Miracle-dz/UniFAR-FTUs huggingface dataset:
A Review of Emergency Incident Prediction, Resource Allocation and Dispatch Models

Emergency response to incidents such as accidents, medical calls, and fires is one of the most pressing problems faced by communities across the globe. In the last fifty years, researchers have developed statistical, analytical, and algorithmic approaches for designing emergency response management (ERM) systems. In this survey, we present models for incident prediction, resource allocation, and dispatch for emergency incidents. We highlight the strengths and weaknesses of prior work in this domain and explore the similarities and differences between different modeling paradigms. Finally, we present future research directions. To the best of our knowledge, our work is the first comprehensive survey that explores the entirety of ERM systems.
Disinformation and Misinformation on Twitter during the Novel Coronavirus Outbreak

As the novel coronavirus spread globally, a growing public panic was expressed over the internet. We examine the public discussion concerning COVID-19 on Twitter. We use a dataset of 67 million tweets from 12 million users collected between January 29, 2020 and March 4, 2020. We categorize users based on their home countries, social identities, and political orientation. We find that news media, government officials, and individual news reporters posted a majority of influential tweets, while the most influential ones are still written by regular users. Tweets mentioning "fake news" URLs and disinformation story-lines are also more likely to be spread by regular users. Unlike real news and normal tweets, tweets containing URLs pointing to "fake news" sites are most likely to be retweeted within the source country and so are less likely to spread internationally.
Boundary-layer effects on electromagnetic and acoustic extraordinary transmission through narrow slits

We study the problem of resonant extraordinary transmission of electromagnetic and acoustic waves through subwavelength slits in an infinite plate, whose thickness is close to a half-multiple of the wavelength. We build on the matched-asymptotics analysis of Holley & Schnitzer (2019 Wave Motion 91, 102381 (doi:10.1016/j.wavemoti.2019.102381)), who considered a single-slit system assuming an idealized formulation where dissipation is neglected and the electromagnetic and acoustic problems are analogous. We here extend that theory to include thin dissipative boundary layers associated with finite conductivity of the plate in the electromagnetic problem and viscous and thermal effects in the acoustic problem, considering both single-slit and slit-array configurations. By considering a distinguished boundary-layer scaling where dissipative and diffractive effects are comparable, we develop accurate analytical approximations that are generally valid near resonance; the electromagnetic–acoustic analogy is preserved up to a single parameter that is provided explicitly for both scenarios. The theory is shown to be in excellent agreement with GHz-microwave and kHz-acoustic experiments in the literature.
Kinematics Based Visual Localization for Skid-Steering Robots: Algorithm and Theory

In recent years, the volume of the commercial mobile robot market has been continuously growing due to the maturity of both hardware and software technology. To build commercial robots, skid-steering mechanical design is of increased popularity due to its manufacturing simplicity and unique hardware properties. However, this causes challenges on software and algorithm design, especially for localization (i.e., determining the robot's rotation and position). While the general localization algorithms have been extensively studied in research communities, there are still fundamental problems that need to be resolved for localizing skid-steering robots. To tackle this problem, we propose a probabilistic sliding-window estimator dedicated to skid-steering robots, using measurements from a monocular camera, the wheel encoders, and optionally an inertial measurement unit (IMU). Specifically, we explicitly model the kinematics of skid-steering robots by both track instantaneous centers of rotation (ICRs) and correction factors, which are capable of compensating for the complexity of track-to-terrain interaction, the imperfectness of mechanical design, terrain conditions and smoothness, and so on. These time and location varying kinematic parameters are estimated online along with other localization states in a tightly-coupled manner. More importantly, we conduct in-depth observability analysis for different sensors and design configurations in this paper, which provides us with theoretical tools in making the correct choice when building real commercial robots. In our experiments, we validate the proposed method by both simulation tests and real-world experiments, which demonstrate that our method outperforms competing methods by wide margins.
Distribution of Chrysaora quinquecirrha in the York River

Distribution, quantitative abundance, and period of maximum set of the polyp stage of the sea nettleChrysaora quinquecirrha DeSor were investigated in the York River, Virginia, from September 1966 to November 1967. The distribution and abundance of the medusoid stage were also determined. Effects of varying salinity and temperature on the polyp were investigated in the laboratory. The polyp stage occurred on oysters and shells dredged in a 12-mile segment of the river. Medusae appeared in tributary creeks in June, 15 to 30 days carlier than in the river. In the river, medusae were observed between 12 June and 2 October; peak abundance was reached during the week of 17–21 July.
Training Deep Spiking Neural Networks

Computation using brain-inspired spiking neural networks (SNNs) with neuromorphic hardware may offer orders of magnitude higher energy efficiency compared to the current analog neural networks (ANNs). Unfortunately, training SNNs with the same number of layers as state of the art ANNs remains a challenge. To our knowledge the only method which is successful in this regard is supervised training of ANN and then converting it to SNN. In this work we directly train deep SNNs using backpropagation with surrogate gradient and find that due to implicitly recurrent nature of feed forward SNN's the exploding or vanishing gradient problem severely hinders their training. We show that this problem can be solved by tuning the surrogate gradient function. We also propose using batch normalization from ANN literature on input currents of SNN neurons. Using these improvements we show that is is possible to train SNN with ResNet50 architecture on CIFAR100 and Imagenette object recognition datasets. The trained SNN falls behind in accuracy compared to analogous ANN but requires several orders of magnitude less inference time steps (as low as 10) to reach good accuracy compared to SNNs obtained by conversion from ANN which require on the order of 1000 time steps.
Linformer: Self-Attention with Linear Complexity

Large transformer models have shown extraordinary success in achieving state-of-the-art results in many natural language processing applications. However, training and deploying these models can be prohibitively costly for long sequences, as the standard self-attention mechanism of the Transformer uses $O(n^2)$ time and space with respect to sequence length. In this paper, we demonstrate that the self-attention mechanism can be approximated by a low-rank matrix. We further exploit this finding to propose a new self-attention mechanism, which reduces the overall self-attention complexity from $O(n^2)$ to $O(n)$ in both time and space. The resulting linear transformer, the \textit{Linformer}, performs on par with standard Transformer models, while being much more memory- and time-efficient.
End-to-end learning for semiquantitative rating of COVID-19 severity on Chest X-rays

In this work we design an end-to-end deep learning architecture for predicting, on Chest X-rays images (CXR), a multi-regional score conveying the degree of lung compromise in COVID-19 patients. Such semi-quantitative scoring system, namely \texttt{Brixia~score}, is applied in serial monitoring of such patients, showing significant prognostic value, in one of the hospitals that experienced one of the highest pandemic peaks in Italy. To solve such a challenging visual task, we adopt a weakly supervised learning strategy structured to handle different tasks (segmentation, spatial alignment, and score estimation) trained with a "from part to whole" procedure involving different datasets. In particular, we exploit a clinical dataset of almost 5,000 CXR annotated images collected in the same hospital. Our BS-Net demonstrates self-attentive behavior and a high degree of accuracy in all processing stages. Through inter-rater agreement tests and a gold standard comparison, we show that our solution outperforms single human annotators in rating accuracy and consistency, thus supporting the possibility of using this tool in contexts of computer-assisted monitoring. Highly resolved (super-pixel level) explainability maps are also generated, with an original technique, to visually help the understanding of the network activity on the lung areas. We also consider other scores proposed in literature and provide a comparison with a recently proposed non-specific approach. We eventually test the performance robustness of our model on a variegated public COVID-19 dataset, for which we also provide \texttt{Brixia~score} annotations, observing good direct generalization and fine-tuning capabilities that highlight the portability of BS-Net in other clinical settings. The CXR dataset along with the source code and the trained model are publicly released for research purposes.
Pebbles versus planetesimals

In the core accretion scenario, a massive core forms first and then accretes an envelope. When discussing how this core forms some divergences appear. First scenarios of planet formation predict the accretion of km-sized bodies, called planetesimals, while more recent works suggest growth by accretion of pebbles, which are cm-sized objects. These two accretion models are often discussed separately and we aim here at comparing the outcomes of the two models with identical initial conditions. We use two distinct codes: one computing planetesimal accretion, the other pebble accretion. Using a population synthesis approach, we compare planet simulations and study the impact of the two solid accretion models, focussing on the formation of single planets. We find that the planetesimal model predicts the formation of more giant planets, while the pebble accretion model forms more super-Earth mass planets. This is due to the pebble isolation mass concept, which prevents planets formed by pebble accretion to accrete gas efficiently before reaching Miso. This translates into a population of planets that are not heavy enough to accrete a consequent envelope but that are in a mass range where type I migration is very efficient. We also find higher gas mass fractions for a given core mass for the pebble model compared to the planetesimal one caused by luminosity differences. This also implies planets with lower densities which could be confirmed observationally. Focusing on giant planets, we conclude that the sensitivity of their formation differs: for the pebble accretion model, the time at which the embryos are formed, as well as the period over which solids are accreted strongly impact the results, while for the planetesimal model it depends on the planetesimal size and on the splitting in the amount of solids available to form planetesimals.
Laplacian-Level Quantum Hydrodynamic Theory for Plasmonics

An accurate description of the optical response of subwavelength metallic particles and nanogap structures is a key problem of plasmonics. Quantum hydrodynamic theory (QHT) has emerged as a powerful method to calculate the optical response of metallic nanoparticles since it takes into account nonlocality and spill-out effects. Nevertheless, the absorption spectra of metallic particles from QHT is affected, at energies higher than the main plasmon peak, by several additional peaks, which are instead largely damped in reference time-dependent density-functional theory calculations. Moreover, we show here that these peaks have a strong dependence on the simulation domain-size so that the numerical convergence of QHT calculations is problematic. In this article, we introduce a QHT method accounting for kinetic energy contributions depending on the Laplacian of the electronic density, thus beyond the gradient-only dependence of conventional QHT. In this way, only the main plasmon peak is obtained, with a numerically stable absorption spectrum and more accurate intensities of the plasmon peak. Thus, the Laplacian-level QHT represents a novel, efficient and accurate platform to study plasmonic systems.
Braids, inner automorphisms and the Andreadakis problem

In this paper, we generalize the tools that were introduced in [Dar19b] in order to study the Andreadakis problem for subgroups of IAn. In particular, we study the behaviour of the Andreadakis problem when we add inner automorphisms to a subgroup of IAn. We notably use this to show that the Andreadakis equality holds for the pure braid group on n strands modulo its center acting on the free group on n-1 generators , that is, for the (pure, based) mapping class group of the n-punctured sphere acting on its fundamental group.
Genetic Improvement for Yield and Fertility of Alfalfa Cultivars Representing Different Eras of Breeding

Alfalfa (Medicago sativa L.) exhibits inbreeding depression for forage yield and also tends to set fewer seeds following self-pollination than following cross-pollination. The objectives of this study were to estimate changes in yield potential, inbreeding depression for yield, and self- and cross-fertilities of alfalfa cultivars adapted to Wisconsin and representing different eras of breeding from 1898 to 1985. In addition, two University of Wisconsin-Madison experimental cultivars with well-known breeding histories were included in the study. The S 0 and S 1 populations derived from each cultivar were evaluated for 3 yr for forage yield [. ]
Multicarving for high-dimensional post-selection inference

We consider post-selection inference for high-dimensional (generalized) linear models. Data carving (Fithian et al., 2014) is a promising technique to perform this task. However, it suffers from the instability of the model selector and hence may lead to poor replicability, especially in high-dimensional settings. We propose the multicarve method inspired by multisplitting, to improve upon stability and replicability. Furthermore, we extend existing concepts to group inference and illustrate the applicability of the methodology also for generalized linear models.
The carcinogenic effect of low doses: the validity of the linear no-threshold relationship

The linear no-threshold relationship (LNT) has been advocated by the International Commission on Radiological Protection (ICRP) for assessing the carcinogenic effect of low dose irradiation (below 200 mSv). Initially, the main argument of its proponents was that risks were unlikely to be underestimated with LNT. Subsequently, it has been argued that: the traversal of a cell nucleus by one track can cause a double-strand break and since the probability of disrepair is never zero there is no inferior limit to the use of LNT; the repair probability of the complex DNA lesion caused by radiation is much smaller than that of the lesion caused by cell metabolism and is not much influenced by dose and dose rate; and this relationship is compatible with epidemiological and experimental data. Gradually, what was initially an hypothesis has become a dogma. The arguments of the opponents to LNT are given. In conclusion, for doses between 200 and 20 mSv the linear extrapolation could provide the superior limit of the risk although the actual risk might be much smaller. Assessment by LNT of the carcinogenic effect for doses below 10 mSv is not scientifically justified and should be banned. Doses below 1 mSv/year should not be taken into account for risk assessment.
Overwintering of Culicoides spp. (Diptera: Ceratopogonidae) in the Hunter Valley, New South Wales

In a study of the overwintering ability of Culicoides spp, at the southern limits of the distribution of Culicoides brevitarsis Kieffer, it was found that C. brevitarsis numbers declined linearly with temperature during winter but were independent of rainfall. Regression equations were used to calculate lower thresholds for C. brevitarsis activity/presence. These thresholds were similar for three different collection methods. Results obtained from light trap data for four other Culicoides spp showed that their relationship with temperature allowed their recovery after winter. Under suitable conditions, oviposition by C. brevitarsis continued into winter and immature stages survived for 1 to 2 months (mainly as pupae/pre‐pupae) in cattle dung, its breeding habitat. Survival of C. brevitarsis was affected by the time spent at temperatures below the estimated thresholds. Reinfestations from coastal regions further north could be responsible for the reappearance of C. brevitarsis in years when they fail to survive in the central coastal region of NSW. Conversely, the other Culicoides spp. recorded appeared capable of maintaining viable populations.
ANGIOSPERM DIVERSITY OF LAWACHARA NATIONAL PARK (BANGLADESH): A PRELIMINARY ASSESSMENT

The present article focuses on an inventory of angiosperm diversity of Lawachara National Park. Using traditional taxonomic techniques data have been collected from the p ark during January to October 2008. In this preliminary assessment, a total of 374 angiosperm species including cultivated ones have been catalogued under 84 families. Nineteen threatened plant species have also been recognized in the p ark. Keywords: Preliminary assessment; a ngiosperm diversity; Lawachara National Park. DOI: 10.3329/bjpt.v17i1. 5387 Bangladesh J. Plant Taxon. 17(1): 9-22, 2010 (June)
Chromatic Learning for Sparse Datasets

Learning over sparse, high-dimensional data frequently necessitates the use of specialized methods such as the hashing trick. In this work, we design a highly scalable alternative approach that leverages the low degree of feature co-occurrences present in many practical settings. This approach, which we call Chromatic Learning (CL), obtains a low-dimensional dense feature representation by performing graph coloring over the co-occurrence graph of features---an approach previously used as a runtime performance optimization for GBDT training. This color-based dense representation can be combined with additional dense categorical encoding approaches, e.g., submodular feature compression, to further reduce dimensionality. CL exhibits linear parallelizability and consumes memory linear in the size of the co-occurrence graph. By leveraging the structural properties of the co-occurrence graph, CL can compress sparse datasets, such as KDD Cup 2012, that contain over 50M features down to 1024, using an order of magnitude fewer features than frequency-based truncation and the hashing trick while maintaining the same test error for linear models. This compression further enables the use of deep networks in this wide, sparse setting, where CL similarly has favorable performance compared to existing baselines for budgeted input dimension.
Growth and Nutrient Accumulation of Winged Bean and Velvet Bean as Cover Crops in a Subtropical Region

We examined biomass dry matter and nutrient uptake of live plant parts, leaf area index, and litter of winged bean (Psophocarpus tetragonolobus) and velvet bean (Mucuna pruriens) 12, 18, 24 and 30 weeks after sowing (WAS). The two plants had similar leaf and stem+petiole biomasses. At 30 WAS winged bean had a significantly lower pod yield than velvet bean. Between 18 and 30 WAS, winged bean produced less litter than velvet bean due to differences in growth stages. The total mulch of live parts and litter of winged bean and velvet bean completely covered the ground by 18 and 12 WAS, respectively. Compared to velvet bean, the leaf and stem+petiole of winged bean had a significantly higher N concentration; significantly higher N uptake at 24 and 30 WAS; significantly lower C/N ratio; and significantly higher P, K and Mg concentrations. In winged bean, P uptake was significantly higher in the leaf at 30 WAS and in the stem+petiole at all harvesting times. The total biomass of the leaf, stem+petiole and litter of winged bean was 317–561 g DM m-2, and their N content was 12.3–17.7 g m-2. The total biomass of live parts and litter of winged bean might be sufficient to suppress weeds and increase soil N. Winged bean is an appropriate legume cover crop and green manure due to its longer growing period and superior ground-covering ability and high N input.
CycleGT: Unsupervised Graph-to-Text and Text-to-Graph Generation via Cycle Training

Two important tasks at the intersection of knowledge graphs and natural language processing are graph-to-text (G2T) and text-tograph (T2G) conversion. Due to the difficulty and high cost of data collection, the supervised data available in the two fields are usually on the magnitude of tens of thousands, for example, 18K in the WebNLG 2017 dataset after preprocessing, which is far fewer than the millions of data for other tasks such as machine translation. Consequently, deep learning models for G2T and T2G suffer largely from scarce training data. We present CycleGT, an unsupervised training method that can bootstrap from fully non-parallel graph and text data, and iteratively back translate between the two forms. Experiments on WebNLG datasets show that our unsupervised model trained on the same number of data achieves performance on par with several fully supervised models. Further experiments on the non-parallel GenWiki dataset verify that our method performs the best among unsupervised baselines. This validates our framework as an effective approach to overcome the data scarcity problem in the fields of G2T and T2G.
Fast and powerful conditional randomization testing via distillation.

We consider the problem of conditional independence testing: given a response Y and covariates (X,Z), we test the null hypothesis that Y⫫X∣Z. The conditional randomization test was recently proposed as a way to use distributional information about X∣Z to exactly and nonasymptotically control Type-I error using any test statistic in any dimensionality without assuming anything about Y∣(X,Z). This flexibility, in principle, allows one to derive powerful test statistics from complex prediction algorithms while maintaining statistical validity. Yet the direct use of such advanced test statistics in the conditional randomization test is prohibitively computationally expensive, especially with multiple testing, due to the requirement to recompute the test statistic many times on resampled data. We propose the distilled conditional randomization test, a novel approach to using state-of-the-art machine learning algorithms in the conditional randomization test while drastically reducing the number of times those algorithms need to be run, thereby taking advantage of their power and the conditional randomization test's statistical guarantees without suffering the usual computational expense. In addition to distillation, we propose a number of other tricks, like screening and recycling computations, to further speed up the conditional randomization test without sacrificing its high power and exact validity. Indeed, we show in simulations that all our proposals combined lead to a test that has similar power to the most powerful existing conditional randomization test implementations, but requires orders of magnitude less computation, making it a practical tool even for large datasets. We demonstrate these benefits on a breast cancer dataset by identifying biomarkers related to cancer stage.