pwc-archive/papers-with-abstracts huggingface dataset:
On Distributed Detection in EH-WSNs With Finite-State Markov Channel and Limited Feedback
Authors: Ghazaleh Ardeshiri, Azadeh Vosoughi
Date: 2022-10-10 00:00:00

We consider a network, tasked with solving binary distributed detection, consisting of N sensors, a fusion center (FC), and a feedback channel from the FC to sensors. Each sensor is capable of harvesting energy and is equipped with a finite size battery to store randomly arrived energy. Sensors process their observations and transmit their symbols to the FC over orthogonal fading channels. The FC fuses the received symbols and makes a global binary decision. We aim at developing adaptive channel-dependent transmit power control policies such that J-divergence based detection metric is maximized at the FC, subject to total transmit power constraint. Modeling the quantized fading channel, the energy arrival, and the battery dynamics as time-homogeneous finite-state Markov chains, and the network lifetime as a geometric random variable, we formulate our power control optimization problem as a discounted infinite-horizon constrained Markov decision process (MDP) problem, where sensors' transmit powers are functions of the battery states, quantized channel gains, and the arrived energies. We utilize stochastic dynamic programming and Lagrangian approach to find the optimal and sub-optimal power control policies. We demonstrate that our sub-optimal policy provides a close-to-optimal performance with a reduced computational complexity and without imposing signaling overhead on sensors.
Advanced Tri-Sectoral Multi-User Millimeter-Wave Smart Repeater
Authors: Kai Dong, Silvia Mura, Marouan Mizmizi, Dario Tagliaferri, Umberto Spagnolini
Date: 2022-10-10 00:00:00

Smart Repeaters (SR) can potentially enhance the coverage in Millimeter-wave (mmWave) wireless communications. However, the angular coverage of the existing two-panel SR is too limited to make the SR a truly cost-effective mmWave range extender. This paper proposes the usage of a tri-sectoral Advanced SR (ASR) to extend the angular coverage with respect to conventional SR. We propose a multi-user precoder optimization for ASR in a downlink multi-carrier communication system to maximize the number of served User Equipments (UEs) while guaranteeing constraints on per-UE rate and time-frequency resources. Numerical results show the benefits of the ASR against conventional SR in terms of both cumulative spectral efficiency and number of served UEs (both improved by an average factor 2), varying the system parameters.
A Learning-based approach for bias elimination in low cost gyroscopes
Authors: Daniel Engelsman, Itzik Klein
Date: 2022-10-10 00:00:00
Tasks: Autonomous Vehicles

Modern sensors play a pivotal role in many operating platforms, as they manage to track the platform dynamics at a relatively low manufacturing costs. Their widespread use can be found starting from autonomous vehicles, through tactical platforms, and ending with household appliances in daily use. Upon leaving the factory, the calibrated sensor starts accumulating different error sources which slowly wear out its precision and reliability. To that end, periodic calibration is needed, to restore intrinsic parameters and realign its readings with the ground truth. While extensive analytic methods exist in the literature, little is proposed using data-driven techniques and their unprecedented approximation capabilities. In this study, we show how bias elimination in low-cost gyroscopes can be performed in considerably shorter operative time, using a unique convolutional neural network structure. The strict constraints of traditional methods are replaced by a learning-based regression which spares the time-consuming averaging time, exhibiting efficient sifting of background noise from the actual bias.
Sampling of Correlated Bandlimited Continuous Signals by Joint Time-vertex Graph Fourier Transform
Authors: Zhongyi Ni, Feng Ji, Hang Sheng, Hui Feng, Bo Hu
Date: 2022-10-10 00:00:00

When sampling multiple signals, the correlation between the signals can be exploited to reduce the overall number of samples. In this paper, we study the sampling theory of multiple correlated signals, using correlation to sample them at the lowest sampling rate. Based on the correlation between signal sources, we model multiple continuous-time signals as continuous time-vertex graph signals. The graph signals are projected onto orthogonal bases to remove spatial correlation and reduce dimensions by graph Fourier transform. When the bandwidths of the original signals and the reduced dimension signals are given, we prove the minimum sampling rate required for recovery of the original signals, and propose a feasible sampling scheme.
A Beam-Space Active Sensing Scheme for Integrated Communication and Sensing Applications
Authors: Saeid K. Dehkordi, Giuseppe Caire
Date: 2022-10-09 00:00:00
Tasks: Integrated sensing and communication, ISAC, parameter estimation

In this paper, we develop an active sensing strategy for a millimeter wave (mmWave) band Integrated Sensing and Communication (ISAC) system adopting a realistic hybrid digital-analog (HDA) architecture. To maintain a desired SNR level, initial beam acquisition (BA) must be established prior to data transmission. In the considered setup, a Base Station (BS) Tx transmits data via a digitally modulated waveform and a co-located radar receiver simultaneously performs radar estimation from the backscattered signal. In this BA scheme, a single common data stream is broadcast over a wide angular sector such that the radar receiver can detect the presence of not yet acquired users and perform coarse parameter estimation (angle of arrival, time of flight, and Doppler). As a result of the HDA architecture, we consider the design of multi-block adaptive RF-domain 'reduction matrices' (from antennas to RF chains) at the radar receiver, to achieve a compromise between the exploration capability in the angular domain and the directivity of the beamforming patterns. Our numerical results demonstrate that the proposed approach is able to reliably detect multiple targets while significantly reducing the initial acquisition time.
Cost-effective photonic super-resolution millimeter-wave joint radar-communication system using self-coherent detection
Authors: Wenlin Bai, Peixuan Li, Xihua Zou, Ningyuan Zhong, Wei Pan, Lianshan Yan, Bin Luo
Date: 2022-10-09 00:00:00
Tasks: Joint Radar-Communication, Super-Resolution

A cost-effective millimeter-wave (MMW) joint radar-communication (JRC) system with super resolution is proposed and experimentally demonstrated, using optical heterodyne up-conversion and self-coherent detection down-conversion techniques. The point lies in the designed coherent dual-band constant envelope linear frequency modulation-orthogonal frequency division multiplexing (LFM-OFDM) signal with opposite phase modulation indexes for the JRC system. Then the self-coherent detection, as a simple and low-cost means, is accordingly facilitated for both de-chirping of MMW radar and frequency down-conversion reception of MMW communication, which circumvents the costly high-speed mixers along with MMW local oscillators and more significantly achieves the real-time decomposition of radar and communication information. Furthermore, a super resolution radar range profile is realized through the coherent fusion processing of dual-band JRC signal. In experiments, a dual-band LFM-OFDM JRC signal centered at 54-GHz and 61-GHz is generated. The dual bands are featured with an identical instantaneous bandwidth of 2 GHz and carry an OFDM signal of 1 GBaud, which help to achieve a 6-Gbit/s data rate for communication and a 1.76-cm range resolution for radar.
Non-intrusive Load Monitoring based on Self-supervised Learning
Authors: Shuyi Chen, Bochao Zhao, Mingjun Zhong, Wenpeng Luan, Yixin Yu
Date: 2022-10-09 00:00:00
Tasks: Non-Intrusive Load Monitoring, Self-Supervised Learning, Zero-Shot Learning

Deep learning models for non-intrusive load monitoring (NILM) tend to require a large amount of labeled data for training. However, it is difficult to generalize the trained models to unseen sites due to different load characteristics and operating patterns of appliances between data sets. For addressing such problems, self-supervised learning (SSL) is proposed in this paper, where labeled appliance-level data from the target data set or house is not required. Initially, only the aggregate power readings from target data set are required to pre-train a general network via a self-supervised pretext task to map aggregate power sequences to derived representatives. Then, supervised downstream tasks are carried out for each appliance category to fine-tune the pre-trained network, where the features learned in the pretext task are transferred. Utilizing labeled source data sets enables the downstream tasks to learn how each load is disaggregated, by mapping the aggregate to labels. Finally, the fine-tuned network is applied to load disaggregation for the target sites. For validation, multiple experimental cases are designed based on three publicly accessible REDD, UK-DALE, and REFIT data sets. Besides, state-of-the-art neural networks are employed to perform NILM task in the experiments. Based on the NILM results in various cases, SSL generally outperforms zero-shot learning in improving load disaggregation performance without any sub-metering data from the target data sets.
Dual-Mode Time Domain Multiplexed Chirp Spread Spectrum
Authors: Ali Waqar Azim, Ahmad Bazzi, Mahrukh Fatima, Raed Shubair, Marwa Chafii
Date: 2022-10-08 00:00:00

We propose a dual-mode (DM) time domain multiplexed (TDM) chirp spread spectrum (CSS) modulation for spectral and energy-efficient low-power wide-area networks (LPWANs). DM-CSS modulation that uses both the even and odd cyclic time shifts has been proposed for LPWANs to achieve noteworthy performance improvement over classical counterparts. However, its spectral efficiency (SE) is half of the in-phase and quadrature (IQ)-TDM-CSS scheme that employs IQ components with both up and down chirps, resulting in a SE that is four times relative to Long Range (LoRa) modulation. Nevertheless, the IQ-TDM-CSS scheme only allows coherent detection. Furthermore, it is also sensitive to carrier frequency and phase offsets, making it less practical for low-cost battery-powered LPWANs for Internet-of-Things (IoT) applications. DM-CSS uses either an up-chirp or a down-chirp. DM-TDM-CSS consists of two chirped symbols that are multiplexed in the time domain. One of these symbols consisting of even and odd frequency shifts (FSs) is chirped using an up-chirp. The second chirped symbol also consists of even and odd FSs, but they are chirped using a down-chirp. It shall be demonstrated that DM-TDM-CSS attains a maximum achievable SE close to IQ-TDM-CSS while also allowing both coherent and non-coherent detection. Additionally, unlike IQ-TDM-CSS, DM-TDM-CSS is robust against carrier frequency and phase offsets.
Over-the-Air Split Learning with MIMO-Based Neural Network and Constellation-Based Activation
Authors: Yuzhi Yang, Zhaoyang Zhang, Zhaohui Yang
Date: 2022-10-08 00:00:00

This paper investigates a communication-efficient split learning (SL) over multiple-input multiple-output (MIMO) communication system. In particular, we mathematically decompose the inter-layer connection of a neural network (NN) to a series of linear precoding and combining transformations using over-the-air computation (OAC), which synergistically form a linear layer in NNs. The precoding and combining matrices are trainable parameters in such a system, whereas the MIMO channel is implicit. The proposed system eliminates the implicit channel estimation through exploiting the channel reciprocity and properly casting the backpropagation process, significantly saving the system costs and further improving the overall efficiency. The practical constellation diagrams are used as the activation function to avoid sending arbitrary analog signals as in the traditional OAC system. Numerical results are illustrated to demonstrate the effectiveness of the proposed scheme.
Quantifying constraints determining independent activation on NMDA receptors mediated currents from evoked and spontaneous synaptic transmission at an individual synapse
Authors: Sat byul Seo, Jianzhong Su
Date: 2022-10-08 00:00:00
Tasks: Chemical Process

A synapse acts on neural transmission through a chemical process called synapses fusion between pre-synaptic and post-synaptic terminals. Presynaptic terminals release neurotransmitters either in response to action potential or spontaneously independent of presynaptic activity. However, it is still unclear the mechanism of evoked and spontaneous neuro-transmission that activate on postsynaptic terminals. To address this question, we examined the possibility that spontaneous and evoked neurotransmissions using mathematical simulations. We aimed to address the biophysical constraints that may determine independent activation on N-methyl-D-asparate (NMDA) receptor mediated currents in response to evoked and spontaneous glutamate molecules releases. In order to identify the spatial relation between spontaneous and evoked glutamate release, we considered quantitative factors, such as size of synapses, inhomogeneity of diffusion mobility, geometry of synaptic cleft, and release rate of neurotransmitter. Simulation results showed that as a synaptic size is smaller and if the cleft space is more cohesive in the peripheral area than the centre area, then there is high possibility of having crosstalk of two signals released from center and edge. When a synaptic size is larger, the cleft space is more affinity in the central area than the external area, and if the geometry of fusion has a narrower space, then those produce more chances of independence of two modes of currents released from center and edge. The computed results match well with existing experimental findings and serve as a road map for further exploration to identify independence of evoked and spontaneous releases.
Rethinking the protein folding problem from a new perspective
Authors: Jorge A. Vila
Date: 2022-10-10 00:00:00
Tasks: Protein Folding

One of the main concerns of Anfinsen was to reveal the connection between the amino acid sequence and their biologically active conformation. This search gave rise to two crucial questions in structural biology, namely, why the proteins fold and how a sequence encodes its folding. As to the why, he proposes a plausible answer, namely, at a given milieu a protein folds into its functional form -- native state -- because such structure represents the lowest free-energy minimum among all feasible conformations -- the thermodynamic hypothesis or Anfinsen dogma. As to the how, this remains as an unsolved challenge and, hence, this inquiry is examined here from a new perspective of protein folding, namely, as an analytic whole -- a notion proposed by Leibnitz and Kant. This new perspective forces us to discuss in detail why the theoretical force-field-based approaches have failed in both their ability to predict the three-dimensional structure of a protein accurately and in their capacity to answer one of the most critical questions in structural biology: how a sequence encodes its folding.
Corticosteroid Activation of Atlantic Sea Lamprey Corticoid Receptor: Allosteric Regulation by the N-terminal Domain
Authors: Yoshinao Katsu, Xiaozhi Lin, Ruigeng Ji, Ze Chen, Yui Kamisaka, Koto Bamba, Michael E. Baker
Date: 2022-10-08 00:00:00
Tasks: Specificity

Lampreys are jawless fish that evolved about 550 million years ago at the base of the vertebrate line. Modern lampreys contain a corticoid receptor (CR), the common ancestor of the glucocorticoid receptor (GR) and mineralocorticoid receptor (MR), which first appear in cartilaginous fish, such as sharks. Until recently, 344 amino acids at the amino terminus of adult lamprey CR were not present in the lamprey CR sequence in GenBank. A search of the recently sequenced lamprey germline genome identified two CR sequences, CR1 and CR2, containing the 344 previously un-identified amino acids at the amino terminus. CR1 also contains a novel four amino acid insertion in the DNA-binding domain (DBD). We studied corticosteroid activation of CR1 and CR2 and found their strongest response was to 11-deoxycorticosterone and 11-deoxycortisol, the two circulating corticosteroids in lamprey. Based on steroid specificity, both CRs are close to elephant shark MR and distant from elephant shark GR. HEK293 cells transfected with full-length CR1 or CR2 and the MMTV promoter have about 3-fold higher steroid-mediated activation compared to HEK293 cells transfected with these CRs and the TAT3 promoter. Deletion of the amino-terminal domain (NTD) of lamprey CR1 and CR2 to form truncated CRs decreased transcriptional activation by about 70% in HEK293 cells transfected with MMTV, but increased transcription by about 6-fold in cells transfected with TAT3, indicating that the promoter has an important effect on NTD regulation of CR transcription by corticosteroids.
Deep Learning in Spatially Resolved Transcriptomics: A Comprehensive Technical View
Authors: Roxana Zahedi Nasab, Mohammad Reza Eftekhariyan Ghamsari, Ahmadreza Argha, Callum Macphillamy, Amin Beheshti, Roohallah Alizadehsani, Nigel H. Lovell, Mohammad Lotfollahi, Hamid Alinejad-Rokny
Date: 2022-10-10 00:00:00
Tasks: Clustering, Deep Learning

Spatially resolved transcriptomics (SRT) has evolved rapidly through various technologies, enabling scientists to investigate both morphological contexts and gene expression profiling at single-cell resolution in parallel. SRT data are complex and multi-modal, comprising gene expression matrices, spatial information, and often high-resolution histology images. Because of this complexity and multi-modality, sophisticated computational algorithms are required to accurately analyze SRT data. Most efforts in this domain have been made to utilize conventional machine learning and statistical approaches, exhibiting sub-optimal results due to the complicated nature of SRT datasets. To address these shortcomings, researchers have recently employed deep learning algorithms including various state-of-the-art methods mainly in spatial clustering, spatially variable gene identification, and alignment. While great progress has been made in developing deep learning-based models for SRT data analysis, further improvement is still needed to create more biologically aware models that consider aspects such as phylogeny-aware clustering or the analysis of small histology image patches. Additionally, strategies for batch effect removal, normalization, and handling overdispersion and zero inflation patterns of gene expression are still needed in the analysis of SRT data using deep learning methods. In this paper, we provide a comprehensive overview of these deep learning methods, including their strengths and limitations. We also highlight new frontiers, current challenges, limitations, and open questions in this field. Also, we provide a comprehensive list of all available SRT databases that can be used as an extensive resource for future studies.
Model Reduction for the Chemical Master Equation: an Information-Theoretic Approach
Authors: K. Öcal, G. Sanguinetti, R. Grima
Date: 2022-10-11 00:00:00

The complexity of mathematical models in biology has rendered model reduction an essential tool in the quantitative biologist's toolkit. For stochastic reaction networks described using the Chemical Master Equation, commonly used methods include time-scale separation, the Linear Mapping Approximation and state-space lumping. Despite the success of these techniques, they appear to be rather disparate and at present no general-purpose approach to model reduction for stochastic reaction networks is known. In this paper we show that most common model reduction approaches for the Chemical Master Equation can be seen as minimising a well-known information-theoretic quantity between the full model and its reduction, the Kullback-Leibler divergence defined on the space of trajectories. This allows us to recast the task of model reduction as a variational problem that can be tackled using standard numerical optimisation approaches. In addition we derive general expressions for the propensities of a reduced system that generalise those found using classical methods. We show that the Kullback-Leibler divergence is a useful metric to assess model discrepancy and to compare different model reduction techniques using three examples from the literature: an autoregulatory feedback loop, the Michaelis-Menten enzyme system and a genetic oscillator.
Cortical Divisive Normalization from Wilson-Cowan Neural Dynamics
Authors: J. Malo, J. J. Esteve-Taboada, M. Bertalmío
Date: 2019-06-19 00:00:00
Tasks: Relation

Divisive Normalization and the Wilson-Cowan equations are influential models of neural interaction and saturation [Carandini and Heeger Nat.Rev.Neurosci. 2012; Wilson and Cowan Kybernetik 1973]. However, they have not been analytically related yet. In this work we show that Divisive Normalization can be obtained from the Wilson-Cowan model. Specifically, assuming that Divisive Normalization is the steady state solution of the Wilson-Cowan differential equation, we find that the kernel that controls neural interactions in Divisive Normalization depends on the Wilson-Cowan kernel but also has a signal-dependent contribution. A standard stability analysis of a Wilson-Cowan model with the parameters obtained from our relation shows that the Divisive Normalization solution is a stable node. This stability demonstrates the consistency of our steady state assumption. The proposed theory provides a physiological foundation (a relation to a dynamical network with fixed wiring among neurons) for the functional suggestions that have been done on the need of signal-dependent Divisive Normalization [e.g. in Coen-Cagli et al., PLoS Comp.Biol. 2012]. Moreover, this theory explains the modifications that had to be introduced ad-hoc in Gaussian kernels of Divisive Normalization in [Martinez et al. Front. Neurosci. 2019] to reproduce contrast responses. The proposed relation implies that the Wilson-Cowan dynamics also reproduces visual masking and subjective image distortion metrics, which up to now had been mainly explained via Divisive Normalization. Finally, this relation allows to apply to Divisive Normalization the methods which up to now had been developed for dynamical systems such as Wilson-Cowan networks.
Autopolyploidy, allopolyploidy, and phylogenetic networks with horizontal arcs
Authors: Katharina T. Huber, Liam J. Maher
Date: 2022-10-11 00:00:00

Polyploidization is an evolutionary process by which a species acquires multiple copies of its complete set of chromosomes. The reticulate nature of the signal left behind by it means that phylogenetic networks offer themselves as a framework to reconstruct the evolutionary past of species affected by it. The main strategy for doing this is to first construct a so called multiple-labelled tree and to then somehow derive such a network from it. The following question therefore arises: How much can be said about that past if such a tree is not readily available? By viewing a polyploid dataset as a certain vector which we call a ploidy (level) profile we show that, among other results, there always exists a phylogenetic network in the form of a beaded phylogenetic tree with additional arcs that realizes a given ploidy profile. Intriguingly, the two end vertices of almost all of these additional arcs can be interpreted as having co-existed in time thereby adding biological realism to our network, a feature that is, in general, not enjoyed by phylogenetic networks. In addition, we show that our network may be viewed as a generator of ploidy profile space, a novel concept similar to phylogenetic tree space that we introduce to be able to compare phylogenetic networks that realize one and the same ploidy profile. We illustrate our findings in terms of a publicly available Viola dataset.