pwc-archive/papers-with-abstracts huggingface dataset:
Big Data for Traffic Estimation and Prediction: A Survey of Data and Tools
Authors: Weiwei Jiang, Jiayun Luo
Date: 2021-03-18 00:00:00
Tasks: Prediction

Big data has been used widely in many areas including the transportation industry. Using various data sources, traffic states can be well estimated and further predicted for improving the overall operation efficiency. Combined with this trend, this study presents an up-to-date survey of open data and big data tools used for traffic estimation and prediction. Different data types are categorized and the off-the-shelf tools are introduced. To further promote the use of big data for traffic estimation and prediction tasks, challenges and future directions are given for future studies.
From Digital Humanities to Quantum Humanities: Potentials and Applications
Authors: Johanna Barzen
Date: 2021-03-17 00:00:00
Tasks: Clustering, Feature Engineering

Quantum computers are becoming real. Therefore, it is promising to use their potentials in different applications areas, which includes research in the humanities. Due to an increasing amount of data that needs to be processed in the digital humanities the use of quantum computers can contribute to this research area. To give an impression on how beneficial such involvement of quantum computers can be when analyzing data from the humanities, a use case from the media science is presented. Therefore, both the theoretical basis and the tooling support for analyzing the data from our digital humanities project MUSE is described. This includes a data analysis pipeline, containing e.g. various approaches for data preparation, feature engineering, clustering, and classification where several steps can be realized classically, but also supported by quantum computers.
Locally-Aware Constrained Games on Networks
Authors: Guanze Peng, Tao Li, Shutian Liu, Juntao Chen, Quanyan Zhu
Date: 2020-11-19 00:00:00

Network games have been instrumental in understanding strategic behaviors over networks for applications such as critical infrastructure networks, social networks, and cyber-physical systems. One critical challenge of network games is that the behaviors of the players are constrained by the underlying physical laws or safety rules, and the players may not have complete knowledge of network-wide constraints. To this end, this paper proposes a game framework to study constrained games on networks, where the players are locally aware of the constraints. We use \textit{awareness levels} to capture the scope of the network constraints that players are aware of. We first define and show the existence of generalized Nash equilibria (GNE) of the game, and point out that higher awareness levels of the players would lead to a larger set of GNE solutions. We use necessary and sufficient conditions to characterize the GNE, and propose the concept of the dual game to show that one can convert a locally-aware constrained game into a two-layer unconstrained game problem. We use linear quadratic games as case studies to corroborate the analytical results, and in particular, show the duality between Bertrand games and Cournot games.%, where each layer comprises an unconstrained game.
Fast ATP-dependent Subunit Rotation in Reconstituted FoF1-ATP Synthase Trapped in Solution
Authors: Thomas Heitkamp, Michael Börsch
Date: 2021-03-22 00:00:00

FoF1-ATP synthases are ubiquitous membrane-bound, rotary motor enzymes that can catalyze ATP synthesis and hydrolysis. Their enzyme kinetics are controlled by internal subunit rotation, by substrate and product concentrations, by mechanical inhibitory mechanisms, but also by the electrochemical potential of protons across the membrane. Single-molecule F\"orster resonance energy transfer (smFRET) has been used to detect subunit rotation within FoF1-ATP synthases embedded in freely diffusing liposomes. We now report that kinetic monitoring of functional rotation can be prolonged from milliseconds to seconds by utilizing an Anti-Brownian electrokinetic trap (ABEL trap). These extended observation times allowed us to observe fluctuating rates of functional rotation for individual FoF1-liposomes in solution. Broad distributions of ATP-dependent catalytic rates were revealed. The buildup of an electrochemical potential of protons was confirmed to limit the maximum rate of ATP hydrolysis. In the presence of ionophores or uncouplers, the fastest subunit rotation speeds measured in single reconstituted FoF1-ATP synthases were 180 full rounds per second. This was much faster than measured by biochemical ensemble averaging, but not as fast as the maximum rotational speed reported previously for isolated single F1 complexes uncoupled from the membrane-embedded Fo complex. Further application of ABEL trap measurements should help resolve the mechanistic causes of such fluctuating rates of subunit rotation.
Normal-mode driven exploration of protein domain motions
Authors: Yves-Henri Sanejouand
Date: 2021-03-22 00:00:00

Domain motions involved in the function of proteins can often be well described as a combination of motions along a handfull of low-frequency modes, that is, with the values of a few normal coordinates. This means that, when the functional motion of a protein is unknown, it should prove possible to predict it, since it amounts to guess a few values. However, without the help of additional experimental data, using normal coordinates for generating accurate conformers far away from the initial one is not so straightforward. To do so, a new approach is proposed: instead of building conformers directly with the values of a subset of normal coordinates, they are built in two steps, the conformer built with normal coordinates being just used for defining a set of distance constraints, the final conformer being built so as to match them. Note that this approach amounts to transform the problem of generating accurate protein conformers using normal coordinates into a better known one: the distance-geometry problem, which is herein solved with the help of the ROSETTA software. In the present study, this approach allowed to rebuild accurately six large amplitude conformational changes, using at most six low-frequency normal coordinates. As a consequence of the low-dimensionality of the corresponding subspace, random exploration also proved enough for generating low-energy conformers close to the known end-point of the conformational change of the LAO binding protein, lysozyme T4 and adenylate kinase.
Reinforcement Learning based on Scenario-tree MPC for ASVs
Authors: Arash Bahari Kordabad, Hossein Nejatbakhsh Esfahani, Anastasios M. Lekkas, Sébastien Gros
Date: 2021-03-22 00:00:00
Tasks: Model Predictive Control, Point Tracking, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In this paper, we present the use of Reinforcement Learning (RL) based on Robust Model Predictive Control (RMPC) for the control of an Autonomous Surface Vehicle (ASV). The RL-MPC strategy is utilized for obstacle avoidance and target (set-point) tracking. A scenario-tree robust MPC is used to handle potential failures of the ship thrusters. Besides, the wind and ocean current are considered as unknown stochastic disturbances in the real system, which are handled via constraints tightening. The tightening and other cost parameters are adjusted by RL, using a Q-learning technique. An economic cost is considered, minimizing the time and energy required to achieve the ship missions. The method is illustrated in simulation on a nonlinear 3-DOF model of a scaled version of the Cybership 2.
Self-Adaptive Manufacturing with Digital Twins
Authors: Tim Bolender, Gereon Bürvenich, Manuela Dalibor, Bernhard Rumpe, Andreas Wortmann
Date: 2021-03-22 00:00:00

Digital Twins are part of the vision of Industry 4.0 to represent, control, predict, and optimize the behavior of Cyber-Physical Production Systems (CPPSs). These CPPSs are long-living complex systems deployed to and configured for diverse environments. Due to specific deployment, configuration, wear and tear, or other environmental effects, their behavior might diverge from the intended behavior over time. Properly adapting the configuration of CPPSs then relies on the expertise of human operators. Digital Twins (DTs) that reify this expertise and learn from it to address unforeseen challenges can significantly facilitate self-adaptive manufacturing where experience is very specific and, hence, insufficient to employ deep learning techniques. We leverage the explicit modeling of domain expertise through case-based reasoning to improve the capabilities of Digital Twins for adapting to such situations. To this effect, we present a modeling framework for self-adaptive manufacturing that supports modeling domain-specific cases, describing rules for case similarity and case-based reasoning within a modular Digital Twin. Automatically configuring Digital Twins based on explicitly modeled domain expertise can improve manufacturing times, reduce wastage, and, ultimately, contribute to better sustainable manufacturing.
Spatial alignment, group strategy and non-kin selection enable the evolution of cooperation
Authors: Xiaoliang Wang, Andrew Harrison
Date: 2021-03-22 00:00:00

This article considers a mechanism to explain the emergence and evolution of social cooperation. Selfish individuals tend to benefit themselves, which makes it hard for the maintenance of cooperation between unrelated individuals. We propose and validate that a smart group strategy can effectively facilitate the evolution of cooperation, provided cooperators spatially align whilst cooperating at a new level of alliance. The general evolutionary model presented here shows that a non-kin selection effect is a possible cause for cooperation between unrelated individuals and highlights that non-kin selection may be a hallmark of biological evolution.
Continuous Prediction of Lower-Limb Kinematics From Multi-Modal Biomedical Signals
Authors: Chunzhi Yi, Feng Jiang, Shengping Zhang, Hao Guo, Chifu Yang, Zhen Ding, Baichun Wei, Xiangyuan Lan, Huiyu Zhou
Date: 2021-03-22 00:00:00
Tasks: Prediction

The fast-growing techniques of measuring and fusing multi-modal biomedical signals enable advanced motor intent decoding schemes of lowerlimb exoskeletons, meeting the increasing demand for rehabilitative or assistive applications of take-home healthcare. Challenges of exoskeletons motor intent decoding schemes remain in making a continuous prediction to compensate for the hysteretic response caused by mechanical transmission. In this paper, we solve this problem by proposing an ahead of time continuous prediction of lower limb kinematics, with the prediction of knee angles during level walking as a case study. Firstly, an end-to-end kinematics prediction network(KinPreNet), consisting of a feature extractor and an angle predictor, is proposed and experimentally compared with features and methods traditionally used in ahead-of-time prediction of gait phases. Secondly, inspired by the electromechanical delay(EMD), we further explore our algorithm's capability of compensating response delay of mechanical transmission by validating the performance of the different sections of prediction time. And we experimentally reveal the time boundary of compensating the hysteretic response. Thirdly, a comparison of employing EMG signals or not is performed to reveal the EMG and kinematic signals collaborated contributions to the continuous prediction. During the experiments, EMG signals of nine muscles and knee angles calculated from inertial measurement unit (IMU) signals are recorded from ten healthy subjects. To the best of our knowledge, this is the first study of continuously predicting lower-limb kinematics in an ahead-of-time manner based on the electromechanical delay (EMD).
PTSC: a New Definition for Structural Controllability under Numerical Perturbations
Authors: Yuan Zhang, Yuanqing Xia
Date: 2021-03-22 00:00:00

This paper proposes a novel notion for structural controllability under structured numerical perturbations, namely the perturbation-tolerant structural controllability (PTSC), on a single-input structured system whose entries can be classified into three categories: fixed zero entries, unknown generic entries whose values are fixed but unknown, and perturbed entries that can take arbitrary complex values. Such a system is PTSC if, for almost all values of the unknown generic entries in the parameter space, the corresponding controllable system realizations can preserve controllability under arbitrary complex-valued perturbations with their structure prescribed by the perturbed entries. This new notion can characterize the generic property in controllability preservation under structured numerical perturbations. We give a necessary and sufficient condition for a single-input system to be PTSC, whose verification has polynomial time complexity. Our results can serve as some feasibility conditions for the conventional structured controllability radius problems from a generic view.
Soft-Median Choice: An Automatic Feature Smoothing Method for Sound Event Detection
Authors: Fengnian Zhao, Ruwei Li, Xin Liu, Liwen Xu
Date: 2020-11-25 00:00:00
Tasks: Event Detection, Sound Event Detection

In Sound Event Detection (SED) systems, the lengths of median filters for post-processing have never been optimized during training due to several problems. No gradient is received by the lengths so they cannot be learned during back-propagation. The median-filtering inserted in the models also causes block in gradient flowing and the smoothing process misleads the model by ignoring errors. To resolve these problems, we provide different channels of features smoothed to different extents along with the original feature, so the model can optimize the weights while cognizing all the errors. We then use a linear layer to integrate the results and produce a linear combination. We further design the soft-median function to dredge the gradient flow. The proposed framework is called Soft-Median Choice (SMC). Experiments show that the SMC block not only automatically smooths the features based on the training set, but also forces the model to extract common features shared by all the frames of a sound event. The performance of the proposed method outperforms the baseline by over 10% of Event-Based F1 Score (EBFS) in both the validation and the evaluation set, and also slightly outperforms the single model of the state-of-the-art SED system.
Reinforcement Learning based on MPC/MHE for Unmodeled and Partially Observable Dynamics
Authors: Hossein Nejatbakhsh Esfahani, Arash Bahari Kordabad, Sebastien Gros
Date: 2021-03-22 00:00:00
Tasks: Model Predictive Control, reinforcement-learning, Reinforcement Learning (RL)

This paper proposes an observer-based framework for solving Partially Observable Markov Decision Processes (POMDPs) when an accurate model is not available. We first propose to use a Moving Horizon Estimation-Model Predictive Control (MHE-MPC) scheme in order to provide a policy for the POMDP problem, where the full state of the real process is not measured and necessarily known. We propose to parameterize both MPC and MHE formulations, where certain adjustable parameters are regarded for tuning the policy. In this paper, for the sake of tackling the unmodeled and partially observable dynamics, we leverage the Reinforcement Learning (RL) to tune the parameters of MPC and MHE schemes jointly, with the closed-loop performance of the policy as a goal rather than model fitting or the MHE performance. Illustrations show that the proposed approach can effectively increase the performance of close-loop control of systems formulated as POMDPs.
Correlated evolution: models and methods
Authors: Guillaume Achaz, Julien Dutheil
Date: 2021-03-22 00:00:00

In this article, we review the different models and methods to understand and estimate correlated evolution within the same genome, individual or species. We describe correlated evolution among traits, among genetic components and finally between traits and genetics.
The black hole of logistics costs of digitizing commodity money
Authors: Boliang Lin, Ruixi Lin
Date: 2021-03-22 00:00:00

In this paper, we reveal the depreciation mechanism of representative money (banknotes) from the perspective of logistics warehousing costs. Although it has long been the dream of economists to stabilize the buying power of the monetary units, the goal we have honest money always broken since the central bank depreciate the currency without limit. From the point of view of modern logistics, the key functions of money are the store of value and low logistics (circulation and warehouse) cost. Although commodity money (such as gold and silver) has the advantages of a wealth store, its disadvantage is the high logistics cost. In comparison to commodity money, credit currency and digital currency cannot protect wealth from loss over a long period while their logistics costs are negligible. We proved that there is not such honest money from the perspective of logistics costs, which is both the store of value like precious metal and without logistics costs in circulation like digital currency. The reason hidden in the back of the depreciation of banknotes is the black hole of storage charge of the anchor overtime after digitizing commodity money. Accordingly, it is not difficult to infer the inevitable collapse of the Bretton woods system. Therefore, we introduce a brand-new currency named honest devalued stable-coin and built a attenuation model of intrinsic value of the honest money based on the change mechanism of storage cost of anchor assets, like gold, which will lay the theoretical foundation for a stable monetary system.
To vote, or not to vote: on the epidemiological impact of electoral campaigns at the time of COVID-19
Authors: Davide Cipullo, Marco Le Moglie
Date: 2021-03-22 00:00:00

Elections are crucial for legitimating modern democracies, and giving all candidates the possibility to run a proper electoral campaign is necessary for elections' success in providing such legitimization. Yet, during a pandemic, the risk that electoral campaigns would enhance the spread of the disease exists and is substantive. In this work, we estimate the causal impact of electoral campaigns on the spread of COVID-19. Exploiting plausibly exogenous variation in the schedule of local elections across Italy, we show that the electoral campaign preceding this latter led to a significant worsening of the epidemiological situation related to the disease. Our results strongly highlight the importance of undertaking stringent measures along the entire electoral process to minimize its epidemiological consequences.
A New Parameterized Family of Stochastic Particle Flow Filters
Authors: Liyi Dai, Fred Daum
Date: 2021-03-17 00:00:00
Tasks: Bayesian Inference

In this paper, we are interested in obtaining answers to the following questions for particle flow filters: Can we provide a theoretical guarantee that particle flow filters give correct results such as unbiased estimates? Are particle flows stable and under what conditions? Can we have one particle flow filter, rather than multiple seemingly different ones? To answer these questions, we first derive a parameterized family of stochastic particle flow filters, in which particle flows are driven by a linear combination of prior knowledge and measurement likelihood information. We then show that several particle flows existing in the literature are special cases of this family. We prove that the particle flows are unbiased under the assumption of linear measurement and Gaussian distributions, and that estimates constructed from the stochastic flows are consistent. We further establish several finite time stability concepts for this new family of stochastic particle flows. The results reported in this paper represent a significant development toward establishing a theoretical foundation for particle flow filters.
American options in the Volterra Heston model
Authors: Etienne Chevalier, Sergio Pulido, Elizabeth Zúñiga
Date: 2021-03-22 00:00:00
Tasks: model

We price American options using kernel-based approximations of the Volterra Heston model. We choose these approximations because they allow simulation-based techniques for pricing. We prove the convergence of American option prices in the approximating sequence of models towards the prices in the Volterra Heston model. A crucial step in the proof is to exploit the affine structure of the model in order to establish explicit formulas and convergence results for the conditional Fourier-Laplace transform of the log price and an adjusted version of the forward variance. We illustrate with numerical examples our convergence result and the behavior of American option prices with respect to certain parameters of the model.
Extracting Semantic Process Information from the Natural Language in Event Logs
Authors: Adrian Rebmann, Han van der Aa
Date: 2021-03-06 00:00:00
Tasks: Attribute, Language Modeling, Language Modelling, Semantic Role Labeling

Process mining focuses on the analysis of recorded event data in order to gain insights about the true execution of business processes. While foundational process mining techniques treat such data as sequences of abstract events, more advanced techniques depend on the availability of specific kinds of information, such as resources in organizational mining and business objects in artifact-centric analysis. However, this information is generally not readily available, but rather associated with events in an ad hoc manner, often even as part of unstructured textual attributes. Given the size and complexity of event logs, this calls for automated support to extract such process information and, thereby, enable advanced process mining techniques. In this paper, we present an approach that achieves this through so-called semantic role labeling of event data. We combine the analysis of textual attribute values, based on a state-of-the-art language model, with a novel attribute classification technique. In this manner, our approach extracts information about up to eight semantic roles per event. We demonstrate the approach's efficacy through a quantitative evaluation using a broad range of event logs and demonstrate the usefulness of the extracted information in a case study.
Transfer learning from High-Resource to Low-Resource Language Improves Speech Affect Recognition Classification Accuracy
Authors: Sara Durrani, Umair Arshad
Date: 2021-03-04 00:00:00
Tasks: Cross-corpus, Diversity, Transfer Learning

Speech Affect Recognition is a problem of extracting emotional affects from audio data. Low resource languages corpora are rear and affect recognition is a difficult task in cross-corpus settings. We present an approach in which the model is trained on high resource language and fine-tune to recognize affects in low resource language. We train the model in same corpus setting on SAVEE, EMOVO, Urdu, and IEMOCAP by achieving baseline accuracy of 60.45, 68.05, 80.34, and 56.58 percent respectively. For capturing the diversity of affects in languages cross-corpus evaluations are discussed in detail. We find that accuracy improves by adding the domain target data into the training data. Finally, we show that performance is improved for low resource language speech affect recognition by achieving the UAR OF 69.32 and 68.2 for Urdu and Italian speech affects.
JPS-daprinfo: A Dataset for Japanese Dialog Act Analysis and People-related Information Detection
Authors: Changzeng Fu
Date: 2021-03-06 00:00:00
Tasks: text-classification, Text Classification

We conducted a labeling work on a spoken Japanese dataset (I-JAS) for the text classification, which contains 50 interview dialogues of two-way Japanese conversation that discuss the participants' past present and future. Each dialogue is 30 minutes long. From this dataset, we selected the interview dialogues of native Japanese speakers as the samples. Given the dataset, we annotated sentences with 13 labels. The labeling work was conducted by native Japanese speakers who have experiences with data annotation. The total amount of the annotated samples is 20130.