pwc-archive/papers-with-abstracts huggingface dataset:
Wideband Sample Rate Converter Using Cascaded Parallel-serial Structure for Synthetic Instrumentation
Authors: Ruiyuan Ming, Peng Ye, Kuojun Yang, Zhixiang Pan, Li Chen, Xuetao Liu
Date: 2023-12-22 00:00:00

A sample rate converter(SRC) is designed to adjust the sampling rate of digital signals flexibly for different application requirements in the broadband signal processing system. In this paper, a novel parallel-serial structure is proposed to improve the bandwidth and flexibility of SRC. The core of this structure is a parallel decimation filter followed by a serial counterpart, the parallel part is designed to process high sampling rate data streams, and the serial part provides high flexibility in decimation factor configuration. A typical combination of cascaded integral comb filter(CIC) and halfband filter is utilized in this structure, the serial recursive loop which limits the processing ability of the CIC filter is transformed into a parallel-pipeline recursive structure. In addition, the symmetry property and zero coefficient of the halfband filter are exploited with the polyphase filter structure to reduce resource utilization and design complexity. In the meantime, the decimation factor of the CIC filter can be adjusted flexibly in a wide range, which is used to improve the system configuration flexibility. This parallel-serial SRC structure was implemented on Xilinx KU115 series field programmable gate array(FPGA), and then applied in a synthetic instrument system. The experiment results demonstrate that the proposed scheme significantly improves the performance of SRC in bandwidth and flexibility.
Magnetic Sensor Compensation Utilizing Factor Graph Estimation
Authors: Frederic W. Lathrop, Clark N. Taylor
Date: 2023-12-22 00:00:00
Tasks: Navigate

Recently, there has been significant interest in the ability to navigate without GPS using the magnetic anomaly field of the Earth (magnav). One of the key technical bottlenecks to achieving magnav is obtaining an accurate magnetic sensor calibration, taking into account own-ship and sensor effects. The Tolles-Lawson magnetic calibration method was developed when airborne magnetic survey aircraft were first employed 80 years ago. In this paper, we present a magnetic calibration algorithm based on a factor graph optimization using inertial measurements as well as inputs from both a vector and scalar magnetometer. The factor graph is well suited for combining multiple sensor inputs and allows accurate calibrations in the presence of large permanent moments as well as estimating a time-varying external magnetic field during the calibration, two problems that are difficult to solve with previous approaches. The ability to accurately calibrate a magnetic sensor in flight (i.e. with the presence of a large platform field and a varying Earth field due to movement) will allow greater flexibility in sensor mounting locations for magnetic anomaly navigation.
Towards Non-contact Muscle Activity Estimation using FMCW Radar
Authors: Kukhokuhle Tsengwa, Stephen Paine, Fred Nicolls, Yumna Albertus, Amir Patel
Date: 2023-12-21 00:00:00

Surface electromyography (sEMG) is a widely used muscle activity monitoring technique. sEMG measures muscle activity through monopolar and bipolar, multi-electrode electrodes. The surface electrodes are placed on the surface of the skin above the target muscle and the received signal can be used to infer the state of the muscle - active, inactive or fatigued - which serves as vital information during neurological and orthopaedic rehabilitation. Additionally, the sEMG signal can also be used for the control of prostheses. sEMG requires contact with the participant's skin and is thus a potentially uncomfortable method for the measurement of muscle activity. Moreover, the setup procedure has been termed time-consuming by sEMG experts and is listed as one of the main barriers to the clinical employment of the technique. Previous studies have shown that architectural changes, particularly muscle deformation, can provide information about the activity of the muscle, providing an alternative to sEMG. In all these studies, the muscle deformation signal is acquired using ultrasound imaging, an approach known as sonomyography (SMG). Despite its advantages, such as improved spatial resolution, SMG is still a contact based approach. In this paper, we propose a non-contact muscle activity monitoring approach that measures the muscle deformation signal using a Frequency Modulated Continuous Wave (FMCW) mmWave radar which we call radiomyography (RMG). In future, this system will enable muscle activation to be measured in an unconstrained and less cumbersome manner for both the person conducting the test and the individual being tested.
Feature-based Transformer with Incomplete Multimodal Brain Images for Diagnosis of Neurodegenerative Diseases
Authors: Xingyu Gao, Feng Shi, Dinggang Shen & Manhua Liu
Date: 2023-07-01 00:00:00
Proceeding: Conference 2023 7

Benefiting from complementary information, multimodal brain imaging analysis has distinct advantages over single-modal methods for the diagnosis of neurodegenerative diseases such as Alzheimer’s disease. However, multi-modal brain images are often incomplete with missing data in clinical practice due to various issues such as motion, medical costs, and scanner availability. Most existing methods attempted to build machine learning models to directly estimate the missing images. However, since brain images are of high dimension, accurate and efficient estimation of missing data is quite challenging, and not all voxels in the brain images are associated with the disease. In this paper, we propose a multimodal feature-based transformer to impute multimodal brain features with missing data for the diagnosis of neurodegenerative disease. The proposed method consists of a feature regression subnetwork and a multimodal fusion subnetwork based on transformer, for completion of the features of missing data and also multimodal diagnosis of disease. Different from previous methods for the generation of missing images, our method imputes high-level and disease-related features for multimodal classification. Experiments on the ADNI database with 1,364 subjects show better performance of our method over the state-of-the-art methods in disease diagnosis with missing multimodal data.
PlayMolecule Viewer: a toolkit for the visualization of molecules and other data
Authors: Mariona Torrens-Fontanals, Panagiotis Tourlas, Stefan Doerr, Gianni de Fabritiis
Date: 2023-12-22 00:00:00
Tasks: Data Visualization, Drug Design

PlayMolecule Viewer is a web-based data visualization toolkit designed to streamline the exploration of data resulting from structural bioinformatics or computer-aided drug design efforts. By harnessing state-of-the-art web technologies such as WebAssembly, PlayMolecule Viewer integrates powerful Python libraries directly within the browser environment, which enhances its capabilities of managing multiple types of molecular data. With its intuitive interface, it allows users to easily upload, visualize, select, and manipulate molecular structures and associated data. The toolkit supports a wide range of common structural file formats and offers a variety of molecular representations to cater to different visualization needs. PlayMolecule Viewer is freely accessible at open.playmolecule.org, ensuring accessibility and availability to the scientific community and beyond.
Identifying topologically associating domains using differential kernels
Authors: Luka Maisuradze, Megan C. King, Ivan V. Surovtsev, Simon G. J. Mochrie, Mark D. Shattuck, Corey S. O'Hern
Date: 2023-12-22 00:00:00

Chromatin is a polymer complex of DNA and proteins that regulates gene expression. The three-dimensional structure and organization of chromatin controls DNA transcription and replication. High-throughput chromatin conformation capture techniques generate Hi-C maps that can provide insight into the 3D structure of chromatin. Hi-C maps can be represented as a symmetric matrix where each element represents the average contact probability or number of contacts between two chromatin loci. Previous studies have detected topologically associating domains (TADs), or self-interacting regions in Hi-C maps within which the contact probability is greater than that outside the region. Many algorithms have been developed to identify TADs within Hi-C maps. However, most TAD identification algorithms are unable to identify nested or overlapping TADs and for a given Hi-C map there is significant variation in the location and number of TADs identified by different methods. We develop a novel method, KerTAD, using a kernel-based technique from computer vision and image processing that is able to accurately identify nested and overlapping TADs. We benchmark this method against state-of-the-art TAD identification methods on both synthetic and experimental data sets. We find that KerTAD consistently has higher true positive rates (TPR) and lower false discovery rates (FDR) than all tested methods for both synthetic and manually annotated experimental Hi-C maps. The TPR for KerTAD is also largely insensitive to increasing noise and sparsity, in contrast to the other methods. We also find that KerTAD is consistent in the number and size of TADs identified across replicate experimental Hi-C maps for several organisms. KerTAD will improve automated TAD identification and enable researchers to better correlate changes in TADs to biological phenomena, such as enhancer-promoter interactions and disease states.
Relationship between Decimal Hill Coefficient, Intermediate Processes and Mesoscopic Fluctuations
Authors: Manuel Eduardo Hernández-García, Jorge Velázquez-Castro
Date: 2023-12-25 00:00:00

The Hill function is relevant for describing enzyme binding and other processes in gene regulatory networks. Despite its theoretical foundation, it is often empirically used as a useful fitting function. Theoretical predictions suggest that the Hill coefficient should be an integer. However, it is often assigned a decimal value. The deterministic approximation of binding processes leads to the derivation of the Hill function, which can be expanded around the fluctuation magnitude to derive mesoscopic corrections. This study establishes the relationships between intermediate processes and the decimal Hill coefficient through a direct relationship between the dissociation constants, both with and without fluctuations. This outcome contributes to a deeper understanding of the underlying processes associated with the decimal Hill coefficient while also enabling the prediction of an effective value of the Hill coefficient from the underlying mechanism. This procedure allows us to have a simplified effective description of complex systems.
Notes on Retroactive Interference Model of Forgetting
Authors: Mikhail Katkov
Date: 2023-12-26 00:00:00
Tasks: model

We present analytical derivation of the minimal and maximal number of items retained in recently introduced Retroactive Interference Model of Forgetting. Also we computed the probability that two items presented at different times are retained in the memory at a later time analytically.
The HCP multi-pipeline dataset: an opportunity to investigate analytical variability in fMRI data analysis
Authors: Elodie Germani, Elisa Fromont, Pierre Maurel, Camille Maumet
Date: 2023-12-22 00:00:00

Results of functional Magnetic Resonance Imaging (fMRI) studies can be impacted by many sources of variability including differences due to: the sampling of the participants, differences in acquisition protocols and material but also due to different analytical choices in the processing of the fMRI data. While variability across participants or across acquisition instruments have been extensively studied in the neuroimaging literature the root causes of analytical variability remain an open question. Here, we share the \textit{HCP multi-pipeline dataset}, including the resulting statistic maps for 24 typical fMRI pipelines on 1,080 participants of the HCP-Young Adults dataset. We share both individual and group results - for 1,000 groups of 50 participants - over 5 motor contrasts. We hope that this large dataset covering a wide range of analysis conditions will provide new opportunities to study analytical variability in fMRI.
Rethinking the external globus pallidus and information flow in cortico-basal ganglia-thalamic circuits
Authors: Cristina Giossi, Jonathan E. Rubin, Aryn Gittis, Timothy Verstynen, Catalina Vich
Date: 2023-12-21 00:00:00

For decades the external globus pallidus (GPe) has been viewed as a passive way-station in the indirect pathway of the cortico-basal ganglia-thalamic (CBGT) circuit, sandwiched between striatal inputs and basal ganglia outputs. According to this model, one-way descending striatal signals in the indirect pathway amplify the suppression of downstream thalamic nuclei by inhibiting GPe activity. Here we revisit this assumption, in light of new and emerging work on the cellular complexity, connectivity, and functional role of the GPe in behavior. We show how, according to this new circuit-level logic, the GPe is ideally positioned for relaying ascending and descending control signals within the basal ganglia. Focusing on the problem of inhibitory control, we illustrate how this bidirectional flow of information allows for the integration of reactive and proactive control mechanisms during action selection. Taken together, this new evidence points to the GPe as being a central hub in the CBGT circuit, participating in bidirectional information flow and linking multifaceted control signals to regulate behavior.
Natural Averaging May Complement Known Biological Constraints in Bi-parental Reproduction's Advantages Over Mono-parental in Conserving Species Quantitative Traits
Authors: Assaf Marron, Smadar Szekely, Irun R. Cohen, David Harel
Date: 2023-12-25 00:00:00
Tasks: Diversity

Commonly recognized evolutionarily relevant effects of sexual reproduction include increased diversity, accelerated adaptation, and constrained accumulation of deleterious mutations, along with a secondary effect of species genotype homogenization. Still, strong published arguments prioritize the contribution of biological mechanisms underlying bi-parental reproduction to maintaining species identity above their contribution to diversity. Here, we contribute to the latter position. In an initial mathematical analysis and simulation, we show that in an environment where copying is prone to error, quantitative polygenic traits that are shared within a parents' generation are transmitted to future generations under bi-parental reproduction with less deviation than under asexual reproduction. Furthermore, we abstract away many biological details, and show that this trait conservation is a general statistical effect, driven by the very nature of mixing of parental traits, separately from DNA repair and from the reproductive failures, barriers and disadvantages induced by biological mechanisms. Since survival of ecosystem interaction networks depends on the ability of individuals to replace the networked function of failing, dying or absent members of the same species, more faithful inheritance of common traits helps sustain species and ecosystems. This sustaining effect may have contributed to the very evolution of sexual reproduction.
Anticipating dengue outbreaks using a novel hybrid ARIMA-ARNN model with exogenous variables
Authors: I. Ghosh, S. Gupta, S. Rana
Date: 2023-12-23 00:00:00
Tasks: Time Series, Time Series Forecasting

Dengue incidence forecasting using hybrid models has been surging in the data rich world. Hybridization of statistical time series forecasting models and machine learning models are explored for dengue forecasting with different degrees of success. In this paper, we propose a multivariate expansion of the hybrid ARIMA-ARNN model. The main motivation is to propose a novel hybridization and apply it to dengue outbreak prediction. The asymptotic stationarity of the proposed model has been established. We check the forecasting capability and robustness of the forecasts through numerical experiments. State-of-the-art forecasting models for multivariate time series data are compared with the proposed model using accuracy metrics. Dengue incidence data from San Juan and Iquitos are utilized along with rainfall as an exogenous variable. Results indicate that the proposed model improves the ARIMAX forecasts in some situations and closely follows it otherwise. The theoretical as well as experimental results reinforce that the proposed model has the potential to act as a candidate for early warning of dengue outbreaks. The proposed model can be readily generalized to incorporate more exogenous variables and also applied to other time series forecasting problems wherever exogenous variable(s) are available.
On the exclusion of exponential autocatalysts by sub-exponential autocatalysts
Authors: Yann Sakref, Olivier Rivoire
Date: 2023-12-22 00:00:00

Selection among autocatalytic species fundamentally depends on their growth law: exponential species, whose number of copies grows exponentially, are mutually exclusive, while sub-exponential ones, whose number of copies grows polynomially, can coexist. Here we consider competitions between autocatalytic species with different growth laws and make the simple yet counterintuitive observation that sub-exponential species can exclude exponential ones while the reverse is, in principle, impossible. This observation has implications for scenarios pertaining to the emergence of natural selection.
Expanding to Arbitrary Study Designs: ANOVA to Estimate Limits of Agreement for MRMC Studies
Authors: Si Wen, Brandon D. Gallas
Date: 2023-12-26 00:00:00

A multi-reader multi-case (MRMC) analysis is applied to account for both reader and case variability when evaluating the clinical performance of a medical imaging device or reader performance under different reading modalities. For a clinical task that measures a quantitative biomarker an agreement analysis, such as limits of agreement (LOA), can be used. In this work, we decompose the total variation in the data using a three-way mixed effect ANOVA model to estimate the MRMC variance of individual differences and the LOA between different reading modalities. There are rules for writing down the expectation of the mean squares in terms of the variance components for fully-crossed data, i.e. data where all the readers read all the cases in all modalities being studied. Sometimes the annotation task is labor-intensive and time-consuming or distributed across sites, so that a fully-crossed study is not practical. In this work, we focus on estimating the MRMC variance in the within- and between-readers and within- and between-modalities LOA for an arbitrary study design. Simulation studies were conducted to validate the LOA variance estimates. The method was also applied to a dataset to compare pathologist performance for assessing the density of stromal tumor infiltrating lymphocytes on different platforms.
The Status and Prospects of Phytoremediation of Heavy Metals
Authors: Aniruddha Acharya, Enrique Perez, Miller Maddox-Mandolini, Hania De La Fuente
Date: 2023-12-21 00:00:00

The release of heavy metals into the agricultural soil and waterbodies has been accelerated due to anthropogenic activities. They are not usually required for biological functions thus, their accumulation in biological system poses serious threat to health and environment globally. Phytoremediation offers a safe, inexpensive, and ecologically sustainable technique to clean habitats contaminated with heavy metals. Though several plants have been identified and used as a potential candidate for such phytoremediation, the technique is still at its formative stage and has been mostly confined to laboratory and greenhouses. However, recently several field studies have shown promising results that can propel large-scale implementation of this technology in industrial sites and urban agriculture. Realistically, the commercialization of this technique is possible if interdisciplinary approach is employed to increase its efficiency. This review presents a comprehensive narration of the status and future of the technique. It illustrates the concept of phytoremediation, the ecological and commercial benefits, and the types of phytoremediation. The candidate plants and factors that influences phytoremediation has been discussed. Finally, the physiological and molecular mechanism along with the future of the technique has been described.
Implied volatility (also) is path-dependent
Authors: Hervé Andrès, Alexandre Boumezoued, Benjamin Jourdain
Date: 2023-12-26 00:00:00

We propose a new model for the coherent forecasting of both the implied volatility surfaces and the underlying asset returns.In the spirit of Guyon and Lekeufack (2023) who are interested in the dependence of volatility indices (e.g. the VIX) on the paths of the associated equity indices (e.g. the S&P 500), we first study how implied volatility can be predicted using the past trajectory of the underlying asset price. Our empirical study reveals that a large part of the movements of the at-the-money-forward implied volatility for up to two years maturities can be explained using the past returns and their squares. Moreover, we show that up to four years of the past evolution of the underlying price should be used for the prediction and that this feedback effect gets weaker when the maturity increases. Building on this new stylized fact, we fit to historical data a parsimonious version of the SSVI parameterization (Gatheral and Jacquier, 2014) of the implied volatility surface relying on only four parameters and show that the two parameters ruling the at-the-money-forward implied volatility as a function of the maturity exhibit a path-dependent behavior with respect to the underlying asset price. Finally, we propose a model for the joint dynamics of the implied volatility surface and the underlying asset price. The latter is modelled using a variant of the path-dependent volatility model of Guyon and Lekeufack and the former is obtained by adding a feedback effect of the underlying asset price onto the two parameters ruling the at-the-money-forward implied volatility in the parsimonious SSVI parameterization and by specifying a hidden semi-Markov diffusion model for the residuals of these two parameters and the two other parameters. Thanks to this model, we are able to simulate highly realistic paths of implied volatility surfaces that are arbitrage-free.
Scalable Agent-Based Modeling for Complex Financial Market Simulations
Authors: Aaron Wheeler, Jeffrey D. Varner
Date: 2023-12-22 00:00:00
Tasks: Decision Making, Distributed Computing

In this study, we developed a computational framework for simulating large-scale agent-based financial markets. Our platform supports trading multiple simultaneous assets and leverages distributed computing to scale the number and complexity of simulated agents. Heterogeneous agents make decisions in parallel, and their orders are processed through a realistic, continuous double auction matching engine. We present a baseline model implementation and show that it captures several known statistical properties of real financial markets (i.e., stylized facts). Further, we demonstrate these results without fitting models to historical financial data. Thus, this framework could be used for direct applications such as human-in-the-loop machine learning or to explore theoretically exciting questions about market microstructure's role in forming the statistical regularities of real markets. To the best of our knowledge, this study is the first to implement multiple assets, parallel agent decision-making, a continuous double auction mechanism, and intelligent agent types in a scalable real-time environment.