pwc-archive/papers-with-abstracts huggingface dataset:
Simulation of snakes using vertical body bending to traverse terrain with large height variation
Authors: Yifeng Zhang, Qihan Xuan, Qiyuan Fu, Chen Li
Date: 2022-07-26 00:00:00
Tasks: Friction

Snake moves across various terrains by bending its elongated body. Recent studies discovered that snakes can use vertical bending to traverse terrain of large height variation, such as horizontally oriented cylinders, a wedge (Jurestovsky, Usher, Astley, 2021, J. Exp. Biol.), and uneven terrain (Fu & Li, 2020, Roy. Soc. Open Sci.; Fu, Astley, Li, 2022 Bioinspiration & Biomimetics). Here, to understand how vertical bending generates propulsion, we developed a dynamic simulation of a snake traversing a wedge (height = 0.05 body length, slope = 27 degrees) and a half cylindrical obstacle (height = 0.1 body length). By propagating down the body an internal torque profile with a maximum around the obstacle, the simulated snake moved forward as observed in the animal. Remarkably, even when frictional drag is low (snake-terrain kinetic friction coefficient of 0.20), the body must push against the wedge with a pressure 5 times that from body weight to generate sufficient forward propulsion to move forward. This indicated that snakes are highly capable of bending vertically to push against the environment to generate propulsion. Testing different controllers revealed that contact force feedback further helps generate and maintain propulsion effectively under unknown terrain perturbations.
Initial Orbit Determination for the CR3BP using Particle Swarm Optimization
Authors: David Zuehlke, Taylor Yow, Daniel Posada, Joseph Nicolich, Christopher W. Hays, Aryslan Malik, Troy Henderson
Date: 2022-07-23 00:00:00
Tasks: GPU, Position

This work utilizes a particle swarm optimizer (PSO) for initial orbit determination for a chief and deputy scenario in the circular restricted three-body problem (CR3BP). The PSO is used to minimize the difference between actual and estimated observations and knowledge of the chief's position with known CR3BP dynamics to determine the deputy's initial state. Convergence is achieved through limiting particle starting positions to feasible positions based on the known chief position, and sensor constraints. Parallel and GPU processing methods are used to improve computation time and provide an accurate initial state estimate for a variety of cislunar orbit geometries.
Regularity-Constrained Fast Sine Transforms
Authors: Taizo Suzuki, Seisuke Kyochi, Yuichi Tanaka
Date: 2022-07-27 00:00:00

This letter proposes a fast implementation of the regularity-constrained discrete sine transform (R-DST). The original DST \textit{leaks} the lowest frequency (DC: direct current) components of signals into high frequency (AC: alternating current) subbands. This property is not desired in many applications, particularly image processing, since most of the frequency components in natural images concentrate in DC subband. The characteristic of filter banks whereby they do not leak DC components into the AC subbands is called \textit{regularity}. While an R-DST has been proposed, it has no fast implementation because of the singular value decomposition (SVD) in its internal algorithm. In contrast, the proposed regularity-constrained fast sine transform (R-FST) is obtained by just appending a regularity constraint matrix as a postprocessing of the original DST. When the DST size is $M\times M$ ($M=2^\ell$, $\ell\in\mathbb{N}_{\geq 1}$), the regularity constraint matrix is constructed from only $M/2-1$ rotation matrices with the angles derived from the output of the DST for the constant-valued signal (i.e., the DC signal). Since it does not require SVD, the computation is simpler and faster than the R-DST while keeping all of its beneficial properties. An image processing example shows that the R-FST has fine frequency selectivity with no DC leakage and higher coding gain than the original DST. Also, in the case of $M=8$, the R-FST saved approximately $0.126$ seconds in a 2-D transformation of $512\times 512$ signals compared with the R-DST because of fewer extra operations.
Propagation Map Reconstruction via Interpolation Assisted Matrix Completion
Authors: Hao Sun, Junting Chen
Date: 2022-07-27 00:00:00
Tasks: Management, Matrix Completion

Constructing a propagation map from a set of scattered measurements finds important applications in many areas, such as localization, spectrum monitoring and management. Classical interpolation-type methods have poor performance in regions with very sparse measurements. Recent advance in matrix completion has the potential to reconstruct a propagation map from sparse measurements, but the spatial resolution is limited. This paper proposes to integrate interpolation with matrix completion to exploit both the spatial correlation and the potential low rank structure of the propagation map. The proposed method first enriches matrix observations using interpolation, and develops the statistics of the interpolation error based on a local polynomial regression model. Then, two uncertainty-aware matrix completion algorithms are developed to exploit the interpolation error statistics. It is numerically demonstrated that the proposed method outperforms Kriging and other state-of-the-art schemes, and reduces the mean squared error (MSE) of propagation map reconstruction by 10%-50% for a medium to large number of measurements.
Implementation and Evaluation of SiPM-Based Photon Counting Receiver for IoT Applications
Authors: Yangchun Li, Danial Chitnis
Date: 2022-07-26 00:00:00
Tasks: Sensitivity

Silicon Photomultipliers (SiPMs) are photon-counting detectors with great potential to improve the sensitivity of optical receivers. Recent studies of SiPMs in communication focus on the speed rather than the power consumption of the receiver. The gain bandwidth product (GBP) of the amplifiers in these post-SiPM readout circuits is significantly higher than the target data rate. Additionally, the SiPM experiments for optical communication are performed using an offline method which uses instruments including oscilloscopes and personal computers to process chunks of the transmitted data. In this work, we have developed an embedded real-time field-programmable gate array (FPGA) based system to evaluate a commercially available 1 mm-sq SiPM. The implemented real-time system achieves data rates from 10 kbps to 1 Mbps with a bit error rate (BER) below 1E-3 approaching the Poisson limit. Results showed that reducing either the dark count rate or increasing the data rate leads to lower dark counts per bit time, hence less power penalty to maintain a probability of error (PE) of 1E-3. The numerically simulated results indicated that to maintain the Poisson limit, the minimum GBP of the amplifier in the post-SiPM readout circuit is 120 MHz based on the proposed setup within the tested data rates. This GBP limitation is determined by the noise floor of the read-out circuit. The analysis of the minimum GBP and electrical power consumption of the receiver in photon counting and BER enables the potential future adoption of this receiver technology when high optical sensitivity is required, such as visible light communications (VLC) for low data rate Internet of Things (IoT) applications.
Bartlett's Delta revisited: Variance-optimal hedging in the lognormal SABR and in the rough Bergomi model
Authors: Martin Keller-Ressel
Date: 2022-07-27 00:00:00

We derive analytic expressions for the variance-optimal hedging strategy and its mean-square hedging error in the lognormal SABR and in the rough Bergomi model. In the SABR model, we show that the variance-optimal hedging strategy coincides with the Delta adjustment of Bartlett [Wilmott magazine 4/6 (2006)]. We show both mathematically and in simulation that the efficiency of the variance-optimal strategy (in comparison to simple Delta hedging) depends strongly on the leverage parameter rho and - in a weaker sense - also on the roughness parameter H of the model, and give a precise quantification of this dependency.
Signature-based models: theory and calibration
Authors: Christa Cuchiero, Guido Gazzani, Sara Svaluto-Ferro
Date: 2022-07-26 00:00:00
Tasks: Time Series, Time Series Analysis

We consider asset price models whose dynamics are described by linear functions of the (time extended) signature of a primary underlying process, which can range from a (market-inferred) Brownian motion to a general multidimensional continuous semimartingale. The framework is universal in the sense that classical models can be approximated arbitrarily well and that the model's parameters can be learned from all sources of available data by simple methods. We provide conditions guaranteeing absence of arbitrage as well as tractable option pricing formulas for so-called sig-payoffs, exploiting the polynomial nature of generic primary processes. One of our main focus lies on calibration, where we consider both time-series and implied volatility surface data, generated from classical stochastic volatility models and also from S&P500 index market data. For both tasks the linearity of the model turns out to be the crucial tractability feature which allows to get fast and accurate calibrations results.
How does artificial intelligence contribute to iEEG research?
Authors: Julia Berezutskaya, Anne-Lise Saive, Karim Jerbi, Marcel van Gerven
Date: 2022-07-26 00:00:00
Tasks: Brain Computer Interface

Artificial intelligence (AI) is a fast-growing field focused on modeling and machine implementation of various cognitive functions with an increasing number of applications in computer vision, text processing, robotics, neurotechnology, bio-inspired computing and others. In this chapter, we describe how AI methods can be applied in the context of intracranial electroencephalography (iEEG) research. IEEG data is unique as it provides extremely high-quality signals recorded directly from brain tissue. Applying advanced AI models to these data carries the potential to further our understanding of many fundamental questions in neuroscience. At the same time, as an invasive technique, iEEG lends itself well to long-term, mobile brain-computer interface applications, particularly for communication in severely paralyzed individuals. We provide a detailed overview of these two research directions in the application of AI techniques to iEEG. That is, (1) the development of computational models that target fundamental questions about the neurobiological nature of cognition (AI-iEEG for neuroscience) and (2) applied research on monitoring and identification of event-driven brain states for the development of clinical brain-computer interface systems (AI-iEEG for neurotechnology). We explain key machine learning concepts, specifics of processing and modeling iEEG data and details of state-of-the-art iEEG-based neurotechnology and brain-computer interfaces.
Early estimates on monkeypox incubation period, generation time and reproduction number in Italy, May-June 2022
Authors: Giorgio Guzzetta, Alessia Mammone, Federica Ferraro, Anna Caraglia, Alessia Rapiti, Valentina Marziano, Piero Poletti, Danilo Cereda, Francesco Vairo, Giovanna Mattei, Francesco Maraglino, Giovanni Rezza, Stefano Merler
Date: 2022-07-27 00:00:00

We analyzed the first 255 PCR-confirmed cases of monkeypox occurred in Italy in 2022. Preliminary estimates indicate: mean incubation period of 9.1 days (95%CI of the mean: 6.5-10.9); mean generation time of 12.5 days (95%CI of the mean: 7.5-17.3); reproduction number in the MSM community of 2.43 (95%CI: 1.82-3.26).
A model for COVID-19 and bacterial pneumonia coinfection with community- and hospital-acquired infections
Authors: Angel G. C. Pérez, David A. Oluyori
Date: 2022-07-27 00:00:00

We propose a new mathematical model to study the coinfection dynamics of COVID-19 and bacterial pneumonia. Our model includes two infection ways for pneumonia, corresponding to community-acquired and hospital-acquired infections. We show that the existence and local stability of equilibria depend on three different parameters, which are interpreted as the basic reproduction numbers of COVID-19, bacterial pneumonia, and bacterial population. Numerical simulations are performed to complement our theoretical analysis, and we show that both diseases can persist if the basic reproduction number of COVID-19 is greater than one.
The History, Current Status, Benefits, and Challenges of 3D Printed Organs
Authors: Alicia Shin
Date: 2022-07-26 00:00:00

There is an increase in demand for organs as transplantation is becoming a common practice to elongate human life. To reach this demand, three-dimensional bioprinting is developing from prior knowledge of scaffolds, growth factors, etc. This review paper aims to determine the current status and future possibilities of three-dimensional bioprinting of organs and evaluate the benefits and challenges, along with the history of its development. Prior research has viewed three-dimensional bioprinting as a technology that will enable safer transplantation without graft rejection and provide demand-based production. However, it faces challenges such as the need to improve biocompatibility and biofunctionality, legal and ethical issues, and the need to improve the technology itself. While the development of three-dimensional printing organs is not yet completed, we are seeing improvements and expecting it to be clinically applied soon.
Interrogation of A Bubble in the Indian Market
Authors: Ganapathy G Gangadharan, N. Suresh
Date: 2022-07-27 00:00:00

Emerging markets such as India provide investors with returns far greater than those in developed markets; taking the average returns from the period 1995 to 2014 the returns are 4.714% to 3.276% of the developed market. The majority of emerging markets commenced joining with the capital market of the world, thus allowing a huge inflow of capital which in turn paved the path for economic growth. Even though the emerging markets provide high returns these may also be an indication of a bubble formation. Detection of a bubble is a tedious task primarily due to the fundamental value of the security being uncertain, and the randomness of the fundamentals of the market makes detecting bubbles an arduous task. Ratios that foretold the financial crisis of 2007- Market Capitalization to GDP, Price to Earnings Ratio, Price to Book Value, Tobins Q. Data is collected from 1999-2000 from various Indian indices such as NIFTY 50, NIFTY NEXT 50, NIFTY BANK, NIFTY 500 S and PBSE SENSEX, S and P BSE 100. The paper utilizes the ratios mentioned above to detect and backtrack various bubble episodes in the Indian market; the methodology used is the Philips et al 2015 right-tailed unit test. The paper is also inclined to take steps to mitigate the effects of a bubble by amending the financial policies and the monetary liquidity of the financial system.
Indian Derivatives Market Evolution and Challenge
Authors: Shruthi B. C., N. Suresh
Date: 2022-07-26 00:00:00

The study is conducted to establish the framework for comparing the relation in performance of the derivatives of BSE and NSE in India and to analyse the relationship of derivatives with the cash market and the market volatility. The exchange-traded equity derivatives were considered for the study. It was found that the performance of derivatives in NSE is a lot higher than in BSE and the NSE is on par with the global exchanges compared to BSE in terms of the number of contracts traded for Stock Index Options and Futures and also Stock Futures. Hence derivatives market needs to strengthen further with the number of contracts traded and turnover in all the derivative instruments with more strong regulations and a robust framework protecting the interest of the investors. This study enables Derivative Industry to progress towards its goals and objectives in a more efficient way.
Affine models with path-dependence under parameter uncertainty and their application in finance
Authors: Benedikt Geuchen, Katharina Oberpriller, Thorsten Schmidt
Date: 2022-07-27 00:00:00

In this work we consider one-dimensional generalized affine processes under the paradigm of Knightian uncertainty (so-called non-linear generalized affine models). This extends and generalizes previous results in Fadina et al. (2019) and L\"utkebohmert et al. (2022). In particular, we study the case when the payoff is allowed to depend on the path, like it is the case for barrier options or Asian options. To this end, we develop the path-dependent setting for the value function which we do by relying on functional It\^o calculus. We establish a dynamic programming principle which then leads to a functional non-linear Kolmogorov equation describing the evolution of the value function. While for Asian options, the valuation can be traced back to PDE methods, this is no longer possible for more complicated payoffs like barrier options. To handle such payoffs in an efficient manner, we approximate the functional derivatives with deep neural networks and show that the numerical valuation under parameter uncertainty is highly tractable. Finally, we consider the application to structural modelling of credit and counterparty risk, where both parameter uncertainty and path-dependence are crucial and the approach proposed here opens the door to efficient numerical methods in this field.
Bayesian Evidential Learning for Few-Shot Classification
Authors: Xiongkun Linghu, Yan Bai, Yihang Lou, Shengsen Wu, Jinze Li, Jianzhong He, Tao Bai
Date: 2022-07-19 00:00:00
Tasks: Classification, Metric Learning, Uncertainty Quantification

Few-Shot Classification(FSC) aims to generalize from base classes to novel classes given very limited labeled samples, which is an important step on the path toward human-like machine learning. State-of-the-art solutions involve learning to find a good metric and representation space to compute the distance between samples. Despite the promising accuracy performance, how to model uncertainty for metric-based FSC methods effectively is still a challenge. To model uncertainty, We place a distribution over class probability based on the theory of evidence. As a result, uncertainty modeling and metric learning can be decoupled. To reduce the uncertainty of classification, we propose a Bayesian evidence fusion theorem. Given observed samples, the network learns to get posterior distribution parameters given the prior parameters produced by the pre-trained network. Detailed gradient analysis shows that our method provides a smooth optimization target and can capture the uncertainty. The proposed method is agnostic to metric learning strategies and can be implemented as a plug-and-play module. We integrate our method into several newest FSC methods and demonstrate the improved accuracy and uncertainty quantification on standard FSC benchmarks.
EEG2Mel: Reconstructing Sound from Brain Responses to Music
Authors: Adolfo G. Ramirez-Aristizabal, Chris Kello
Date: 2022-07-28 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Information Retrieval, Retrieval

Information retrieval from brain responses to auditory and visual stimuli has shown success through classification of song names and image classes presented to participants while recording EEG signals. Information retrieval in the form of reconstructing auditory stimuli has also shown some success, but here we improve on previous methods by reconstructing music stimuli well enough to be perceived and identified independently. Furthermore, deep learning models were trained on time-aligned music stimuli spectrum for each corresponding one-second window of EEG recording, which greatly reduces feature extraction steps needed when compared to prior studies. The NMED-Tempo and NMED-Hindi datasets of participants passively listening to full length songs were used to train and validate Convolutional Neural Network (CNN) regressors. The efficacy of raw voltage versus power spectrum inputs and linear versus mel spectrogram outputs were tested, and all inputs and outputs were converted into 2D images. The quality of reconstructed spectrograms was assessed by training classifiers which showed 81% accuracy for mel-spectrograms and 72% for linear spectrograms (10% chance accuracy). Lastly, reconstructions of auditory music stimuli were discriminated by listeners at an 85% success rate (50% chance) in a two-alternative match-to-sample task.
Visual Recognition by Request
Authors: Chufeng Tang, Lingxi Xie, Xiaopeng Zhang, Xiaolin Hu, Qi Tian
Date: 2022-07-28 00:00:00
Proceeding: CVPR 2023 1
Tasks: Instance Segmentation, Semantic Segmentation

Humans have the ability of recognizing visual semantics in an unlimited granularity, but existing visual recognition algorithms cannot achieve this goal. In this paper, we establish a new paradigm named visual recognition by request (ViRReq) to bridge the gap. The key lies in decomposing visual recognition into atomic tasks named requests and leveraging a knowledge base, a hierarchical and text-based dictionary, to assist task definition. ViRReq allows for (i) learning complicated whole-part hierarchies from highly incomplete annotations and (ii) inserting new concepts with minimal efforts. We also establish a solid baseline by integrating language-driven recognition into recent semantic and instance segmentation methods, and demonstrate its flexible recognition ability on CPP and ADE20K, two datasets with hierarchical whole-part annotations.
Initialization and Alignment for Adversarial Texture Optimization
Authors: Xiaoming Zhao, Zhizhen Zhao, Alexander G. Schwing
Date: 2022-07-28 00:00:00
Tasks: Texture Synthesis

While recovery of geometry from image and video data has received a lot of attention in computer vision, methods to capture the texture for a given geometry are less mature. Specifically, classical methods for texture generation often assume clean geometry and reasonably well-aligned image data. While very recent methods, e.g., adversarial texture optimization, better handle lower-quality data obtained from hand-held devices, we find them to still struggle frequently. To improve robustness, particularly of recent adversarial texture optimization, we develop an explicit initialization and an alignment procedure. It deals with complex geometry due to a robust mapping of the geometry to the texture map and a hard-assignment-based initialization. It deals with misalignment of geometry and images by integrating fast image-alignment into the texture refinement optimization. We demonstrate efficacy of our texture generation on a dataset of 11 scenes with a total of 2807 frames, observing 7.8% and 11.1% relative improvements regarding perceptual and sharpness measurements.
Physics-informed neural networks for diffraction tomography
Authors: Amirhossein Saba, Carlo Gigli, Ahmed B. Ayoub, Demetri Psaltis
Date: 2022-07-28 00:00:00
Tasks: Tomographic Reconstructions

We propose a physics-informed neural network as the forward model for tomographic reconstructions of biological samples. We demonstrate that by training this network with the Helmholtz equation as a physical loss, we can predict the scattered field accurately. It will be shown that a pretrained network can be fine-tuned for different samples and used for solving the scattering problem much faster than other numerical solutions. We evaluate our methodology with numerical and experimental results. Our physics-informed neural networks can be generalized for any forward and inverse scattering problem.