Miracle-dz/UniFAR-FTUs huggingface dataset:
A competing risks interpretation of Hawkes processes

We give a construction of the Hawkes process as a piecewise competing risks model. We argue that the most natural interpretation of the self-excitation kernel is the hazard function of a defective random variable. This establishes a link between desired qualitative features of the process and a parametric form for the kernel, which we illustrate using examples from the literature. Two families of cure rate models taken from the survival analysis literature are proposed as new models for the self-excitation kernel. Fi-nally, we show that the competing risks viewpoint leads to a general simulation algorithm which avoids inverting the compensator of the point process or performing an accept-reject step, and is therefore fast and quite general.
Real-Time Control of Battery Energy Storage Systems to Provide Ancillary Services Considering Voltage-Dependent Capability of DC-AC Converters

Frequency response and voltage support are vital ancillary services for power grids. In this paper, we design and experimentally validate a real-time control framework for battery energy storage systems (BESSs) to provide ancillary services to power grids. The objective of the control system is to utilize the full capability of the BESSs to provide ancillary services. We take the voltage-dependent capability curve of the DC-AC converter and the security requirements of BESSs as constraints of the control system. The initial power set-points are obtained based on the droop control approach. To guarantee the feasibility of the power set-points with respect to both the converter capability and BESS security constraints, the final power set-points calculation is formulated as a nonconvex optimization problem. A convex and computationally efficient reformulation of the original control problem is then proposed. We prove that the proposed convex optimization gives the global optimal solution to the original nonconvex problem. We improve the computational performance of this algorithm by discretizing the feasible region of the optimization model. We achieve a 100 ms update time of the controller setpoint computation in the experimental validation of the utility-scale 720 kVA/560 kWh BESS on the EPFL campus.
Necessary and sufficient graphical conditions for optimal adjustment sets in causal graphical models with hidden variables

The problem of selecting optimal backdoor adjustment sets to estimate causal effects in graphical models with hidden and conditioned variables is addressed. Previous work has defined optimality as achieving the smallest asymptotic estimation variance and derived an optimal set for the case without hidden variables. For the case with hidden variables there can be settings where no optimal set exists and currently only a sufficient graphical optimality criterion of limited applicability has been derived. In the present work optimality is characterized as maximizing a certain adjustment information which allows to derive a necessary and sufficient graphical criterion for the existence of an optimal adjustment set and a definition and algorithm to construct it. Further, the optimal set is valid if and only if a valid adjustment set exists and has higher (or equal) adjustment information than the Adjust-set proposed in Perkovi{\'c} et al. [Journal of Machine Learning Research, 18: 1--62, 2018] for any graph. The results translate to minimal asymptotic estimation variance for a class of estimators whose asymptotic variance follows a certain information-theoretic relation. Numerical experiments indicate that the asymptotic results also hold for relatively small sample sizes and that the optimal adjustment set or minimized variants thereof often yield better variance also beyond that estimator class. Surprisingly, among the randomly created setups more than 90\% fulfill the optimality conditions indicating that also in many real-world scenarios graphical optimality may hold. Code is available as part of the python package \url{https://github.com/jakobrunge/tigramite}.
Modeling Social Readers: Novel Tools for Addressing Reception from Online Book Reviews

Readers' responses to literature have received scant attention in computational literary studies. The rise of social media offers an opportunity to capture a segment of these responses while data-driven analysis of these responses can provide new critical insight into how people"read". Posts discussing an individual book on Goodreads, a social media platform that hosts user discussions of popular literature, are referred to as"reviews", and consist of plot summaries, opinions, quotes, or some mixture of these. Since these reviews are written by readers, computationally modeling them allows one to discover the overall non-professional discussion space about a work, including an aggregated summary of the work's plot, an implicit ranking of the importance of events, and the readers' impressions of main characters. We develop a pipeline of interlocking computational tools to extract a representation of this reader generated shared narrative model. Using a corpus of reviews of five popular novels, we discover the readers' distillation of the main storylines in a novel, their understanding of the relative importance of characters, as well as the readers' varying impressions of these characters. In so doing, we make three important contributions to the study of infinite vocabulary networks: (i) an automatically derived narrative network that includes meta-actants; (ii) a new sequencing algorithm, REV2SEQ, that generates a consensus sequence of events based on partial trajectories aggregated from the reviews; and (iii) a new"impressions"algorithm, SENT2IMP, that provides finer, non-trivial and multi-modal insight into readers' opinions of characters.
Quinone-Mediated Microbial Goethite Reduction and Transformation of Redox Mediator, Anthraquinone-2,6-Disulfonate (AQDS)

ABSTRACT The aim of current study is to identify the kinetic characteristics and elucidate the possible transformation pathways of the interaction between the redox mediator (anthraquinone-2,6-disulfonate, AQDS) and goethite during the process of microbial goethite reduction by Shewanella putrefaciens, a dissimilatory iron reduction bacterium (DIRB). Speciations of both AQDS and microbially reduced ferrous iron are used to characterize the interaction process among S. putrefaciens, AQDS and goethite. Due to the complexities of the natural environment, two pre-incubation reaction systems of the “DIRB–goethite” and the “DIRB–AQDS” are introduced to investigate the dynamics of goethite reduction and redox transformation of AQDS. Results show that the characteristics of the microbial goethite reduction and the kinetic transformation between two species of the redox mediator are different in two pre-incubation reaction systems. Both abiotic and enzymatic reactions and their coupling regulate the kinetic process for “redox mediatoriron” interaction in the presence of DIRB. This study will help to understand the characteristics and mechanism of microbial reduction of the Fe(III) oxide and transformation of redox mediator.
Sentiment and Emotion Classification of Epidemic Related Bilingual data from Social Media

In recent years, sentiment analysis and emotion classification are two of the most abundantly used techniques in the field of Natural Language Processing (NLP). Although sentiment analysis and emotion classification are used commonly in applications such as analyzing customer reviews, the popularity of candidates contesting in elections, and comments about various sporting events; however, in this study, we have examined their application for epidemic outbreak detection. Early outbreak detection is the key to deal with epidemics effectively, however, the traditional ways of outbreak detection are time-consuming which inhibits prompt response from the respective departments. Social media platforms such as Twitter, Facebook, Instagram, etc. allow the users to express their thoughts related to different aspects of life, and therefore, serve as a substantial source of information in such situations. The proposed study exploits the bilingual (Urdu and English) data from Twitter and NEWS websites related to the dengue epidemic in Pakistan, and sentiment analysis and emotion classification are performed to acquire deep insights from the data set for gaining a fair idea related to an epidemic outbreak. Machine learning and deep learning algorithms have been used to train and implement the models for the execution of both tasks. The comparative performance of each model has been evaluated using accuracy, precision, recall, and f1-measure.
Consistent Density Estimation Under Discrete Mixture Models

This work considers a problem of estimating a mixing probability density $f$ in the setting of discrete mixture models. The paper consists of three parts. The first part focuses on the construction of an $L_1$ consistent estimator of $f$. In particular, under the assumptions that the probability measure $\mu$ of the observation is atomic, and the map from $f$ to $\mu$ is bijective, it is shown that there exists an estimator $f_n$ such that for every density $f$ $\lim_{n\to \infty} \mathbb{E} \left[ \int |f_n -f | \right]=0$. The second part discusses the implementation details. Specifically, it is shown that the consistency for every $f$ can be attained with a computationally feasible estimator. The third part, as a study case, considers a Poisson mixture model. In particular, it is shown that in the Poisson noise setting, the bijection condition holds and, hence, estimation can be performed consistently for every $f$.
Trust-Aware Planning: Modeling Trust Evolution in Longitudinal Human-Robot Interaction

Trust between team members is an essential requirement for any successful cooperation. Thus, engendering and maintaining the fellow team members’ trust becomes a central responsibility for any member trying to not only successfully participate in the task but to ensure the team achieves its goals. The problem of trust management is particularly challenging in mixed human-robot teams where the human and the robot may have different models about the task at hand and thus may have different expectations regarding the current course of action and forcing the robot to focus on the costly explicable behavior. We propose a computational model for capturing and modulating trust in such longitudinal human-robot interaction, where the human adopts a supervisory role. In our model, the robot integrates human’s trust and their expectations from the robot into its planning process to build and maintain trust over the interaction horizon. By establishing the required level of trust, the robot can focus on maximizing the team goal by eschewing explicit explanatory or explica-ble behavior without worrying about the human supervisor monitoring and intervening to stop behaviors they may not necessarily understand. We model this reasoning about trust levels as a meta reasoning process over individual planning tasks. We additionally validate our model through a human subject experiment.
Sulfur-doped covalent triazine-based frameworks for enhanced photocatalytic hydrogen evolution from water under visible light

Here, we show for the first time that the photocatalytic activity of covalent triazine-based frameworks (CTFs) can be efficiently improved using a facile sulfur-doping approach. The sulfur-doped CTFs show superior photocatalytic activity and stability in hydrogen evolution from water under visible light irradiation over pristine CTFs and g-C3N4. The significantly improved catalytic efficiency was attributed to sulfur-doping in the frameworks, which results in enhanced adsorption of visible light, reduced recombination of free charge carriers, and rapid separation and transportation of photogenerated electron–holes.
Decentralized Algorithms for Wasserstein Barycenters

In this thesis, we consider the Wasserstein barycenter problem of discrete probability measures from computational and statistical sides in two scenarios: (I) the measures are given and we need to compute their Wasserstein barycenter, and (ii) the measures are generated from a probability distribution and we need to calculate the population barycenter of the distribution defined by the notion of Fr\'echet mean. The statistical focus is estimating the sample size of measures necessary to calculate an approximation for Fr\'echet mean (barycenter) of a probability distribution with a given precision. For empirical risk minimization approaches, the question of the regularization is also studied together with proposing a new regularization which contributes to the better complexity bounds in comparison with quadratic regularization. The computational focus is developing algorithms for calculating Wasserstein barycenters: both primal and dual algorithms which can be executed in a decentralized manner. The motivation for dual approaches is closed-forms for the dual formulation of entropy-regularized Wasserstein distances and their derivatives, whereas the primal formulation has closed-form expression only in some cases, e.g., for Gaussian measures. Moreover, the dual oracle returning the gradient of the dual representation for entropy-regularized Wasserstein distance can be computed for a cheaper price in comparison with the primal oracle returning the gradient of the entropy-regularized Wasserstein distance. The number of dual oracle calls, in this case, will also be less, i.e., the square root of the number of primal oracle calls. This explains the successful application of the first-order dual approaches for the Wasserstein barycenter problem.
Gaining Insights on Student Course Selection in Higher Education with Community Detection

Gaining insight into course choices holds significant value for universities, especially those who aim for flexibility in their programs and wish to adapt quickly to changing demands of the job market. However, little emphasis has been put on utilizing the large amount of educational data to understand these course choices. Here, we use network analysis of the course selection of all students who enrolled in an undergraduate program in engineering, psychology, business or computer science at a Nordic university over a five year period. With these methods, we have explored student choices to identify their distinct fields of interest. This was done by applying community detection to a network of courses, where two courses were connected if a student had taken both. We compared our community detection results to actual major specializations within the computer science department and found strong similarities. To compliment this analysis, we also used directed networks to identify the"typical"student, by looking at students' general course choices by semester. We found that course choices diversify as programs progress, meaning that attempting to understand course choices by identifying a"typical"student gives less insight than understanding what characterizes course choice diversity. Analysis with our proposed methodology can be used to offer more tailored education, which in turn allows students to follow their interests and adapt to the ever-changing career market.
Defeating Super-Reactive Jammers With Deception Strategy: Modeling, Signal Detection, and Performance Analysis

This paper develops a novel framework to defeat a super-reactive jammer, one of the most difficult jamming attacks to deal with in practice. Specifically, the jammer has an unlimited power budget and is equipped with the self-interference suppression capability to simultaneously attack and listen to the transmitter’s activities. Consequently, dealing with super-reactive jammers is very challenging. Thus, we introduce a smart deception mechanism to attract the jammer to continuously attack the channel and then leverage jamming signals to transmit data based on the ambient backscatter communication technology. To detect the backscattered signals, the maximum likelihood detector can be adopted. However, this method is notorious for its high computational complexity and requires the model of the current propagation environment as well as channel state information. Hence, we propose a deep learning-based detector that can dynamically adapt to any channels and noise distributions. With a Long Short-Term Memory network, our detector can learn the received signals’ dependencies to achieve a performance close to that of the optimal maximum likelihood detector. Through simulation and theoretical results, we demonstrate that with our approaches, the more power the jammer uses to attack the channel, the better bit error rate performance the transmitter can achieve.
Interdépendance entre géométrie, adsorption et transport dans les matériaux à porosité hiérarchique

Cette these, par une etude combinant modelisation et experiences, s’attache a comprendre l’interdependance entre porosite, transport et adsorption dans les materiaux poreux hierarchiques. Le transport et l’adsorption sont simules dans les materiaux poreux a l’aide d’un modele de Lattice Boltzmann etendu pour prendre en compte l’adsorption. Ce modele mesoscopique permet de simuler le comportement d’un fluide present au sein des pores du materiau ainsi que d’especes en solution. La synthese des materiaux poreux hierarchiques par ice-templating et la caracterisation de la geometrie des echantillons par tomographie aux rayons X a permis de realiser des simulations sur des geometries reelles. La comparaison des resultats de simulation et des resultats experimentaux a prouve la capacite du modele a reproduire le comportement du fluide et des especes dans le materiau. Grâce au modele employe, l’interdependance entre transport et adsorption a pu etre mise en evidence, montrant notamment que le flux de fluide pouvait avoir une influence sur la densite adsorbee. Une etude sur l’influence de la geometrie et de la rugosite sur le couplage entre transport et adsorption a ete menee sur des geometries bien caracterisees. Enfin, la modification du modele de Lattice-Boltzmann pour rendre compte de l’effet d’un pic de traceurs a ete developpe. Cette modification permet notamment d’observer la cinetique hors regime stationnaire des traceurs dans le materiau et de rendre compte de l’effet du flux sur l’accessibilite des sites d’adsorption.
BLM-17m: A Large-Scale Dataset for Black Lives Matter Topic Detection on Twitter

Protection of human rights is one of the most important problems of the modern world. In this paper, we construct a Twitter dataset that covers one of the most significant human rights contradiction in recent years which affected the whole world: the George Floyd incident. We propose a labeled dataset for topic detection that contains about 17 million tweets. These Tweets are collected from 25 May 2020 to 21 August 2020, covering about 90 days from the start of the incident. We labeled the dataset by monitoring most trending news topics from global and local newspapers and used TF-IDF and LDA as baselines. We evaluated the results of these two methods with three different k values for precision, recall and F1-score.
Towards A Multi-agent System for Online Hate Speech Detection

This paper envisions a multi-agent system for detecting the presence of hate speech in online social media platforms such as Twitter and Facebook. We introduce a novel framework employing deep learning techniques to coordinate the channels of textual and im-age processing. Our experimental results aim to demonstrate the effectiveness of our methods for classifying online content, training the proposed neural network model to effectively detect hateful instances in the input. We conclude with a discussion of how our system may be of use to provide recommendations to users who are managing online social networks, showcasing the immense potential of intelligent multi-agent systems towards delivering social good.
Modern Subsampling Methods for Large-Scale Least Squares Regression

Subsampling methods aim to select a subsample as a surrogate for the observed sample. As a powerful technique for large-scale data analysis, various subsampling methods are developed for more effective coefficient estimation and model prediction. This review presents some cutting-edge subsampling methods based on the large-scale least squares estimation. Two major families of subsampling methods are introduced: the randomized subsampling approach and the optimal subsampling approach. The former aims to develop a more effective data-dependent sampling probability while the latter aims to select a deterministic subsample in accordance with certain optimality criteria. Real data examples are provided to compare these methods empirically, respecting both the estimation accuracy and the computing time.
Chemical post-processing of magneto-hydrodynamical simulations of star-forming regions: robustness and pitfalls

A common approach to model complex chemistry in numerical simulations is via postprocessing of existingmagneto-hydrodynamic simulations, relying on computing the evolution of chemistry over the dynamic history of a subset of particles from within the raw simulation. Here, we validate such a technique, assessing its ability to recover the abundances of chemical species, using the chemistry package krome. We also assess, for the first time, the importance of the main free input parameters, by means of a direct comparison with a self-consistent state-of-the-art simulation in which chemistry was directly coupled to hydrodynamics. We have found that the post-processing is highly reliable, with an accuracy at the percent level, even when the most relaxed input parameters are employed. In particular, our results show that the number of particles used does not affect significantly the average properties, although it suppresses the appearance of possibly important spatial features. On the other hand, the choice of the integration time-step plays a crucial role. Longer integration time-steps can produce large errors, as the post-processing solution will be forced towards chemical equilibrium, a condition that does not always necessarily apply. When the interpolation-based reconstruction of chemical properties is performed, the errors further increase up to a factor of ∼ 2. Concluding, our results suggest that this technique is extremely useful when exploring the relative quantitative effect of different chemical parameters and/or networks, without the need of rerunning simulations multiple times, but some care should be taken in the choice of particles sub-sample and integration time-step.
Self-Improving Semantic Perception on a Construction Robot

—We propose a novel robotic system that can improve its semantic perception during deployment. Contrary to the established approach of learning semantics from large datasets and deploying ﬁxed models, we propose a framework in which semantic models are continuously updated on the robot to adapt to the deployment environments. Our system therefore tightly couples multi-sensor perception and localisation to continuously learn from self-supervised pseudo labels. We study this system in the context of a construction robot registering LiDAR scans of cluttered environments against building models. Our experiments show how the robot’s semantic perception improves during deployment and how this translates into improved 3D localisation by ﬁltering the clutter out of the LiDAR scan, even across drastically different environments. We further study the risk of catastrophic forgetting that such a continuous learning setting poses. We ﬁnd memory replay an effective measure to reduce forgetting and show how the robotic system can improve even when switching between different environments. On average, our system improves by 60% in segmentation and 10% in localisation compared to deployment of a ﬁxed model, and it keeps this improvement up while adapting to further environments.