pwc-archive/papers-with-abstracts huggingface dataset:
Influential Papers in Artificial Intelligence and Paediatrics: Assessing RPYS by Experts Review
Authors: Peter Kokol, Jernej Završnik, Helena Blažun Vošner
Date: 2021-10-27 00:00:00

The use of artificial intelligence in paediatrics has vastly increased in the last few years. Interestingly, no historical bibliometric study analysing the knowledge development in this specific paediatric field has been performed yet, thus our study aimed to close this gap. References Publication Years Spectrography (RPYS), more precisely CitedReferenceExplorer (CRE) software tool was employed to achieve this aim. We identified 28 influential papers and domain experts validation showed that both, the RPYS method and CRE tool performed adequately in the identification process.
Rigid Motion Invariant Statistical Shape Modeling based on Discrete Fundamental Forms
Authors: Felix Ambellan, Stefan Zachow, Christoph von Tycowicz
Date: 2021-11-05 00:00:00
Tasks: Hippocampus, Specificity

We present a novel approach for nonlinear statistical shape modeling that is invariant under Euclidean motion and thus alignment-free. By analyzing metric distortion and curvature of shapes as elements of Lie groups in a consistent Riemannian setting, we construct a framework that reliably handles large deformations. Due to the explicit character of Lie group operations, our non-Euclidean method is very efficient allowing for fast and numerically robust processing. This facilitates Riemannian analysis of large shape populations accessible through longitudinal and multi-site imaging studies providing increased statistical power. Additionally, as planar configurations form a submanifold in shape space, our representation allows for effective estimation of quasi-isometric surfaces flattenings. We evaluate the performance of our model w.r.t. shape-based classification of hippocampus and femur malformations due to Alzheimer's disease and osteoarthritis, respectively. In particular, we outperform state-of-the-art classifiers based on geometric deep learning as well as statistical shape modeling especially in presence of sparse training data. We evaluate the performance of our model w.r.t. shape-based classification of pathological malformations of the human knee and show that it outperforms the standard Euclidean as well as a recent nonlinear approach especially in presence of sparse training data. To provide insight into the model's ability of capturing biological shape variability, we carry out an analysis of specificity and generalization ability.
Impact of Strategic Electric Vehicles Driving Behavior on the Grid
Authors: Benoît Sohet, Yezekael Hayel, Olivier Beaude, Alban Jeandin
Date: 2021-11-12 00:00:00

In the context of transport electrification, a model coupling Electric Vehicles (EV) driving and charging decisions is considered. While a Traffic Assignment Problem (TAP) is considered for the driving part, the charging operation is done with an exact load flow on a simple distribution network. This setting allows assessing precisely the coupled impact of driving and charging decisions. In particular, to schedule the charging need coming from the driving part, different charging strategies are defined and compared according to a cost metric based on the load flow computation. In view of this metric, the proposed non-load flow based strategies can perform similarly to a load flow based one, and with significantly reduced computation time and data need. Numerical simulations show how a transportation toll can influence the charging results.
Towards 6G Internet of Things: Recent Advances, Use Cases, and Open Challenges
Authors: Zakria Qadir, Hafiz Suliman Munawar, Nasir Saeed, Khoa Le
Date: 2021-11-12 00:00:00

Smart services based on the Internet of Everything (IoE) are gaining considerable popularity due to the ever-increasing demands of wireless networks. This demands the appraisal of the wireless networks with enhanced properties as next-generation communication systems. Although 5G networks show great potential to support numerous IoE based services, it is not adequate to meet the complete requirements of the new smart applications. Therefore, there is an increased demand for envisioning the 6G wireless communication systems to overcome the major limitations in the existing 5G networks. Moreover, incorporating artificial intelligence in 6G will provide solutions for very complex problems relevant to network optimization. Furthermore, to add further value to the future 6G networks, researchers are investigating new technologies, such as THz and quantum communications. The requirements of future 6G wireless communications demand to support massive data-driven applications and the increasing number of users. This paper presents recent advances in the 6G wireless networks, including the evolution from 1G to 5G communications, the research trends for 6G, enabling technologies, and state-of-the-art 6G projects.
AC-VC: Non-parallel Low Latency Phonetic Posteriorgrams Based Voice Conversion
Authors: Damien Ronssin, Milos Cernak
Date: 2021-11-12 00:00:00
Tasks: Voice Conversion

This paper presents AC-VC (Almost Causal Voice Conversion), a phonetic posteriorgrams based voice conversion system that can perform any-to-many voice conversion while having only 57.5 ms future look-ahead. The complete system is composed of three neural networks trained separately with non-parallel data. While most of the current voice conversion systems focus primarily on quality irrespective of algorithmic latency, this work elaborates on designing a method using a minimal amount of future context thus allowing a future real-time implementation. According to a subjective listening test organized in this work, the proposed AC-VC system achieves parity with the non-causal ASR-TTS baseline of the Voice Conversion Challenge 2020 in naturalness with a MOS of 3.5. In contrast, the results indicate that missing future context impacts speaker similarity. Obtained similarity percentage of 65% is lower than the similarity of current best voice conversion systems.
Disentangling Physical Parameters for Anomalous Sound Detection Under Domain Shifts
Authors: Kota Dohi, Takashi Endo, Yohei Kawaguchi
Date: 2021-11-12 00:00:00
Tasks: Anomaly Detection, Disentanglement, Unsupervised Anomaly Detection

To develop a sound-monitoring system for machines, a method for detecting anomalous sound under domain shifts is proposed. A domain shift occurs when a machine's physical parameters change. Because a domain shift changes the distribution of normal sound data, conventional unsupervised anomaly detection methods can output false positives. To solve this problem, the proposed method constrains some latent variables of a normalizing flows (NF) model to represent physical parameters, which enables disentanglement of the factors of domain shifts and learning of a latent space that is invariant with respect to these domain shifts. Anomaly scores calculated from this domain-shift-invariant latent space are unaffected by such shifts, which reduces false positives and improves the detection performance. Experiments were conducted with sound data from a slide rail under different operation velocities. The results show that the proposed method disentangled the velocity to obtain a latent space that was invariant with respect to domain shifts, which improved the AUC by 13.2% for Glow with a single block and 2.6% for Glow with multiple blocks.
A Bayesian Nash equilibrium-based moving target defense against stealthy sensor attacks
Authors: David Umsonst, Serkan Sarıtaş, György Dán, Henrik Sandberg
Date: 2021-11-12 00:00:00

We present a moving target defense strategy to reduce the impact of stealthy sensor attacks on feedback systems. The defender periodically and randomly switches between thresholds from a discrete set to increase the uncertainty for the attacker and make stealthy attacks detectable. However, the defender does not know the exact goal of the attacker but only the prior of the possible attacker goals. Here, we model one period with a constant threshold as a Bayesian game and use the Bayesian Nash equilibrium concept to find the distribution for the choice of the threshold in that period, which takes the defender's uncertainty about the attacker into account. To obtain the equilibrium distribution, the defender minimizes its cost consisting of the cost for false alarms and the cost induced by the attack. We present a necessary and sufficient condition for the existence of a moving target defense and formulate a linear program to determine the moving target defense. Furthermore, we present a closed-form solution for the special case when the defender knows the attacker's goals. The results are numerically evaluated on a four-tank process.
Ramp metering: modeling, simulations and control issues
Authors: Cédric Join, Hassane Abouaïssa, Michel Fliess
Date: 2021-11-12 00:00:00

The aim of ramp metering is to improve the highway traffic conditions by an appropriate regulation of the inflow from the on-ramps to the highway mainstream. Our presentation rests on several improvements: 1) Our simulation techniques do not need contrarily to other approaches any heuristic fundamental law. 2) There is no need of crucial time-varying quantities, like the critical density, which is most difficult to estimate correctly online. 3) Our feedback loop, which is stemming from model-free control, is easy to implement and shows an excellent robustness with respect to model mismatch. Several computer experiments are displayed and discussed.
Data-Driven Pole Placement in LMI Regions with Robustness Constraints
Authors: Sayak Mukherjee, Ramij R. Hossain
Date: 2021-11-12 00:00:00
Tasks: LEMMA

This paper proposes a robust learning methodology to place the closed-loop poles in desired convex regions in the complex plane. We considered the system state and input matrices to be unknown and can only use the measurements of the system trajectories. The closed-loop pole placement problem in the linear matrix inequality (LMI) regions is considered a classic robust control problem; however, that requires knowledge about the state and input matrices of the linear system. We bring in ideas from the behavioral system theory and persistency of excitation condition-based fundamental lemma to develop a data-driven counterpart that satisfies multiple closed-loop robustness specifications, such as $\mathcal{D}$-stability and mixed $H_2/H_{\infty}$ performance specifications. Our formulations lead to data-driven semi-definite programs (SDPs) that are coupled with sufficient theoretical guarantees. We validate the theoretical results with numerical simulations on a third-order dynamic system.
Matryoshka and Disjoint Cluster Synchronization of Networks
Authors: Amirhossein Nazerian, Shirin Panahi, Ian Leifer, David Phillips, Hernan Makse, Francesco Sorrentino
Date: 2021-11-11 00:00:00

The main motivation for this paper is to present a definition of network synchronizability for the case of cluster synchronization (CS), in an analogous fashion to Barahona and Pecora for the case of complete synchronization. We find this problem to be substantially more complex than the original one. We distinguish between the two cases of networks with intertwined clusters and no intertwined clusters and between {the two cases that the master stability function is negative either in a bounded range or in an unbounded range of its argument. We first obtain a definition of synchronizability that applies to each individual cluster within a network and then attempt to generalize this definition to the entire network. For CS, the synchronous solution of each cluster may be stable independent of the stability of the other clusters, which results in possibly different ranges in which each cluster synchronizes (isolated CS.) For each pair of clusters, we distinguish between three different cases: Matryoshka Cluster Synchronization (when the range of the stability of the synchronous solution for one cluster is included in that of the other cluster), Partially Disjoint Cluster Synchronization (when the ranges of stability of the synchronous solutions partially overlap), and Complete Disjoint Cluster Synchronization (when the ranges of stability of the synchronous solutions do not overlap.)
Greater than the parts: A review of the information decomposition approach to causal emergence
Authors: Pedro A. M. Mediano, Fernando E. Rosas, Andrea I. Luppi, Henrik J. Jensen, Anil K. Seth, Adam B. Barrett, Robin L. Carhart-Harris, Daniel Bor
Date: 2021-11-12 00:00:00

Emergence is a profound subject that straddles many scientific disciplines, including the formation of galaxies and how consciousness arises from the collective activity of neurons. Despite the broad interest that exists on this concept, the study of emergence has suffered from a lack of formalisms that could be used to guide discussions and advance theories. Here we summarise, elaborate on, and extend a recent formal theory of causal emergence based on information decomposition, which is quantifiable and amenable to empirical testing. This theory relates emergence with information about a system's temporal evolution that cannot be obtained from the parts of the system separately. This article provides an accessible but rigorous introduction to the framework, discussing the merits of the approach in various scenarios of interest. We also discuss several interpretation issues and potential misunderstandings, while highlighting the distinctive benefits of this formalism.
Understanding hesitancy with revealed preferences across COVID-19 vaccine types
Authors: Kristóf Kutasi, Júlia Koltai, Ágnes Szabó-Morvai, Gergely Röst, Márton Karsai, Péter Biró, Balázs Lengyel
Date: 2021-11-11 00:00:00

Many countries have secured larger quantities of COVID-19 vaccines than their populace is willing to take. This abundance and variety of vaccines created a historical moment to understand vaccine hesitancy better. Never before were more types of vaccines available for an illness and the intensity of vaccine-related public discourse is unprecedented. Yet, the heterogeneity of hesitancy by vaccine types has been neglected so far, even though factual or believed vaccine characteristics and patient attributes are known to influence acceptance. We address this problem by analysing acceptance and assessment of five vaccine types using information collected with a nationally representative survey at the end of the third wave of the COVID-19 pandemic in Hungary, where a unique portfolio of vaccines were available to the public in large quantities. Our special case enables us to quantify revealed preferences across vaccine types since one could evaluate a vaccine unacceptable and even could reject an assigned vaccine to wait for another type. We find that the source of information that respondents trust characterizes their attitudes towards vaccine types differently and leads to divergent vaccine hesitancy. Believers of conspiracy theories were significantly more likely to evaluate the mRNA vaccines (Pfizer and Moderna) unacceptable while those who follow the advice of politicians evaluate vector-based (AstraZeneca and Sputnik) or whole-virus vaccines (Sinopharm) acceptable with higher likelihood. We illustrate that the rejection of non-desired and re-selection of preferred vaccines fragments the population by the mRNA versus other type of vaccines while it generally improves the assessment of the received vaccine. These results highlight that greater variance of available vaccine types and individual free choice are desirable conditions that can widen the acceptance of vaccines in societies.
Bounds for Treatment Effects in the Presence of Anticipatory Behavior
Authors: Aibo Gong
Date: 2021-11-12 00:00:00

In program evaluations, units can often anticipate the implementation of a new policy before it occurs. Such anticipatory behavior can lead to units' outcomes becoming dependent on their future treatment assignments. In this paper, I employ a potential-outcomes framework to analyze the treatment effect with anticipation. I start with a classical difference-in-differences model with two time periods and provide identified sets with easy-to-implement estimation and inference strategies for causal parameters. Empirical applications and generalizations are provided. I illustrate my results by analyzing the effect of an early retirement incentive program for teachers, which the target units were likely to anticipate, on student achievement. The empirical results show the result can be overestimated by up to 30\% in the worst case and demonstrate the potential pitfalls of failing to consider anticipation in policy evaluation.
Can Air Pollution Save Lives? Air Quality and Risky Behaviors on Roads
Authors: Wen Hsu, Bing-Fang Hwang, Chau-Ren Jung, Yau-Huo Jimmy Shr
Date: 2021-11-12 00:00:00

Air pollution has been linked to elevated levels of risk aversion. This paper provides the first evidence showing that such effect reduces life-threatening risky behaviors. We study the impact of air pollution on traffic accidents caused by risky driving behaviors, using the universe of accident records and high-resolution air quality data of Taiwan from 2009 to 2015. We find that air pollution significantly decreases accidents caused by driver violations, and that this effect is nonlinear. In addition, our results suggest that air pollution primarily reduces road users' risky behaviors through visual channels rather than through the respiratory system.
Non-Standard Choice in Matching Markets
Authors: Gian Caspari, Manshu Khanna
Date: 2021-11-12 00:00:00

We explore the possibility of designing matching mechanisms that can accommodate non-standard choice behavior. We pin down the necessary and sufficient conditions on participants' choice behavior for the existence of stable and incentive compatible mechanisms. Our results imply that well-functioning matching markets can be designed to adequately accommodate a plethora of choice behaviors, including the standard behavior consistent with preference maximization. To illustrate the significance of our results in practice, we show that a simple modification in a commonly used matching mechanism enables it to accommodate non-standard choice behavior.
HLT-NUS SUBMISSION FOR 2020 NIST Conversational Telephone Speech SRE
Authors: Rohan Kumar Das, Ruijie Tao, Haizhou Li
Date: 2021-11-12 00:00:00
Tasks: Domain Adaptation, Speaker Recognition

This work provides a brief description of Human Language Technology (HLT) Laboratory, National University of Singapore (NUS) system submission for 2020 NIST conversational telephone speech (CTS) speaker recognition evaluation (SRE). The challenge focuses on evaluation under CTS data containing multilingual speech. The systems developed at HLT-NUS consider time-delay neural network (TDNN) x-vector and ECAPA-TDNN systems. We also perform domain adaption of probabilistic linear discriminant analysis (PLDA) model and adaptive s-norm on our systems. The score level fusion of TDNN x-vector and ECAPA-TDNN systems is carried out, which improves the final system performance of our submission to 2020 NIST CTS SRE.
Diffuse arrays that autocorrelate and project as delta-like points
Authors: I. D. Svalbe, D. M. Paganin, T. C. Petersen
Date: 2021-11-12 00:00:00

Diffuse two-dimensional integer-valued arrays are demonstrated that have delta-like aperiodic autocorrelation and, simultaneously, the array sums form delta-like projections along several directions. The delta-projected views show a single sharp spike at the central ray. When such arrays are embedded in larger blocks of two-dimensional data, their location can be fixed precisely via the fast and simple intersection of the back-projected central rays along two or more directions. This mechanism complements localization of the same array from its delta-like autocorrelation, which, although more robust, is slower and more complex to compute.
Photonics-based de-chirping and leakage cancellation for frequency-modulated continuous-wave radar system
Authors: Taixia Shi, Dingding Liang, Moxuan Han, Yang Chen
Date: 2021-11-12 00:00:00

A photonics-based leakage cancellation and echo signal de-chirping approach for frequency-modulated continuous-wave radar systems is proposed based on a dual-drive Mach-Zehnder modulator (DD-MZM), with its performance evaluated by the radar measurement and imaging. The de-chirp reference signal and the leakage cancellation reference signal are combined and applied to the upper arm of the DD-MZM, while the received signal including the leakage signal and echo signals is applied to the lower arm of the DD-MZM. When the amplitudes and delays of the leakage cancellation reference signal and the leakage signal are precisely matched and the DD-MZM is biased at the minimum transmission point, the leakage signal is canceled in the optical domain. The de-chirped signals are obtained after the leakage-free optical signal is detected in a photodetector. An experiment is performed. The cancellation depth of the de-chirped leakage signal is around 23 dB when the center frequency and bandwidth of the linearly frequency-modulated signal are 11.5 and 2 GHz. The leakage cancellation scheme is used in a radar system. When the leakage cancellation is not employed, the leakage signal will seriously affect the imaging results and distance measurement accuracy of the radar system. When the leakage cancellation is applied, the imaging results of multiple targets can be clearly distinguished, and the error of the distance measurement results is significantly reduced to 10 cm.
Digital-assisted photonic analog wideband multipath self-interference cancellation
Authors: Moxuan Han, Taixia Shi, Yang Chen
Date: 2021-11-12 00:00:00

A digital-assisted photonic analog wideband radio-frequency multipath self-interference cancellation (SIC) and frequency downconversion method based on a dual-drive Mach-Zehnder modulator and the recursive least square (RLS) algorithm is proposed and demonstrated for in-band full-duplex systems. Besides the reference for the direct-path self-interference (SI) signal, the RLS algorithm is used to construct another reference for the residual SI signal from the direct path and the SI signals from the reflection paths. The proposed method can solve the performance limitation in the previously reported SIC methods of constructing the multipath SI signal using a single reference caused by the limited dynamic range of the digital-to-analog converter when the direct-path SI signal is much stronger than the sub-weak reflection-path SI signals. An experiment is performed. When the carrier frequency of the multipath SI signal is 10 GHz and the direct-path SI signal is much stronger than the sub-weak multipath SI signal, the cancellation depths of about 26.7 and 26.1 dB are realized with SI baud rates of 0.5 and 1 Gbaud. When the direct-path SI signal and sub-weak multipath SI signal own closer power, the corresponding cancellation depths are 24.7 and 20.8 dB, respectively.
A new technique for compression of data sets
Authors: Anatoli Torokhti
Date: 2021-11-12 00:00:00
Tasks: Data Compression

Data compression techniques are characterized by four key performance indices which are (i) associated accuracy, (ii) compression ratio, (iii) computational work, and (iv) degree of freedom. The method of data compression developed in this paper allows us to substantially improve all the four issues above. The proposed transform $F$ is presented in the form of a sum with $p-1$ terms, $F_1,..., F_{p-1}$, where each term is a particular sub-transform presented by a first degree polynomial. For $j=1,\ldots,p-1$, each sub-transform $F_{j}$ is determined from interpolation-like conditions. This device provides the transform flexibility to incorporate variation of observed data and leads to performance improvement. The transform $F$ has two degrees of freedom, the number of sub-transforms and associated compression ratio associated with each sub-transform $F_{j}$.