pwc-archive/papers-with-abstracts huggingface dataset:
3D Single Source Localization Based on Euclidean Distance Matrices
Authors: Klaus Brümann, Simon Doclo
Date: 2022-05-18 00:00:00
Tasks: Position

A popular approach for 3D source localization using multiple microphones is the steered-response power method, where the source position is directly estimated by maximizing a function of three continuous position variables. Instead of directly estimating the source position, in this paper we propose an indirect, distance-based method for 3D source localization. Based on properties of Euclidean distance matrices (EDMs), we reformulate the 3D source localization problem as the minimization of a cost function of a single variable, namely the distance between the source and the reference microphone. Using the known microphone geometry and estimated time-differences of arrival (TDOAs) between the microphones, we show how the 3D source position can be computed based on this variable. In addition, instead of using a single TDOA estimate per microphone pair, we propose an extension that enables to select the most appropriate estimate from a set of candidate TDOA estimates, which is especially relevant in reverberant environments with strong early reflections. Experimental results for different source and microphone constellations show that the proposed EDM-based method consistently outperforms the steered-response power method, especially when the source is close to the microphones.
U-Former: Improving Monaural Speech Enhancement with Multi-head Self and Cross Attention
Authors: Xinmeng Xu, Jianjun Hao
Date: 2022-05-18 00:00:00
Tasks: Decoder, Speech Enhancement

For supervised speech enhancement, contextual information is important for accurate spectral mapping. However, commonly used deep neural networks (DNNs) are limited in capturing temporal contexts. To leverage long-term contexts for tracking a target speaker, this paper treats the speech enhancement as sequence-to-sequence mapping, and propose a novel monaural speech enhancement U-net structure based on Transformer, dubbed U-Former. The key idea is to model long-term correlations and dependencies, which are crucial for accurate noisy speech modeling, through the multi-head attention mechanisms. For this purpose, U-Former incorporates multi-head attention mechanisms at two levels: 1) a multi-head self-attention module which calculate the attention map along both time- and frequency-axis to generate time and frequency sub-attention maps for leveraging global interactions between encoder features, while 2) multi-head cross-attention module which are inserted in the skip connections allows a fine recovery in the decoder by filtering out uncorrelated features. Experimental results illustrate that the U-Former obtains consistently better performance than recent models of PESQ, STOI, and SSNR scores.
A Survey on Hyperspectral Image Restoration: From the View of Low-Rank Tensor Approximation
Authors: Na Liu, Wei Li, Yinjian Wang, Rao Tao, Qian Du, Jocelyn Chanussot
Date: 2022-05-18 00:00:00
Tasks: Deblurring, Denoising, Image Restoration, Super-Resolution

The ability of capturing fine spectral discriminative information enables hyperspectral images (HSIs) to observe, detect and identify objects with subtle spectral discrepancy. However, the captured HSIs may not represent true distribution of ground objects and the received reflectance at imaging instruments may be degraded, owing to environmental disturbances, atmospheric effects and sensors' hardware limitations. These degradations include but are not limited to: complex noise (i.e., Gaussian noise, impulse noise, sparse stripes, and their mixtures), heavy stripes, deadlines, cloud and shadow occlusion, blurring and spatial-resolution degradation and spectral absorption, etc. These degradations dramatically reduce the quality and usefulness of HSIs. Low-rank tensor approximation (LRTA) is such an emerging technique, having gained much attention in HSI restoration community, with ever-growing theoretical foundation and pivotal technological innovation. Compared to low-rank matrix approximation (LRMA), LRTA is capable of characterizing more complex intrinsic structure of high-order data and owns more efficient learning abilities, being established to address convex and non-convex inverse optimization problems induced by HSI restoration. This survey mainly attempts to present a sophisticated, cutting-edge, and comprehensive technical survey of LRTA toward HSI restoration, specifically focusing on the following six topics: Denoising, Destriping, Inpainting, Deblurring, Super--resolution and Fusion. The theoretical development and variants of LRTA techniques are also elaborated. For each topic, the state-of-the-art restoration methods are compared by assessing their performance both quantitatively and visually. Open issues and challenges are also presented, including model formulation, algorithm design, prior exploration and application concerning the interpretation requirements.
Leveraging Global Binary Masks for Structure Segmentation in Medical Images
Authors: Mahdieh Kazemimoghadam, Zi Yang, Lin Ma, Mingli Chen, Weiguo Lu, Xuejun Gu
Date: 2022-05-13 00:00:00
Tasks: Image Segmentation, Medical Image Segmentation, Organ Segmentation, Position, Segmentation, Semantic Segmentation

Deep learning (DL) models for medical image segmentation are highly influenced by intensity variations of input images and lack generalization due to primarily utilizing pixels' intensity information for inference. Acquiring sufficient training data is another challenge limiting models' applications. We proposed to leverage the consistency of organs' anatomical shape and position information in medical images. We introduced a framework leveraging recurring anatomical patterns through global binary masks for organ segmentation. Two scenarios were studied.1) Global binary masks were the only model's (i.e. U-Net) input, forcing exclusively encoding organs' position and shape information for segmentation/localization.2) Global binary masks were incorporated as an additional channel functioning as position/shape clues to mitigate training data scarcity. Two datasets of the brain and heart CT images with their ground-truth were split into (26:10:10) and (12:3:5) for training, validation, and test respectively. Training exclusively on global binary masks led to Dice scores of 0.77(0.06) and 0.85(0.04), with the average Euclidian distance of 3.12(1.43)mm and 2.5(0.93)mm relative to the center of mass of the ground truth for the brain and heart structures respectively. The outcomes indicate that a surprising degree of position and shape information is encoded through global binary masks. Incorporating global binary masks led to significantly higher accuracy relative to the model trained on only CT images in small subsets of training data; the performance improved by 4.3-125.3% and 1.3-48.1% for 1-8 training cases of the brain and heart datasets respectively. The findings imply the advantages of utilizing global binary masks for building generalizable models and to compensate for training data scarcity.
Subspace-Based Detection and Localization in Distributed MIMO Radars
Authors: Yangming Lai, Luca Venturino, Emanuele Grossi, Wei Yi
Date: 2022-05-18 00:00:00

In this paper, we consider a distributed multiple-input multiple-output (MIMO) radar which radiates waveforms with non-ideal cross- and auto-correlation functions and derive a novel subspace-based procedure to detect and localize multiple prospective targets. The proposed solution solves a sequence of composite binary hypothesis testing problems by resorting to the generalized information criterion (GIC); in particular, at each step, it aims to detect and localize one additional target, upon removing the interference caused by the previously-detected targets. An illustrative example is provided.
A matrix-inverse-free implementation of the MU-MIMO WMMSE beamforming algorithm
Authors: Lissy Pellaco, Joakim Jaldén
Date: 2022-05-18 00:00:00

The WMMSE beamforming algorithm is a popular approach to address the NP-hard weighted sum rate (WSR) maximization beamforming problem. Although it efficiently finds a local optimum, it requires matrix inverses, eigendecompositions, and bisection searches, operations that are problematic for real-time implementation. In our previous work, we considered the MU-MISO case and effectively replaced such operations by resorting to a first-order method. Here, we consider the more general and challenging MU-MIMO case. Our earlier approach does not generalize to this scenario and cannot be applied to replace all the hard-to-parallelize operations that appear in the MU-MIMO case. Thus, we propose to leverage a reformulation of the auxiliary WMMSE function given by Hu et al. By applying gradient descent and Schulz iterations, we formulate the first variant of the WMMSE algorithm applicable to the MU-MIMO case that is free from matrix inverses and other serial operations and hence amenable to both real-time implementation and deep unfolding. From a theoretical viewpoint, we establish its convergence to a stationary point of the WSR maximization problem. From a practical viewpoint, we show that in a deep-unfolding-based implementation, the matrix-inverse-free WMMSE algorithm attains, within a fixed number of iterations, a WSR comparable to the original WMMSE algorithm truncated to the same number of iterations, yet with significant implementation advantages in terms of parallelizability and real-time execution.
Experimental Comparison of Cap and Cup Probabilistically Shaped PAM for O-Band IM/DD Transmission System
Authors: Md Sabbir-Bin Hossain, Georg Boecherer, Talha Rahman, Nebojsa Stojanovic, Patrick Schulte, Stefano Calabrò, Jinlong Wei, Christian Bluemm, Tom Wettlin, Changsong Xie, Maxim Kuschnerov, Stephan Pachnicke
Date: 2022-05-18 00:00:00
Tasks: Sensitivity

For 200Gbit/s net rates, uniform PAM-4, 6 and 8 are experimentally compared against probabilistic shaped PAM-8 cap and cup variants. In back-to-back and 20km measurements, cap shaped 80GBd PAM-8 outperforms 72GBd PAM-8 and 83GBd PAM-6 by up to 3.50dB and 0.8dB in receiver sensitivity, respectively
Complexity Measures for Quantifying Changes in Electroencephalogram in Alzheimers Disease
Authors: Ali H. Al-Nuaimi, Emmanuel Jammeh, Lingfen Sun, Emmanuel Ifeachor
Date: 2022-05-17 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Specificity

Alzheimers disease (AD) is a progressive disorder that affects cognitive brain functions and starts many years before its clinical manifestations. A biomarker that provides a quantitative measure of changes in the brain due to AD in the early stages would be useful for early diagnosis of AD, but this would involve dealing with large numbers of people because up to 50% of dementia sufferers do not receive a formal diagnosis. Thus, there is a need for accurate, low-cost, and easy-to-use biomarkers that could be used to detect AD in its early stages. Potentially, electroencephalogram (EEG) based biomarkers can play a vital role in early diagnosis of AD as they can fulfill these needs. This is a cross-sectional study that aims to demonstrate the usefulness of EEG complexity measures in early AD diagnosis. We have focused on the three complexity methods which have shown the greatest promise in the detection of AD, Tsallis entropy (TsEn), Higuchi Fractal Dimension (HFD), and Lempel-Ziv complexity (LZC) methods. Unlike previous approaches, in this study, the complexity measures are derived from EEG frequency bands (instead of the entire EEG) as EEG activities have significant association with AD and this has led to enhanced performance. The results show that AD patients have significantly lower TsEn, HFD, and LZC values for specific EEG frequency bands and for specific EEG channels and that this information can be used to detect AD with a sensitivity and specificity of more than 90%.
Brain waves are a repetition of a pause and an activity
Authors: Chika Koyama
Date: 2022-05-18 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Rhythm

Brain waves still cannot reliably distinguish between awake and asleep states. Here, I present new original indices, voltage subthreshold wave $\tau$ and abovethreshold wave burst, for advanced LFP/EEG readings. Assuming that $\tau$ is a microwave that fluctuates every sample such as the equipotential, the total number of $\tau$ ($N\tau$) is inferred to be the maximum, and the amplitude of burst (Abst) is inferred to be the minimum. In fact, they invariably had a mean $\tau$ duration ($M\tau$) of 2-3 sample intervals in any case. In addition, $\tau$ and burst exhibited self-similarity for sample frequency while occupying approximately 30% and 70% of LFP in the natural state, respectively. Its threshold and Abst were correlated with the vigilance state and decreased to 70% by doubling the sample frequency. The dose of sevoflurane, which inhibits and synchronizes neural activity, was linearly correlated with decreases in the threshold and $N\tau$. Thus, $\tau$ could reflect the uncertainty of the membrane potential. I propose that $\tau$ and burst represent a pause and an activity such as the rhythm of the brain.
Pattern formation of parasite-host model induced by fear effect
Authors: Yong Ye, Yi Zhao, Jiaying Zhou
Date: 2022-05-18 00:00:00

In this paper, based on the epidemiological microparasite model, a parasite-host model is established by considering the fear effect of susceptible individuals on infectors. We explored the pattern formation with the help of numerical simulation, and analyzed the effects of fear effect, infected host mortality, population diffusion rate and reducing reproduction ability of infected hosts on population activities in different degrees. Theoretically, we give the general conditions for the stability of the model under non-diffusion and considering the Turing instability caused by diffusion. Our results indicate how fear affects the distribution of the uninfected and infected hosts in the habitat and quantify the influence of the fear factor on the spatiotemporal pattern of the population. In addition, we analyze the influence of natural death rate, reproduction ability of infected hosts, and diffusion level of uninfected (infected) hosts on the spatiotemporal pattern, respectively. The results present that the growth of pattern induced by intensified fear effect follows the certain rule: cold spots $\rightarrow$ cold spots-stripes $\rightarrow$ cold stripes $\rightarrow$ hot stripes $\rightarrow$ hot spots-stripes $\rightarrow$ hot spots. Interestingly, the natural mortality and fear effect take the opposite effect on the growth order of the pattern. From the perspective of biological significance, we find that the degree of fear effect can reshape the distribution of population to meet the previous rule.
Modelling macroparasitic diseases dynamics
Authors: Gonzalo Maximiliano Lopez, Juan Pablo Aparicio
Date: 2022-05-17 00:00:00

In this work we present a general framework for the modeling of the transmission dynamics of macroparasites which do not reproduce within the host like Ascaris lumbricoides, Trichuris trichiura, Necator americanus y Ancylostoma duodenale. The basic models are derived from general probabilistic models for the parasite density-dependent mating probability. Here we considered the particular, and common case, of a negative binomial distribution for the number of parasites in hosts. We find the basic reproductive number and we show that the system exhibit a saddle-node bifurcation at some value of the basic reproduction number. We also found the equilibria and basic reproduction number of a model for the more general case of heteregeneous host populations.
Investigating the concentration of High Yield Investment Programs in the United Kingdom
Authors: Sharad Agarwal, Marie Vasek
Date: 2022-04-21 00:00:00
Tasks: valid

Ponzi schemes that offer absurdly high rates of return by relying on more and more people paying into the scheme have been documented since at least the mid-1800s. Ponzi schemes have shifted online in the Internet age, and some are re-branded as HYIPs or High Yield Investment Programs. This paper focuses on understanding HYIPs' continuous presence and presents various possible reasons behind their existence in today's world. A look into the countries where these schemes purport to exist, we find that 62.89% of all collected HYIPs claim to be in the United Kingdom (UK), and a further 55.56% are officially registered in the UK as a 'limited company' with a registration number provided by the UK Companies House, a UK agency that registers companies. We investigate other factors influencing these schemes, including the HYIPs' social media platforms and payment processors. The lifetime of the HYIPs helps to understand the success/failure of the investment schemes and helps indicate the schemes that could attract more investors. Using Cox proportional regression analysis, we find that having a valid UK address significantly affects the lifetime of an HYIP.
Robust Distortion Risk Measures
Authors: Carole Bernard, Silvana M. Pesenti, Steven Vanduffel
Date: 2022-05-18 00:00:00

The robustness of risk measures to changes in underlying loss distributions (distributional uncertainty) is of crucial importance in making well-informed decisions. In this paper, we quantify, for the class of distortion risk measures with an absolutely continuous distortion function, its robustness to distributional uncertainty by deriving its largest (smallest) value when the underlying loss distribution has a known mean and variance and, furthermore, lies within a ball - specified through the Wasserstein distance - around a reference distribution. We employ the technique of isotonic projections to provide for these distortion risk measures a complete characterisation of sharp bounds on their value, and we obtain quasi-explicit bounds in the case of Value-at-Risk and Range-Value-at-Risk. We extend our results to account for uncertainty in the first two moments and provide applications to portfolio optimisation and to model risk assessment.
Well Posedness of Utility Maximization Problems Under Partial Information in a Market with Gaussian Drift
Authors: Abdelali Gabih, Hakam Kondakji, Ralf Wunderlich
Date: 2022-05-17 00:00:00

This paper investigates well posedness of utility maximization problems for financial markets where stock returns depend on a hidden Gaussian mean reverting drift process. Since that process is potentially unbounded, well posedness cannot be guaranteed for utility functions which are not bounded from above. For power utility with relative risk aversion smaller than that of log-utility this leads to restrictions on the choice of model parameters such as the investment horizon and parameters controlling the variance of the asset price and drift processes. We derive sufficient conditions to the model parameters leading to bounded maximum expected utility of terminal wealth for models with full and partial information.
Risks and Returns of Uniswap V3 Liquidity Providers
Authors: Lioba Heimbach, Eric Schertenleib, Roger Wattenhofer
Date: 2022-05-18 00:00:00
Tasks: Management

Trade execution on Decentralized Exchanges (DEXes) is automatic and does not require individual buy and sell orders to be matched. Instead, liquidity aggregated in pools from individual liquidity providers enables trading between cryptocurrencies. The largest DEX measured by trading volume, Uniswap V3, promises a DEX design optimized for capital efficiency. However, Uniswap V3 requires far more decisions from liquidity providers than previous DEX designs. In this work, we develop a theoretical model to illustrate the choices faced by Uniswap V3 liquidity providers and their implications. Our model suggests that providing liquidity on Uniswap V3 is highly complex and requires many considerations from a user. Our supporting data analysis of the risks and returns of real Uniswap V3 liquidity providers underlines that liquidity providing in Uniswap V3 is incredibly complicated, and performances can vary wildly. While there are simple and profitable strategies for liquidity providers in liquidity pools characterized by negligible price volatilities, these strategies only yield modest returns. Instead, significant returns can only be obtained by accepting increased financial risks and at the cost of active management. Thus, providing liquidity has become a game reserved for sophisticated players with the introduction of Uniswap V3, where retail traders do not stand a chance.
Market Making via Reinforcement Learning in China Commodity Market
Authors: Junshu Jiang, Thomas Dierckx, Duxiang Xiao, Wim Schoutens
Date: 2022-05-18 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Market makers play an essential role in financial markets. A successful market maker should control inventory and adverse selection risks and provide liquidity to the market. As an important methodology in control problems, Reinforcement Learning enjoys the advantage of data-driven and less rigid assumptions, receiving great attention in the market-making field since 2018. However, although the China Commodity market has the biggest trading volume on agricultural products, nonferrous metals, and some other sectors, the study of applying RL to Market Making in China market is still rare. In this thesis, we try to fill the gap. Our contribution is threefold: We develop the Automatic Trading System and verify the feasibility of applying Reinforcement Learning in the China Commodity market. Also, we probe the agent's behavior by analyzing how it reacts to different environmental conditions.
Representation Learning for Content-Sensitive Anomaly Detection in Industrial Networks
Authors: Fabian Kopp
Date: 2022-04-20 00:00:00
Tasks: Anomaly Detection, Intrusion Detection, Network Intrusion Detection, Representation Learning

Using a convGRU-based autoencoder, this thesis proposes a framework to learn spatial-temporal aspects of raw network traffic in an unsupervised and protocol-agnostic manner. The learned representations are used to measure the effect on the results of a subsequent anomaly detection and are compared to the application without the extracted features. The evaluation showed, that the anomaly detection could not effectively be enhanced when applied on compressed traffic fragments for the context of network intrusion detection. Yet, the trained autoencoder successfully generates a compressed representation (code) of the network traffic, which hold spatial and temporal information. Based on the models residual loss, the autoencoder is also capable of detecting anomalies by itself. Lastly, an approach for a kind of model interpretability (LRP) was investigated in order to identify relevant areas within the raw input data, which is used to enrich alerts generated by an anomaly detection method.