pwc-archive/papers-with-abstracts huggingface dataset:
Economic prospects of the Russian-Chinese partnership in the logistics projects of the Eurasian Economic Union and the Silk Road Economic Belt: a scientific literature review
Authors: Elena Rudakova, Alla Pavlova, Oleg Antonov, Kira Kuntsevich, Yue Yang
Date: 2021-07-07 00:00:00

The authors of the article have reviewed the scientific literature on the development of the Russian-Chinese cooperation in the field of combining economic and logistics projects of the Eurasian Economic Union and the Silk Road Economic Belt. The opinions of not only Russian, but also Chinese experts on these projects are indicated, which provides the expansion of the vision of the concept of the New Silk Road in both countries.
Estimating the economic value of ultrafine particles information: A contingent valuation method
Authors: Eunjung Cho, Youngsang Cho
Date: 2021-07-07 00:00:00

Global concern regarding ultrafine particles (UFPs), which are particulate matter (PM) with a diameter of less than 100nm, is increasing. These particles-with more serious health effects than PM less than 2.5 micrometers (PM2.5)-are difficult to measure using the current methods because their characteristics are different from those of other air pollutants. Therefore, a new monitoring system is required to obtain accurate UFPs information, which will raise the financial burden of the government and people. In this study, we estimated the economic value of UFPs information by evaluating the willingness-to-pay (WTP) for the UFPs monitoring and reporting system. We used the contingent valuation method (CVM) and the one-and-one-half-bounded dichotomous choice (OOHBDC) spike model. We analyzed how the respondents' socio-economic variables, as well as their cognition level of PM, affected their WTP. Therefore, we collected WTP data of 1,040 Korean respondents through an online survey. The estimated mean WTP for building a UFPs monitoring and reporting system is KRW 6,958.55-7,222.55 (USD 6.22-6.45) per household per year. We found that people satisfied with the current air pollutant information, and generally possessing relatively greater knowledge of UFPs, have higher WTP for a UFPs monitoring and reporting system. The results can be used to establish new policies response to PM including UFPs.
Decreasing Incomes Increase Selfishness
Authors: Nickolas Gagnon, Riccardo D. Saulle, Henrik W. Zaunbrecher
Date: 2021-07-06 00:00:00

We use a controlled laboratory experiment to study the causal impact of income decreases within a time period on redistribution decisions at the end of that period, in an environment where we keep fixed the sum of incomes over the period. First, we investigate the effect of a negative income trend (intra-personal decrease), which means a decreasing income compared to one's recent past. Second, we investigate the effect ofa negative income trend relative to the income trend of another person (inter-personal decrease). If intra-personal or inter-personal decreases create dissatisfaction for an individual, that person may become more selfish to obtain compensation. We formal-ize both effects in a multi-period model augmenting a standard model of inequality aversion. Overall, conditional on exhibiting sufficiently-strong social preferences, we find that individuals indeed behave more selfishly when they experience decreasing incomes. While many studies examine the effect of income inequality on redistribution decisions, we delve into the history behind one's income to isolate the effect of income changes.
Unsupervised Domain Adaptation for Small Bowel Segmentation using Disentangled Representation
Authors: Seung Yeon Shin, SungWon Lee, Ronald M. Summers
Date: 2021-07-06 00:00:00
Tasks: Disentanglement, Domain Adaptation, Segmentation, Unsupervised Domain Adaptation

We present a novel unsupervised domain adaptation method for small bowel segmentation based on feature disentanglement. To make the domain adaptation more controllable, we disentangle intensity and non-intensity features within a unique two-stream auto-encoding architecture, and selectively adapt the non-intensity features that are believed to be more transferable across domains. The segmentation prediction is performed by aggregating the disentangled features. We evaluated our method using intravenous contrast-enhanced abdominal CT scans with and without oral contrast, which are used as source and target domains, respectively. The proposed method showed clear improvements in terms of three different metrics compared to other domain adaptation methods that are without the feature disentanglement. The method brings small bowel segmentation closer to clinical application.
Deep Unfolding with Normalizing Flow Priors for Inverse Problems
Authors: Xinyi Wei, Hans van Gorp, Lizeth Gonzalez Carabarin, Daniel Freedman, Yonina C. Eldar, Ruud J. G. van Sloun
Date: 2021-07-06 00:00:00
Tasks: Deblurring, Denoising, Image Denoising

Many application domains, spanning from computational photography to medical imaging, require recovery of high-fidelity images from noisy, incomplete or partial/compressed measurements. State of the art methods for solving these inverse problems combine deep learning with iterative model-based solvers, a concept known as deep algorithm unfolding. By combining a-priori knowledge of the forward measurement model with learned (proximal) mappings based on deep networks, these methods yield solutions that are both physically feasible (data-consistent) and perceptually plausible. However, current proximal mappings only implicitly learn such image priors. In this paper, we propose to make these image priors fully explicit by embedding deep generative models in the form of normalizing flows within the unfolded proximal gradient algorithm. We demonstrate that the proposed method outperforms competitive baselines on various image recovery tasks, spanning from image denoising to inpainting and deblurring.
Time Encoding of Finite-Rate-of-Innovation Signals
Authors: Abijith Jagannath Kamath, Sunil Rudresh, Chandra Sekhar Seelamantula
Date: 2021-07-07 00:00:00

Time-encoding of continuous-time signals is an alternative sampling paradigm to conventional methods such as Shannon's sampling. In time-encoding, the signal is encoded using a sequence of time instants where an event occurs, and hence fall under event-driven sampling methods. Time-encoding can be designed agnostic to the global clock of the sampling hardware, which makes sampling asynchronous. Moreover, the encoding is sparse. This makes time-encoding energy efficient. However, the signal representation is nonstandard and in general, nonuniform. In this paper, we consider time-encoding of finite-rate-of-innovation signals, and in particular, periodic signals composed of weighted and time-shifted versions of a known pulse. We consider encoding using both crossing-time-encoding machine (C-TEM) and integrate-and-fire time-encoding machine (IF-TEM). We analyze how time-encoding manifests in the Fourier domain and arrive at the familiar sum-of-sinusoids structure of the Fourier coefficients that can be obtained starting from the time-encoded measurements via a suitable linear transformation. Thereafter, standard FRI techniques become applicable. Further, we extend the theory to multichannel time-encoding such that each channel operates with a lower sampling requirement. We also study the effect of measurement noise, where the temporal measurements are perturbed by additive noise. To combat the effect of noise, we propose a robust optimization framework to simultaneously denoise the Fourier coefficients and estimate the annihilating filter accurately. We provide sufficient conditions for time-encoding and perfect reconstruction using C-TEM and IF-TEM, and furnish extensive simulations to substantiate our findings.
Quantum Graph Convolutional Neural Networks
Authors: Jin Zheng, Qing Gao, Yanxuan Lv
Date: 2021-07-07 00:00:00
Tasks: Graph Classification

At present, there are a large number of quantum neural network models to deal with Euclidean spatial data, while little research have been conducted on non-Euclidean spatial data. In this paper, we propose a novel quantum graph convolutional neural network (QGCN) model based on quantum parametric circuits and utilize the computing power of quantum systems to accomplish graph classification tasks in traditional machine learning. The proposed QGCN model has a similar architecture as the classical graph convolutional neural networks, which can illustrate the topology of the graph type data and efficiently learn the hidden layer representation of node features as well. Numerical simulation results on a graph dataset demonstrate that the proposed model can be effectively trained and has good performance in graph level classification tasks.
MagNI: A Magnetoelectrically Powered and Controlled Wireless Neurostimulating Implant
Authors: Zhanghao Yu, Joshua C. Chen, Fatima T. Alrashdan, Benjamin W. Avants, Yan He, Amanda Singer, Jacob T. Robinson, Kaiyuan Yang
Date: 2021-07-07 00:00:00

This paper presents the first wireless and programmable neural stimulator leveraging magnetoelectric (ME) effects for power and data transfer. Thanks to low tissue absorption, low misalignment sensitivity and high power transfer efficiency, the ME effect enables safe delivery of high power levels (a few milliwatts) at low resonant frequencies (~250 kHz) to mm-sized implants deep inside the body (30-mm depth). The presented MagNI (Magnetoelectric Neural Implant) consists of a 1.5-mm$^2$ 180-nm CMOS chip, an in-house built 4x2 mm ME film, an energy storage capacitor, and on-board electrodes on a flexible polyimide substrate with a total volume of 8.2 mm$^3$ . The chip with a power consumption of 23.7 $\mu$W includes robust system control and data recovery mechanisms under source amplitude variations (1-V variation tolerance). The system delivers fully-programmable bi-phasic current-controlled stimulation with patterns covering 0.05-to-1.5-mA amplitude, 64-to-512-$\mu$s pulse width, and 0-to-200Hz repetition frequency for neurostimulation.
Deep-Learning Based Auction-Driven Beamforming for Wireless Information and Power Transfer
Authors: Ali Bayat, Sonia Aissa
Date: 2021-07-07 00:00:00
Tasks: LEMMA

In this paper, we design a deep learning based resource allocation framework, in the form of an auction, for simultaneous information and power transfer from a hybrid access point (AP) to information devices and energy harvesting devices, respectively. Using Myerson's lemma and the concept of virtual welfare maximization, we develop an optimal dominant-strategy incentive-compatible mechanism for the AP to maximize its expected revenue, based on the devices' bid profiles, valuation distributions, demand profiles, and channel state information. In so doing, we formulate the revenue maximization problem, which is a mixed-integer non-linear program, and propose an efficient Branch-and-Bound (BnB) algorithm to solve the problem using semidefinite relaxation technique in each branch. Since the problem has exponential time complexity, using BnB algorithms can be impractical for real-time applications. To circumvent this, a deep neural network (DNN) is proposed, and trained to predict the optimal mechanism for beamforming the data and the energy towards the information and energy devices, respectively. We use the BnB algorithm to solve the problem offline and populate the training dataset. The proposed DNN architecture is indeed a multi-layer perceptron, which is trained well to map the heterogeneous input to the desired output with high accuracy. Furthermore, we propose a heuristic iterative solution whose accuracy performance is comparable to that of the DNN-based solution. The heuristic solution has polynomial time complexity whereas the DNN-based solution has linear time complexity.
Towards Scalable and Channel-Robust Radio Frequency Fingerprint Identification for LoRa
Authors: Guanxiong Shen, Junqing Zhang, Alan Marshall, Joseph Cavallaro
Date: 2021-07-06 00:00:00
Tasks: Data Augmentation, Metric Learning

Radio frequency fingerprint identification (RFFI) is a promising device authentication technique based on the transmitter hardware impairments. In this paper, we propose a scalable and robust RFFI framework achieved by deep learning powered radio frequency fingerprint (RFF) extractor. Specifically, we leverage the deep metric learning to train an RFF extractor, which has excellent generalization ability and can extract RFFs from previously unseen devices. Any devices can be enrolled via the pre-trained RFF extractor and the RFF database can be maintained efficiently for allowing devices to join and leave. Wireless channel impacts the RFF extraction and is tackled by exploiting channel independent feature and data augmentation. We carried out extensive experimental evaluation involving 60 commercial off-the-shelf LoRa devices and a USRP N210 software defined radio platform. The results have successfully demonstrated that our framework can achieve excellent generalization abilities for device classification and rogue device detection as well as effective channel mitigation.
Transmission Dynamics of COVID-19 Pandemic Non-pharmaceutical Interventions and Vaccination
Authors: Bin-Guo Wang, Shunxiang Huang, Yongping Xiong, Ming-Zhen Xin, Jing Li, Jiangqian Zhang, Zhihui Ma
Date: 2021-07-07 00:00:00

Non-pharmaceutical interventions(NPIs) play an important role in the early stage control of COVID-19 pandemic. Vaccination is considered to be the inevitable course to stop the spread of SARS-CoV-2. Based on the mechanism, a SVEIR COVID-19 model with vaccination and NPIs is proposed. By means of the basic reproduction number $R_{0}$, it is shown that the disease-free equilibrium is globally attractive if $\mathscr{R}_{0}<1$, and COVID-19 is uniform persistence if $\mathscr{R}_{0}>1$. Taking Indian dates for example in the numerical simulation, we find that our dynamical results fits well with the statistical dates. Consequently, we forecast the spreading trend of COVID-19 pandemic in India. Furthermore, our results imply that improving the intensity of NPIs will greatly reduce the number of confirmed cases. Especially, NPIs are indispensable even if all the people were vaccinated when the efficiency of vaccine is relatively low. By simulating the relation ships of the basic reproduction number $\mathscr{R}_{0}$, the vaccination rate and the efficiency of vaccine, we find that it is impossible to achieve the herd immunity without NPIs when the efficiency of vaccine is lower than $76.9\%$. Therefore, the herd immunity area is defined by the evolution of relationships between the vaccination rate and the efficiency of vaccine. In the study of two patchy, we give the conditions for India and China to be open to navigation. Furthermore, an appropriate dispersal of population between India and China is obtained. A discussion completes the paper.
MicroRNAs -- targeting and target prediction
Authors: Takaya Saito, Pål Sætrom
Date: 2015-07-07 00:00:00
Tasks: Prediction

MicroRNAs (miRNAs) are a class of small noncoding RNAs that can regulate many genes by base pairing to sites in mRNAs. The functionality of miRNAs overlaps that of short interfering RNAs (siRNAs), and many features of miRNA targeting have been revealed experimentally by studying miRNA-mimicking siRNAs. This review outlines the features associated with animal miRNA targeting and describes currently available prediction tools.
Sramm: short read alignment mapping metrics
Authors: Alvin Chon, Xiaoqiu Huang
Date: 2021-07-06 00:00:00

Short Read Alignment Mapping Metrics (SRAMM): is an efficient and versatile command line tool providing additional short read mapping metrics, filtering, and graphs. Short read aligners report MAPing Quality (MAPQ), but these methods generally are neither standardized nor well described in literature or software manuals. Additionally, third party mapping quality programs are typically computationally intensive or designed for specific applications. SRAMM efficiently generates multiple different concept-based mapping scores to provide for an informative post alignment examination and filtering process of aligned short reads for various downstream applications. SRAMM is compatible with Python 2.6+ and Python 3.6+ on all operating systems. It works with any short read aligner that generates SAM/BAM/CRAM file outputs and reports 'AS' tags. It is freely available under the MIT license at http://github.com/achon/sramm.
Biases and Variability from Costly Bayesian Inference
Authors: Arthur Prat-Carrabin, Florent Meyniel, Misha Tsodyks, Rava Azeredo da Silveira
Date: 2021-07-07 00:00:00
Tasks: Bayesian Inference

When humans infer underlying probabilities from stochastic observations, they exhibit biases and variability that cannot be explained on the basis of sound, Bayesian manipulations of probability. This is especially salient when beliefs are updated as a function of sequential observations. We introduce a theoretical framework in which biases and variability emerge from a trade-off between Bayesian inference and a cognitive cost of carrying out probabilistic computations. We consider two forms of the cost: a precision cost and an unpredictability cost; these penalize beliefs that are less entropic and less deterministic, respectively. We apply our framework to the case of a Bernoulli variable: the bias of a coin is inferred from a sequence of coin flips. Theoretical predictions are qualitatively different depending on the form of the cost. A precision cost induces overestimation of small probabilities on average and a limited memory of past observations, and, consequently, a fluctuating bias. An unpredictability cost induces underestimation of small probabilities and a fixed bias that remains appreciable even for nearly unbiased observations. The case of a fair (equiprobable) coin, however, is singular, with non-trivial and slow fluctuations of the inferred bias. The proposed framework of costly Bayesian inference illustrates the richness of a 'resource-rational' (or 'bounded-rational') picture of seemingly irrational human cognition.
Mid infrared spectroscopy and milk quality traits: a data analysis competition at the "International Workshop on Spectroscopy and Chemometrics 2021"
Authors: Maria Frizzarin, Antonio Bevilacqua, Bhaskar Dhariyal, Katarina Domijan, Federico Ferraccioli, Elena Hayes, Georgiana Ifrim, Agnieszka Konkolewska, Thach Le Nguyen, Uche Mbaka, Giovanna Ranzato, Ashish Singh, Marco Stefanucci, Alessandro Casa
Date: 2021-07-05 00:00:00

A chemometric data analysis challenge has been arranged during the first edition of the "International Workshop on Spectroscopy and Chemometrics", organized by the Vistamilk SFI Research Centre and held online in April 2021. The aim of the competition was to build a calibration model in order to predict milk quality traits exploiting the information contained in mid-infrared spectra only. Three different traits have been provided, presenting heterogeneous degrees of prediction complexity thus possibly requiring trait-specific modelling choices. In this paper the different approaches adopted by the participants are outlined and the insights obtained from the analyses are critically discussed.
Pseudo-Model-Free Hedging for Variable Annuities via Deep Reinforcement Learning
Authors: Wing Fung Chong, Haoen Cui, YuXuan Li
Date: 2021-07-07 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This paper proposes a two-phase deep reinforcement learning approach, for hedging variable annuity contracts with both GMMB and GMDB riders, which can address model miscalibration in Black-Scholes financial and constant force of mortality actuarial market environments. In the training phase, an infant reinforcement learning agent interacts with a pre-designed training environment, collects sequential anchor-hedging reward signals, and gradually learns how to hedge the contracts. As expected, after a sufficient number of training steps, the trained reinforcement learning agent hedges, in the training environment, equally well as the correct Delta while outperforms misspecified Deltas. In the online learning phase, the trained reinforcement learning agent interacts with the market environment in real time, collects single terminal reward signals, and self-revises its hedging strategy. The hedging performance of the further trained reinforcement learning agent is demonstrated via an illustrative example on a rolling basis to reveal the self-revision capability on the hedging strategy by online learning.
Faults in Deep Reinforcement Learning Programs: A Taxonomy and A Detection Approach
Authors: Amin Nikanjam, Mohammad Mehdi Morovati, Foutse khomh, Houssem Ben Braiek
Date: 2021-01-01 00:00:00
Tasks: Deep Reinforcement Learning, Fault Detection, OpenAI Gym, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

A growing demand is witnessed in both industry and academia for employing Deep Learning (DL) in various domains to solve real-world problems. Deep Reinforcement Learning (DRL) is the application of DL in the domain of Reinforcement Learning (RL). Like any software systems, DRL applications can fail because of faults in their programs. In this paper, we present the first attempt to categorize faults occurring in DRL programs. We manually analyzed 761 artifacts of DRL programs (from Stack Overflow posts and GitHub issues) developed using well-known DRL frameworks (OpenAI Gym, Dopamine, Keras-rl, Tensorforce) and identified faults reported by developers/users. We labeled and taxonomized the identified faults through several rounds of discussions. The resulting taxonomy is validated using an online survey with 19 developers/researchers. To allow for the automatic detection of faults in DRL programs, we have defined a meta-model of DRL programs and developed DRLinter, a model-based fault detection approach that leverages static analysis and graph transformations. The execution flow of DRLinter consists in parsing a DRL program to generate a model conforming to our meta-model and applying detection rules on the model to identify faults occurrences. The effectiveness of DRLinter is evaluated using 15 synthetic DRLprograms in which we injected faults observed in the analyzed artifacts of the taxonomy. The results show that DRLinter can successfully detect faults in all synthetic faulty programs.
Answering Chinese Elementary School Social Study Multiple Choice Questions
Authors: Daniel Lee, Chao-Chun Liang, Keh-Yih Su
Date: 2021-06-26 00:00:00
Tasks: Multiple-choice, Negation, Reading Comprehension

We present a novel approach to answer the Chinese elementary school Social Study Multiple Choice questions. Although BERT has demonstrated excellent performance on Reading Comprehension tasks, it is found not good at handling some specific types of questions, such as Negation, All-of-the-above, and None-of-the-above. We thus propose a novel framework to cascade BERT with a Pre-Processor and an Answer-Selector modules to tackle the above challenges. Experimental results show the proposed approach effectively improves the performance of BERT, and thus demonstrate the feasibility of supplementing BERT with additional modules.
A Century of Controversy over the Foundations of Mathematics
Authors: G. J. Chaitin
Date: 1999-08-27 00:00:00

This is the transcript of a lecture given at UMass-Lowell in which I compare and contrast the work of Godel and of Turing and my own work on incompleteness. I also discuss randomness in physics vs randomness in pure mathematics.