Miracle-dz/UniFAR-FTUs huggingface dataset:
Unmanned Aerial Systems for Wildland and Forest Fires: Sensing, Perception, Cooperation and Assistance

Wildfires represent an important natural risk causing economic losses, human death and important environmental damage. In recent years, we witness an increase in fire intensity and frequency. Research has been conducted towards the development of dedicated solutions for wildland and forest fire assistance and fighting. Systems were proposed for the remote detection and tracking of fires. These systems have shown improvements in the area of efficient data collection and fire characterization within small scale environments. However, wildfires cover large areas making some of the proposed ground-based systems unsuitable for optimal coverage. To tackle this limitation, Unmanned Aerial Systems (UAS) were proposed. UAS have proven to be useful due to their maneuverability, allowing for the implementation of remote sensing, allocation strategies and task planning. They can provide a low-cost alternative for the prevention, detection and real-time support of firefighting. In this paper we review previous work related to the use of UAS in wildfires. Onboard sensor instruments, fire perception algorithms and coordination strategies are considered. In addition, we present some of the recent frameworks proposing the use of both aerial vehicles and Unmanned Ground Vehicles (UV) for a more efficient wildland firefighting strategy at a larger scale.
Towards Character-Level Transformer NMT by Finetuning Subword Systems

Applying the Transformer architecture on the character level usually requires very deep architectures that are difficult and slow to train. A few approaches have been proposed that partially overcome this problem by using explicit segmentation into tokens. We show that by initially training a subword model based on this segmentation and then finetuning it on characters, we can obtain a neural machine translation model that works at the character level without requiring segmentation. Without changing the vanilla 6-layer Transformer Base architecture, we train purely character-level models. Our character-level models better capture morphological phenomena and show much higher robustness towards source-side noise at the expense of somewhat worse overall translation quality. Our study is a significant step towards high-performance character-based models that are not extremely large.
Combining boundary‐conforming finite element meshes on moving domains using a sliding mesh approach

For most finite element simulations, boundary‐conforming meshes have significant advantages in terms of accuracy or efficiency. This is particularly true for complex domains. However, with increased complexity of the domain, generating a boundary‐conforming mesh becomes more difficult and time consuming. One might therefore decide to resort to an approach where individual boundary‐conforming meshes are pieced together in a modular fashion to form a larger domain. This article presents a stabilized finite element formulation for fluid and temperature equations on sliding meshes. It couples the solution fields of multiple subdomains whose boundaries slide along each other on common interfaces. Thus, the method allows to use highly tuned boundary‐conforming meshes for each subdomain that are only coupled at the overlapping boundary interfaces. In contrast to standard overlapping or fictitious domain methods the coupling is broken down to few interfaces with reduced geometric dimension. The formulation consists of the following key ingredients: the coupling of the solution fields on the overlapping surfaces is imposed weakly using a stabilized version of Nitsche's method. It ensures mass and energy conservation at the common interfaces. Additionally, we allow to impose weak Dirichlet boundary conditions at the nonoverlapping parts of the interfaces. We present a detailed numerical study for the resulting stabilized formulation. It shows optimal convergence behavior of the interface coupling for both Newtonian and generalized Newtonian material models. Simulations of flow of plastic melt inside single‐screw as well as twin‐screw extruders demonstrate the applicability of the method to complex and relevant industrial applications.
A Method for Vehicle Collision Risk Assessment through Inferring Driver's Braking Actions in Near-Crash Situations

Driving information and data under potential vehicle crashes create opportunities for extensive real-world observations of driver behaviors and relevant factors that significantly influence the driving safety in emergency scenarios. Furthermore, the availability of such data also enhances the collision avoidance systems (CASs) by evaluating driver's actions in near-crash scenarios and providing timely warnings. These applications motivate the need for heuristic tools capable of inferring relationship among driving risk, driver/vehicle characteristics, and road environment. In this paper, we acquired amount of real-world driving data and built a comprehensive dataset, which contains multiple "driver-vehicle-road" attributes. The proposed method works in two steps. In the first step, a variable precision rough set (VPRS) based classification technique is applied to draw a reduced core subset from field driving dataset, which presents the essential attributes set most relevant to driving safety assessment. In the second step, we design a decision strategy by introducing mutual information entropy to quantify the significance of each attribute, then a representative index through accumulation of weighted "driver-vehicle-road" factors is calculated to reflect the driving risk for actual situation. The performance of the proposed method is demonstrated in an offline analysis of the driving data collected in field trials, where the aim is to infer the emergency braking actions in next short term. The results indicate that our proposed model is a good alternative for providing improved warnings in real-time because of its high prediction accuracy and stability.
Polymer scission in turbulent flows

Abstract Polymers in a turbulent flow are subject to intense strain, which can cause their scission and thereby limit the experimental study and application of phenomena such as turbulent drag reduction and elastic turbulence. In this paper, we study polymer scission in homogeneous isotropic turbulence, through a combination of stochastic modelling, based on a Gaussian time-decorrelated random flow, and direct numerical simulations (DNS) with both one-way (passive) and two-way (active) coupling of the polymers, modelled as bead-spring chains, and the flow. For the first scission of passive polymers, the stochastic model yields analytical predictions which are found to be in good agreement with results from the DNS, for the temporal evolution of the fraction of unbroken polymers and the statistics of the survival of polymers. The impact of scission on the dynamics of a turbulent polymer solution is investigated through DNS with two-way coupling (active polymers). Our results indicate that the reduction of kinetic energy dissipation due to feedback from stretched polymers is an inherently transient effect, which is lost as the polymers break up. Thus, the overall dissipation reduction is maximized by an intermediate polymer relaxation time, for which polymers stretch significantly but without breaking too quickly. We also study the dynamics of the polymer fragments which form after scission; these daughter polymers can themselves undergo subsequent, repeated, breakups to produce a hierarchical population of polymers with a range of relaxation times and scission rates.
Informational Space of Meaning for Scientific Texts

In Natural Language Processing, automatic extracting the meaning of texts constitutes an important problem. Our focus is the computational analysis of meaning of short scientific texts (abstracts or brief reports). In this paper, a vector space model is developed for quantifying the meaning of words and texts. We introduce the Meaning Space, in which the meaning of a word is represented by a vector of Relative Information Gain (RIG) about the subject categories that the text belongs to, which can be obtained from observing the word in the text. This new approach is applied to construct the Meaning Space based on Leicester Scientific Corpus (LSC) and Leicester Scientific Dictionary-Core (LScDC). The LSC is a scientific corpus of 1,673,350 abstracts and the LScDC is a scientific dictionary which words are extracted from the LSC. Each text in the LSC belongs to at least one of 252 subject categories of Web of Science (WoS). These categories are used in construction of vectors of information gains. The Meaning Space is described and statistically analysed for the LSC with the LScDC. The usefulness of the proposed representation model is evaluated through top-ranked words in each category. The most informative n words are ordered. We demonstrated that RIG-based word ranking is much more useful than ranking based on raw word frequency in determining the science-specific meaning and importance of a word. The proposed model based on RIG is shown to have ability to stand out topic-specific words in categories. The most informative words are presented for 252 categories. The new scientific dictionary and the 103,998 x 252 Word-Category RIG Matrix are available online. Analysis of the Meaning Space provides us with a tool to further explore quantifying the meaning of a text using more complex and context-dependent meaning models that use co-occurrence of words and their combinations.
Immunoregulatory Properties of Break Down Products of Human Choriogonadotropin

textabstractReproductive function in the female is cyclic. A series of functional interrelationships between the hypothalamus, the anterior pituitary, and the ovaries leads to the monthly rupture of an ovarian follicle and extrusion of an egg (“ovulation”), which is then transported to the fallopian tubes to be fertilized. Should fertilization fail to occur, menstruation ensues within 14 days and the hormonal and morphological events that led to ovulation are repeated. Ovulation occurs around day 14 of the menstrual cycle, followed by fertilization as egg and sperm unite within 24 hours. The first three days of development occur within the fallopian tube. Upon arrival within the uterus the conceptus develops into a blastocyst (Figure 1) and begins to make mRNA for human chorionic gonadotropin (hCG), the first hormone signal from the early embryo. By day 6 after fertilization the blastocyst initiates implantation into the maternal endometrium or uterine lining. Within a few days of fertilization the blastocyst becomes a spherical structure composed of two layers. The outside layer of cells become trophoblasts and the inside of a group of cells called the inner cell mass (Figure 2A) will develop into the fetus and ultimately the baby. In addition to making hCG, the trophoblasts mediate the implantation process by attaching to, and eventually invading into the endometrium (Figure 2B). Once firmly attached to the endometrium the developing conceptus grows and continues to expand into the endometrium. One of the basic paradigms which is established even within the first week of gestation is that the embryonic/fetal cells are always separated from maternal tissues and blood by a layer of cytotrophoblasts (mononuclear trophoblasts) and syncytiotrophoblasts (multinucleated trophoblasts) (Figure 2C-F). This is critical not only for nutrient exchange, but also to protect the developing fetus from maternal immunologic attack (1). Implantation is regulated by a complex interplay between trophoblasts and endometrium. On the one hand trophoblasts have a potent invasive capacity and if allowed to invade unchecked, spread throughout the uterus. The endometrium, on the other hand, controls trophoblast invasion by secreting locally acting factors (i.e. cytokines and protease inhibitors), which modulate trophoblast invasion. Within the placenta the syncytiotrophoblasts generate high levels of hCG which modulates cytotrophoblast differentiation towards a non-invasive hormone secreting villous-type trophoblast. The closer the trophoblasts are to the endometrium the less hCG is made, allowing the trophoblasts to differentiate into anchoring type cells (2). Trophoblasts that leave the placenta and migrate within the endo and myometrium are induced to make proteases and protease inhibitors, to further facilitate trophoblast invasion into the maternal tissues (2). Ultimately, normal implantation and placentation is a balance between regulatory gradients created by both the trophoblasts and the endometrium.
Analysing Lexical Semantic Change with Contextualised Word Representations

This paper presents the first unsupervised approach to lexical semantic change that makes use of contextualised word representations. We propose a novel method that exploits the BERT neural language model to obtain representations of word usages, clusters these representations into usage types, and measures change along time with three proposed metrics. We create a new evaluation dataset and show that the model representations and the detected semantic shifts are positively correlated with human judgements. Our extensive qualitative analysis demonstrates that our method captures a variety of synchronic and diachronic linguistic phenomena. We expect our work to inspire further research in this direction.
Probabilistic pointwise convergence problem of some dispersive equations

In this paper, we investigate the almost surely pointwise convergence problem of free KdV equation, free wave equation, free elliptic and non-elliptic Schrodinger equation respectively. We firstly establish some estimates related to the Wiener decomposition of frequency spaces which are just Lemmas 2.1-2.6 in this paper. Secondly, by using Lemmas 2.1-2.6, 3.1, we establish the probabilistic estimates of some random series which are just Lemmas 3.2-3.11 in this paper. Finally, combining the density theorem in L$^{2}$ with Lemmas 3.2-3.11, we obtain almost surely pointwise convergence of the solutions to corresponding equations with randomized initial data in $L^{2}$, which require much less regularity of the initial data than the rough data case. At the same time, we present the probabilistic density theorem, which is Lemma 3.11 in this paper.
Modeling Long Context for Task-Oriented Dialogue State Generation

Based on the recently proposed transferable dialogue state generator (TRADE) that predicts dialogue states from utterance-concatenated dialogue context, we propose a multi-task learning model with a simple yet effective utterance tagging technique and a bidirectional language model as an auxiliary task for task-oriented dialogue state generation. By enabling the model to learn a better representation of the long dialogue context, our approaches attempt to solve the problem that the performance of the baseline significantly drops when the input dialogue context sequence is long. In our experiments, our proposed model achieves a 7.03% relative improvement over the baseline, establishing a new state-of-the-art joint goal accuracy of 52.04% on the MultiWOZ 2.0 dataset.
Extending machine learning classification capabilities with histogram reweighting

We propose the use of Monte Carlo histogram reweighting to extrapolate predictions of machine learning methods. In our approach, we treat the output from a convolutional neural network as an observable in a statistical system, enabling its extrapolation over continuous ranges in parameter space. We demonstrate our proposal using the phase transition in the two-dimensional Ising model. By interpreting the output of the neural network as an order parameter, we explore connections with known observables in the system and investigate its scaling behavior. A finite-size scaling analysis is conducted based on quantities derived from the neural network that yields accurate estimates for the critical exponents and the critical temperature. The method improves the prospects of acquiring precision measurements from machine learning in physical systems without an order parameter and those where direct sampling in regions of parameter space might not be possible.
The Lyapunov exponents and the neighbourhood of periodic orbits

We show that the Lyapunov exponents of a periodic orbit can be easily obtained from the eigenvalues of the monodromy matrix. It turns out that the Lyapunov exponents of simply stable periodic orbits are all zero, simply unstable periodic orbits have only one positive Lyapunov exponent, doubly unstable periodic orbits have two different positive Lyapunov exponents and the two positive Lyapunov exponents of complex unstable periodic orbits are equal. We present a numerical example for periodic orbits in a realistic galactic potential. Moreover, the center manifold theorem allowed us to show that stable, simply unstable and doubly unstable periodic orbits are the mothers of families of, respectively, regular, partially and fully chaotic orbits in their neighbourhood.
DR-SPAAM: A Spatial-Attention and Auto-regressive Model for Person Detection in 2D Range Data

Detecting persons using a 2D LiDAR is a challenging task due to the low information content of 2D range data. To alleviate the problem caused by the sparsity of the LiDAR points, current state-of-the-art methods fuse multiple previous scans and perform detection using the combined scans. The downside of such a backward looking fusion is that all the scans need to be aligned explicitly, and the necessary alignment operation makes the whole pipeline more expensive – often too expensive for real-world applications. In this paper, we propose a person detection network which uses an alternative strategy to combine scans obtained at different times. Our method, Distance Robust SPatial Attention and Auto-regressive Model (DR-SPAAM), follows a forward looking paradigm. It keeps the intermediate features from the backbone network as a template and recurrently updates the template when a new scan becomes available. The updated feature template is in turn used for detecting persons currently in the scene. On the DROW dataset, our method outperforms the existing state-of-the-art, while being approximately four times faster, running at 87.2 FPS on a laptop with a dedicated GPU and at 22.6 FPS on an NVIDIA Jetson AGX embedded GPU. We release our code in PyTorch and a ROS node including pre-trained models.
Supervised MPC control of large-scale electricity networks via clustering methods

This paper describes a control approach for large-scale electricity networks, with the goal of efficiently coordinating distributed generators to balance unexpected load variations with respect to nominal forecasts. To mitigate the difficulties due to the size of the problem, the proposed methodology is divided in two steps. First, the network is partitioned into clusters, composed of several dispatchable and non dispatchable generators, storage systems, and loads. A clustering algorithm is designed with the aim of obtaining clusters with the following characteristics: (i) they must be compact, keeping the distance between generators and loads as small as possible; (ii) they must be able to internally balance load variations to the maximum possible extent. Once the network clustering has been completed, a two layer control system is designed. At the lower layer, a local Model Predictive Controller is associated to each cluster for managing the available generation and storage elements to compensate local load variations. If the local sources are not sufficient to balance the cluster's load variations, a power request is sent to the supervisory layer, which optimally distributes additional resources available from the other clusters of the network. To enhance the scalability of the approach, the supervisor is implemented relying on a fully distributed optimization algorithm. The IEEE 118-bus system is used to test the proposed design procedure in a non trivial scenario.
CMRNet++: Map and Camera Agnostic Monocular Visual Localization in LiDAR Maps

Localization is a critically essential and crucial enabler of autonomous robots. While deep learning has made significant strides in many computer vision tasks, it is still yet to make a sizeable impact on improving capabilities of metric visual localization. One of the major hindrances has been the inability of existing Convolutional Neural Network (CNN)-based pose regression methods to generalize to previously unseen places. Our recently introduced CMRNet effectively addresses this limitation by enabling map independent monocular localization in LiDAR-maps. In this paper, we now take it a step further by introducing CMRNet++, which is a significantly more robust model that not only generalizes to new places effectively, but is also independent of the camera parameters. We enable this capability by combining deep learning with geometric techniques, and by moving the metric reasoning outside the learning process. In this way, the weights of the network are not tied to a specific camera. Extensive evaluations of CMRNet++ on three challenging autonomous driving datasets, i.e., KITTI, Argoverse, and Lyft5, show that CMRNet++ outperforms CMRNet as well as other baselines by a large margin. More importantly, for the first-time, we demonstrate the ability of a deep learning approach to accurately localize without any retraining or fine-tuning in a completely new environment and independent of the camera parameters.
Modelling the role of media induced fear conditioning in mitigating post-lockdown COVID-19 pandemic: perspectives on India

Several countries that have been successful in constraining the severity of COVID-19 pandemic via "lockdown" are now considering to slowly end it, mainly because of enormous socio-economic side-effects. An abrupt ending of lockdown can increase the basic reproductive number and undo everything; therefore, carefully designed exit strategies are needed to sustain its benefits post upliftment. To study the role of fear conditioning on mitigating the spread of COVID-19 in post-lockdown phase, in this work, we propose an age- and social contact- structures dependent Susceptible, Feared, Exposed, Infected and Recovered (SFEIR) model. Simulating the SFEIR model on Indian population with fear conditioning via mass media (like, television, community radio, internet and print media) along with positive reinforcement, it is found that increase in fraction of feared people results in the significant decrease in the growth of infected population. The present study suggests that, during post-lockdown phase, media induced fear conditioning in conjunction with closure of schools for about one more year can serve as an important non-pharmaceutical intervention to substantially mitigate this pandemic in India. The proposed SFEIR model, by quantifying the influence of media in inducing fear conditioning, underlies the importance of community driven changes in country specific mitigation of COVID-19 spread in post-lockdown phase.
A new kernel-based approach for spectral estimation

The paper addresses the problem to estimate the power spectral density of an ARMA zero mean Gaussian process. We propose a kernel based maximum entropy spectral estimator. The latter searches the optimal spectrum over a class of high order autoregressive models while the penalty term induced by the kernel matrix promotes regularity and exponential decay to zero of the impulse response of the corresponding one-step ahead predictor. Moreover, the proposed method also provides a minimum phase spectral factor of the process. Numerical experiments showed the effectiveness of the proposed method.