pwc-archive/papers-with-abstracts huggingface dataset:
Frequency-based Ultrasonic Backscatter Modulation for Passive Sensing Applications
Authors: Muhammad Junaid Akhtar, Alp Timucin Toymus, Kivanc Esat, Levent Beker
Date: 2021-09-21 00:00:00

Ultrasonic backscatter communication has gained popularity in recent years where piezoceramic resonators are used as acoustic antennas. Currently, backscatter communication revolves around the amplitude modulation of the echo signal by the sensor, which can be modeled as a variable shunt impedance. Amplitude based sensing is prone to high levels of noise, motion artifacts, and has low efficiency with respect to power usage and bandwidth. To overcome these shortcomings, we present here, a frequency-based sensing method for ultrasonic backscatter communication. This system exhibits a frequency shift in the ultrasonic sensor's parallel resonance when its shunt capacitance is varied, similar to an inductive near field link. The concept is simulated using an equivalent end-to-end model in SPICE that matches well with the experimental observation. To monitor the resonance frequency shift in the experiments a variety of methods are employed including ringdown measurements, chirp spectroscopy, bode analysis and phase locked loop. All the methods provde substantial proof for feasibility of frequency-based sensing in ultrasonic backscatter communication enabling passive sensor motes for sensing applications.
Who's the Daddy? Infidelity Favours Father-Son Facial Dissimilarity
Authors: James V Stone
Date: 2021-08-16 00:00:00

Evolutionary theory predicts that children fare better if they resemble their father. However, if a man is promiscuous then his children tend to be (unwittingly) raised within other families; such children should fare better if they do not resemble their father. This suggests that father-son facial similarity should decrease as a function of male promiscuity. To test this hypothesis, the degree of similarity between each of 37 pairs of father-son photographs was rated by 83 participants. The promiscuity score of a father was defined as the number of sexual relationships he had been involved in. A linear regression of father-son similarity ratings against fathers' promiscuity scores indicated that the slope of the fitted line was significantly less than zero (slope=-0.176, p < 0.001), and that fathers' promiscuity scores account for 75% of the variance father-son facial similarity ratings. Additionally, a significant negative non-parametric correlation (Spearman's rho rho=-0.886, p=0.019) between father-son similarity ratings and fathers' promiscuity scores was found.
Structural optimization of biohydrogen production: Impact of pretreatments on volatile fatty acids and biogas parameters
Authors: Mahmood Mahmoodi-Eshkaftakia, Gustavo Mockaitis
Date: 2021-09-22 00:00:00

The present study aims to describe an innovative approach that enables the system to achieve high yielding for biohydrogen (bio-H$_2$) production using xylose as a by-product of lignocellulosic biomass processing. A hybrid optimization technique, structural modelling, desirability analysis, and genetic algorithm could determine the optimum input factors to maximize useful biogas parameters, especially bio-H$_2$ and CH$_4$. As found, the input factors (pretreatment, digestion time and biogas relative pressure) and volatile fatty acids (acetic acid, propionic acid and butyric acid) had indirectly and significantly impacted the bio-H$_2$ and desirability score. The pretreatment factor had the most effect on bio-H$_2$ and CH$_4$ production among the factors, and after that, were propionic acid and digestion time. The optimization method showed that the best pretreatment was acidic pretreatment, digestion time > 20 h, relative pressure in a range of 300-800 mbar, acetic acid in a range of 90-200 mg/L, propionic acid in a range of 20-150 mg/L, and butyric acid in a range of 250-420 mg/L. These values caused to produce H$_2$ > 10.2 mmol/L, CH$_4$ > 3.9 mmol/L, N$_2$ < 15.3 mmol/L, CO$_2$ < 19.5 mmol/L, total biogas > 0.31 L, produced biogas > 0.10 L, and accumulated biogas > 0.41 L.
Evaluation of Dynamic Cointegration-Based Pairs Trading Strategy in the Cryptocurrency Market
Authors: Masood Tadi, Irina Kortchmeski
Date: 2021-09-22 00:00:00

This research aims to demonstrate a dynamic cointegration-based pairs trading strategy, including an optimal look-back window framework in the cryptocurrency market, and evaluate its return and risk by applying three different scenarios. We employ the Engle-Granger methodology, the Kapetanios-Snell-Shin (KSS) test, and the Johansen test as cointegration tests in different scenarios. We calibrate the mean-reversion speed of the Ornstein-Uhlenbeck process to obtain the half-life used for the asset selection phase and look-back window estimation. By considering the main limitations in the market microstructure, our strategy exceeds the naive buy-and-hold approach in the Bitmex exchange. Another significant finding is that we implement a numerous collection of cryptocurrency coins to formulate the model's spread, which improves the risk-adjusted profitability of the pairs trading strategy. Besides, the strategy's maximum drawdown level is reasonably low, which makes it useful to be deployed. The results also indicate that a class of coins has better potential arbitrage opportunities than others. This research has some noticeable advantages, making it stand out from similar studies in the cryptocurrency market. First is the accuracy of data in which minute-binned data create the signals in the formation period. Besides, to backtest the strategy during the trading period, we simulate the trading signals using best bid/ask quotes and market trades. We exclusively take the order execution into account when the asset size is already available at its quoted price (with one or more period gaps after signal generation). This action makes the backtesting much more realistic.
Solution Representations of Solving Problems for the Black-Scholes equations and Application to the Pricing Options on Bond with Credit Risk
Authors: Hyong-Chol O, Tae-Song Kim, Tae-Song Choe
Date: 2021-08-17 00:00:00

In this paper is investigated the pricing problem of options on bonds with credit risk based on analysis on two kinds of solving problems for the Black-Scholes equations. First, a solution representation of the Black-Scholes equation with the maturity payoff function which is the product of the power function, normal distribution function and characteristic function is provided. Then a solution representation of a special terminal boundary value problem of the Black-Sholes equation is provided and its monotonicity is proved. The simplest case of the structural model of the credit bond is studied and its pricing formula is provided, and based on the results, the pricing model of option on corporate bond with credit risk is transformed into a terminal boundary value problem of the Black-Scholes equation with some special maturity payoff functions and the solution formula is obtained. Using it, we provide the pricing formulae of the puttable and callable bonds with credit risk.
Fractional Growth Portfolio Investment
Authors: Anthony E. Brockwell
Date: 2021-09-07 00:00:00
Tasks: Time Series, Time Series Analysis

We review some fundamental concepts of investment from a mathematical perspective, concentrating specifically on fractional-Kelly portfolios, which allocate a fraction of wealth to a growth-optimal portfolio while the remainder collects (or pays) interest at a risk-free rate. We elucidate a coherent continuous-parameter time-series framework for analysis of these portfolios, explaining relationships between Sharpe ratios, growth rates, and leverage. We see how Kelly's criterion prescribes the same leverage as Markowitz mean-variance optimization. Furthermore, for fractional Kelly portfolios, we state a simple distributional relationship between portfolio Sharpe ratio, the fractional coefficient, and portfolio log-returns. These results provide critical insight into realistic expectations of growth for different classes of investors, from individuals to quantitative trading operations. We then illustrate application of the results by analyzing performance of various bond and equity mixes for an investor. We also demonstrate how the relationships can be exploited by a simple method-of-moments calculation to estimate portfolio Sharpe ratios and levels of risk deployment, given a fund's reported returns.
Unsupervised Movement Detection in Indoor Positioning Systems of Production Halls
Authors: Jonathan Flossdorf, Anne Meyer, Dmitri Artjuch, Jaques Schneider, Carsten Jentsch
Date: 2021-08-21 00:00:00

Consider indoor positioning systems (IPS) in production halls where objects equipped with sensors send their current position. Beside its large volume, the analyzation of the resulting raw data is challenging due to the susceptibility towards noise. Reasons are accuracy issues and undesired awakenings of sensors that occur due to the dynamics of logistic processes (e.g.~vibrations of passing forklifts). We propose a tailor-made statistical procedure for these challenges and combine visual analytics with movement detection. Contrary to common stay-point algorithms, we do not only distinguish between stops and moves, but also consider undesired awakenings. This leads to a more detailed interpretation scheme offering usages for online (e.g.~monitoring of orders) and offline applications (e.g.~detection of problematic areas). The approach does not require other information than the raw IPS output and enables an ad-hoc analysis. We underline our findings in an extensive case study with real IPS data of our industry partner.
An Evaluation of Anomaly Detection and Diagnosis in Multivariate Time Series
Authors: Astha Garg, Wenyu Zhang, Jules Samaran, Savitha Ramasamy, Chuan-Sheng Foo
Date: 2021-09-23 00:00:00
Tasks: Anomaly Detection, Time Series, Time Series Analysis, Time Series Anomaly Detection

Several techniques for multivariate time series anomaly detection have been proposed recently, but a systematic comparison on a common set of datasets and metrics is lacking. This paper presents a systematic and comprehensive evaluation of unsupervised and semi-supervised deep-learning based methods for anomaly detection and diagnosis on multivariate time series data from cyberphysical systems. Unlike previous works, we vary the model and post-processing of model errors, i.e. the scoring functions independently of each other, through a grid of 10 models and 4 scoring functions, comparing these variants to state of the art methods. In time-series anomaly detection, detecting anomalous events is more important than detecting individual anomalous time-points. Through experiments, we find that the existing evaluation metrics either do not take events into account, or cannot distinguish between a good detector and trivial detectors, such as a random or an all-positive detector. We propose a new metric to overcome these drawbacks, namely, the composite F-score ($Fc_1$), for evaluating time-series anomaly detection. Our study highlights that dynamic scoring functions work much better than static ones for multivariate time series anomaly detection, and the choice of scoring functions often matters more than the choice of the underlying model. We also find that a simple, channel-wise model - the Univariate Fully-Connected Auto-Encoder, with the dynamic Gaussian scoring function emerges as a winning candidate for both anomaly detection and diagnosis, beating state of the art algorithms.
Masks Fusion with Multi-Target Learning For Speech Enhancement
Authors: Liangchen Zhou, Wenbin Jiang, Jingyan Xu, Fei Wen, Peilin Liu
Date: 2021-09-23 00:00:00
Tasks: Speech Enhancement

Recently, deep neural network (DNN) based time-frequency (T-F) mask estimation has shown remarkable effectiveness for speech enhancement. Typically, a single T-F mask is first estimated based on DNN and then used to mask the spectrogram of noisy speech in an order to suppress the noise. This work proposes a multi-mask fusion method for speech enhancement. It simultaneously estimates two complementary masks, e.g., ideal ratio mask (IRM) and target binary mask (TBM), and then fuse them to obtain a refined mask for speech enhancement. The advantage of the new method is twofold. First, simultaneously estimating multiple complementary masks brings benefit endowed by multi-target learning. Second, multi-mask fusion can exploit the complementarity of multiple masks to boost the performance of speech enhancement. Experimental results show that the proposed method can achieve significant PESQ improvement and reduce the recognition error rate of back-end over traditional masking-based methods. Code is available at https://github.com/lc-zhou/mask-fusion.
Graph Neural Netwrok with Interaction Pattern for Group Recommendation
Authors: Bojie Wang, Yuheng Lu
Date: 2021-09-21 00:00:00
Tasks: Graph Neural Network

With the development of social platforms, people are more and more inclined to combine into groups to participate in some activities, so group recommendation has gradually become a problem worthy of research. For group recommendation, an important issue is how to obtain the characteristic representation of the group and the item through personal interaction history, and obtain the group's preference for the item. For this problem, we proposed the model GIP4GR (Graph Neural Network with Interaction Pattern For Group Recommendation). Specifically, our model use the graph neural network framework with powerful representation capabilities to represent the interaction between group-user-items in the topological structure of the graph, and at the same time, analyze the interaction pattern of the graph to adjust the feature output of the graph neural network, the feature representations of groups, and items are obtained to calculate the group's preference for items. We conducted a lot of experiments on two real-world datasets to illustrate the superior performance of our model.
Temporal Inference with Finite Factored Sets
Authors: Scott Garrabrant
Date: 2021-09-23 00:00:00
Tasks: Causal Inference

We propose a new approach to temporal inference, inspired by the Pearlian causal inference paradigm - though quite different from Pearl's approach formally. Rather than using directed acyclic graphs, we make use of factored sets, which are sets expressed as Cartesian products. We show that finite factored sets are powerful tools for inferring temporal relations. We introduce an analog of d-separation for factored sets, conditional orthogonality, and we demonstrate that this notion is equivalent to conditional independence in all probability distributions on a finite factored set.
Finding a Balanced Degree of Automation for Summary Evaluation
Authors: Shiyue Zhang, Mohit Bansal
Date: 2021-09-23 00:00:00
Proceeding: EMNLP 2021 11
Tasks: Natural Language Inference, Semantic Role Labeling, Triplet

Human evaluation for summarization tasks is reliable but brings in issues of reproducibility and high costs. Automatic metrics are cheap and reproducible but sometimes poorly correlated with human judgment. In this work, we propose flexible semiautomatic to automatic summary evaluation metrics, following the Pyramid human evaluation method. Semi-automatic Lite2Pyramid retains the reusable human-labeled Summary Content Units (SCUs) for reference(s) but replaces the manual work of judging SCUs' presence in system summaries with a natural language inference (NLI) model. Fully automatic Lite3Pyramid further substitutes SCUs with automatically extracted Semantic Triplet Units (STUs) via a semantic role labeling (SRL) model. Finally, we propose in-between metrics, Lite2.xPyramid, where we use a simple regressor to predict how well the STUs can simulate SCUs and retain SCUs that are more difficult to simulate, which provides a smooth transition and balance between automation and manual evaluation. Comparing to 15 existing metrics, we evaluate human-metric correlations on 3 existing meta-evaluation datasets and our newly-collected PyrXSum (with 100/10 XSum examples/systems). It shows that Lite2Pyramid consistently has the best summary-level correlations; Lite3Pyramid works better than or comparable to other automatic metrics; Lite2.xPyramid trades off small correlation drops for larger manual effort reduction, which can reduce costs for future data collection. Our code and data are publicly available at: https://github.com/ZhangShiyue/Lite2-3Pyramid
Safe-Planner: A Single-Outcome Replanner for Computing Strong Cyclic Policies in Fully Observable Non-Deterministic Domains
Authors: Vahid Mokhtari, Ajay Suresha Sathya, Nikolaos Tsiogkas, Wilm Decre
Date: 2021-09-23 00:00:00

Replanners are efficient methods for solving non-deterministic planning problems. Despite showing good scalability, existing replanners often fail to solve problems involving a large number of misleading plans, i.e., weak plans that do not lead to strong solutions, however, due to their minimal lengths, are likely to be found at every replanning iteration. The poor performance of replanners in such problems is due to their all-outcome determinization. That is, when compiling from non-deterministic to classical, they include all compiled classical operators in a single deterministic domain which leads replanners to continually generate misleading plans. We introduce an offline replanner, called Safe-Planner (SP), that relies on a single-outcome determinization to compile a non-deterministic domain to a set of classical domains, and ordering heuristics for ranking the obtained classical domains. The proposed single-outcome determinization and the heuristics allow for alternating between different classical domains. We show experimentally that this approach can allow SP to avoid generating misleading plans but to generate weak plans that directly lead to strong solutions. The experiments show that SP outperforms state-of-the-art non-deterministic solvers by solving a broader range of problems. We also validate the practical utility of SP in real-world non-deterministic robotic tasks.
DeepRare: Generic Unsupervised Visual Attention Models
Authors: Phutphalla Kong, Matei Mancas, Bernard Gosselin, Kimtho Po
Date: 2021-09-23 00:00:00
Tasks: CPU

Human visual system is modeled in engineering field providing feature-engineered methods which detect contrasted/surprising/unusual data into images. This data is "interesting" for humans and leads to numerous applications. Deep learning (DNNs) drastically improved the algorithms efficiency on the main benchmark datasets. However, DNN-based models are counter-intuitive: surprising or unusual data is by definition difficult to learn because of its low occurrence probability. In reality, DNN-based models mainly learn top-down features such as faces, text, people, or animals which usually attract human attention, but they have low efficiency in extracting surprising or unusual data in the images. In this paper, we propose a new visual attention model called DeepRare2021 (DR21) which uses the power of DNNs feature extraction and the genericity of feature-engineered algorithms. This algorithm is an evolution of a previous version called DeepRare2019 (DR19) based on a common framework. DR21 1) does not need any training and uses the default ImageNet training, 2) is fast even on CPU, 3) is tested on four very different eye-tracking datasets showing that the DR21 is generic and is always in the within the top models on all datasets and metrics while no other model exhibits such a regularity and genericity. Finally DR21 4) is tested with several network architectures such as VGG16 (V16), VGG19 (V19) and MobileNetV2 (MN2) and 5) it provides explanation and transparency on which parts of the image are the most surprising at different levels despite the use of a DNN-based feature extractor. DeepRare2021 code can be found at https://github.com/numediart/VisualAttention-RareFamil}.
Deep Learning for Ultrasound Beamforming
Authors: Ruud JG van Sloun, Jong Chul Ye, Yonina C Eldar
Date: 2021-09-23 00:00:00
Tasks: Deep Learning, Diagnostic, Image Reconstruction, Management

Diagnostic imaging plays a critical role in healthcare, serving as a fundamental asset for timely diagnosis, disease staging and management as well as for treatment choice, planning, guidance, and follow-up. Among the diagnostic imaging options, ultrasound imaging is uniquely positioned, being a highly cost-effective modality that offers the clinician an unmatched and invaluable level of interaction, enabled by its real-time nature. Ultrasound probes are becoming increasingly compact and portable, with the market demand for low-cost pocket-sized and (in-body) miniaturized devices expanding. At the same time, there is a strong trend towards 3D imaging and the use of high-frame-rate imaging schemes; both accompanied by dramatically increasing data rates that pose a heavy burden on the probe-system communication and subsequent image reconstruction algorithms. With the demand for high-quality image reconstruction and signal extraction from less (e.g unfocused or parallel) transmissions that facilitate fast imaging, and a push towards compact probes, modern ultrasound imaging leans heavily on innovations in powerful digital receive channel processing. Beamforming, the process of mapping received ultrasound echoes to the spatial image domain, naturally lies at the heart of the ultrasound image formation chain. In this chapter on Deep Learning for Ultrasound Beamforming, we discuss why and when deep learning methods can play a compelling role in the digital beamforming pipeline, and then show how these data-driven systems can be leveraged for improved ultrasound image reconstruction.
Robin Hood and Matthew Effects: Differential Privacy Has Disparate Impact on Synthetic Data
Authors: Georgi Ganev, Bristena Oprisanu, Emiliano De Cristofaro
Date: 2021-09-23 00:00:00

Generative models trained with Differential Privacy (DP) can be used to generate synthetic data while minimizing privacy risks. We analyze the impact of DP on these models vis-a-vis underrepresented classes/subgroups of data, specifically, studying: 1) the size of classes/subgroups in the synthetic data, and 2) the accuracy of classification tasks run on them. We also evaluate the effect of various levels of imbalance and privacy budgets. Our analysis uses three state-of-the-art DP models (PrivBayes, DP-WGAN, and PATE-GAN) and shows that DP yields opposite size distributions in the generated synthetic data. It affects the gap between the majority and minority classes/subgroups; in some cases by reducing it (a "Robin Hood" effect) and, in others, by increasing it (a "Matthew" effect). Either way, this leads to (similar) disparate impacts on the accuracy of classification tasks on the synthetic data, affecting disproportionately more the underrepresented subparts of the data. Consequently, when training models on synthetic data, one might incur the risk of treating different subpopulations unevenly, leading to unreliable or unfair conclusions.