pwc-archive/papers-with-abstracts huggingface dataset:
Non-sliced Optical Arbitrary Waveform Measurement (OAWM) Using a Silicon Photonic Receiver Chip
Authors: Daniel Drayss, Dengyang Fang, Christoph Füllner, Wolfgang Freude, Sebastian Randel, Christian Koos
Date: 2023-10-21 00:00:00

Comb-based optical arbitrary waveform measurement (OAWM) techniques can overcome the bandwidth limitations of conventional coherent detection schemes and may have disruptive impact on a wide range of scientific and industrial applications. Over the previous years, different OAWM schemes have been demonstrated, showing the performance and the application potential of the concept in laboratory experiments. However, these demonstrations still relied on discrete fiber-optic components or on combinations of discrete coherent receivers with integrated optical slicing filters that require complex tuning procedures to achieve the desired performance. In this paper, we demonstrate the first wavelength-agnostic OAWM front-end that is integrated on a compact silicon photonic chip and that neither requires slicing filters nor active controls. Our OAWM system comprises four IQ receivers, which are accurately calibrated using a femtosecond mode-locked laser and which offer a total acquisition bandwidth of 170 GHz. Using sinusoidal test signals, we measure a signal-to-noise-and-distortion ratio (SINAD) of 30 dB for the reconstructed signal, which corresponds to an effective number of bits (ENOB) of 4.7 bit, where the underlying electronic analog-to-digital converters (ADC) turn out to be the main limitation. The performance of the OAWM system is further demonstrated by receiving 64QAM data signals at symbol rates of up to 100 GBd, achieving constellation signal-to-noise ratios (CSNR) that are on par with those obtained for conventional coherent receivers. In a theoretical scalability analysis, we show that increasing the channel count of non-sliced OAWM systems can improve both the acquisition bandwidth and the signal quality. We believe that our work represents a key step towards out-of-lab use of highly compact OAWM systems that rely on chip-scale integrated optical front-ends.
Economics for the Global Economic Order: The Tragedy of Epic Fail Equilibria
Authors: Shiro Armstrong, Danny Quah
Date: 2023-10-27 00:00:00

This paper casts within a unified economic framework some key challenges for the global economic order: de-globalization; the rising impracticability of global cooperation; and the increasingly confrontational nature of Great Power competition. In these, economics has been weaponised in the service of national interest. This need be no bad thing. History provides examples where greater openness and freer trade emerge from nations seeking only to advance their own self-interests. But the cases described in the paper provide mixed signals. We find that some developments do draw on a growing zero-sum perception to economic and political engagement. That zero-sum explanation alone, however, is crucially inadequate. Self-serving nations, even when believing the world zero-sum, have under certain circumstances produced outcomes that have benefited all. In other circumstances, perfectly-predicted losses have instead resulted on all sides. Such lose-lose outcomes -- epic fail equilibria -- generalize the Prisoner's Dilemma game and are strictly worse than zero-sum. In our analysis, Third Nations -- those not frontline in Great Power rivalry -- can serve an essential role in averting epic fail outcomes. The policy implication is that Third Nations need to provide platforms that will gently and unobtrusively nudge Great Powers away from epic-fail equilibria and towards inadvertent cooperation.
Relative Transfer Function Vector Estimation for Acoustic Sensor Networks Exploiting Covariance Matrix Structure
Authors: Wiebke Middelberg, Henri Gode, Simon Doclo
Date: 2023-10-27 00:00:00

In many multi-microphone algorithms for noise reduction, an estimate of the relative transfer function (RTF) vector of the target speaker is required. The state-of-the-art covariance whitening (CW) method estimates the RTF vector as the principal eigenvector of the whitened noisy covariance matrix, where whitening is performed using an estimate of the noise covariance matrix. In this paper, we consider an acoustic sensor network consisting of multiple microphone nodes. Assuming uncorrelated noise between the nodes but not within the nodes, we propose two RTF vector estimation methods that leverage the block-diagonal structure of the noise covariance matrix. The first method modifies the CW method by considering only the diagonal blocks of the estimated noise covariance matrix. In contrast, the second method only considers the off-diagonal blocks of the noisy covariance matrix, but cannot be solved using a simple eigenvalue decomposition. When applying the estimated RTF vector in a minimum variance distortionless response beamformer, simulation results for real-world recordings in a reverberant environment with multiple noise sources show that the modified CW method performs slightly better than the CW method in terms of SNR improvement, while the off-diagonal selection method outperforms a biased RTF vector estimate obtained as the principal eigenvector of the noisy covariance matrix.
Cascaded Multi-task Adaptive Learning Based on Neural Architecture Search
Authors: Yingying Gao, Shilei Zhang, Zihao Cui, Chao Deng, Junlan Feng
Date: 2023-10-23 00:00:00
Tasks: Neural Architecture Search

Cascading multiple pre-trained models is an effective way to compose an end-to-end system. However, fine-tuning the full cascaded model is parameter and memory inefficient and our observations reveal that only applying adapter modules on cascaded model can not achieve considerable performance as fine-tuning. We propose an automatic and effective adaptive learning method to optimize end-to-end cascaded multi-task models based on Neural Architecture Search (NAS) framework. The candidate adaptive operations on each specific module consist of frozen, inserting an adapter and fine-tuning. We further add a penalty item on the loss to limit the learned structure which takes the amount of trainable parameters into account. The penalty item successfully restrict the searched architecture and the proposed approach is able to search similar tuning scheme with hand-craft, compressing the optimizing parameters to 8.7% corresponding to full fine-tuning on SLURP with an even better performance.
TabAttention: Learning Attention Conditionally on Tabular Data
Authors: Michal K. Grzeszczyk, Szymon Płotka, Beata Rebizant, Katarzyna Kosińska-Kaczyńska, Michał Lipa, Robert Brawura-Biskupski-Samaha, Przemysław Korzeniowski, Tomasz Trzciński, Arkadiusz Sitek
Date: 2023-10-27 00:00:00

Medical data analysis often combines both imaging and tabular data processing using machine learning algorithms. While previous studies have investigated the impact of attention mechanisms on deep learning models, few have explored integrating attention modules and tabular data. In this paper, we introduce TabAttention, a novel module that enhances the performance of Convolutional Neural Networks (CNNs) with an attention mechanism that is trained conditionally on tabular data. Specifically, we extend the Convolutional Block Attention Module to 3D by adding a Temporal Attention Module that uses multi-head self-attention to learn attention maps. Furthermore, we enhance all attention modules by integrating tabular data embeddings. Our approach is demonstrated on the fetal birth weight (FBW) estimation task, using 92 fetal abdominal ultrasound video scans and fetal biometry measurements. Our results indicate that TabAttention outperforms clinicians and existing methods that rely on tabular and/or imaging data for FBW prediction. This novel approach has the potential to improve computer-aided diagnosis in various clinical workflows where imaging and tabular data are combined. We provide a source code for integrating TabAttention in CNNs at https://github.com/SanoScience/Tab-Attention.
CPIA Dataset: A Comprehensive Pathological Image Analysis Dataset for Self-supervised Learning Pre-training
Authors: Nan Ying, Yanli Lei, Tianyi Zhang, Shangqing Lyu, Chunhui Li, Sicheng Chen, Zeyu Liu, Yu Zhao, Guanglei Zhang
Date: 2023-10-27 00:00:00
Tasks: Self-Supervised Learning, Transfer Learning, whole slide images

Pathological image analysis is a crucial field in computer-aided diagnosis, where deep learning is widely applied. Transfer learning using pre-trained models initialized on natural images has effectively improved the downstream pathological performance. However, the lack of sophisticated domain-specific pathological initialization hinders their potential. Self-supervised learning (SSL) enables pre-training without sample-level labels, which has great potential to overcome the challenge of expensive annotations. Thus, studies focusing on pathological SSL pre-training call for a comprehensive and standardized dataset, similar to the ImageNet in computer vision. This paper presents the comprehensive pathological image analysis (CPIA) dataset, a large-scale SSL pre-training dataset combining 103 open-source datasets with extensive standardization. The CPIA dataset contains 21,427,877 standardized images, covering over 48 organs/tissues and about 100 kinds of diseases, which includes two main data types: whole slide images (WSIs) and characteristic regions of interest (ROIs). A four-scale WSI standardization process is proposed based on the uniform resolution in microns per pixel (MPP), while the ROIs are divided into three scales artificially. This multi-scale dataset is built with the diagnosis habits under the supervision of experienced senior pathologists. The CPIA dataset facilitates a comprehensive pathological understanding and enables pattern discovery explorations. Additionally, to launch the CPIA dataset, several state-of-the-art (SOTA) baselines of SSL pre-training and downstream evaluation are specially conducted. The CPIA dataset along with baselines is available at https://github.com/zhanglab2021/CPIA_Dataset.
A Control-Bounded Quadrature Leapfrog ADC
Authors: Hampus Malmberg, Fredrik Feyling, Jose M. de la Rosa
Date: 2023-10-27 00:00:00

In this paper, the design flexibility of the control-bounded analog-to-digital converter principle is demonstrated. A band-pass analog-to-digital converter is considered as an application and case study. We show how a low-pass control-bounded analog-to-digital converter can be translated into a band-pass version where the guaranteed stability, converter bandwidth, and signal-to-noise ratio are preserved while the center frequency for conversion can be positioned freely. The proposed converter is validated with behavioral simulations on several filter orders, center frequencies, and oversampling ratios. Additionally, we consider an op-amp circuit realization where the effects of first-order op-amp non-idealities are shown. Finally, robustness against component variations is demonstrated by Monte Carlo simulations.
Probabilistic Constellation Shaping for OFDM-Based ISAC Signaling
Authors: Zhen Du, Fan Liu, Yifeng Xiong, Tony Xiao Han, Weijie Yuan, Yuanhao Cui, Changhua Yao, Yonina C. Eldar
Date: 2023-10-27 00:00:00
Tasks: ISAC

Integrated Sensing and Communications (ISAC) has garnered significant attention as a promising technology for the upcoming sixth-generation wireless communication systems (6G). In pursuit of this goal, a common strategy is that a unified waveform, such as Orthogonal Frequency Division Multiplexing (OFDM), should serve dual-functional roles by enabling simultaneous sensing and communications (S&C) operations. However, the sensing performance of an OFDM communication signal is substantially affected by the randomness of the data symbols mapped from bit streams. Therefore, achieving a balance between preserving communication capability (i.e., the randomness) while improving sensing performance remains a challenging task. To cope with this issue, in this paper we analyze the ambiguity function of the OFDM communication signal modulated by random data. Subsequently, a probabilistic constellation shaping (PCS) method is proposed to devise the probability distributions of constellation points, which is able to strike a scalable S&C tradeoff of the random transmitted signal. Finally, the superiority of the proposed PCS method over conventional uniformly distributed constellations is validated through numerical simulations.
New Fast Transform for Orthogonal Frequency Division Multiplexing
Authors: Said Boussakta, Mounir T. Hamood, Mohammed Sh. Ahmed
Date: 2023-10-27 00:00:00

In this paper, a new fast and low complexity transform is introduced for orthogonal frequency division multiplexing (OFDM) wireless systems. The new transform combines the effects of fast complex-Walsh-Hadamard transform (CHT) and the fast Fourier transform (FFT) into a single unitary transform named in this paper as the complex transition transform (CTT). The development of a new algorithm for fast calculation of the CT transform called FCT is found to have all the desirable properties such as in-place computation, simple indexing scheme and considerably lower arithmetic complexity than existing algorithms. Furthermore, a new OFDM system using the FCT algorithm is introduced and its performance has been evaluated. The proposed CT-OFDM achieves a noticeable reduction in peak-to-average-power-ratio (PAPR) and a significant improvement in the bit-error-rate (BER) performance compared with the conventional OFDM.
Vision-Based Reconfigurable Intelligent Surface Beam Tracking for mmWave Communications
Authors: Juan Sanchez, Xuesong Cai, Fredrik Tufvesson
Date: 2023-10-27 00:00:00

Reconfigurable intelligent surfaces have emerged as a technology with the potential to enhance wireless communication performance for 5G and beyond. However, the technology comes with challenges in areas such as complexity, power consumption, and cost. This paper demonstrates a computer vision-based reconfigurable intelligent surface beamforming algorithm that addresses complexity and cost issues and analyzes the multipath components that arise from the insertion of such a device into the wireless channel. The results show that a reconfigurable intelligent surface can provide an additional multipath component. The power of this additional path can be critical in blockage scenarios, and a capacity increase can be perceived in both line-of-sight and non line-of-sight scenarios.
SCAN-MUSIC: An Efficient Super-resolution Algorithm for Single Snapshot Wide-band Line Spectral Estimation
Authors: Zetao Fei, Hai Zhang
Date: 2023-10-27 00:00:00
Tasks: subspace methods, Super-Resolution

We propose an efficient algorithm for reconstructing one-dimensional wide-band line spectra from their Fourier data in a bounded interval $[-\Omega,\Omega]$. While traditional subspace methods such as MUSIC achieve super-resolution for closely separated line spectra, their computational cost is high, particularly for wide-band line spectra. To address this issue, we proposed a scalable algorithm termed SCAN-MUSIC that scans the spectral domain using a fixed Gaussian window and then reconstructs the line spectra falling into the window at each time. For line spectra with cluster structure, we further refine the proposed algorithm using the annihilating filter technique. Both algorithms can significantly reduce the computational complexity of the standard MUSIC algorithm with a moderate loss of resolution. Moreover, in terms of speed, their performance is comparable to the state-of-the-art algorithms, while being more reliable for reconstructing line spectra with cluster structure. The algorithms are supplemented with theoretical analyses of error estimates, sampling complexity, computational complexity, and computational limit.
RTNH+: Enhanced 4D Radar Object Detection Network using Combined CFAR-based Two-level Preprocessing and Vertical Encoding
Authors: Seung-Hyun Kong, Dong-Hee Paek, Sangjae Cho
Date: 2023-10-19 00:00:00
Tasks: 3D Object Detection, Object, object-detection, Object Detection, Radar Object Detection

Four-dimensional (4D) Radar is a useful sensor for 3D object detection and the relative radial speed estimation of surrounding objects under various weather conditions. However, since Radar measurements are corrupted with invalid components such as noise, interference, and clutter, it is necessary to employ a preprocessing algorithm before the 3D object detection with neural networks. In this paper, we propose RTNH+ that is an enhanced version of RTNH, a 4D Radar object detection network, by two novel algorithms. The first algorithm is the combined constant false alarm rate (CFAR)-based two-level preprocessing (CCTP) algorithm that generates two filtered measurements of different characteristics using the same 4D Radar measurements, which can enrich the information of the input to the 4D Radar object detection network. The second is the vertical encoding (VE) algorithm that effectively encodes vertical features of the road objects from the CCTP outputs. We provide details of the RTNH+, and demonstrate that RTNH+ achieves significant performance improvement of 10.14\% in ${{AP}_{3D}^{IoU=0.3}}$ and 16.12\% in ${{AP}_{3D}^{IoU=0.5}}$ over RTNH.
Is Channel Independent strategy optimal for Time Series Forecasting?
Authors: Yuan Peiwen, Zhu Changsheng
Date: 2023-10-18 00:00:00
Tasks: Clustering, Time Series, Time Series Forecasting

There has been an emergence of various models for long-term time series forecasting. Recent studies have demonstrated that a single linear layer, using Channel Dependent (CD) or Channel Independent (CI) modeling, can even outperform a large number of sophisticated models. However, current research primarily considers CD and CI as two complementary yet mutually exclusive approaches, unable to harness these two extremes simultaneously. And it is also a challenging issue that both CD and CI are static strategies that cannot be determined to be optimal for a specific dataset without extensive experiments. In this paper, we reconsider whether the current CI strategy is the best solution for time series forecasting. First, we propose a simple yet effective strategy called CSC, which stands for $\mathbf{C}$hannel $\mathbf{S}$elf-$\mathbf{C}$lustering strategy, for linear models. Our Channel Self-Clustering (CSC) enhances CI strategy's performance improvements while reducing parameter size, for exmpale by over 10 times on electricity dataset, and significantly cutting training time. Second, we further propose Channel Rearrangement (CR), a method for deep models inspired by the self-clustering. CR attains competitive performance against baselines. Finally, we also discuss whether it is best to forecast the future values using the historical values of the same channel as inputs. We hope our findings and methods could inspire new solutions beyond CD/CI.
Deep Learning Algorithm for Advanced Level-3 Inverse-Modeling of Silicon-Carbide Power MOSFET Devices
Authors: Massimo Orazio Spata, Sebastiano Battiato, Alessandro Ortis, Francesco Rundo, Michele Calabretta, Carmelo Pino, Angelo Messina
Date: 2023-10-16 00:00:00
Tasks: Deep Learning

Inverse modelling with deep learning algorithms involves training deep architecture to predict device's parameters from its static behaviour. Inverse device modelling is suitable to reconstruct drifted physical parameters of devices temporally degraded or to retrieve physical configuration. There are many variables that can influence the performance of an inverse modelling method. In this work the authors propose a deep learning method trained for retrieving physical parameters of Level-3 model of Power Silicon-Carbide MOSFET (SiC Power MOS). The SiC devices are used in applications where classical silicon devices failed due to high-temperature or high switching capability. The key application of SiC power devices is in the automotive field (i.e. in the field of electrical vehicles). Due to physiological degradation or high-stressing environment, SiC Power MOS shows a significant drift of physical parameters which can be monitored by using inverse modelling. The aim of this work is to provide a possible deep learning-based solution for retrieving physical parameters of the SiC Power MOSFET. Preliminary results based on the retrieving of channel length of the device are reported. Channel length of power MOSFET is a key parameter involved in the static and dynamic behaviour of the device. The experimental results reported in this work confirmed the effectiveness of a multi-layer perceptron designed to retrieve this parameter.
Phylogenetic invariants: straightforward from the general Markov to equivariant models
Authors: Marta Casanellas, Jesús Fernández-Sánchez
Date: 2023-10-27 00:00:00

In the last decade, some algebraic tools have been successfully applied to phylogenetic reconstruction. These tools are mainly based on the knowledge of equations describing algebraic varieties associated to phylogenetic trees evolving under Markov processes of molecular substitution, the so called phylogenetic invariants. Although the theory involved allows to explicitly obtain these equations for all equivariant models (which include some of the most popular nucleotide substitution models), practical uses of these algebraic tools have been restricted to the case of the general Markov model. Arguably, one of the reasons for this restriction is that knowledge of linear representation theory is required before making these equations explicit. With the aim of enlarging the practical uses of algebraic phylogenetics, in this paper we prove that phylogenetic invariants for trees evolving under equivariant models can be derived from phylogenetic invariants for the general Markov model, without the need of representation theory. Our main result states that the algebraic variety corresponding to a phylogenetic tree evolving under an equivariant model is an irreducible component of the variety corresponding to the same tree under the general Markov model cut with the linear space defined by the model. We also prove that, for any equivariant model, those phylogenetic invariants that are relevant for practical uses (e.g. tree reconstruction) can be simply deduced from a single rank constraint on the matrices obtained by flattening the joint distribution at the leaves of the tree. This condition can be easily tested from singular values of the matrices and extends our results from trees to phylogenetic networks.
Leveraging Machine Learning Models for Peptide-Protein Interaction Prediction
Authors: Song Yin, Xuenan Mi, Diwakar Shukla
Date: 2023-10-27 00:00:00
Tasks: Prediction, Specificity

Peptides play a pivotal role in a wide range of biological activities through participating in up to 40% protein-protein interactions in cellular processes. They also demonstrate remarkable specificity and efficacy, making them promising candidates for drug development. However, predicting peptide-protein complexes by traditional computational approaches, such as Docking and Molecular Dynamics simulations, still remains a challenge due to high computational cost, flexible nature of peptides, and limited structural information of peptide-protein complexes. In recent years, the surge of available biological data has given rise to the development of an increasing number of machine learning models for predicting peptide-protein interactions. These models offer efficient solutions to address the challenges associated with traditional computational approaches. Furthermore, they offer enhanced accuracy, robustness, and interpretability in their predictive outcomes. This review presents a comprehensive overview of machine learning and deep learning models that have emerged in recent years for the prediction of peptide-protein interactions.