pwc-archive/papers-with-abstracts huggingface dataset:
Language-based Audio Retrieval Task in DCASE 2022 Challenge
Authors: Huang Xie, Samuel Lipping, Tuomas Virtanen
Date: 2022-06-13 00:00:00
Tasks: Audio captioning, Retrieval

Language-based audio retrieval is a task, where natural language textual captions are used as queries to retrieve audio signals from a dataset. It has been first introduced into DCASE 2022 Challenge as Subtask 6B of task 6, which aims at developing computational systems to model relationships between audio signals and free-form textual descriptions. Compared with audio captioning (Subtask 6A), which is about generating audio captions for audio signals, language-based audio retrieval (Subtask 6B) focuses on ranking audio signals according to their relevance to natural language textual captions. In DCASE 2022 Challenge, the provided baseline system for Subtask 6B was significantly outperformed, with top performance being 0.276 in mAP@10. This paper presents the outcome of Subtask 6B in terms of submitted systems' performance and analysis.
Convex Quantization Preserves Logconcavity
Authors: Pol del Aguila Pla, Aleix Boquet-Pujadas, Joakim Jaldén
Date: 2022-06-11 00:00:00
Tasks: Quantization

A logconcave likelihood is as important to proper statistical inference as a convex cost function is important to variational optimization. Quantization is often disregarded when writing likelihood models, ignoring the limitations of the physical detectors used to collect the data. These two facts call for the question: would including quantization in likelihood models preclude logconcavity? are the true data likelihoods logconcave? We provide a general proof that the same simple assumption that leads to logconcave continuous-data likelihoods also leads to logconcave quantized-data likelihoods, provided that convex quantization regions are used.
Integration of Physics-Based and Data-Driven Models for Hyperspectral Image Unmixing
Authors: Jie Chen, Min Zhao, Xiuheng Wang, Cédric Richard, Susanto Rahardja
Date: 2022-06-11 00:00:00
Tasks: Hyperspectral Unmixing

Spectral unmixing is one of the most important quantitative analysis tasks in hyperspectral data processing. Conventional physics-based models are characterized by clear interpretation. However they may not be suitable for analyzing scenes with unknown complex physical characteristics. Data-driven methods have developed rapidly in recent years, in particular deep learning methods because they possess superior capability in modeling complex and nonlinear systems. Simply transferring these methods as black-boxes to conduct unmixing may lead to low physical interpretability and generalization ability. This article reviews hyperspectral unmixing works that integrate advantages of both physics-based models and data-driven methods by means of deep neural network structures design, prior design and loss design. Most of these methods derive from a common mathematical optimization framework, and combine good interpretability with high accuracy.
Physics-informed EDFA Gain Model Based on Active Learning
Authors: Xiaomin Liu, Yuli Chen, Yihao Zhang, Yichen Liu, Lilin Yi, Weisheng Hu, Qunbi Zhuge
Date: 2022-06-13 00:00:00
Tasks: Active Learning

We propose a physics-informed EDFA gain model based on the active learning method. Experimental results show that the proposed modelling method can reach a higher optimal accuracy and reduce ~90% training data to achieve the same performance compared with the conventional method.
Robust Adaptive Beamforming via Worst-Case SINR Maximization with Nonconvex Uncertainty Sets
Authors: Yongwei Huang, Hao Fu, Sergiy A. Vorobyov, Zhi-Quan Luo
Date: 2022-06-13 00:00:00
Tasks: valid

This paper considers a formulation of the robust adaptive beamforming (RAB) problem based on worst-case signal-to-interference-plus-noise ratio (SINR) maximization with a nonconvex uncertainty set for the steering vectors. The uncertainty set consists of a similarity constraint and a (nonconvex) double-sided ball constraint. The worst-case SINR maximization problem is turned into a quadratic matrix inequality (QMI) problem using the strong duality of semidefinite programming. Then a linear matrix inequality (LMI) relaxation for the QMI problem is proposed, with an additional valid linear constraint. Necessary and sufficient conditions for the tightened LMI relaxation problem to have a rank-one solution are established. When the tightened LMI relaxation problem still has a high-rank solution, the LMI relaxation problem is further restricted to become a bilinear matrix inequality (BLMI) problem. We then propose an iterative algorithm to solve the BLMI problem that finds an optimal/suboptimal solution for the original RAB problem by solving the BLMI formulations. To validate our results, simulation examples are presented to demonstrate the improved array output SINR of the proposed robust beamformer.
Data-Driven Denoising of Stationary Accelerometer Signals
Authors: Daniel Engelsman, Itzik Klein
Date: 2022-06-13 00:00:00
Tasks: Benchmarking, Denoising

Modern navigation solutions are largely dependent on the performances of the standalone inertial sensors, especially at times when no external sources are available. During these outages, the inertial navigation solution is likely to degrade over time due to instrumental noises sources, particularly when using consumer low-cost inertial sensors. Conventionally, model-based estimation algorithms are employed to reduce noise levels and enhance meaningful information, thus improving the navigation solution directly. However, guaranteeing their optimality often proves to be challenging as sensors performance differ in manufacturing quality, process noise modeling, and calibration precision. In the literature, most inertial denoising models are model-based when recently several data-driven approaches were suggested primarily for gyroscope measurements denoising. Data-driven approaches for accelerometer denoising task are more challenging due to the unknown gravity projection on the accelerometer axes. To fill this gap, we propose several learning-based approaches and compare their performances with prominent denoising algorithms, in terms of pure noise removal, followed by stationary coarse alignment procedure. Based on the benchmarking results, obtained in field experiments, we show that: (i) learning-based models perform better than traditional signal processing filtering; (ii) non-parametric kNN algorithm outperforms all state of the art deep learning models examined in this study; (iii) denoising can be fruitful for pure inertial signal reconstruction, but moreover for navigation-related tasks, as both errors are shown to be reduced up to one order of magnitude.
Mismatched Estimation in the Distance Geometry Problem
Authors: Mahmoud Abdelkhalek, Dror Baron, Chau-Wai Wong
Date: 2022-06-12 00:00:00

We investigate mismatched estimation in the context of the distance geometry problem (DGP). In the DGP, for a set of points, we are given noisy measurements of pairwise distances between the points, and our objective is to determine the geometric locations of the points. A common approach to deal with noisy measurements of pairwise distances is to compute least-squares estimates of the locations of the points. However, these least-squares estimates are likely to be suboptimal, because they do not necessarily maximize the correct likelihood function. In this paper, we argue that more accurate estimates can be obtained when an estimation procedure using the correct likelihood function of noisy measurements is performed. Our numerical results demonstrate that least-squares estimates can be suboptimal by several dB.
Effect of Strong Time-Varying Transmission Distance on LEO Satellite-Terrestrial Deliveries
Authors: Yuanyuan Ma, Tiejun Lv, TingTing Li, Gaofeng Pan, Yunfei Chen, Mohamed-Slim Alouini
Date: 2022-06-11 00:00:00

In this paper, we investigate the effect of the strong time-varying transmission distance on the performance of the low-earth orbit (LEO) satellite-terrestrial transmission (STT) system. We propose a new analytical framework using finite-state Markov channel (FSMC) model and time discretization method. Moreover, to demonstrate the applications of the proposed framework, the performances of two adaptive transmissions, rate-adaptive transmission (RAT) and power-adaptive transmission (PAT) schemes, are evaluated for the cases when the transmit power or the transmission rate at the LEO satellite is fixed. Closed-form expressions for the throughput, energy efficiency (EE), and delay outage rate (DOR) of the considered systems are derived and verified, which are capable of addressing the capacity, energy efficiency, and outage rate performance of the considered LEO STT scenarios with the proposed analytical framework.
Reconfigurable Intelligent Surface-Aided 6G Massive Access: Coupled Tensor Modeling and Sparse Bayesian Learning
Authors: Xiaodan Shao, Lei Cheng, Xiaoming Chen, Chongwen Huang, Derrick Wing Kwan Ng
Date: 2022-06-11 00:00:00
Tasks: Tensor Decomposition, Variational Inference

This paper investigates a reconfigurable intelligent surface (RIS)-aided unsourced random access (URA) scheme for the sixth-generation (6G) wireless networks with massive sporadic traffic devices. First of all, this paper proposes a novel joint active device separation (the message recovery of active device) and channel estimation architecture for the RIS-aided URA. Specifically, the RIS passive reflection is optimized before the successful device separation. Then, by associating the data sequences to multiple rank-one tensors and exploiting the angular sparsity of the RIS-BS channel, the detection problem is cast as a high-order coupled tensor decomposition problem without the need of exploiting pilot sequences. However, the inherent coupling among multiple sparse device-RIS channels, together with the unknown number of active devices make the detection problem at hand deviate from the widely-used coupled tensor decomposition format. To overcome this challenge, this paper judiciously devises a probabilistic model that captures both the element-wise sparsity from the angular channel model and the low-rank property due to the sporadic nature of URA. Then, based on such a probabilistic model, a iterative detection algorithm is developed under the framework of sparse variational inference, where each update iteration is obtained in a closed-form and the number of active devices can be automatically estimated for effectively avoiding the overfitting of noise. Extensive simulation results confirm the excellence of the proposed URA algorithm, especially for the case of a large number of reflecting elements for accommodating a significantly large number of devices.
Bispectrum-based Cross-frequency Functional Connectivity: Classification of Alzheimer's disease
Authors: Dominik Klepl, Fei He, Min Wu, Daniel J. Blackburn, Ptolemaios G. Sarrigiannis
Date: 2022-06-10 00:00:00
Tasks: Classification, EEG, Electroencephalogram (EEG), Functional Connectivity

Alzheimer's disease (AD) is a neurodegenerative disease known to affect brain functional connectivity (FC). Linear FC measures have been applied to study the differences in AD by splitting neurophysiological signals such as electroencephalography (EEG) recordings into discrete frequency bands and analysing them in isolation. We address this limitation by quantifying cross-frequency FC in addition to the traditional within-band approach. Cross-bispectrum, a higher-order spectral analysis, is used to measure the nonlinear FC and is compared with the cross-spectrum, which only measures the linear FC within bands. Each frequency coupling is then used to construct an FC network, which is in turn vectorised and used to train a classifier. We show that fusing features from networks improves classification accuracy. Although both within-frequency and cross-frequency networks can be used to predict AD with high accuracy, our results show that bispectrum-based FC outperforms cross-spectrum suggesting an important role of cross-frequency FC.
Reducing the Control Overhead of Intelligent Reconfigurable Surfaces Via a Tensor-Based Low-Rank Factorization Approach
Authors: Bruno Sokal, Paulo R. B. Gomes, André L. F. de Almeida, Behrooz Makki, Gabor Fodor
Date: 2022-06-10 00:00:00
Tasks: Tensor Decomposition

Passive intelligent reconfigurable surfaces (IRS) are becoming an attractive component of cellular networks due to their ability of shaping the propagation environment and thereby improving the coverage. While passive IRS nodes incorporate a great number of phase-shifting elements and a controller entity, the phase-shifts are typically determined by the cellular base station (BS) due to its computational capability. Since the fine granularity control of the large number of phase-shifters may become prohibitive in practice, it is important to reduce the control overhead between the BS and the IRS controller. To this end, in this paper we propose a low-rank approximation of the near-optimal phase-shifts, which would incur prohibitively high communication overhead on the BS-IRS controller links. The key idea is to represent the potentially large IRS phase-shift vector using a low-rank tensor model. This is achieved by factorizing a tensorized version of the IRS phase-shift vector, where each component is modeled as the Kronecker product of a predefined number of factors of smaller sizes, which can be obtained via tensor decomposition algorithms. We show that the proposed low-rank models drastically reduce the required feedback requirements associated with the BS-IRS control links. Our simulation results indicate that the proposed method is especially attractive in scenarios with a strong line of sight component, in which case nearly the same spectral efficiency is reached as in the cases with near-optimal phase-shifts, but with a drastically reduced communication overhead.
SHREC 2022: Protein-ligand binding site recognition
Authors: Luca Gagliardi, Andrea Raffo, Ulderico Fugacci, Silvia Biasotti, Walter Rocchia, Hao Huang, Boulbaba Ben Amor, Yi Fang, Yuanyuan Zhang, Xiao Wang, Charles Christoffer, Daisuke Kihara, Apostolos Axenopoulos, Stelios Mylonas, Petros Daras
Date: 2022-06-13 00:00:00
Tasks: Drug Design, Molecular Docking

This paper presents the methods that have participated in the SHREC 2022 contest on protein-ligand binding site recognition. The prediction of protein-ligand binding regions is an active research domain in computational biophysics and structural biology and plays a relevant role for molecular docking and drug design. The goal of the contest is to assess the effectiveness of computational methods in recognizing ligand binding sites in a protein based on its geometrical structure. Performances of the segmentation algorithms are analyzed according to two evaluation scores describing the capacity of a putative pocket to contact a ligand and to pinpoint the correct binding region. Despite some methods perform remarkably, we show that simple non-machine-learning approaches remain very competitive against data-driven algorithms. In general, the task of pocket detection remains a challenging learning problem which suffers of intrinsic difficulties due to the lack of negative examples (data imbalance problem).
The human and mammalian cerebrum scale by computational power and information resistance
Authors: Marc H. E. de Lussanet
Date: 2013-08-06 00:00:00

The cerebrum of mammals spans a vast range of sizes and yet has a very regular structure. The amount of folding of the cortical surface and the proportion of white matter gradually increase with size, but the underlying mechanisms remain elusive. Here, two laws are derived to fully explain these cerebral scaling relations. The first law holds that the long-range information flow in the cerebrum is determined by the total cortical surface (i.e., the number of neurons) and the increasing information resistance of long-range connections. Despite having just one free parameter, the first law fits the mammalian cerebrum better than any existing function, both across species and within humans. According to the second law, the white matter volume scales, with a few minor corrections, to the cortical surface area. It follows from the first law that large cerebrums have much local processing and little global information flow. Moreover, paradoxically, a further increase in long-range connections would decrease the efficiency of information flow.
Moving towards FAIR practices in epidemiological research
Authors: Montserrat Garcia-Closas, Thomas U. Ahearn, Mia M. Gaudet, Amber N. Hurson, Jeya Balaji Balasubramanian, Parichoy Pal Choudhury, Nicole M. Gerlanc, Bhaumik Patel, Daniel Russ, Mustapha Abubakar, Neal D. Freedman, Wendy S. W. Wong, Stephen J. Chanock, Amy Berrington de Gonzalez, Jonas S Almeida
Date: 2022-06-13 00:00:00
Tasks: Epidemiology, Fairness

Reproducibility and replicability of research findings are central to the scientific integrity of epidemiology. In addition, many research questions require combiningdata from multiple sources to achieve adequate statistical power. However, barriers related to confidentiality, costs, and incentives often limit the extent and speed of sharing resources, both data and code. Epidemiological practices that follow FAIR principles can address these barriers by making resources (F)indable with the necessary metadata , (A)ccessible to authorized users and (I)nteroperable with other data, to optimize the (R)e-use of resources with appropriate credit to its creators. We provide an overview of these principles and describe approaches for implementation in epidemiology. Increasing degrees of FAIRness can be achieved by moving data and code from on-site locations to the Cloud, using machine-readable and non-proprietary files, and developing open-source code. Adoption of these practices will improve daily work and collaborative analyses, and facilitate compliance with data sharing policies from funders and scientific journals. Achieving a high degree of FAIRness will require funding, training, organizational support, recognition, and incentives for sharing resources. But these costs are amply outweighed by the benefits of making research more reproducible, impactful, and equitable by facilitating the re-use of precious research resources by the scientific community.
Human lumbar spine injury risk in dynamic combined compression and flexion loading
Authors: Sophia K. Tushak, Bronislaw D. Gepner, Jason L. Forman, Jason J. Hallman, Bengt Pipkorn, Jason R. Kerrigan
Date: 2022-06-13 00:00:00

Anticipating changes to vehicle interiors with future automated driving systems, the automobile industry recently has focused attention on crash response in relaxed postures with increased seatback recline. Prior research found that this posture may result in greater risk of lumbar spine injury in the event of a frontal crash. This study developed a lumbar spine injury risk function that estimated injury risk as a function of simultaneously applied compression force and flexion moment. Force and moment failure data from 40 compression-flexion tests were utilized in a Weibull survival model, including appropriate data censoring. A mechanics-based injury metric was formulated, where lumbar spine compression force and flexion moment were normalized to specimen geometry. Subject age was incorporated as a covariate to further improve model fit. A weighting factor was included to adjust the influence of force and moment, and parameter optimization yielded a value of 0.11. Thus, the normalized compression force component had a greater effect on injury risk than the normalized flexion moment component. Additionally, as force was nominally increased, less moment was required to produce injury for a given age and specimen geometry. The resulting injury risk function can be utilized to improve occupant safety in the field.
Modeling membrane curvature generation using mechanics and machine learning
Authors: Sage Malingen, Padmini Rangamani
Date: 2022-06-10 00:00:00
Tasks: BIG-bench Machine Learning

The deformation of cellular membranes regulates trafficking processes, such as exocytosis and endocytosis. Classically, the Helfrich continuum model is used to characterize the forces and mechanical parameters that cells tune to accomplish membrane shape changes. While this classical model effectively captures curvature generation, one of the core challenges in using it to approximate a biological process is selecting a set of mechanical parameters (including bending modulus and membrane tension) from a large set of reasonable values. We used the Helfrich model to generate a large synthetic dataset from a random sampling of realistic mechanical parameters and used this dataset to train machine learning models. These models produced promising results, accurately classifying model behavior and predicting membrane shape from mechanical parameters. We also note emerging methods in machine learning that can leverage the physical insight of the Helfrich model to improve performance and draw greater insight into how cells control membrane shape change.
Noisy circumnutations facilitate self-organized shade avoidance in sunflowers
Authors: Chantal Nguyen, Imri Dromi, Aharon Kempinski, Gabriella E. C. Gall, Orit Peleg, Yasmine Meroz
Date: 2022-06-11 00:00:00
Tasks: Navigate

Circumnutations are widespread in plants and typically associated with exploratory movements, however a quantitative understanding of their role remains elusive. In this study we report, for the first time, the role of noisy circumnutations in facilitating an optimal growth pattern within a crowded group of mutually shading plants. We revisit the problem of self-organization observed for sunflowers, mediated by shade response interactions. Our analysis reveals that circumnutation movements conform to a bounded random walk characterized by a remarkably broad distribution of velocities, covering three orders of magnitude. In motile animal systems such wide distributions of movement velocities are frequently identified with enhancement of behavioral processes, suggesting that circumnutations may serve as a source of functional noise. To test our hypothesis, we developed a parsimonious model of interacting growing disks, informed by experiments, successfully capturing the characteristic dynamics of individual and multiple interacting plants. Employing our simulation framework we examine the role of circumnutations in the system, and find that the observed breadth of the velocity distribution confers advantageous effects by facilitating exploration of potential configurations, leading to an optimized arrangement with minimal shading. These findings represent the first report of functional noise in plant movements, and establishes a theoretical foundation for investigating how plants navigate their environment by employing computational processes such as task-oriented processes, optimization, and active sensing.
Hellinger distance to normal distribution as market invariant
Authors: Mesrop T. Mesropyan, Vardan G. Bardakhchyan
Date: 2022-06-12 00:00:00
Tasks: Sensitivity

Main purpose of distance based portfolio constructions is in portfolio imitation. Here we construct portfolio based on Hellinger distance from normal distribution. We empirically found that minimum of this distance drastically varies from market to market. Thus we suppose that it may be regarded as a form of market invariant, in a sense of helpful tool for market segmentation. We analyze its sensitivity. Though mean sensitivity was small it showed extreme sensitivity in some cases.