlo sampling. This approach enables accurate reconstruction without fully sampled training data. Its performance was tested on the dataset acquired by a 3D joint myocardial T1 and T1rho mapping sequence. The T1 and T1rho maps were estimated via a dictionary matching method from the reconstructed images. Experimental results show that the proposed method outperforms traditional compressed sensing and existing self-supervised deep learning MRI reconstruction methods. It also achieves high quality T1 and T1rho parametric maps close to the reference maps, even at a high acceleration rate of 14.
Deep Learning Based Active Spatial Channel Gain Prediction Using a Swarm of Unmanned Aerial Vehicles
Authors: Enes Krijestorac, Danijela Cabric
Date: 2023-10-06 00:00:00
Tasks: Prediction

Prediction of wireless channel gain (CG) across space is a necessary tool for many important wireless network design problems. In this paper, we develop prediction methods that use environment-specific features, namely building maps and CG measurements, to achieve high prediction accuracy. We assume that measurements are collected using a swarm of coordinated unmanned aerial vehicles (UAVs). We develop novel active prediction approaches which consist of both methods for UAV path planning for optimal measurement collection and methods for prediction of CG across space based on the collected measurements. We propose two active prediction approaches based on deep learning (DL) and Kriging interpolation. The first approach does not rely on the location of the transmitter and utilizes 3D maps to compensate for the lack of it. We utilize DL to incorporate 3D maps into prediction and reinforcement learning for optimal path planning for the UAVs based on DL prediction. The second active prediction approach is based on Kriging interpolation, which requires known transmitter location and cannot utilize 3D maps. We train and evaluate the two proposed approaches in a ray-tracing-based channel simulator. Using simulations, we demonstrate the importance of active prediction compared to prediction based on randomly collected measurements of channel gain. Furthermore, we show that using DL and 3D maps, we can achieve high prediction accuracy even without knowing the transmitter location. We also demonstrate the importance of coordinated path planning for active prediction when using multiple UAVs compared to UAVs collecting measurements independently in a greedy manner.
An Automatic Cost Learning Framework for Image Steganography Using Deep Reinforcement Learning
Authors: Weixuan Tang, Bin Li, Mauro Barni, Jin Li, Jiwu Huang
Date: 2020-09-25 00:00:00
Proceeding: journal 2020 9
Tasks: Deep Reinforcement Learning, Image Steganography, reinforcement-learning

Abstract— Automatic cost learning for steganography based on deep neural networks is receiving increasing attention.
Steganographic methods under such a framework have been shown to achieve better security performance than methods
adopting hand-crafted costs. However, they still exhibit some limitations that prevent a full exploitation of their potentiality,
including using a function-approximated neural-network-based embedding simulator and a coarse-grained optimization objective without explicitly using pixel-wise information. In this article, we propose a new embedding cost learning framework called SPAR-RL (Steganographic Pixel-wise Actions and Rewards with Reinforcement Learning) that overcomes the above limitations. In SPAR-RL, an agent utilizes a policy network which decomposes the embedding process into pixel-wise actions and aims at maximizing the total rewards from a simulated steganalytic environment, while the environment employs an environment network for pixel-wise reward assignment. A sampling process is utilized to emulate the message embedding of an optimal embedding simulator. Through the iterative interactions between the agent and the environment, the policy network learns a secure embedding policy which can be converted into pixel-wise embedding costs for practical message embedding. Experimental results demonstrate that the proposed framework achieves state-of-the-art security performance against various modern steganalyzers, and outperforms existing cost learning frameworks with regard to learning stability and efficiency.
Simplifying recombinant protein production: Combining Golden Gate cloning with a standardized protein purification scheme
Authors: Sonja Zweng, Gabriel Mendoza-Rojas, Florian Altegoer
Date: 2023-10-10 00:00:00

Recombinant protein production is pivotal in molecular biology, enabling profound insights into cellular processes through biophysical, biochemical, and structural analyses of the purified samples. The demand for substantial biomolecule quantities often presents challenges, particularly for eukaryotic proteins. Escherichia coli expression systems have evolved to address these issues, offering advanced features such as solubility tags, posttranslational modification capabilities, and modular plasmid libraries. Nevertheless, existing tools are often complex, which limits their accessibility and necessitates streamlined systems for rapid screening under standardized conditions. Based on the Golden Gate cloning method, we have developed a simple 'one-pot' approach for the generation of expression constructs using strategically chosen tags like hexahistidine, SUMO, MBP, GST, and GB1 to enhance solubility and expression. Tags are removable via TEV protease cleavage, and the system allows visual cloning verification through mScarlet fluorescence. We provide a comprehensive protocol encompassing oligonucleotide design, cloning, expression, Ni-NTA affinity chromatography, and size-exclusion chromatography. This method therefore streamlines prokaryotic and eukaryotic protein production, rendering it accessible to standard molecular biology laboratories with basic protein biochemical equipment.
RawAlign: Accurate, Fast, and Scalable Raw Nanopore Signal Mapping via Combining Seeding and Alignment
Authors: Joël Lindegger, Can Firtina, Nika Mansouri Ghiasi, Mohammad Sadrosadati, Mohammed Alser, Onur Mutlu
Date: 2023-10-08 00:00:00

Nanopore sequencers generate raw electrical signals representing the contents of a biological sequence molecule passing through the nanopore. These signals can be analyzed directly, avoiding basecalling entirely. We observe that while existing proposals for raw signal analysis typically do well in all metrics for small genomes (e.g., viral genomes), they all perform poorly for large genomes (e.g., the human genome). Our goal is to analyze raw nanopore signals in an accurate, fast, and scalable manner. To this end, we propose RawAlign, the first work to integrate fine-grained signal alignment into the state-of-the-art raw signal mapper. To enable accurate, fast, and scalable mapping with alignment, RawAlign implements three algorithmic improvements and hardware acceleration via a vectorized implementation of fine-grained alignment. Together, these significantly reduce the overhead of typically computationally expensive fine-grained alignment. Our extensive evaluations on different use cases and various datasets show RawAlign provides 1) the most accurate mapping for large genomes and 2) and on-par performance compared to RawHash (between 0.80x-1.08x), while achieving better performance than UNCALLED and Sigmap by on average (geo. mean) 2.83x and 2.06x, respectively. Availability: https://github.com/CMU-SAFARI/RawAlign.
Multilevel Monte Carlo for a class of Partially Observed Processes in Neuroscience
Authors: Mohamed Maama, Ajay Jasra, Kengo Kamatani
Date: 2023-10-10 00:00:00

In this paper we consider Bayesian parameter inference associated to a class of partially observed stochastic differential equations (SDE) driven by jump processes. Such type of models can be routinely found in applications, of which we focus upon the case of neuroscience. The data are assumed to be observed regularly in time and driven by the SDE model with unknown parameters. In practice the SDE may not have an analytically tractable solution and this leads naturally to a time-discretization. We adapt the multilevel Markov chain Monte Carlo method of [11], which works with a hierarchy of time discretizations and show empirically and theoretically that this is preferable to using one single time discretization. The improvement is in terms of the computational cost needed to obtain a pre-specified numerical error. Our approach is illustrated on models that are found in neuroscience.
Identifying epileptogenic abnormality by decomposing intracranial EEG and MEG power spectra
Authors: Csaba Kozma, Gabrielle Schroeder, Tom Owen, Jane de Tisi, Andrew W. McEvoy, Anna Miserocchi, John Duncan, Yujiang Wang, Peter N. Taylor
Date: 2023-10-10 00:00:00
Tasks: EEG

Identifying abnormal electroencephalographic activity is crucial in diagnosis and treatment of epilepsy. Recent studies showed that decomposing brain activity into periodic (oscillatory) and aperiodic (trend across all frequencies) components may illuminate drivers of changes in spectral activity. Using iEEG data from 234 subjects, we constructed a normative map and compared this with a separate cohort of 63 patients with refractory focal epilepsy being considered for neurosurgery. The normative map was computed using three approaches: (i) relative complete band power, (ii) relative band power with the aperiodic component removed (iii) the aperiodic exponent. Corresponding abnormalities were also calculated for each approach in the separate patient cohort. We investigated the spatial profiles of the three approaches, assessed their localizing ability, and replicated our findings in a separate modality using MEG. The normative maps of relative complete band power and relative periodic band power had similar spatial profiles. In the aperiodic normative map, exponent values were highest in the temporal lobe. Abnormality estimated through the complete band power robustly distinguished between good and bad outcome patients. Neither periodic band power nor aperiodic exponent abnormalities distinguished seizure outcome groups. Combining periodic and aperiodic abnormalities improved performance, similar to the complete band power approach. Our findings suggest that sparing cerebral tissue that generates abnormalities in either periodic or aperiodic activity may lead to a poor surgical outcome. Both periodic and aperiodic abnormalities are necessary to distinguish patient outcomes, with neither sufficient in isolation. Future studies could investigate whether periodic or aperiodic abnormalities are affected by the cerebral location or pathology.
Modeling motor control in continuous-time Active Inference: a survey
Authors: Matteo Priorelli, Federico Maggiore, Antonella Maselli, Francesco Donnarumma, Domenico Maisto, Francesco Mannella, Ivilin Peev Stoianov, Giovanni Pezzulo
Date: 2023-10-08 00:00:00
Tasks: Decision Making, Survey

The way the brain selects and controls actions is still widely debated. Mainstream approaches based on Optimal Control focus on stimulus-response mappings that optimize cost functions. Ideomotor theory and cybernetics propose a different perspective: they suggest that actions are selected and controlled by activating action effects and by continuously matching internal predictions with sensations. Active Inference offers a modern formulation of these ideas, in terms of inferential mechanisms and prediction-error-based control, which can be linked to neural mechanisms of living organisms. This article provides a technical illustration of Active Inference models in continuous time and a brief survey of Active Inference models that solve four kinds of control problems; namely, the control of goal-directed reaching movements, active sensing, the resolution of multisensory conflict during movement and the integration of decision-making and motor control. Crucially, in Active Inference, all these different facets of motor control emerge from the same optimization process - namely, the minimization of Free Energy - and do not require designing separate cost functions. Therefore, Active Inference provides a unitary perspective on various aspects of motor control that can inform both the study of biological control mechanisms and the design of artificial and robotic systems.
Synaptic delay induced macroscopic dynamics of the large-scale network of Izhikevich neurons
Authors: Liang Chen, Sue Ann Campbell
Date: 2023-10-06 00:00:00

We consider a large network of Izhikevich neurons. Each neuron has a quadratic integrate-and-fire type model with a recovery variable modelling spike frequency adaptation (SFA). We introduce a biologically motivated synaptic current expression and a delay in the synaptic transmission. Following the Ott-Antonsen theory, we reduce the network model to a mean-field system of delayed differential equations. Numerical bifurcation analysis allows us to locate higher-codimension bifurcations and to identify the regions in the parameter space where the network exhibits changes in the macroscopic dynamics, including transitions between states where the individual neurons exhibit asynchronous tonic firing and different types of synchronous bursting. We investigate the impact of the heterogeneity of the quenched input current, the SFA mechanism and the synaptic delay on macroscopic dynamics. In the limit that the heterogeneity goes to zero, our perturbation and bifurcation analysis shows that the behaviour of the mean-field model remains consistent, although this limit breaks an assumption of the model reduction. For a single population of neurons with SFA, the synaptic delay has little effect on the generation of COs for weak coupling, but favours their emergence beyond that, and even induces new macroscopic dynamics. In particular, Torus bifurcations may occur, and these are a crucial mechanism for the emergence of population bursting with two nested frequencies. We discuss how these solutions may relate to cross-frequency coupling which is potentially relevant for understanding healthy and pathological brain functions.
Of heading, posture and body rotations derived from data acquired by animal-borne accelerometers, magnetometers and gyrometers, kernel density estimation of the corresponding spherical distributions, and fine-scale movement reconstruction
Authors: Simon Benhamou
Date: 2023-10-09 00:00:00
Tasks: Density Estimation

This paper provides the mathematical expressions, formulated in classical trigonometric terms, that are required (i) to compute an animal's body orientations and rotations in 3D space from data acquired at high-frequency by on-board accelerometers, magnetometers and gyrometers, (ii) to relate 3D rotations to changes in 3D orientation, (iii) to represent their spherical distributions through kernel density estimation (KDE), and (iv) to reconstruct fine-scale movements. The way the huge amount of data that is acquired by these on-board devices can be more easily managed is also considered.