pwc-archive/papers-with-abstracts huggingface dataset:
A Topological Loss Function: Image Denoising on a Low-Light Dataset
Authors: Alexandra Malyugina, Nantheera Anantrasirichai, David Bull
Date: 2022-08-09 00:00:00
Tasks: Denoising, Image Denoising

Although image denoising algorithms have attracted significant research attention, surprisingly few have been proposed for, or evaluated on, noise from imagery acquired under real low-light conditions. Moreover, noise characteristics are often assumed to be spatially invariant, leading to edges and textures being distorted after denoising. Here, we introduce a novel topological loss function which is based on persistent homology. The method performs in the space of image patches, where topological invariants are calculated and represented in persistent diagrams. The loss function is a combination of $\ell_1$ or $\ell_2$ losses with the new persistence-based topological loss. We compare its performance across popular denoising architectures and loss functions, training the networks on our new comprehensive dataset of natural images captured in low-light conditions -- BVI-LOWLIGHT. Analysis reveals that this approach outperforms existing methods, adapting well to complex structures and suppressing common artifacts.
Breaking the accuracy and resolution limitation of filter- and frequency-to-time mapping-based time and frequency acquisition methods by broadening the filter bandwidth
Authors: Pengcheng Zuo, Dong Ma, Xiaowei Li, Yang Chen
Date: 2022-08-09 00:00:00

In this paper, the filter- and frequency-to-time mapping (FTTM)-based photonics-assisted time and frequency acquisition methods are comprehensively analyzed and the accuracy and resolution limitation in the fast sweep scenario is broken by broadening the filter bandwidth. It is found that when the sweep speed is very fast, the width of the generated pulse via FTTM is mainly determined by the impulse response of the filter. In this case, appropriately increasing the filter bandwidth can significantly reduce the pulse width, so as to improve the measurement accuracy and resolution. FTTM-based short-time Fourier transform (STFT) and microwave frequency measurement using the stimulated Brillouin scattering (SBS) effect is demonstrated by comparing the results with and without SBS gain spectrum broadening and the improvement of measurement accuracy and frequency resolution is well confirmed. The frequency measurement accuracy of the system is improved by around 25 times compared with the former work using a similar sweep speed, while the frequency resolution of the STFT is also much improved compared with our former results.
Evaluation of a beam switching smart antenna array for use in traffic telematics V2X applications
Authors: Hagen Ußler, Oliver Michler
Date: 2022-08-09 00:00:00

With the digitalization of transportation new use cases for digital information services are emerging. For example, prioritization of road users at traffic signals, especially emergency vehicles, is a desired goal. Requirements for the necessary communication link between road user and C-ITS station are often inadequately met. In particular, increasing communication distances while minimizing latencies are key requirements for timely influencing the control of traffic light signal programs. This paper presents the automotive application of a fast-switchable antenna array with targeted transmission direction selection and high antenna gain. In addition to antenna design requirements, theoretical analyses to increase transmission distance are performed using radio propagation simulation. Furthermore, practical evaluation is performed both in laboratory and test field environment using continuous wave and C-ITS service measurements. An automatic switching of antenna sectors based on geolocation is implemented and discussed from a scientific point of view. As a result, using an adaptive antenna array in traffic telematics environments is proposed to provide more robust communication links and increase the radio transmission distance.
Ultra Lite Convolutional Neural Network for Fast Automatic Modulation Classification in Low-Resource Scenarios
Authors: Lantu Guo, Yu Wang, Yun Lin, Haitao Zhao, Guan Gui
Date: 2022-08-09 00:00:00
Tasks: Classification, Data Augmentation

Automatic modulation classification (AMC) is a key technique for designing non-cooperative communication systems, and deep learning (DL) is applied effectively to AMC for improving classification accuracy. However, most of the DL-based AMC methods have a large number of parameters and high computational complexity, and they cannot be directly applied to low-resource scenarios with limited computing power and storage space. In this letter, we propose a fast AMC method with lightweight and low-complexity using ultra lite convolutional neural network (ULCNN) consisting of data augmentation, complex-valued convolution, separable convolution, channel attention, and channel shuffle. Simulation results demonstrate that our proposed ULCNN-based AMC method achieves an average accuracy of 62.47% on RML2016.10a and only 9,751 parameters. Moreover, ULCNN is verified on a typical edge device (Raspberry Pi), where the interference time per sample is about 0.775 ms. The reproducible code can be downloaded from GitHub\footnote{https://github.com/BeechburgPieStar/Ultra-Lite-Convolutional-Neural-Network-for-Automatic-Modulation-Classification}.
Two-Factor Biometric Verification with ECG: Two Cancelable Approaches
Authors: Jui-Kun Chiu, Tzu-Yun Lin, Wei-Shen Hsu, Shun-Chi Wu
Date: 2022-08-09 00:00:00
Tasks: Vocal Bursts Valence Prediction

Biometric authentication relies on an individual's physiological or behavioral traits to verify their identity before granting access permission to a system or device without remembering anything. Although electrocardiograms (ECGs) have been considered a biometric trait, an ECG biometric recognition system that operates in verification mode is rarely considered. This study proposes two two-factor cancelable biometric verification schemes that enable identity recognition using ECGs. Using bioconvolving and minimum average correlation energy biometric filters, revocable and irreversible templates can be constructed to avoid privacy invasion and security concerns associated with ECG biometric recognition. An interquartile range-based method is adopted to determine if an identity match exists, enabling identity verification under the influence of inter-beat variation. The experimental results indicated that the proposed schemes could achieve an equal error rate as low as 1% when the inter-beat variation was properly addressed.
Compressive Sensing Based Sparse MIMO Array Optimization for Wideband Near-Field Imaging
Authors: Shuoguang Wang, Shiyong Li, Ahmad Hoorfar, Ke Miao, Guoqiang Zhao, Houjun Sun
Date: 2022-08-09 00:00:00
Tasks: Compressive Sensing

In the area of near-field millimeter-wave imaging, the generalized sparse array synthesis (SAS) method is in great demand. The traditional methods usually employ the greedy algorithms, which may have the convergence problem. This paper proposes a convex optimization model for the multiple-input multiple-output (MIMO) array design based on the compressive sensing (CS) approach. We generate a block shaped reference pattern, to be used as an optimizing target. The pattern occupies the entire imaging area of interest in order to involve the effect of each pixel into the optimization model. In MIMO scenarios, we can fix the transmit subarray and synthesize the receive subarray, and vice versa, or doing the synthesis sequentially. The problems associated with focusing, sidelobes suppression, and grating lobes suppression of the synthesized array are examined in details. Numerical and experimental results demonstrate that the synthesized sparse array can offer better image qualities than the sparse arrays with equally spaced or randomly spaced antennas with the same number of antenna elements.
Waveform Design for Mutual Interference Mitigation in Automotive Radar
Authors: Arindam Bose, Bo Tang, Wenjie Huang, Mojtaba Soltanalian, Jian Li
Date: 2022-08-08 00:00:00

The mutual interference between similar radar systems can result in reduced radar sensitivity and increased false alarm rates. To address the synchronous and asynchronous interference mitigation problems in similar radar systems, we first propose herein two slow-time coding schemes to modulate the pulses within a coherent processing interval (CPI) for a single-input-single-output (SISO) scenario. Specifically, the first coding scheme relies on Doppler shifting and the second one is devised based on an optimization approach. We further extend our discussion to the more general case of multiple-input-multiple-output (MIMO) radars and propose an efficient algorithm to design waveforms to mitigate mutual interference in such systems. The proposed coding schemes are computationally efficient in practice and the incorporation of the coding schemes requires only a slight modification of the existing systems. Our numerical examples indicate that the proposed coding schemes can reduce the interference power level in a desired area of the cross-ambiguity function significantly.
Enhanced drug uptake on application of electroporation in a single-cell model
Authors: Nilay Mondal, K. S. Yadav, D. C. Dalal
Date: 2022-08-01 00:00:00

Electroporation method is a useful tool for delivering drugs into various diseased tissues in the human body. As a result of an applied electric field, drug particles enter the intracellular compartment through the temporarily permeabilized cell membrane. Consequently, electroporation method allows better penetration of the drug into the diseased tissue and improves treatment clinically. In this study, a more generalized model of drug transport in a single-cell is proposed. The model is able to capture non-homogeneous drug transport in the cell due to non-uniform cell membrane permeabilization. Several numerical experiments are conducted to understand the effects of electric field and drug permeability on drug uptake into the cell. Through investigation, the appropriate electric field and drug permeability are identified that lead to sufficient drug uptake into the cell. This model can be used by experimentalists to get information prior to conduct any experiment, and it may help reduce the number of actual experiments that might be conducted otherwise.
Graph neural networks for the prediction of molecular structure-property relationships
Authors: Jan G. Rittig, Qinghe Gao, Manuel Dahmen, Alexander Mitsos, Artur M. Schweidtmann
Date: 2022-07-25 00:00:00
Tasks: Drug Discovery, Molecular Property Prediction, Prediction, Property Prediction

Molecular property prediction is of crucial importance in many disciplines such as drug discovery, molecular biology, or material and process design. The frequently employed quantitative structure-property/activity relationships (QSPRs/QSARs) characterize molecules by descriptors which are then mapped to the properties of interest via a linear or nonlinear model. In contrast, graph neural networks, a novel machine learning method, directly work on the molecular graph, i.e., a graph representation where atoms correspond to nodes and bonds correspond to edges. GNNs allow to learn properties in an end-to-end fashion, thereby avoiding the need for informative descriptors as in QSPRs/QSARs. GNNs have been shown to achieve state-of-the-art prediction performance on various property predictions tasks and represent an active field of research. We describe the fundamentals of GNNs and demonstrate the application of GNNs via two examples for molecular property prediction.
r/K selection of GC content in prokaryotes
Authors: Lucio Aliperti Car, Gonzalo Farfañuk, Luciana L. Couso, Alfonso Soler-Bistué, Ariel A. Aptekmann, Ignacio E. Sánchez
Date: 2022-08-09 00:00:00
Tasks: Diversity

The GC content of prokaryotic genomes is species-specific and takes values from 16 to 77 percent. There are currently no accepted explanations for this diversity of selection for GC content. We analyzed the known correlations between GC content, genome size and amino acid cost in thousands of prokaryotes, together with new or recently compiled data on cell shape and volume, duplication times, motility, nutrient assimilation, sporulation, defense mechanisms and Gram staining. GC content integrates well with these traits into r/K selection theory when phenotypic plasticity is considered. High GC content prokaryotes are r-strategists with cheaper descendants thanks to a lower average amino acid metabolic cost and a smaller cell volume, colonize unstable environments thanks to flagella and a bacillus form and are generalists in terms of resource opportunism and the ability to defend themselves from various hazards. Low GC content prokaryotes are K-strategists specialized for stable environments that maintain homeostasis via a high-cost outer cell membrane and endospore formation as a response to nutrient deprivation and attain a higher nutrient-to-biomass yield. The lower proteome cost of high GC content prokaryotes is driven by the association between GC-rich codons and cheaper amino acids in the genetic code, while the correlation between GC content and genome size may be partly due to a shift in the functional repertoire of genomes driven by r/K selection. In all, we show that molecular diversity in the GC content of prokaryotes and the corresponding species diversity may be a consequence of ecological r/K selection
Context sequence theory: a common explanation for multiple types of learning
Authors: Yu Mingcan, Wang Junying
Date: 2022-07-17 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Although principles of neuroscience like reinforcement learning, visual perception and attention have been applied in machine learning models, there is a huge gap between machine learning and mammalian learning. Based on the advances in neuroscience, we propose the context sequence theory to give a common explanation for multiple types of learning in mammals and hope that can provide a new insight into the construct of machine learning models.
Validity of Web-based, Self-directed, NeuroCognitive Performance Test in MCI
Authors: P. Murali Doraiswamy, Terry E. Goldberg, Min Qian, Alexandra R. Linares, Adaora Nwosu, Izael Nino, Jessica D'Antonio, Julia Phillips, Charlie Ndouli, Caroline Hellegers, Andrew M. Michael, Jeffrey R. Petrella, Howards Andrews, Joel Sneed, Davangere P. Devanand
Date: 2022-07-11 00:00:00
Tasks: Skills Assessment

Digital cognitive tests offer several potential advantages over established paper-pencil tests but have not yet been fully evaluated for the clinical evaluation of mild cognitive impairment. The NeuroCognitive Performance Test (NCPT) is a web-based, self-directed, modular battery intended for repeated assessments of multiple cognitive domains. Our objective was to examine its relationship with the ADAS-Cog and MMSE as well as with established paper-pencil tests of cognition and daily functioning in MCI. We used Spearman correlations, regressions and principal components analysis followed by a factor analysis (varimax rotated) to examine our objectives. In MCI subjects, the NCPT composite is significantly correlated with both a composite measure of established tests (r=0.78, p<0.0001) as well as with the ADAS-Cog (r=0.55, p<0.0001). Both NCPT and paper-pencil test batteries had a similar factor structure that included a large g component with a high eigenvalue. The correlation for the analogous tests (e.g. Trails A and B, learning memory tests) were significant (p<0.0001). Further, both the NCPT and established tests significantly (p< 0.01) predicted the University of California San Diego Performance-Based Skills Assessment and Functional Activities Questionnaire, measures of daily functioning. The NCPT, a web-based, self-directed, computerized test, shows high concurrent validity with established tests and hence offers promise for use as a research or clinical tool in MCI. Despite limitations such as a relatively small sample, absence of control group and cross-sectional nature, these findings are consistent with the growing literature on the promise of self-directed, web-based cognitive assessments for MCI.
Modeling the Role of Land Conversion on the Spread of an Epizootic Disease
Authors: Dustin G. Padilla, Xiaoqian Gong, Charles Perrings
Date: 2022-08-08 00:00:00

Land conversion and the resulting contact between domesticated and wild species has arguably been the single largest contributor to the emergence of novel epizootic and zoonotic diseases in the past century. An unintended consequence of these interactions is zoonotic or epizootic disease spillovers from wild species to humans and their domesticates. Disease spillovers are edge effects of land conversion and are sensitive to the size and shape of converted areas. We combine spatial metrics from landscape ecology with theoretical epidemiological models to understand how the size and shape of land conversion affect epizootic and zoonotic disease transmission of single and two species populations. We show that the less compact the converted area, and the the greater the depth of the contact zone, the more rapidly will an introduced disease spread through the domesticated population.
A Review of Machine Learning and Algorithmic Methods for Protein Phosphorylation Sites Prediction
Authors: Farzaneh Esmaili, Mahdi Pourmirzaei, Shahin Ramazi, Seyedehsamaneh Shojaeilangari, Elham Yavari
Date: 2022-08-04 00:00:00
Tasks: Prediction

Post-translational modifications (PTMs) have key roles in extending the functional diversity of proteins and as a result, regulating diverse cellular processes in prokaryotic and eukaryotic organisms. Phosphorylation modification is a vital PTM that occurs in most proteins and plays a significant role in many biological processes. Disorders in the phosphorylation process lead to multiple diseases including neurological disorders and cancers. The purpose of this review paper is to organize this body of knowledge associated with phosphorylation site (p-site) prediction to facilitate future research in this field. At first, we comprehensively reviewed all related databases and introduced all steps regarding dataset creation, data preprocessing and method evaluation in p-site prediction. Next, we investigated p-sites prediction methods which fall into two computational groups: Algorithmic and Machine Learning (ML). Additionally, it was shown that there are basically two main approaches for p-sites prediction by ML: conventional and End-to-End deep learning methods, which were given an overview for both of them. Moreover, this study introduced the most important feature extraction techniques which have mostly been used in p-site prediction. Finally, we created three test sets from new proteins related to the 2022th released version of the dbPTM database based on general and human species. Evaluation of the available online tools on the test sets showed quite poor performance for p-sites prediction. Keywords: Phosphorylation, Machine Learning, Deep Learning, Post Translation Modification, Databases
Risk-averse Stochastic Optimization for Farm Management Practices and Cultivar Selection Under Uncertainty
Authors: Faezeh Akhavizadegan, Javad Ansarifar, Lizhi Wang, Sotirios V. Archontoulis
Date: 2022-07-17 00:00:00
Tasks: Bayesian Optimization, Management, Stochastic Optimization

Optimizing management practices and selecting the best cultivar for planting play a significant role in increasing agricultural food production and decreasing environmental footprint. In this study, we develop optimization frameworks under uncertainty using conditional value-at-risk in the stochastic programming objective function. We integrate the crop model, APSIM, and a parallel Bayesian optimization algorithm to optimize the management practices and select the best cultivar at different levels of risk aversion. This approach integrates the power of optimization in determining the best decisions and crop model in simulating nature's output corresponding to various decisions. As a case study, we set up the crop model for 25 locations across the US Corn Belt. We optimized the management options (planting date, N fertilizer amount, fertilizing date, and plant density in the farm) and cultivar options (cultivars with different maturity days) three times: a) before, b) at planting and c) after a growing season with known weather. Results indicated that the proposed model produced meaningful connections between weather and optima decisions. Also, we found risk-tolerance farmers get more expected yield than risk-averse ones in wet and non-wet weathers.
Context-Specific Independence in Bayesian Networks
Authors: Craig Boutilier, Nir Friedman, Moises Goldszmidt, Daphne Koller
Date: 2013-02-13 00:00:00

Bayesian networks provide a language for qualitatively representing the conditional independence properties of a distribution. This allows a natural and compact representation of the distribution, eases knowledge acquisition, and supports effective inference algorithms. It is well-known, however, that there are certain independencies that we cannot capture qualitatively within the Bayesian network structure: independencies that hold only in certain contexts, i.e., given a specific assignment of values to certain variables. In this paper, we propose a formal notion of context-specific independence (CSI), based on regularities in the conditional probability tables (CPTs) at a node. We present a technique, analogous to (and based on) d-separation, for determining when such independence holds in a given network. We then focus on a particular qualitative representation scheme - tree-structured CPTs - for capturing CSI. We suggest ways in which this representation can be used to support effective inference algorithms. In particular, we present a structural decomposition of the resulting network which can improve the performance of clustering algorithms, and an alternative algorithm based on cutset conditioning.
An open access database for the evaluation of respiratory sound classification algorithms
Authors: Bruno M Rocha, Dimitris Filos, Luís Mendes, Gorkem Serbes, Sezer Ulukaya, Yasemin P Kahya, Nikša Jakovljevic, Tatjana L Turukalo, Ioannis M Vogiatzis, Eleni Perantoni, Evangelos Kaimakamis, Pantelis Natsiavas, Ana Oliveira, Cristina Jácome, Alda Marques, Nicos Maglaveras, Rui Pedro Paiva, Ioanna Chouvarda, Paulo de Carvalho
Date: 2019-05-22 00:00:00
Proceeding: Physiological Measurement 2019 5
Tasks: Sound Classification

###Objective: Over the last few decades, there has been significant interest in the automatic analysis of respiratory sounds. However, currently there are no publicly available large databases with which new algorithms can be evaluated and compared. Further developments in the field are dependent on the creation of such databases.

###Approach: This paper describes a public respiratory sound database, which was compiled for an international competition, the first scientific challenge of the IFMBE's International Conference on Biomedical and Health Informatics. The database includes 920 recordings acquired from 126 participants and two sets of annotations. One set contains 6898 annotated respiratory cycles, some including crackles, wheezes, or a combination of both, and some with no adventitious respiratory sounds. In the other set, precise locations of 10 775 events of crackles and wheezes were annotated.

###Main results: The best system that participated in the challenge achieved an average score of 52.5% with the respiratory cycle annotations and an average score of 91.2% with the event annotations.

###Significance: The creation and public release of this database will be useful to the research community and could bring attention to the respiratory sound classification problem.