pwc-archive/papers-with-abstracts huggingface dataset:
Compressive Spectrum Sensing Using Sampling-Controlled Block Orthogonal Matching Pursuit
Authors: Liyang Lu, Wenbo Xu, Yue Wang, Zhi Tian
Date: 2022-11-14 00:00:00

This paper proposes two novel schemes of wideband compressive spectrum sensing (CSS) via block orthogonal matching pursuit (BOMP) algorithm, for achieving high sensing accuracy in real time. These schemes aim to reliably recover the spectrum by adaptively adjusting the number of required measurements without inducing unnecessary sampling redundancy. To this end, the minimum number of required measurements for successful recovery is first derived in terms of its probabilistic lower bound. Then, a CSS scheme is proposed by tightening the derived lower bound, where the key is the design of a nonlinear exponential indicator through a general-purpose sampling-controlled algorithm (SCA). In particular, a sampling-controlled BOMP (SC-BOMP) is developed through a holistic integration of the existing BOMP and the proposed SCA. For fast implementation, a modified version of SC-BOMP is further developed by exploring the block orthogonality in the form of sub-coherence of measurement matrices, which allows more compressive sampling in terms of smaller lower bound of the number of measurements. Such a fast SC-BOMP scheme achieves a desired tradeoff between the complexity and the performance. Simulations demonstrate that the two SC-BOMP schemes outperform the other benchmark algorithms.
Misspecified Cramér-Rao Bound of RIS-aided Localization under Geometry Mismatch
Authors: Pinjun Zheng, Hui Chen, Tarig Ballal, Henk Wymeersch, Tareq Y. Al-Naffouri
Date: 2022-11-13 00:00:00

In 5G/6G wireless systems, reconfigurable intelligent surfaces (RIS) can play a role as a passive anchor to enable and enhance localization in various scenarios. However, most existing RIS-aided localization works assume that the geometry of the RIS is perfectly known, which is not realistic in practice due to calibration errors. In this work, we derive the misspecified Cram\'er-Rao bound (MCRB) for a single-input-single-output RIS-aided localization system with RIS geometry mismatch. Specifically, unlike most existing works that use numerical methods, we propose a closed-form solution to the pseudo-true parameter determination problem for MCRB analysis. Simulation results demonstrate the validity of the derived pseudo-true parameters and MCRB, and show that the RIS geometry mismatch causes performance saturation in the high signal-to-noise ratio regions.
Two-dimensional gridless super-resolution method for ISAR imaging
Authors: Mohammad Roueinfar, Mohammad Hossein Kahaei
Date: 2022-11-13 00:00:00
Tasks: Super-Resolution, Vocal Bursts Valence Prediction

We are focused on improving the resolution of images of moving targets in Inverse Synthetic Aperture Radar (ISAR) imaging. This could be achieved by recovering the scattering points of a target that have stronger reflections than other target points, leading to increasing the higher Radar Cross Section (RCS) of a target. These points, however, are sparse and when the received data is incomplete, moving targets would not be properly recognizable in ISAR images. To increase the resolution in ISAR imaging, we propose the 2-Dimensional Reweighted Trace Minimization (2D-RWTM) method to retrieve frequencies of sparse scattering points in both range and cross-range directions. This method is a gridless super-resolution method, which does not depend on fitting the scattering point on the grids, leading to less complexity compared to the other methods. Using computer simulations, the proposed 2D-RWTM is compared to the Atomic Norm Minimization (ANM) in terms of the Mean Squared Errors (MSE). The results show that using the proposed method, the scattering points of a target are successfully recovered. It is shown that by selecting different weighting matrices and scattering points adjacent to each other, the recovery in ISAR imaging is still successful.
ECE496Y Final Report: Edge Machine Learning for Detecting Freezing of Gait in Parkinson's Patients
Authors: Purnoor Ghuman, Tyama Lyall, Usama Mahboob, Alia Aamir, Xilin Liu
Date: 2022-11-13 00:00:00

Parkinson's disease is a common neurological disease, entailing a multitude of motor deficiency symptoms. In this project, we developed a device with an uploaded edge machine learning algorithm that can detect the onset of freezing of gait symptoms in a Parkinson's patient. The algorithm achieved an accuracy of 83.7% in a validation using data from ten patients. The model was deployed in a microcontroller Arduino Nano 33 BLE Sense Board model and validated in real-time operation with data streamed to the microcontroller from a computer.
Quantum Dot Solar cells
Authors: Husien Salama
Date: 2022-11-13 00:00:00
Tasks: Band Gap

There remains wide interest in solar cells being made using inexpensive materials and simple device manufacturing techniques to harvest ever-increasing amounts of energy. New semiconductor materials and new quantum nanostructures are exploited to fabricate high-efficiency next-generation solar cells. Quantum dots have offered an attractive option for photovoltaics. A single photon absorbed by a quantum dot produces more than one bound electron-hole pair, or exciton, thereby doubling normal conversion efficiency numbers seen in single-junction silicon cells. One potential is to use the solar cell configuration which incorporates. Quantum Dots Super Lattice (QDSL). QDSL provides a mechanism for the enhancement of solar sales due to their higher band gap and absorption coefficient when compared to their bulk material counterpart. The mini bands in the conduction, as well as the valence band of a QDSL, play an important role in solar cells because the photogenerated carriers are collected via transport using mini bands. In recent years, a lot of research has been done on crystal growth, structural, electrical, and optical properties of thin films and nanostructures as well as fabrication processes and characterization of photovoltaic devices. In this paper, all the recent developments in future generation quantum dot solar cells like a tandem, intermediate band, and solution-processed band alignment engineering from several research works will be presented.
A Shared Cluster-based Stochastic Channel Model for Integrated Sensing and Communication Systems
Authors: Yameng Liu, Jianhua Zhang, Yuxiang Zhang, Zhiqiang Yuan, Guangyi Liu
Date: 2022-11-12 00:00:00
Tasks: Integrated sensing and communication, ISAC

Integrated Sensing And Communication (ISAC) has been recognized as a promising technology in the 6G communication. A realistic channel model is a prerequisite for designing ISAC systems. Most existing channel models independently generate the communication and sensing channels under the same framework. However, due to the multiplexing of hardware resources and the same environment, signals enabled for communication and sensing may experience shared propagation scatterers. This practical sharing feature necessities the joint generation of communication and sensing channels for realistic modeling, where the shared clusters (contributed by the shared scatterers) should be reconstructed.In this paper, we first conduct communication and sensing channel measurements for an indoor scenario at 28 GHz. The power-angular-delay profiles of multipath components are obtained, and the shared scatterers by communication and sensing channels are intuitively observed. Then, a stochastic ISAC channel model is proposed to capture the sharing feature, where shared and non-shared clusters by the two channels are dfined and superimposed. To extract those clusters from measured ISAC channels, a KPowerMeans-based joint clustering algorithm is novelly introduced. Finally, stochastic channel characteristics are analyzed, and empirical simulations validate that the channel Sharing Degree (SD) increases with more shared clusters. The proposed model can realistically capture the sharing feature of ISAC channels and is able to evaluate and simulate the channel SD values, which is valuable for the design and deployment of ISAC systems.
Design of Integrated Communication System under UAV Network
Authors: Sang Wanchao, Gao Hui, Xu Wenjun
Date: 2022-11-12 00:00:00

It is becoming a key feature of UAV communication network to integrate sensing function into communication equipment to realize sensing communication integration.The existing perceptual communication fusion schemes focus on the design under a single waveform and single frequency band, and lack of discussion on the adaptation of scene and algorithm complexity.The current perception mode of UAV focuses on radar active detection and perception, and there is a problem of time delay in perception information sharing.For this reason, this paper first proposes a waveform library idea based on perceptual information drive to achieve the best cost performance ratio of the communication scheme adopted by UAVs in different flight scenarios.Based on the hybrid waveform concept, this paper proposes a design scheme of active passive fusion synchronous sensing, and matches with an adaptive sensing algorithm based on compressed sensing.Through simulation, it is proved that the overhead caused by sharing perceptual information among UAVs can be reduced, And improve the passive side sensing accuracy.
Transforming RIS-Assisted Passive Beamforming from Tedious to Simple: A Relaxation Algorithm for Rician Channel
Authors: Xuehui Dong, Rujing Xiong, Tiebin Mi, Yuan Xie, Robert Caiming Qiu
Date: 2022-11-12 00:00:00

This paper investigates the problem of maximizing the signal-to-noise ratio (SNR) in reconfigurable intelligent surface (RIS)-assisted MISO communication systems. The problem will be reformulated as a complex quadratic form problem with unit circle constraints. We proved that the SNR maximizing problem has a closed-form global optimal solution when it is a rank-one problem, whereas the former researchers regarded it as an optimization problem. Moreover, We propose a relaxation algorithm (RA) that relaxes the constraints to that of Rayleigh's quotient problem and then projects the solution back, where the SNR obtained by RA achieves much the same SNR as the upper bound but with significantly low time consumption. Then we asymptotically analyze its performance when the transmitter antennas n_t and the number of units of RIS N grow large together, with N/n_t -> c. Finally, our numerical simulations show that RA achieves over 98% of the performance of the upper bound and takes below 1% time consumption of manifold optimization (MO) and 0.1% of semidefinite relaxation (SDR).
Training self-supervised peptide sequence models on artificially chopped proteins
Authors: Gil Sadeh, Zichen Wang, Jasleen Grewal, Huzefa Rangwala, Layne Price
Date: 2022-11-09 00:00:00
Tasks: Data Augmentation, Language Modeling, Language Modelling, Representation Learning, Zero-Shot Learning

Representation learning for proteins has primarily focused on the global understanding of protein sequences regardless of their length. However, shorter proteins (known as peptides) take on distinct structures and functions compared to their longer counterparts. Unfortunately, there are not as many naturally occurring peptides available to be sequenced and therefore less peptide-specific data to train with. In this paper, we propose a new peptide data augmentation scheme, where we train peptide language models on artificially constructed peptides that are small contiguous subsets of longer, wild-type proteins; we refer to the training peptides as "chopped proteins". We evaluate the representation potential of models trained with chopped proteins versus natural peptides and find that training language models with chopped proteins results in more generalized embeddings for short protein sequences. These peptide-specific models also retain information about the original protein they were derived from better than language models trained on full-length proteins. We compare masked language model training objectives to three novel peptide-specific training objectives: next-peptide prediction, contrastive peptide selection and evolution-weighted MLM. We demonstrate improved zero-shot learning performance for a deep mutational scan peptides benchmark.
A universal DNA computing model for solving NP-hard subset problems
Authors: Enqiang Zhu, Xianhang Luo, Chanjuan Liu, Xiaolong Shi, Jin Xu
Date: 2022-11-14 00:00:00

DNA computing, a nontraditional computing mechanism, provides a feasible and effective method for solving NP-hard problems because of the vast parallelism and high-density storage of DNA molecules. Although DNA computing has been exploited to solve various intractable computational problems, such as the Hamiltonian path problem, SAT problem, and graph coloring problem, there has been little discussion of designing universal DNA computing-based models, which can solve a class of problems. In this paper, by leveraging the dynamic and enzyme-free properties of DNA strand displacement, we propose a universal model named DCMSubset for solving subset problems in graph theory. The model aims to find a minimum (or maximum) set satisfying given constraints. For each element x involved in a given problem, DCMSubset uses an exclusive single-stranded DNA molecule to model x as well as a specific DNA complex to model the relationship between x and other elements. Based on the proposed model, we conducted simulation and biochemical experiments on three kinds of subset problems, a minimum dominating set, maximum independent set, and minimum vertex cover. We observed that DCMSubset can also be used to solve the graph coloring problem. Moreover, we extended DCMSubset to a model for solving the SAT problem. The results of experiments showed the feasibility and university of the proposed method. Our results highlighted the potential for DNA strand displacement to act as a computation tool to solve NP-hard problems.
A Unified Theory of Dual-Process Control
Authors: Ted Moskovitz, Kevin Miller, Maneesh Sahani, Matthew M. Botvinick
Date: 2022-11-13 00:00:00
Tasks: Decision Making

Dual-process theories play a central role in both psychology and neuroscience, figuring prominently in fields ranging from executive control to reward-based learning to judgment and decision making. In each of these domains, two mechanisms appear to operate concurrently, one relatively high in computational complexity, the other relatively simple. Why is neural information processing organized in this way? We propose an answer to this question based on the notion of compression. The key insight is that dual-process structure can enhance adaptive behavior by allowing an agent to minimize the description length of its own behavior. We apply a single model based on this observation to findings from research on executive control, reward-based learning, and judgment and decision making, showing that seemingly diverse dual-process phenomena can be understood as domain-specific consequences of a single underlying set of computational principles.
In vivo labeling and quantitative imaging of neurons using MRI
Authors: Shana Li, Xiang Xu, Canjun Li, Ziyan Xu, Qiong Ye, Yan Zhang, Chunlei Cang, Jie Wen
Date: 2022-11-13 00:00:00

Mammalian brain is a complex organ that contains billions of neurons. These neurons form various neural circuits that control the perception, cognition, emotion and behavior. Developing in vivo neuronal labeling and imaging techniques is crucial for studying the structure and function of neural circuits. In vivo techniques can provide true physiological information that cannot be provided by ex vivo methods. In this study, we describe a new strategy for in vivo neuronal labeling and quantification using MRI. To demonstrate the ability of this new method, we used neurotropic virus to deliver oatp1a1 gene to the target neural circuit. OATP1A1 protein is expressed on the neuronal membrane and can increase the uptake of a specific MRI contrast agent (Gd-EOB-DTPA). By using T1-weighted images for observation, labeled neurons "light up" on MRI. We further use a dynamic-contrast-enhancement based method to obtain measures that provide quantitative information of labeled neurons in vivo.
An algebraic approach to spike-time neural codes in the hippocampus
Authors: Federico W. Pasini, Alexandra N. Busch, Ján Mináč, Krishnan Padmanabhan, Lyle Muller
Date: 2022-11-12 00:00:00
Tasks: Hippocampus, Open-Ended Question Answering

Although temporal coding through spike-time patterns has long been of interest in neuroscience, the specific structures that could be useful for spike-time codes remain highly unclear. Here, we introduce a new analytical approach, using techniques from discrete mathematics, to study spike-time codes. We focus on the phenomenon of ``phase precession'' in the rodent hippocampus. During navigation and learning on a physical track, specific cells in a rodent's brain form a highly structured pattern relative to the oscillation of local population activity. Studies of phase precession largely focus on its well established role in synaptic plasticity and memory formation. Comparatively less attention has been paid to the fact that phase precession represents one of the best candidates for a spike-time neural code. The precise nature of this code remains an open question. Here, we derive an analytical expression for an operator mapping points in physical space, through individual spike times, to complex numbers. The properties of this operator highlight a specific relationship between past and future in hippocampal spike patterns. Importantly, this approach generalizes beyond the specific phenomenon studied here, providing a new technique to study the neural codes within spike-time sequences found during sensory coding and motor behavior. We then introduce a novel spike-based decoding algorithm, based on this operator, that successfully decodes a simulated animal's trajectory using only the animal's initial position and a pattern of spike times. This decoder is robust to noise in spike times and works on a timescale almost an order of magnitude shorter than typically used with decoders that work on average firing rate. These results illustrate the utility of a discrete approach, based on the symmetries in spike patterns, to provide insight into the structure and function of neural systems.
Spatio-Temporal Attention in Multi-Granular Brain Chronnectomes for Detection of Autism Spectrum Disorder
Authors: James Orme-Rogers, Ajitesh Srivastava
Date: 2022-10-30 00:00:00

The traditional methods for detecting autism spectrum disorder (ASD) are expensive, subjective, and time-consuming, often taking years for a diagnosis, with many children growing well into adolescence and even adulthood before finally confirming the disorder. Recently, graph-based learning techniques have demonstrated impressive results on resting-state functional magnetic resonance imaging (rs-fMRI) data from the Autism Brain Imaging Data Exchange (ABIDE). We introduce IMAGIN, a multI-granular, Multi-Atlas spatio-temporal attention Graph Isomorphism Network, which we use to learn graph representations of dynamic functional brain connectivity (chronnectome), as opposed to static connectivity (connectome). The experimental results demonstrate that IMAGIN achieves a 5-fold cross-validation accuracy of 79.25%, which surpasses the current state-of-the-art by 1.5%. In addition, analysis of the spatial and temporal attention scores provides further validation for the neural basis of autism.
Joint Graph Convolution for Analyzing Brain Structural and Functional Connectome
Authors: Yueting Li, Qingyue Wei, Ehsan Adeli, Kilian M. Pohl, Qingyu Zhao
Date: 2022-10-27 00:00:00
Tasks: Graph Learning

The white-matter (micro-)structural architecture of the brain promotes synchrony among neuronal populations, giving rise to richly patterned functional connections. A fundamental problem for systems neuroscience is determining the best way to relate structural and functional networks quantified by diffusion tensor imaging and resting-state functional MRI. As one of the state-of-the-art approaches for network analysis, graph convolutional networks (GCN) have been separately used to analyze functional and structural networks, but have not been applied to explore inter-network relationships. In this work, we propose to couple the two networks of an individual by adding inter-network edges between corresponding brain regions, so that the joint structure-function graph can be directly analyzed by a single GCN. The weights of inter-network edges are learnable, reflecting non-uniform structure-function coupling strength across the brain. We apply our Joint-GCN to predict age and sex of 662 participants from the public dataset of the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) based on their functional and micro-structural white-matter networks. Our results support that the proposed Joint-GCN outperforms existing multi-modal graph learning approaches for analyzing structural and functional networks.
Mechanism of Universal Quantum Computation in the Brain
Authors: Aman Chawla, Salvatore Domenic Morgera
Date: 2022-10-26 00:00:00

In this paper the authors extend [1] and provide more details of how the brain may act like a quantum computer. In particular, positing the difference between voltages on two axons as the environment for ions undergoing spatial superposition, we argue that evolution in the presence of metric perturbations will differ from that in the absence of these waves. This differential state evolution will then encode the information being processed by the tract due to the interaction of the quantum state of the ions at the nodes with the `controlling' potential. Upon decoherence, which is equal to a measurement, the final spatial state of the ions is decided and it also gets reset by the next impulse initiation time. Under synchronization, several tracts undergo such processes in synchrony and therefore the picture of a quantum computing circuit is complete. Under this model, based on the number of axons in the corpus callosum alone [2], we estimate that upwards of 15 million quantum states might be prepared and evolved every millisecond in this white matter tract, far greater processing than any present quantum computer can accomplish.
Exploratory Control with Tsallis Entropy for Latent Factor Models
Authors: Ryan Donnelly, Sebastian Jaimungal
Date: 2022-11-14 00:00:00
Tasks: Q-Learning

We study optimal control in models with latent factors where the agent controls the distribution over actions, rather than actions themselves, in both discrete and continuous time. To encourage exploration of the state space, we reward exploration with Tsallis Entropy and derive the optimal distribution over states - which we prove is $q$-Gaussian distributed with location characterized through the solution of an FBS$\Delta$E and FBSDE in discrete and continuous time, respectively. We discuss the relation between the solutions of the optimal exploration problems and the standard dynamic optimal control solution. Finally, we develop the optimal policy in a model-agnostic setting along the lines of soft $Q$-learning. The approach may be applied in, e.g., developing more robust statistical arbitrage trading strategies.
Credit Default Swaps and the mixed-fractional CEV model
Authors: Axel A. Araneda
Date: 2022-11-14 00:00:00

This paper explores the capabilities of the Constant Elasticity of Variance model driven by a mixed-fractional Brownian motion (mfCEV) [Axel A. Araneda. The fractional and mixed-fractional CEV model. Journal of Computational and Applied Mathematics, 363:106-123, 2020] to address default-related financial problems, particularly the pricing of Credit Default Swaps. The increase in both, the probability of default and the CDS spreads under mixed-fractional diffusion compared to the standard Brownian case, improves the lower empirical performance of the standard Constant Elasticity of Variance model (CEV), yielding a more realistic model for credit events.