pwc-archive/papers-with-abstracts huggingface dataset:
Classification of Human Monkeypox Disease Using Deep Learning Models and Attention Mechanisms
Authors: Md. Enamul Haque, Md. Rayhan Ahmed, Razia Sultana Nila, Salekul Islam
Date: 2022-11-21 00:00:00
Tasks: Transfer Learning

As the world is still trying to rebuild from the destruction caused by the widespread reach of the COVID-19 virus, and the recent alarming surge of human monkeypox disease outbreaks in numerous countries threatens to become a new global pandemic too. Human monkeypox disease syndromes are quite similar to chickenpox, and measles classic symptoms, with very intricate differences such as skin blisters, which come in diverse forms. Various deep-learning methods have shown promising performances in the image-based diagnosis of COVID-19, tumor cell, and skin disease classification tasks. In this paper, we try to integrate deep transfer-learning-based methods, along with a convolutional block attention module (CBAM), to focus on the relevant portion of the feature maps to conduct an image-based classification of human monkeypox disease. We implement five deep-learning models, VGG19, Xception, DenseNet121, EfficientNetB3, and MobileNetV2, along with integrated channel and spatial attention mechanisms, and perform a comparative analysis among them. An architecture consisting of Xception-CBAM-Dense layers performed better than the other models at classifying human monkeypox and other diseases with a validation accuracy of 83.89%.
Hyperspectral Demosaicing of Snapshot Camera Images Using Deep Learning
Authors: Eric L. Wisotzky, Charul Daudkhane, Anna Hilsmann, Peter Eisert
Date: 2022-11-21 00:00:00
Tasks: Deep Learning, Demosaicking

Spectral imaging technologies have rapidly evolved during the past decades. The recent development of single-camera-one-shot techniques for hyperspectral imaging allows multiple spectral bands to be captured simultaneously (3x3, 4x4 or 5x5 mosaic), opening up a wide range of applications. Examples include intraoperative imaging, agricultural field inspection and food quality assessment. To capture images across a wide spectrum range, i.e. to achieve high spectral resolution, the sensor design sacrifices spatial resolution. With increasing mosaic size, this effect becomes increasingly detrimental. Furthermore, demosaicing is challenging. Without incorporating edge, shape, and object information during interpolation, chromatic artifacts are likely to appear in the obtained images. Recent approaches use neural networks for demosaicing, enabling direct information extraction from image data. However, obtaining training data for these approaches poses a challenge as well. This work proposes a parallel neural network based demosaicing procedure trained on a new ground truth dataset captured in a controlled environment by a hyperspectral snapshot camera with a 4x4 mosaic pattern. The dataset is a combination of real captured scenes with images from publicly available data adapted to the 4x4 mosaic pattern. To obtain real world ground-truth data, we performed multiple camera captures with 1-pixel shifts in order to compose the entire data cube. Experiments show that the proposed network outperforms state-of-art networks.
How to Backpropagate through Hungarian in Your DETR?
Authors: Lingji Chen, Alok Sharma, Chinmay Shirore, Chengjie Zhang, Balarama Raju Buddharaju
Date: 2022-11-12 00:00:00
Tasks: Decoder

The DEtection TRansformer (DETR) approach, which uses a transformer encoder-decoder architecture and a set-based global loss, has become a building block in many transformer based applications. However, as originally presented, the assignment cost and the global loss are not aligned, i.e., reducing the former is likely but not guaranteed to reduce the latter. And the issue of gradient is ignored when a combinatorial solver such as Hungarian is used. In this paper we show that the global loss can be expressed as the sum of an assignment-independent term, and an assignment-dependent term which can be used to define the assignment cost matrix. Recent results on generalized gradients of optimal assignment cost with respect to parameters of an assignment problem are then used to define generalized gradients of the loss with respect to network parameters, and backpropagation is carried out properly. Our experiments using the same loss weights show interesting convergence properties and a potential for further performance improvements.
Semi-Supervised Specific Emitter Identification Method Using Metric-Adversarial Training
Authors: Xue Fu, Yang Peng, Yuchao Liu, Yun Lin, Guan Gui, Haris Gacanin, Fumiyuki Adachi
Date: 2022-11-28 00:00:00
Tasks: Decision Making, Metric Learning

Specific emitter identification (SEI) plays an increasingly crucial and potential role in both military and civilian scenarios. It refers to a process to discriminate individual emitters from each other by analyzing extracted characteristics from given radio signals. Deep learning (DL) and deep neural networks (DNNs) can learn the hidden features of data and build the classifier automatically for decision making, which have been widely used in the SEI research. Considering the insufficiently labeled training samples and large unlabeled training samples, semi-supervised learning-based SEI (SS-SEI) methods have been proposed. However, there are few SS-SEI methods focusing on extracting the discriminative and generalized semantic features of radio signals. In this paper, we propose an SS-SEI method using metric-adversarial training (MAT). Specifically, pseudo labels are innovatively introduced into metric learning to enable semi-supervised metric learning (SSML), and an objective function alternatively regularized by SSML and virtual adversarial training (VAT) is designed to extract discriminative and generalized semantic features of radio signals. The proposed MAT-based SS-SEI method is evaluated on an open-source large-scale real-world automatic-dependent surveillance-broadcast (ADS-B) dataset and WiFi dataset and is compared with state-of-the-art methods. The simulation results show that the proposed method achieves better identification performance than existing state-of-the-art methods. Specifically, when the ratio of the number of labeled training samples to the number of all training samples is 10\%, the identification accuracy is 84.80\% under the ADS-B dataset and 80.70\% under the WiFi dataset. Our code can be downloaded from https://github.com/lovelymimola/MAT-based-SS-SEI.
Defending Adversarial Attacks on Deep Learning Based Power Allocation in Massive MIMO Using Denoising Autoencoders
Authors: Rajeev Sahay, Minjun Zhang, David J. Love, Christopher G. Brinton
Date: 2022-11-28 00:00:00
Tasks: Deep Learning, Denoising, regression

Recent work has advocated for the use of deep learning to perform power allocation in the downlink of massive MIMO (maMIMO) networks. Yet, such deep learning models are vulnerable to adversarial attacks. In the context of maMIMO power allocation, adversarial attacks refer to the injection of subtle perturbations into the deep learning model's input, during inference (i.e., the adversarial perturbation is injected into inputs during deployment after the model has been trained) that are specifically crafted to force the trained regression model to output an infeasible power allocation solution. In this work, we develop an autoencoder-based mitigation technique, which allows deep learning-based power allocation models to operate in the presence of adversaries without requiring retraining. Specifically, we develop a denoising autoencoder (DAE), which learns a mapping between potentially perturbed data and its corresponding unperturbed input. We test our defense across multiple attacks and in multiple threat models and demonstrate its ability to (i) mitigate the effects of adversarial attacks on power allocation networks using two common precoding schemes, (ii) outperform previously proposed benchmarks for mitigating regression-based adversarial attacks on maMIMO networks, (iii) retain accurate performance in the absence of an attack, and (iv) operate with low computational overhead.
Separation-Free Spectral Super-Resolution via Convex Optimization
Authors: Zai Yang, Yi-Lin Mo, Gongguo Tang, Zongben Xu
Date: 2022-11-28 00:00:00
Tasks: Miscellaneous, Spectral Super-Resolution, Super-Resolution

Atomic norm methods have recently been proposed for spectral super-resolution with flexibility in dealing with missing data and miscellaneous noises. A notorious drawback of these convex optimization methods however is their lower resolution in the high signal-to-noise (SNR) regime as compared to conventional methods such as ESPRIT. In this paper, we devise a simple weighting scheme in existing atomic norm methods and show that the resolution of the resulting convex optimization method can be made arbitrarily high in the absence of noise, achieving the so-called separation-free super-resolution. This is proved by a novel, kernel-free construction of the dual certificate whose existence guarantees exact super-resolution using the proposed method. Numerical results corroborating our analysis are provided.
A 97 fJ/Conversion Neuron-ADC with Reconfigurable Sampling and Static Power Reduction
Authors: Jinbo Chen, Hui Wu, Jie Yang, Mohamad Sawan
Date: 2022-11-28 00:00:00

A bio-inspired Neuron-ADC with reconfigurable sampling and static power reduction for biomedical applications is proposed in this work. The Neuron-ADC leverages level-crossing sampling and a bio-inspired refractory circuit to compressively converts bio-signal to digital spikes and information-of-interest. The proposed design can not only avoid dissipating ADC energy on unnecessary data but also achieve reconfigurable sampling, making it appropriate for either low power operation or high accuracy conversion when dealing with various kinds of bio-signals. Moreover, the proposed dynamic comparator can reduce static power up to 41.1% when tested with a 10 kHz sinusoidal input. Simulation results of 40 nm CMOS process show that the Neuron-ADC achieves a maximum ENOB of 6.9 bits with a corresponding FoM of 97 fJ/conversion under 0.6 V supply voltage.
Fast DFT Computation for Signals with Structured Support
Authors: Charantej Reddy P, Aditya Siripuram, Brad Osgood
Date: 2022-11-28 00:00:00

Suppose an $N-$length signal has known frequency support of size $k$. Given sample access to this signal, how fast can we compute the DFT? The answer to this question depends on the structure of the frequency support. We first identify some frequency supports for which (an ideal) $O(k \log k)$ complexity is achievable, referred to as homogeneous sets. We give a generalization of radix-2 that enables $O(k\log k)$ computation of signals with homogeneous frequency support. Using homogeneous sets as building blocks, we construct more complicated support structures for which the complexity of $O(k\log k)$ is achievable. We also investigate the relationship of DFT computation with additive structure in the support and provide partial converses.
Performance Analysis of Free-Space Information Sharing in Full-Duplex Semantic Communications
Authors: Hongyang Du, Jiacheng Wang, Dusit Niyato, Jiawen Kang, Zehui Xiong, Dong In Kim, Boon Hee Soong
Date: 2022-11-27 00:00:00
Tasks: Mixed Reality

In next-generation Internet services, such as Metaverse, the mixed reality (MR) technique plays a vital role. Yet the limited computing capacity of the user-side MR headset-mounted device (HMD) prevents its further application, especially in scenarios that require a lot of computation. One way out of this dilemma is to design an efficient information sharing scheme among users to replace the heavy and repetitive computation. In this paper, we propose a free-space information sharing mechanism based on full-duplex device-to-device (D2D) semantic communications. Specifically, the view images of MR users in the same real-world scenario may be analogous. Therefore, when one user (i.e., a device) completes some computation tasks, the user can send his own calculation results and the semantic features extracted from the user's own view image to nearby users (i.e., other devices). On this basis, other users can use the received semantic features to obtain the spatial matching of the computational results under their own view images without repeating the computation. Using generalized small-scale fading models, we analyze the key performance indicators of full-duplex D2D communications, including channel capacity and bit error probability, which directly affect the transmission of semantic information. Finally, the numerical analysis experiment proves the effectiveness of our proposed methods.
Reconfigurable Intelligent Surface-Empowered Code Index Modulation for High-Rate SISO Systems
Authors: Fatih Cogen, Burak Ahmet Ozden, Erdogan Aydin, Nihat Kabaoglu
Date: 2022-11-26 00:00:00

In this study, a novel index modulation based communication system is proposed by combining the recently popular code index modulation-spread spectrum (CIM-SS) and reconfigurable intelligent surface (RIS) techniques. This technique is called CIM-RIS in short. In this proposed system, in addition to the traditional modulated symbols, the spreading code indices also carry data by being embedded in the signal, and the reflection/scattering properties of the signals are voluntarily controlled via the RIS technique. Consequently, the proposed system consumes little energy while transmitting extra bits of information compared to the traditional RIS. Average bit-error error (ABER) analysis of the proposed system is carried out and the system complexity, energy efficiency, and throughput analyses are obtained. Performance analysis of the system is carried out on Rayleigh fading channels for the M-ary quadrature amplitude modulation (QAM) technique. It has been shown by computer simulations that the CIM-RIS scheme has better error performance, faster data transmission speed, and lower transmission energy, compared to traditional RIS, transmit spatial modulation aided RIS (TSM-RIS) and transmit quadrature spatial modulation based RIS (TQSM-RIS) techniques.
Variational Learning Algorithms For Channel Estimation in RIS-assisted mmWave Systems
Authors: Milind Nakul, Anupama Rajoriya, Rohit Budhiraja
Date: 2022-11-26 00:00:00

We consider the problem of estimating the channel in reconfigurable intelligent surface (RIS) assisted millimeter wave (mmWave) systems. We propose two variational expectation maximization (VEM) based algorithms for channel estimation in RIS-aided wireless systems. The first algorithm is a structured mean field-based sparse Bayesian learning (SM-SBL) algorithm that exploits the doubly-structured sparsity and the individual sparsity of the elements of the channel. To exploit the sparsities, we propose a column-wise coupled Gaussian prior. We next design the factorized mean field-based algorithm based on the prior we propose. This algorithm called the factorized mean field SBL (FM-SBL) algorithm, addresses the time complexities of the SM-SBL algorithm without sacrificing channel estimation accuracy. We show using extensive numerical investigations that the i) proposed SM-SBL and FM-SBL algorithms outperform several existing algorithms and ii) FM-SBL has lower time complexity than the SM-SBL algorithm.
Ergodic robust maximization of asymptotic growth under stochastic volatility
Authors: David Itkin, Benedikt Koch, Martin Larsson, Josef Teichmann
Date: 2022-11-28 00:00:00

We consider an asymptotic robust growth problem under model uncertainty and in the presence of (non-Markovian) stochastic covariance. We fix two inputs representing the instantaneous covariance for the asset process $X$, which depends on an additional stochastic factor process $Y$, as well as the invariant density of $X$ together with $Y$. The stochastic factor process $Y$ has continuous trajectories but is not even required to be a semimartingale. Our setup allows for drift uncertainty in $X$ and model uncertainty for the local dynamics of $Y$. This work builds upon a recent paper of Kardaras & Robertson, where the authors consider an analogous problem, however, without the additional stochastic factor process. Under suitable, quite weak assumptions we are able to characterize the robust optimal trading strategy and the robust optimal growth rate. The optimal strategy is shown to be functionally generated and, remarkably, does not depend on the factor process $Y$. Our result provides a comprehensive answer to a question proposed by Fernholz in 2002. Mathematically, we use a combination of partial differential equation (PDE), calculus of variations and generalized Dirichlet form techniques.
Endogenous distress contagion in a dynamic interbank model: how possible future losses may spell doom today
Authors: Zachary Feinstein, Andreas Sojmark
Date: 2022-11-28 00:00:00

We introduce a dynamic and stochastic interbank model with an endogenous notion of distress contagion, arising from rational worries about future defaults and ensuing losses. This entails a mark-to-market valuation adjustment for interbank claims, leading to a forward-backward approach to the equilibrium dynamics whereby future default probabilities are needed to determine today's balance sheets. Distinct from earlier models, the resulting distress contagion acts, endogenously, as a stochastic volatility term that exhibits clustering and down-market spikes. Furthermore, by incorporating multiple maturities, we provide a novel framework for constructing systemic interbank term structures, reflecting the intertemporal risk of contagion. We present the analysis in two parts: first, the simpler single maturity setting that extends the classical interbank network literature and, then, the multiple maturity setting for which we can examine how systemic risk materialises in the shape of the resulting term structures.
Optimal Investment with Correlated Stochastic Volatility Factors
Authors: Maxim Bichuch, Jean-Pierre Fouque
Date: 2019-08-20 00:00:00

The problem of portfolio allocation in the context of stocks evolving in random environments, that is with volatility and returns depending on random factors, has attracted a lot of attention. The problem of maximizing a power utility at a terminal time with only one random factor can be linearized thanks to a classical distortion transformation. In the present paper, we address the situation with several factors using a perturbation technique around the case where these factors are perfectly correlated reducing the problem to the case with a single factor. Our proposed approximation requires to solve numerically two linear equations in lower dimension instead of a fully non-linear HJB equation. A rigorous accuracy result is derived by constructing sub- and super- solutions so that their difference is at the desired order of accuracy. We illustrate our result with a particular model for which we have explicit formulas for the approximation. In order to keep the notations as explicit as possible, we treat the case with one stock and two factors and we describe an extension to the case with two stocks and two factors.
Application of Molecular Topology to the Prediction of Antioxidant Activity in a Group of Phenolic Compounds
Authors: Jaime Barros Silva Filho, Fernando de Souza Bastos, Diogo da Silva Machado, Maria Luiza Ferreira Delfim
Date: 2022-11-25 00:00:00

The study of compounds with antioxidant capabilities is of great interest to the scientific community, as it has implications in several areas, from Agricultural Sciences to Biological Sciences, including Food Engineering, Medicine and Pharmacy. In applications related to human health, it is known that antioxidant activity can delay or inhibit oxidative damage to cells, reducing damage caused by free radicals, helping in the treatment, or even preventing or postponing the onset of various diseases. Among the compounds that have antioxidant properties, there are several classes of Phenolic Compounds, which include several compounds with different chemical structures. In this work, based on the molecular branching of compounds and their intramolecular charge distributions, and using Molecular Topology, we propose a significant topological-mathematical model to evaluate the potential of candidate compounds to have an antioxidant function.
Non-Coding RNAs Improve the Predictive Power of Network Medicine
Authors: Deisy Morselli Gysi, Albert-Laszlo Barabasi
Date: 2022-11-27 00:00:00
Tasks: Diagnostic

Network Medicine has improved the mechanistic understanding of disease, offering quantitative insights into disease mechanisms, comorbidities, and novel diagnostic tools and therapeutic treatments. Yet, most network-based approaches rely on a comprehensive map of protein-protein interactions, ignoring interactions mediated by non-coding RNAs (ncRNAs). Here, we systematically combine experimentally confirmed binding interactions mediated by ncRNA with protein-protein interactions, constructing the first comprehensive network of all physical interactions in the human cell. We find that the inclusion of ncRNA, expands the number of genes in the interactome by 46% and the number of interactions by 107%, significantly enhancing our ability to identify disease modules. Indeed, we find that 132 diseases, lacked a statistically significant disease module in the protein-based interactome, but have a statistically significant disease module after inclusion of ncRNA-mediated interactions, making these diseases accessible to the tools of network medicine. We show that the inclusion of ncRNAs helps unveil disease-disease relationships that were not detectable before and expands our ability to predict comorbidity patterns between diseases. Taken together, we find that including non-coding interactions improves both the breath and the predictive accuracy of network medicine.
Are advanced methods necessary to improve infant fNIRS data analysis? An assessment of baseline-corrected averaging, general linear model (GLM) and multivariate pattern analysis (MVPA) based approaches
Authors: Maria Laura Filippetti, Javier Andreu-Perez, Carina de Klerk, Chloe Richmond, Silvia Rigato
Date: 2022-11-27 00:00:00

In the last decade, fNIRS has provided a non-invasive method to investigate neural activation in developmental populations. Despite its increasing use in developmental cognitive neuroscience, there is little consistency or consensus on how to pre-process and analyse infant fNIRS data. With this registered report, we investigated the feasibility of applying more advanced statistical analyses to infant fNIRS data and compared the most commonly used baseline-corrected averaging, General Linear Model (GLM)-based univariate, and Multivariate Pattern Analysis (MVPA) approaches, to show how the conclusions one would draw based on these different analysis approaches converge or differ. The different analysis methods were tested using a face inversion paradigm where changes in brain activation in response to upright and inverted face stimuli were measured in thirty 4-to-6-month-old infants. By including more standard approaches together with recent machine learning techniques, we aim to inform the fNIRS community on alternative ways to analyse infant fNIRS datasets.