pwc-archive/papers-with-abstracts huggingface dataset:
Adaptive Transmit Waveform Design
Authors: David A. Hague
Date: 2021-11-16 00:00:00

Recent research efforts in the Anti-Submarine Warfare (ASW) community have focused on developing sonar systems that adapt to their acoustic environment, referred to as "cognitive" sonars. Cognitive active sonar systems utilize principles of the perception action cycle of cognition to leverage information gathered from earlier sensing interactions with the underwater acoustic environment. This in turn informs the selection of system parameters to optimize target detection, classification, localization, and tracking performance in that acoustic environment. Of the many system parameters such a cognitive sonar system could potentially adapt, the acoustic signal transmitted into the medium, also known as the transmit waveform, has a profound impact on system performance. Many of the physical characteristics of the acoustic environment are contained in the return echo signal that is composed of amplitude scaled (target strength), time-delayed (target range) and Doppler shifted (target range-rate) echoes of the transmit waveform. This paper briefly describes a spectrally compact adaptive FM waveform model using Multi-Tone Sinusoidal Frequency Modulation (MTSFM). The MTSFM waveform's frequency and phase modulation functions are composed of a finite set of weighted sinusoidal harmonics. The weights for each harmonic are utilized as a discrete set of design coefficients. Adjusting these coefficients results in constant amplitude, spectrally compact FM waveforms with unique characteristics. The adaptability of the MTSFM combined with its transmitter friendly properties make it an attractive waveform type for a variety of active sonar applications and may provide a cognitive sonar system the ability to generate a complementary set of finely tuned waveforms for the novel scenarios and environments that it may encounter.
The "Bayesian" brain, with a bit less Bayes
Authors: Eelke Spaak
Date: 2021-11-17 00:00:00
Tasks: Action Generation, Bayesian Inference

The idea that the brain is a probabilistic (Bayesian) inference machine, continuously trying to figure out the hidden causes of its inputs, has become very influential in cognitive (neuro)science over recent decades. Here I present a relatively straightforward generalization of this idea: the primary computational task that the brain is faced with is to track the probabilistic structure of observations themselves, without recourse to hidden states. Taking this starting point seriously turns out to have considerable explanatory power, and several key ideas are developed from it: (1) past experience, encoded in prior expectations, has an influence over the future that is analogous to regularization as known from machine learning; (2) action generation (interpreted as constraint satisfaction) is a special case of such regularization; (3) the concept of attractors in dynamical systems provides a useful lens through which prior expectations, regularization, and action induction can be viewed; these thus appear as different perspectives on the same phenomenon; (4) the phylogenetically ancient imperative of acting to ensure and thereby observe conditions beneficial for survival is likely the same as that which underlies perceptual inference. The Bayesian brain hypothesis has been touted as promising to deliver a "unified science of mind and action". In this paper, I sketch an informal step towards fulfilling that promise, while avoiding some pitfalls that other such attempts have fallen prey to.
Reconsidering Spatial Priors In EEG Source Estimation: Does White Matter Contribute to EEG Rhythms?
Authors: PK Douglas, DB Douglas
Date: 2021-11-17 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

Electroencephalogram (EEG) has been a core tool used in functional neuroimaging in humans for nearly a hundred years. Because it is inexpensive, easy to implement, and noninvasive, it also represents an excellent candidate modality for use in the BCI setting. Nonetheless, a complete understanding of how EEG measurements (voltage fluctuations) relate to information processing in the brain remains somewhat elusive. A deeper understanding of the neuroanatomical underpinnings of the EEG signal may help explain inter-individual variability in evoked and induced potentials, which may improve BCI therapies targeted to the individual. According to classic biophysical models, EEG fluctuations are primarily a reflection of locally synchronized neuronal oscillations within the gray matter oriented approximately orthogonal to the scalp. In contrast, global models ignore local signals due to dendritic processing, and suggest that propagation delays due to white matter architecture are responsible for the EEG signal, and are capable of explaining the coherence between numerous rhythms (e.g., alpha) at spatially distinct areas of the scalp. Recently, combined local-global models suggest that the EEG signal may reflect a superposition of local processing along with global contributors including transduction along white matter tracts in the brain. Incorporating both local and global (e.g., white matter) priors into EEG source models may therefore improve source estimates. These models may also help disentangle which aspects of the EEG signal are predicted to colocalize spatially with measurements from functional MRI (fMRI). Here, we explore the possibility that white matter conductivity contributes to EEG measurements via a generative model based on classic axonal transduction models, and discuss its potential implications for source estimation.
Optimal consumption and portfolio selection with Epstein-Zin utility under general constraints
Authors: Zixin Feng, Dejian Tian
Date: 2021-11-17 00:00:00

The paper investigates the consumption-investment problem for an investor with Epstein-Zin utility in an incomplete market. Closed, not necessarily convex, constraints are imposed on strategies. The optimal consumption and investment strategies are characterized via a quadratic backward stochastic differential equation (BSDE). Due to the stochastic market environment, the solution to this BSDE is unbounded and thereby the BMO argument breaks down. After establishing the martingale optimality criterion, by delicately selecting Lyapunov functions, the verification theorem is ultimately obtained. Besides, several examples and numerical simulations for the optimal strategies are provided and illustrated.
Impermanent Loss in Uniswap v3
Authors: Stefan Loesch, Nate Hindman, Mark B Richardson, Nicholas Welch
Date: 2021-11-17 00:00:00

AMMs are autonomous smart contracts deployed on a blockchain that make markets between different assets that live on that chain. In this paper we are examining a specific class of AMMs called Constant Function Market Makers whose trading profile, ignoring fees, is determined by their bonding curve. This class of AMM suffers from what is commonly referred to as Impermanent Loss, which we have previously identified as the Gamma component of the associated self-financing trading strategy and which is the risk that LP providers wager against potential fee earnings. The recent Uniswap v3 release has popularized the concept of leveraged liquidity provision - wherein the trading range in which liquidity is provided is reduced and achieves a higher degree of capital efficiency through elimination of unused collateral. This leverage increases the fees earned, but it also increases the risk taken, ie the IL. Fee levels on Uniswap v3 are well publicized so, in this paper, we focus on calculating the IL. We found that for the 17 pools we analyzed, covering 43% of TVL and chosen by size, composite tokens and data availability, total fees earned since inception until the cut-off date was $199.3m. We also found that the total IL suffered by LPs during this period was USD 260.1m, meaning that in aggregate those LPs would have been better off by USD 60.8m had they simply HODLd.
A Universal End-to-End Approach to Portfolio Optimization via Deep Learning
Authors: Chao Zhang, Zihao Zhang, Mihai Cucuringu, Stefan Zohren
Date: 2021-11-17 00:00:00
Tasks: Portfolio Optimization

We propose a universal end-to-end framework for portfolio optimization where asset distributions are directly obtained. The designed framework circumvents the traditional forecasting step and avoids the estimation of the covariance matrix, lifting the bottleneck for generalizing to a large amount of instruments. Our framework has the flexibility of optimizing various objective functions including Sharpe ratio, mean-variance trade-off etc. Further, we allow for short selling and study several constraints attached to objective functions. In particular, we consider cardinality, maximum position for individual instrument and leverage. These constraints are formulated into objective functions by utilizing several neural layers and gradient ascent can be adopted for optimization. To ensure the robustness of our framework, we test our methods on two datasets. Firstly, we look at a synthetic dataset where we demonstrate that weights obtained from our end-to-end approach are better than classical predictive methods. Secondly, we apply our framework on a real-life dataset with historical observations of hundreds of instruments with a testing period of more than 20 years.
Physics-guided Loss Functions Improve Deep Learning Performance in Inverse Scattering
Authors: Zicheng Liu, Mayank Roy, Dilip K. Prasad, Krishna Agarwal
Date: 2021-11-13 00:00:00

Solving electromagnetic inverse scattering problems (ISPs) is challenging due to the intrinsic nonlinearity, ill-posedness, and expensive computational cost. Recently, deep neural network (DNN) techniques have been successfully applied on ISPs and shown potential of superior imaging over conventional methods. In this paper, we analyse the analogy between DNN solvers and traditional iterative algorithms and discuss how important physical phenomena cannot be effectively incorporated in the training process. We show the importance of including near-field priors in the learning process of DNNs. To this end, we propose new designs of loss functions which incorporate multiple-scattering based near-field quantities (such as scattered fields or induced currents within domain of interest). Effects of physics-guided loss functions are studied using a variety of numerical experiments. Pros and cons of the investigated ISP solvers with different loss functions are summarized.
Volumetric Quantitative Ablation Margins for Assessment of Ablation Completeness in Thermal Ablation of Liver Tumors
Authors: Raluca-Maria Sandu, Iwan Polucci, Simeon J. S. Ruiter, Raphael Sznitman, Koert P. de Jong, Jacob Freedman, Stefan Weber and Pascale Tinguely
Date: 2021-03-10 00:00:00
Proceeding: Frontiers in Oncology, Cancer Imaging and Image-directed interventions 2021 3
Tasks: Decision Making

In thermal ablation of liver tumors, complete coverage of the tumor volume by the ablation volume with a sufficient ablation margin is the most important factor for treatment success. Evaluation of ablation completeness is commonly performed by visual inspection in 2D and is prone to inter-reader variability. This work aimed to introduce a standardized approach for evaluation of ablation completeness after CT-guided thermal ablation of liver tumors, using volumetric quantitative ablation margins (QAM).

Methods: A QAM computation metric based on volumetric segmentations of tumor and ablation areas and signed Euclidean surface distance maps was developed, including a novel algorithm to address QAM computation in subcapsular tumors. The code for QAM computation was verified in artificial examples of tumor and ablation spheres simulating varying scenarios of ablation margins. The applicability of the QAM metric was investigated in representative cases extracted from a prospective database of colorectal liver metastases (CRLM) treated with stereotactic microwave ablation (SMWA).

Results: Applicability of the proposed QAM metric was confirmed in artificial and clinical example cases. Numerical and visual options of data presentation displaying substrata of QAM distributions were proposed. For subcapsular tumors, the underestimation of tumor coverage by the ablation volume when applying an unadjusted QAM method was confirmed, supporting the benefits of using the proposed algorithm for QAM computation in these cases. The computational code for developed QAM was made publicly available, encouraging the use of a standard and objective metric in reporting ablation completeness and margins.

Conclusion: The proposed volumetric approach for QAM computation including a novel algorithm to address subcapsular liver tumors enables precision and reproducibility in the assessment of ablation margins. The quantitative feedback on ablation completeness opens possibilities for intra-operative decision making and for refined analyses on predictability and consistency of local tumor control after thermal ablation of liver tumors
Joint Inference for Neural Network Depth and Dropout Regularization
Authors: Kishan K C, Rui Li, MohammadMahdi Gilany
Date: 2021-11-08 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: Continual Learning, Model Selection

Dropout regularization methods prune a neural network's pre-determined backbone structure to avoid overfitting. However, a deep model still tends to be poorly calibrated with high confidence on incorrect predictions. We propose a unified Bayesian model selection method to jointly infer the most plausible network depth warranted by data, and perform dropout regularization simultaneously. In particular, to infer network depth we define a beta process over the number of hidden layers which allows it to go to infinity. Layer-wise activation probabilities induced by the beta process modulate neuron activation via binary vectors of a conjugate Bernoulli process. Experiments across domains show that by adapting network depth and dropout regularization to data, our method achieves superior performance comparing to state-of-the-art methods with well-calibrated uncertainty estimates. In continual learning, our method enables neural networks to dynamically evolve their depths to accommodate incrementally available data beyond their initial structures, and alleviate catastrophic forgetting.
A Novel Compartmental Approach to Modeling COVID-19 Disease Dynamics and Analyzing the Effect of Common Preventative Measures
Authors: Caden Lin
Date: 2021-11-17 00:00:00

As of December 2020, the COVID-19 pandemic has infected over 75 million people, making it the deadliest pandemic in modern history. This study develops a novel compartmental epidemiological model specific to the SARS-CoV-2 virus and analyzes the effect of common preventative measures such as testing, quarantine, social distancing, and vaccination. By accounting for the most prevalent interventions that have been enacted to minimize the spread of the virus, the model establishes a paramount foundation for future mathematical modeling of COVID-19 and other modern pandemics. Specifically, the model expands on the classic SIR model and introduces separate compartments for individuals who are in the incubation period, asymptomatic, tested-positive, quarantined, vaccinated, or deceased. It also accounts for variable infection, testing, and death rates. I first analyze the outbreak in Santa Clara County, California, and later generalize the findings. The results show that, although all preventative measures reduce the spread of COVID-19, quarantine and social distancing mandates reduce the infection rate and subsequently are the most effective policies, followed by vaccine distribution and, finally, public testing. Thus, governments should concentrate resources on enforcing quarantine and social distancing policies. In addition, I find mathematical proof that the relatively high asymptomatic rate and long incubation period are driving factors of COVID-19's rapid spread.
Unsupervised Online Learning for Robotic Interestingness with Visual Memory
Authors: Chen Wang, Yuheng Qiu, Wenshan Wang, Yafei Hu, Seungchan Kim, Sebastian Scherer
Date: 2021-11-18 00:00:00
Tasks: Translation

Autonomous robots frequently need to detect "interesting" scenes to decide on further exploration, or to decide which data to share for cooperation. These scenarios often require fast deployment with little or no training data. Prior work considers "interestingness" based on data from the same distribution. Instead, we propose to develop a method that automatically adapts online to the environment to report interesting scenes quickly. To address this problem, we develop a novel translation-invariant visual memory and design a three-stage architecture for long-term, short-term, and online learning, which enables the system to learn human-like experience, environmental knowledge, and online adaption, respectively. With this system, we achieve an average of 20% higher accuracy than the state-of-the-art unsupervised methods in a subterranean tunnel environment. We show comparable performance to supervised methods for robot exploration scenarios showing the efficacy of our approach. We expect that the presented method will play an important role in the robotic interestingness recognition exploration tasks.
Perceiving and Modeling Density is All You Need for Image Dehazing
Authors: Tian Ye, Mingchao Jiang, Yunchen Zhang, Liang Chen, ErKang Chen, Pen Chen, Zhiyong Lu
Date: 2021-11-18 00:00:00
Tasks: All, Image Dehazing, Single Image Dehazing

In the real world, the degradation of images taken under haze can be quite complex, where the spatial distribution of haze is varied from image to image. Recent methods adopt deep neural networks to recover clean scenes from hazy images directly. However, due to the paradox caused by the variation of real captured haze and the fixed degradation parameters of the current networks, the generalization ability of recent dehazing methods on real-world hazy images is not ideal.To address the problem of modeling real-world haze degradation, we propose to solve this problem by perceiving and modeling density for uneven haze distribution. We propose a novel Separable Hybrid Attention (SHA) module to encode haze density by capturing features in the orthogonal directions to achieve this goal. Moreover, a density map is proposed to model the uneven distribution of the haze explicitly. The density map generates positional encoding in a semi-supervised way. Such a haze density perceiving and modeling capture the unevenly distributed degeneration at the feature level effectively. Through a suitable combination of SHA and density map, we design a novel dehazing network architecture, which achieves a good complexity-performance trade-off. The extensive experiments on two large-scale datasets demonstrate that our method surpasses all state-of-the-art approaches by a large margin both quantitatively and qualitatively, boosting the best published PSNR metric from 28.53 dB to 33.49 dB on the Haze4k test dataset and from 37.17 dB to 38.41 dB on the SOTS indoor test dataset.
Near-Optimal Quantum Algorithms for Multivariate Mean Estimation
Authors: Arjan Cornelissen, Yassine Hamoudi, Sofiene Jerbi
Date: 2021-11-18 00:00:00

We propose the first near-optimal quantum algorithm for estimating in Euclidean norm the mean of a vector-valued random variable with finite mean and covariance. Our result aims at extending the theory of multivariate sub-Gaussian estimators to the quantum setting. Unlike classically, where any univariate estimator can be turned into a multivariate estimator with at most a logarithmic overhead in the dimension, no similar result can be proved in the quantum setting. Indeed, Heinrich ruled out the existence of a quantum advantage for the mean estimation problem when the sample complexity is smaller than the dimension. Our main result is to show that, outside this low-precision regime, there is a quantum estimator that outperforms any classical estimator. Our approach is substantially more involved than in the univariate setting, where most quantum estimators rely only on phase estimation. We exploit a variety of additional algorithmic techniques such as amplitude amplification, the Bernstein-Vazirani algorithm, and quantum singular value transformation. Our analysis also uses concentration inequalities for multivariate truncated statistics. We develop our quantum estimators in two different input models that showed up in the literature before. The first one provides coherent access to the binary representation of the random variable and it encompasses the classical setting. In the second model, the random variable is directly encoded into the phases of quantum registers. This model arises naturally in many quantum algorithms but it is often incomparable to having classical samples. We adapt our techniques to these two settings and we show that the second model is strictly weaker for solving the mean estimation problem. Finally, we describe several applications of our algorithms, notably in measuring the expectation values of commuting observables and in the field of machine learning.
Hybrid Super Intelligence and Polymetric Analysis
Authors: Vladislav Dorofeev, Petro Trokhimchuk
Date: 2021-11-18 00:00:00

The problem of possible applications Polymetric Analysis for the resolution problems of artificial Intelligence is discussed. As example the hybrid super intelligence system by N. Moiseev type was selected. The bond between polymetric analysis and hybrid super intelligence system was shown. In operational sense polymetric analysis is more general system. Therefore main principles of Moiseev concept may be unify with the help of polymetric analysis. Main peculiarities of this unification are analyzed.
A big data intelligence marketplace and secure analytics experimentation platform for the aviation industry
Authors: Dimitrios Miltiadou, Stamatis Pitsios, Dimitrios Spyropoulos, Dimitrios Alexandrou, Fenareti Lampathaki, Domenico Messina, Konstantinos Perakis
Date: 2021-11-18 00:00:00
Tasks: Data Integration

The unprecedented volume, diversity and richness of aviation data that can be acquired, generated, stored, and managed provides unique capabilities for the aviation-related industries and pertains value that remains to be unlocked with the adoption of the innovative Big Data Analytics technologies. Despite the large efforts and investments on research and innovation, the Big Data technologies introduce a number of challenges to its adopters. Besides the effective storage and access to the underlying big data, efficient data integration and data interoperability should be considered, while at the same time multiple data sources should be effectively combined by performing data exchange and data sharing between the different stakeholders. However, this reveals additional challenges for the crucial preservation of the information security of the collected data, the trusted and secure data exchange and data sharing, as well as the robust data access control. The current paper aims to introduce the ICARUS big data-enabled platform that aims provide a multi-sided platform that offers a novel aviation data and intelligence marketplace accompanied by a trusted and secure analytics workspace. It holistically handles the complete big data lifecycle from the data collection, data curation and data exploration to the data integration and data analysis of data originating from heterogeneous data sources with different velocity, variety and volume in a trusted and secure manner.