pwc-archive/papers-with-abstracts huggingface dataset:
Integrated Sensing, Communication, and Powering (ISCAP): Towards Multi-functional 6G Wireless Networks
Authors: Yilong Chen, Zixiang Ren, Jie Xu, Yong Zeng, Derrick Wing Kwan Ng, Shuguang Cui
Date: 2024-01-07 00:00:00
Tasks: Integrated sensing and communication, ISAC, Management

This article presents a novel multi-functional system for a sixth-generation (6G) wireless network with integrated sensing, communication, and powering (ISCAP), which unifies integrated sensing and communication (ISAC) and wireless information and power transfer (WIPT) techniques. The multi-functional ISCAP network promises to enhance resource utilization efficiency, reduce network costs, and improve overall performance through versatile operational modes. Specifically, a multi-functional base station (BS) can enable multi-functional transmission, by exploiting the same radio signals to perform target/environment sensing, wireless communication, and wireless power transfer (WPT), simultaneously. Besides, the three functions can be intelligently coordinated to pursue mutual benefits,i.e., wireless sensing can be leveraged to enable light-training or even training-free WIPT by providing side-channel information, and the BS can utilize WPT to wirelessly charge low-power devices for ensuring sustainable ISAC. Furthermore, multiple multi-functional BSs can cooperate in both transmission and reception phases for efficient interference management, multi-static sensing, and distributed energy beamforming. For these operational modes, we discuss the technical challenges and potential solutions, particularly focusing on the fundamental performance tradeoff limits, transmission protocol design, as well as waveform and beamforming optimization. Finally, interesting research directions are identified.
Reconfigurable Holographic Surface Aided Wireless Simultaneous Localization and Mapping
Authors: Haobo Zhang, Ziang Yang, Hongliang Zhang, Boya Di, Lingyang Song
Date: 2024-01-07 00:00:00
Tasks: Autonomous Driving, Simultaneous Localization and Mapping

As a crucial facilitator of future autonomous driving applications, wireless simultaneous localization and mapping (SLAM) has drawn growing attention recently. However, the accuracy of existing wireless SLAM schemes is limited because the antenna gain is constrained given the cost budget due to the expensive hardware components such as phase arrays. To address this issue, we propose a reconfigurable holographic surface (RHS)-aided SLAM system in this paper. The RHS is a novel type of low-cost antenna that can cut down the hardware cost by replacing phased arrays in conventional SLAM systems. However, compared with a phased array where the phase shifts of parallelfed signals are adjusted, the RHS exhibits a different radiation model because its amplitude-controlled radiation elements are series-fed by surface waves, implying that traditional schemes cannot be applied directly. To address this challenge, we propose an RHS-aided beam steering method for sensing the surrounding environment and design the corresponding SLAM algorithm. Simulation results show that the proposed scheme can achieve more than there times the localization accuracy that traditional wireless SLAM with the same cost achieves.
Multi-Channel Multi-Domain based Knowledge Distillation Algorithm for Sleep Staging with Single-Channel EEG
Authors: Chao Zhang, Yiqiao Liao, Siqi Han, Milin Zhang, Zhihua Wang, Xiang Xie
Date: 2024-01-07 00:00:00
Tasks: EEG, Knowledge Distillation, Sleep Staging

This paper proposed a Multi-Channel Multi-Domain (MCMD) based knowledge distillation algorithm for sleep staging using single-channel EEG. Both knowledge from different domains and different channels are learnt in the proposed algorithm, simultaneously. A multi-channel pre-training and single-channel fine-tuning scheme is used in the proposed work. The knowledge from different channels in the source domain is transferred to the single-channel model in the target domain. A pre-trained teacher-student model scheme is used to distill knowledge from the multi-channel teacher model to the single-channel student model combining with output transfer and intermediate feature transfer in the target domain. The proposed algorithm achieves a state-of-the-art single-channel sleep staging accuracy of 86.5%, with only 0.6% deterioration from the state-of-the-art multi-channel model. There is an improvement of 2% compared to the baseline model. The experimental results show that knowledge from multiple domains (different datasets) and multiple channels (e.g. EMG, EOG) could be transferred to single-channel sleep staging.
Channel Mapping Based on Interleaved Learning with Complex-Domain MLP-Mixer
Authors: Zirui Chen, Zhaoyang Zhang, Zhaohui Yang, Lei Liu
Date: 2024-01-07 00:00:00
Tasks: Representation Learning

In multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems, representing the whole channel only based on partial subchannels will significantly reduce the channel acquisition overhead. For such a channel mapping task, inspired by the intrinsic coupling across the space and frequency domains, this letter proposes to use interleaved learning with partial antenna and subcarrier characteristics to represent the whole MIMO-OFDM channel. Specifically, we design a complex-domain multilayer perceptron (MLP)-Mixer (CMixer), which utilizes two kinds of complex-domain MLP modules to learn the space and frequency characteristics respectively and then interleaves them to couple the learned properties. The complex-domain computation facilitates the learning on the complex-valued channel data, while the interleaving tightens the coupling of space and frequency domains. These two designs jointly reduce the learning burden, making the physics-inspired CMixer more effective on channel representation learning than existing data-driven approaches. Simulation shows that the proposed scheme brings 4.6~10dB gains in mapping accuracy compared to existing schemes under different settings. Besides, ablation studies show the necessity of complex-domain computation as well as the extent to which the interleaved learning matches the channel properties.
A Closed-loop Brain-Machine Interface SoC Featuring a 0.2$μ$J/class Multiplexer Based Neural Network
Authors: Chao Zhang, Yongxiang Guo, Dawid Sheng, Zhixiong Ma, Chao Sun, Yuwei Zhang, Wenxin Zhao, Fenyan Zhang, Tongfei Wang, Xing Sheng, Milin Zhang
Date: 2024-01-07 00:00:00
Tasks: Sleep Staging

This work presents the first fabricated electrophysiology-optogenetic closed-loop bidirectional brain-machine interface (CL-BBMI) system-on-chip (SoC) with electrical neural signal recording, on-chip sleep staging and optogenetic stimulation. The first multiplexer with static assignment based table lookup solution (MUXnet) for multiplier-free NN processor was proposed. A state-of-the-art average accuracy of 82.4% was achieved with an energy consumption of only 0.2$\mu$J/class in sleep staging task.
Real-Time Asphalt Pavement Layer Thickness Prediction Using Ground-Penetrating Radar Based on a Modified Extended Common Mid-Point (XCMP) Approach
Authors: Siqi Wang, Zhen Leng, Xin Sui, Weiguang Zhang, Tao Ma, Zehui Zhu
Date: 2024-01-07 00:00:00
Tasks: Dielectric Constant, Edge Detection, GPR, Prediction

The conventional surface reflection method has been widely used to measure the asphalt pavement layer dielectric constant using ground-penetrating radar (GPR). This method may be inaccurate for in-service pavement thickness estimation with dielectric constant variation through the depth, which could be addressed using the extended common mid-point method (XCMP) with air-coupled GPR antennas. However, the factors affecting the XCMP method on thickness prediction accuracy haven't been studied. Manual acquisition of key factors is required, which hinders its real-time applications. This study investigates the affecting factors and develops a modified XCMP method to allow automatic thickness prediction of in-service asphalt pavement with non-uniform dielectric properties through depth. A sensitivity analysis was performed, necessitating the accurate estimation of time of flights (TOFs) from antenna pairs. A modified XCMP method based on edge detection was proposed to allow real-time TOFs estimation, then dielectric constant and thickness predictions. Field tests using a multi-channel GPR system were performed for validation. Both the surface reflection and XCMP setups were conducted. Results show that the modified XCMP method is recommended with a mean prediction error of 1.86%, which is more accurate than the surface reflection method (5.73%).
Quantifying T cell morphodynamics and migration in 3D collagen matrices
Authors: Yeeren I. Low
Date: 2024-01-07 00:00:00

T cells undergo large shape changes (morphodynamics) when migrating. While progress has been made elucidating the molecular basis of cell migration, statistical characterization of morphodynamics and migration has been limited, particularly in physiologically realistic 3D environments. A previous study (H. Cavanagh et al., J. R. Soc. Interface 19: 20220081) found discrete states of dynamics as well as periodic oscillations of shape. However, we show that these results are due to artifacts of the analysis methods. Here, we present a revised analysis of the data, applying a method based on an underdamped Langevin equation. We find that different shape modes have different correlation times. We also find novel non-Gaussian effects. This study provides a framework in which quantitative comparisons of cell morphodynamics and migration can be made, e.g. between different biological conditions or mechanistic models.
Modelling and calibration of pair-rule protein patterns in Drosophila embryo: From Even-skipped and Fushi-tarazu to Wingless expression networks
Authors: Catarina Dias, Rui Dilão
Date: 2024-01-05 00:00:00

We modelled and calibrated the distributions of the seven-stripe patterns of Even-skipped (\textit{Eve}) and Fushi-tarazu (\textit{Ftz}) pair-rule proteins along the anteroposterior axis of the \textit{Drosphila} embryo, established during early development. We have identified the putative repressive combinations for five \textit{Eve} enhancers, and we have explored the relationship between \textit{Eve} and \textit{Ftz} for complementary patterns. The regulators of \textit{Eve} and \textit{Ftz} are stripe-specific DNA enhancers with embryo position-dependent activation rates and are regulated by the gap family of proteins. We achieved remarkable data matching of the \textit{Eve} stripe pattern, and the calibrated model reproduces gap gene mutation experiments. Extended work inferring the Wingless (\textit{Wg}) fourteen stripe pattern from \textit{Eve} and \textit{Ftz} enhancers have been proposed, clarifying the hierarchical structure of \textit{Drosphila}'s genetic expression network during early development.
Low-cost, portable, easy-to-use kiosks to facilitate home-cage testing of non-human primates during vision-based behavioral tasks
Authors: Hamidreza Ramezanpour, Christopher Giverin, Kohitij Kar
Date: 2024-01-08 00:00:00

Non-human primates (NHPs), especially rhesus macaques, have played a significant role in our current understanding of the neural computations underlying human vision. Apart from the established homologies in the visual brain areas between these two species, and our extended abilities to probe detailed neural mechanisms in monkeys at multiple scales, one major factor that makes NHPs an extremely appealing animal model of human-vision is their ability to perform human-like visual behavior. Traditionally, such behavioral studies have been conducted in controlled laboratory settings. Such in-lab studies offer the experimenter a tight control over many experimental variables like overall luminance, eye movements (via eye tracking), auditory interference etc. However, there are several constraints related to such experiments. These include, 1) limited total experimental time, 2) requirement of dedicated human experimenters for the NHPs, 3) requirement of additional lab-space for the experiments, 4) NHPs often need to undergo invasive surgeries for a head-post implant, 5) additional time and training required for chairing and head restraints of monkeys. To overcome these limitations, many laboratories are now adapting home-cage behavioral training and testing of NHPs. Home-cage behavioral testing enables the administering of many vision-based behavioral tasks simultaneously across multiple monkeys with much reduced human personnel requirements, no NHP head restraint, and provide NHPs access to the experiments without specific time constraints. To enable more open-source development of this technology, here we provide the details of operating and building a portable, easy-to-use kiosk for conducting home-cage vision-based behavioral tasks in NHPs.
The interplay of common noise and finite pulses on biological neurons
Authors: Afifurrahman, Mohd Hafiz Mohd, Farah Aini Abdullah
Date: 2024-01-07 00:00:00

The response of neurons is highly sensitive to the stimulus. The stimulus can be associated with a direct injection in vitro experimentation (e.g., time dependent and independent inputs); or post-synaptic potentials resulting from the interaction of many neurons. A typical incoming stimulus resembles a noise which in principle can be described as a random variable. In computational neuroscience, the noise has been extensively studied for different setups. In this study, we investigate the effect of noisy inputs in a minimal network of two identical leaky integrate-and-fire (LIF) neurons interacting with finite pulses. In particular, we consider a Gaussian white noise as a standard function for stochastic modelling of neurons, while taking into account the pulse width as an elementary component for the signal transmission. By exploring the role of noise and finite pulses, the two neurons show a synchronous spiking behaviour characterized by fluctuations in the interspike intervals. Above some critical values the synchronous regime collapses onto asynchronous dynamics. The abrupt change in such dynamics is accompanied by a hysteresis, i.e., the coexistence of synchronous and asynchronous firing behaviour.
How to optimize neuroscience data utilization and experiment design for advancing primate visual and linguistic brain models?
Authors: Greta Tuckute, Dawn Finzi, Eshed Margalit, Joel Zylberberg, SueYeon Chung, Alona Fyshe, Evelina Fedorenko, Nikolaus Kriegeskorte, Jacob Yates, Kalanit Grill-Spector, Kohitij Kar
Date: 2024-01-07 00:00:00
Tasks: Experimental Design

In recent years, neuroscience has made significant progress in building large-scale artificial neural network (ANN) models of brain activity and behavior. However, there is no consensus on the most efficient ways to collect data and design experiments to develop the next generation of models. This article explores the controversial opinions that have emerged on this topic in the domain of vision and language. Specifically, we address two critical points. First, we weigh the pros and cons of using qualitative insights from empirical results versus raw experimental data to train models. Second, we consider model-free (intuition-based) versus model-based approaches for data collection, specifically experimental design and stimulus selection, for optimal model development. Finally, we consider the challenges of developing a synergistic approach to experimental design and model building, including encouraging data and model sharing and the implications of iterative additions to existing models. The goal of the paper is to discuss decision points and propose directions for both experimenters and model developers in the quest to understand the brain.
A how-to guide for code-sharing in biology
Authors: Richard J. Abdill, Emma Talarico, Laura Grieneisen
Date: 2024-01-05 00:00:00

Computational biology continues to spread into new fields, becoming more accessible to researchers trained in the wet lab who are eager to take advantage of growing datasets, falling costs, and novel assays that present new opportunities for discovery even outside of the much-discussed developments in artificial intelligence. However, guidance for implementing these techniques is much easier to find than guidance for reporting their use, leaving biologists to guess which details and files are relevant. Here, we provide a set of recommendations for sharing code, with an eye toward guiding those who are comparatively new to applying open science principles to their computational work. Additionally, we review existing literature on the topic, summarize the most common tips, and evaluate the code-sharing policies of the most influential journals in biology, which occasionally encourage code-sharing but seldom require it. Taken together, we provide a user manual for biologists who seek to follow code-sharing best practices but are unsure where to start.
Singular effect of linkage on long term genetic gain in the infinitesimal model
Authors: Elise Tourrette, Olivier C. Martin
Date: 2024-01-08 00:00:00

During the founding of the field of quantitative genetics, Fisher formulated in 1918 his ``infinitesimal model'' that provided a novel mathematical framework to describe the Mendelian transmission of quantitative traits. If the infinitely many genes in that model are assumed to segregate independently during reproduction, corresponding to having no linkage, directional selection asymptotically leads to a constant genetic gain at each generation. In reality, genes are subject to strong linkage because they lie on chromosomes and thus segregate in a correlated way. Various approximations have been used in the past to study that more realistic case of the infinitesimal model with the expectation that the asymptotic gain per generation is modestly decreased. To treat this system even in the strong linkage limit, we take the genes to lie on continuous chromosomes. Surprisingly, the consequences of genetic linkage are in fact rather singular, changing the nature of the long-term gain per generation: the asymptotic gain vanishes rather than being simply decreased. Nevertheless, the per-generation gain tends to zero sufficiently slowly for the total gain, accumulated over generations, to be unbounded.
On the selection and effectiveness of pseudo-absences for species distribution modeling with deep learning
Authors: Robin Zbinden, Nina van Tiel, Benjamin Kellenberger, Lloyd Hughes, Devis Tuia
Date: 2024-01-03 00:00:00

Species distribution modeling is a highly versatile tool for understanding the intricate relationship between environmental conditions and species occurrences. However, the available data often lacks information on confirmed species absence and is limited to opportunistically sampled, presence-only observations. To overcome this limitation, a common approach is to employ pseudo-absences, which are specific geographic locations designated as negative samples. While pseudo-absences are well-established for single-species distribution models, their application in the context of multi-species neural networks remains underexplored. Notably, the significant class imbalance between species presences and pseudo-absences is often left unaddressed. Moreover, the existence of different types of pseudo-absences (e.g., random and target-group background points) adds complexity to the selection process. Determining the optimal combination of pseudo-absences types is difficult and depends on the characteristics of the data, particularly considering that certain types of pseudo-absences can be used to mitigate geographic biases. In this paper, we demonstrate that these challenges can be effectively tackled by integrating pseudo-absences in the training of multi-species neural networks through modifications to the loss function. This adjustment involves assigning different weights to the distinct terms of the loss function, thereby addressing both the class imbalance and the choice of pseudo-absence types. Additionally, we propose a strategy to set these loss weights using spatial block cross-validation with presence-only data. We evaluate our approach using a benchmark dataset containing independent presence-absence data from six different regions and report improved results when compared to competing approaches.
Quantitative in vitro to in vivo extrapolation for human toxicology and drug development
Authors: Luca Cattelani, Giusy del Giudice, Angela Serra, Michele Fratello, Laura Aliisa Saarimäki, Vittorio Fortino, Antonio Federico, Periklis Tsiros, Marika Mannerström, Tarja Toimela, Tommaso Serchi, Iseult Lynch, Philip Doganis, Haralambos Sarimveis, Dario Greco
Date: 2024-01-06 00:00:00

Traditional animal testing for toxicity is expensive, time consuming, ethically questioned, sometimes inaccurate because of the necessity to extrapolate from animal to human, and in most cases not formally validated according to modern standards. This is driving regulatory bodies and companies in backing alternative methods focusing on in silico and in vitro approaches. These are complex to implement and validate, and their wide adoption is not yet established despite legal directives providing an imperative. It is difficult to link a cell level response to effects on a whole organism, but the advances in high-throughput toxicogenomics towards elucidating the mechanism of action of substances are gradually reducing this gap and fostering the adoption of Next Generation Safety Assessment approaches. Quantitative in vitro to in vivo extrapolation (QIVIVE) methods hold the promise to reveal how to use in vitro -omics data to predict the potential for in vivo toxicity. They could improve lead compounds prioritisation, reduce time and costs, also in numbers of animal lives, and help with the complexity of extrapolating between species. We provide a description of QIVIVE state of the art, including how the problems of dosing and timing are being approached, how in silico simulation can take into account the variability of individuals, and how multiple techniques can be integrated to face complex tasks like the prediction of long term toxicity, including a close look into the open problems and challenges ahead.
Volatility models in practice: Rough, Path-dependent or Markovian?
Authors: Eduardo Abi Jaber, Shaun, Li
Date: 2024-01-07 00:00:00

We present an empirical study examining several claims related to option prices in rough volatility literature using SPX options data. Our results show that rough volatility models with the parameter $H \in (0,1/2)$ are inconsistent with the global shape of SPX smiles. In particular, the at-the-money SPX skew is incompatible with the power-law shape generated by these models, which increases too fast for short maturities and decays too slowly for longer maturities. For maturities between one week and three months, rough volatility models underperform one-factor Markovian models with the same number of parameters. When extended to longer maturities, rough volatility models do not consistently outperform one-factor Markovian models. Our study identifies a non-rough path-dependent model and a two-factor Markovian model that outperform their rough counterparts in capturing SPX smiles between one week and three years, with only 3 to 4 parameters.