pwc-archive/papers-with-abstracts huggingface dataset:
Channel Capacity of Starch and Glucose Molecular Communications in the Small Intestine Digestive Tract
Authors: Dixon Vimalajeewa, Sasitharan Balasubramaniam
Date: 2021-07-16 00:00:00

The emerging field of Molecular Communication (MC) aims to characterize biological-based signaling environment through information that are encoded into molecules. Since the birth of this field, a number of different applications and biological systems have been characterized using MC theory. This study proposes a new application and direction for MC, focusing on the digestive system, where we characterize and model the starch and glucose propagation along the small intestine. Based on the advection-diffusion and reaction mechanisms, we define a channel capacity for the small intestine digestive tract that is dependent on the starch to glucose conversion, velocity flow within the tract, viscosity of the digest product, and length of the tract and position of the receivers for glucose absorption. The numerical results from the derived channel capacity model shows that the SI digestive capacity depends both on physiological factors of the digestive system and type of consumed food, where the digestive capacity is greater for shorter gastric emptying time, low viscosity of the digest product and efficient enzyme activity. We believe that our digital MC model of the digestive tract and lead to personalized diet for each individual, which can potentially avoid a number of different diseases (e.g., celiac disease).
A mechanistic model for airborne and direct human-to-human transmission of COVID-19: Effect of mitigation strategies and immigration of infectious persons
Authors: Saheb Pal, Indrajit Ghosh
Date: 2021-07-27 00:00:00

The COVID-19 pandemic is the most significant global crisis since World War II that affected almost all the countries of our planet. To control the COVID-19 pandemic outbreak, it is necessary to understand how the virus is transmitted to a susceptible individual and eventually spread in the community. The primary transmission pathway of COVID-19 is human-to-human transmission through infectious droplets. However, a recent study by Greenhalgh et al. (Lancet: 397:1603-1605, 2021) demonstrates 10 scientific reasons behind the airborne transmission of SARS-COV-2. In the present study, we introduce a novel mathematical model of COVID-19 that considers the transmission of free viruses in the air besides the transmission of direct contact with an infected person. The basic reproduction number of the epidemic model is calculated using the next-generation operator method and observed that it depends on both the transmission rate of direct contact and free virus contact. The local and global stability of disease-free equilibrium (DFE) is well established. Analytically it is found that there is a forward bifurcation between the DFE and an endemic equilibrium using central manifold theory. Next, we used the nonlinear least-squares technique to identify the best-fitted parameter values in the model from the observed COVID-19 mortality data of two major districts of India. Using estimated parameters for Bangalore urban and Chennai, different control scenarios for mitigation of the disease are investigated. Results indicate that when a vaccine crisis is there, the public health authorities should prefer to vaccinate the susceptible people compared to the recovered persons who are now healthy. Along with face mask use, treatment of hospitalized patients and vaccination of susceptibles, immigration should be allowed in a supervised manner so that economy of the overall society remains healthy.
Mortality in Germany during the Covid-19 pandemic
Authors: Alois Pichler, Dana Uhlig
Date: 2021-06-25 00:00:00

The Covid-19 pandemic still causes severe impacts on society and the economy. This paper studies excess mortality during the pandemic years 2020 and 2021 in Germany empirically with a special focus on the life insurer's perspective. Our conclusions are based on official counts of German governmental offices on the living and deaths of the entire population. Conclusions, relevant for actuaries and specific insurance business lines, including portfolios of pension, life, and health insurance contracts, are provided.
Quasi-sure essential supremum and applications to finance
Authors: Laurence Carassus
Date: 2021-07-27 00:00:00

When uncertainty is modelled by a set of non-dominated and non-compact probability measures, a notion of essential supremum for a family of real-valued functions is developed in terms of upper semi-analytic functions. We show how the properties postulated on the initial functions carry over to their quasi-sure essential supremum. We propose various applications to financial problems with frictions. We analyse super-replication and prove a bi-dual characterization of the super-hedging cost. We also study a weak no-arbitrage condition called Absence of Instantaneous Profit (AIP) under which prices are finite. This requires new results on the aggregation of quasi-sure statements.
Bitcoin option pricing: A market attention approach
Authors: Alvaro Guinea Julia, Alet Roux
Date: 2021-07-26 00:00:00

A model is proposed for Bitcoin prices that takes into account market attention. Market attention, modeled by a mean-reverting Cox-Ingersoll-Ross processes, affects the volatility of Bitcoin returns, with some delay. The model is affine and tractable, with closed formulae for the conditional characteristic functions with respect to both the conventional and a delayed filtration. This leads to semi-closed formulae for European call and put prices. A maximum likelihood estimation procedure is provided, as well as a method for changing to a risk-neutral measure. The model compares very well against classical and attention-based models when tested on real data.
Proof of non-convergence of the short-maturity expansion for the SABR model
Authors: Alan L. Lewis, Dan Pirjol
Date: 2021-07-26 00:00:00

We study the convergence properties of the short maturity expansion of option prices in the uncorrelated log-normal ($\beta=1$) SABR model. In this model the option time-value can be represented as an integral of the form $V(T) = \int_{0}^\infty e^{-\frac{u^2}{2T}} g(u) du$ with $g(u)$ a "payoff function" which is given by an integral over the McKean kernel $G(s,t)$. We study the analyticity properties of the function $g(u)$ in the complex $u$-plane and show that it is holomorphic in the strip $|\Im(u) |< \pi$. Using this result we show that the $T$-series expansion of $V(T)$ and implied volatility are asymptotic (non-convergent for any $T>0$). In a certain limit which can be defined either as the large volatility limit $\sigma_0\to \infty$ at fixed $\omega=1$, or the small vol-of-vol limit $\omega\to 0$ limit at fixed $\omega\sigma_0$, the short maturity $T$-expansion for the implied volatility has a finite convergence radius $T_c = \frac{1.32}{\omega\sigma_0}$.
Robustness and sensitivity analyses for rough Volterra stochastic volatility models
Authors: Jan Matas, Jan Pospíšil
Date: 2021-07-26 00:00:00
Tasks: Sensitivity

In this paper, we analyze the robustness and sensitivity of various continuous-time rough Volterra stochastic volatility models in relation to the process of market calibration. Model robustness is examined from two perspectives: the sensitivity of option price estimates and the sensitivity of parameter estimates to changes in the option data structure. The following sensitivity analysis consists of statistical tests to determine whether a given studied model is sensitive to changes in the option data structure based on the distribution of parameter estimates. Empirical study is performed on a data set consisting of Apple Inc. equity options traded on four different days in April and May 2015. In particular, the results for RFSV, rBergomi and $\alpha$RFSV models are provided and compared to the results for Heston, Bates, and AFSVJD models.
LOB modeling using Hawkes processes with a state-dependent factor
Authors: Emmanouil Sfendourakis, Ioane Muni Toke
Date: 2021-07-27 00:00:00

A point process model for order flows in limit order books is proposed, in which the conditional intensity is the product of a Hawkes component and a state-dependent factor. In the LOB context, state observations may include the observed imbalance or the observed spread. Full technical details for the computationally-efficient estimation of such a process are provided, using either direct likelihood maximization or EM-type estimation. Applications include models for bid and ask market orders, or for upwards and downwards price movements. Empirical results on multiple stocks traded in Euronext Paris underline the benefits of state-dependent formulations for LOB modeling, e.g. in terms of goodness-of-fit to financial data.
Local-Global Fusion Network for Video Super-Resolution
Authors: Dewei Su, Hua Wang, Longcun Jin, Xianfang Sun, Xinyi Peng
Date: 2020-09-22 00:00:00
Proceeding: IEEE Access 2020 9
Tasks: Optical Flow Estimation, Super-Resolution, Video Super-Resolution

The goal of video super-resolution technique is to address the problem of effectively restoring high-resolution (HR) videos from low-resolution (LR) ones. Previous methods commonly used optical flow to perform frame alignment and designed a framework from the perspective of space and time. However, inaccurate optical flow estimation may occur easily which leads to inferior restoration effects. In addition, how to effectively fuse the features of various video frames remains a challenging problem. In this paper, we propose a Local-Global Fusion Network (LGFN) to solve the above issues from a novel viewpoint. As an alternative to optical flow, deformable convolutions (DCs) with decreased multi-dilation convolution units (DMDCUs) are applied for efficient implicit alignment. Moreover, a structure with two branches, consisting of a Local Fusion Module (LFM) and a Global Fusion Module (GFM), is proposed to combine information from two different aspects. Specifically, LFM focuses on the relationship between adjacent frames and maintains the temporal consistency while GFM attempts to take advantage of all related features globally with a video shuffle strategy. Benefiting from our advanced network, experimental results on several datasets demonstrate that our LGFN can not only achieve comparative performance with state-of-the-art methods but also possess reliable ability on restoring a variety of video frames. The results on benchmark datasets of our LGFN are presented on https://github.com/BIOINSu/LGFN and the source code will be released as soon as the paper is accepted.
Real-World Super-Resolution via Kernel Estimation and Noise Injection
Authors: Xiaozhong Ji, Yun Cao, Ying Tai, Chengjie Wang, Jilin Li, Feiyue Huang
Date: 2020-06-19 00:00:00
Proceeding: CVPRW 2020 6
Tasks: Image Super-Resolution, Super-Resolution, Video Super-Resolution

Recent state-of-the-art super-resolution methods have achieved impressive performance on ideal datasets regardless of blur and noise. However, these methods always fail in real-world image super-resolution, since most of them adopt simple bicubic downsampling from high-quality images to construct Low-Resolution (LR) and High-Resolution (HR) pairs for training which may lose track of frequency-related details. To address this issue, we focus on designing a novel degradation framework for real- world images by estimating various blur kernels as well as real noise distributions. Based on our novel degradation framework, we can acquire LR images sharing a common domain with real-world images. Then, we propose a real- world super-resolution model aiming at better perception. Extensive experiments on synthetic noise data and real- world images demonstrate that our method outperforms the state-of-the-art methods, resulting in lower noise and better visual quality. In addition, our method is the winner of NTIRE 2020 Challenge on both tracks of Real-World Super-Resolution, which significantly outperforms other competitors by large margins.
Location-free Spectrum Cartography
Authors: Yves Teganya, Daniel Romero, Luis Miguel Lopez Ramos, Baltasar Beferull-Lozano
Date: 2018-12-30 00:00:00
Tasks: Spectrum Cartography

Spectrum cartography constructs maps of metrics such as channel gain or received signal power across a geographic area of interest using spatially distributed sensor measurements. Applications of these maps include network planning, interference coordination, power control, localization, and cognitive radios to name a few. Since existing spectrum cartography techniques require accurate estimates of the sensor locations, their performance is drastically impaired by multipath affecting the positioning pilot signals, as occurs in indoor or dense urban scenarios. To overcome such a limitation, this paper introduces a novel paradigm for spectrum cartography, where estimation of spectral maps relies on features of these positioning signals rather than on location estimates. Specific learning algorithms are built upon this approach and offer a markedly improved estimation performance than existing approaches relying on localization, as demonstrated by simulation studies in indoor scenarios.
SimROD: A Simple Adaptation Method for Robust Object Detection
Authors: Rindra Ramamonjison, Amin Banitalebi-Dehkordi, Xinyu Kang, Xiaolong Bai, Yong Zhang
Date: 2021-07-28 00:00:00
Proceeding: ICCV 2021 10
Tasks: Domain Adaptation, Object, object-detection, Object Detection, Pseudo Label, Robust Object Detection

This paper presents a Simple and effective unsupervised adaptation method for Robust Object Detection (SimROD). To overcome the challenging issues of domain shift and pseudo-label noise, our method integrates a novel domain-centric augmentation method, a gradual self-labeling adaptation procedure, and a teacher-guided fine-tuning mechanism. Using our method, target domain samples can be leveraged to adapt object detection models without changing the model architecture or generating synthetic data. When applied to image corruptions and high-level cross-domain adaptation benchmarks, our method outperforms prior baselines on multiple domain adaptation benchmarks. SimROD achieves new state-of-the-art on standard real-to-synthetic and cross-camera setup benchmarks. On the image corruption benchmark, models adapted with our method achieved a relative robustness improvement of 15-25% AP50 on Pascal-C and 5-6% AP on COCO-C and Cityscapes-C. On the cross-domain benchmark, our method outperformed the best baseline performance by up to 8% AP50 on Comic dataset and up to 4% on Watercolor dataset.
Fast Wireless Sensor Anomaly Detection based on Data Stream in Edge Computing Enabled Smart Greenhouse
Authors: Yihong Yang, Sheng Ding, YuWen Liu, Shunmei Meng, Xiaoxiao Chi, Rui Ma, Chao Yan
Date: 2021-07-28 00:00:00
Tasks: Anomaly Detection, Decision Making, Edge-computing

Edge computing enabled smart greenhouse is a representative application of Internet of Things technology, which can monitor the environmental information in real time and employ the information to contribute to intelligent decision-making. In the process, anomaly detection for wireless sensor data plays an important role. However, traditional anomaly detection algorithms originally designed for anomaly detection in static data have not properly considered the inherent characteristics of data stream produced by wireless sensor such as infiniteness, correlations and concept drift, which may pose a considerable challenge on anomaly detection based on data stream, and lead to low detection accuracy and efficiency. First, data stream usually generates quickly which means that it is infinite and enormous, so any traditional off-line anomaly detection algorithm that attempts to store the whole dataset or to scan the dataset multiple times for anomaly detection will run out of memory space. Second, there exist correlations among different data streams, which traditional algorithms hardly consider. Third, the underlying data generation process or data distribution may change over time. Thus, traditional anomaly detection algorithms with no model update will lose their effects. Considering these issues, a novel method (called DLSHiForest) on basis of Locality-Sensitive Hashing and time window technique in this paper is proposed to solve these problems while achieving accurate and efficient detection. Comprehensive experiments are executed using real-world agricultural greenhouse dataset to demonstrate the feasibility of our approach. Experimental results show that our proposal is practicable in addressing challenges of traditional anomaly detection while ensuring accuracy and efficiency.
The Who in XAI: How AI Background Shapes Perceptions of AI Explanations
Authors: Upol Ehsan, Samir Passi, Q. Vera Liao, Larry Chan, I-Hsiang Lee, Michael Muller, Mark O. Riedl
Date: 2021-07-28 00:00:00
Tasks: Explainable Artificial Intelligence (XAI)

Explainability of AI systems is critical for users to take informed actions. Understanding "who" opens the black-box of AI is just as important as opening it. We conduct a mixed-methods study of how two different groups--people with and without AI background--perceive different types of AI explanations. Quantitatively, we share user perceptions along five dimensions. Qualitatively, we describe how AI background can influence interpretations, elucidating the differences through lenses of appropriation and cognitive heuristics. We find that (1) both groups showed unwarranted faith in numbers for different reasons and (2) each group found value in different explanations beyond their intended design. Carrying critical implications for the field of XAI, our findings showcase how AI generated explanations can have negative consequences despite best intentions and how that could lead to harmful manipulation of trust. We propose design interventions to mitigate them.
Uncertainty-Aware Credit Card Fraud Detection Using Deep Learning
Authors: Maryam Habibpour, Hassan Gharoun, Mohammadreza Mehdipour, AmirReza Tajally, Hamzeh Asgharnezhad, Afshar Shamsi, Abbas Khosravi, Miadreza Shafie-khah, Saeid Nahavandi, Joao P. S. Catalao
Date: 2021-07-28 00:00:00
Tasks: Deep Learning, Fraud Detection, Uncertainty Quantification

Countless research works of deep neural networks (DNNs) in the task of credit card fraud detection have focused on improving the accuracy of point predictions and mitigating unwanted biases by building different network architectures or learning models. Quantifying uncertainty accompanied by point estimation is essential because it mitigates model unfairness and permits practitioners to develop trustworthy systems which abstain from suboptimal decisions due to low confidence. Explicitly, assessing uncertainties associated with DNNs predictions is critical in real-world card fraud detection settings for characteristic reasons, including (a) fraudsters constantly change their strategies, and accordingly, DNNs encounter observations that are not generated by the same process as the training distribution, (b) owing to the time-consuming process, very few transactions are timely checked by professional experts to update DNNs. Therefore, this study proposes three uncertainty quantification (UQ) techniques named Monte Carlo dropout, ensemble, and ensemble Monte Carlo dropout for card fraud detection applied on transaction data. Moreover, to evaluate the predictive uncertainty estimates, UQ confusion matrix and several performance metrics are utilized. Through experimental results, we show that the ensemble is more effective in capturing uncertainty corresponding to generated predictions. Additionally, we demonstrate that the proposed UQ methods provide extra insight to the point predictions, leading to elevate the fraud prevention process.
Toward Integrated Human-machine Intelligence for Civil Engineering: An Interdisciplinary Perspective
Authors: Cheng Zhang, Jinwoo Kim, JungHo Jeon, Jinding Xing, Changbum Ahn, Pingbo Tang, Hubo Cai
Date: 2021-07-28 00:00:00
Tasks: Decision Making

The purpose of this paper is to examine the opportunities and barriers of Integrated Human-Machine Intelligence (IHMI) in civil engineering. Integrating artificial intelligence's high efficiency and repeatability with humans' adaptability in various contexts can advance timely and reliable decision-making during civil engineering projects and emergencies. Successful cases in other domains, such as biomedical science, healthcare, and transportation, showed the potential of IHMI in data-driven, knowledge-based decision-making in numerous civil engineering applications. However, whether the industry and academia are ready to embrace the era of IHMI and maximize its benefit to the industry is still questionable due to several knowledge gaps. This paper thus calls for future studies in exploring the value, method, and challenges of applying IHMI in civil engineering. Our systematic review of the literature and motivating cases has identified four knowledge gaps in achieving effective IHMI in civil engineering. First, it is unknown what types of tasks in the civil engineering domain can be assisted by AI and to what extent. Second, the interface between human and AI in civil engineering-related tasks need more precise and formal definition. Third, the barriers that impede collecting detailed behavioral data from humans and contextual environments deserve systematic classification and prototyping. Lastly, it is unknown what expected and unexpected impacts will IHMI have on the AEC industry and entrepreneurship. Analyzing these knowledge gaps led to a list of identified research questions. This paper will lay the foundation for identifying relevant studies to form a research roadmap to address the four knowledge gaps identified.
Adaptive Precision Training (AdaPT): A dynamic fixed point quantized training approach for DNNs
Authors: Lorenz Kummer, Kevin Sidak, Tabea Reichmann, Wilfried Gansterer
Date: 2021-07-28 00:00:00
Tasks: Quantization

Quantization is a technique for reducing deep neural networks (DNNs) training and inference times, which is crucial for training in resource constrained environments or applications where inference is time critical. State-of-the-art (SOTA) quantization approaches focus on post-training quantization, i.e., quantization of pre-trained DNNs for speeding up inference. While work on quantized training exists, most approaches require refinement in full precision (usually single precision) in the final training phase or enforce a global word length across the entire DNN. This leads to suboptimal assignments of bit-widths to layers and, consequently, suboptimal resource usage. In an attempt to overcome such limitations, we introduce AdaPT, a new fixed-point quantized sparsifying training strategy. AdaPT decides about precision switches between training epochs based on information theoretic conditions. The goal is to determine on a per-layer basis the lowest precision that causes no quantization-induced information loss while keeping the precision high enough such that future learning steps do not suffer from vanishing gradients. The benefits of the resulting fully quantized DNN are evaluated based on an analytical performance model which we develop. We illustrate that an average speedup of 1.27 compared to standard training in float32 with an average accuracy increase of 0.98% can be achieved for AlexNet/ResNet on CIFAR10/100 and we further demonstrate these AdaPT trained models achieve an average inference speedup of 2.33 with a model size reduction of 0.52.