Miracle-dz/UniFAR-FTUs huggingface dataset:
Rainfall-Runoff Prediction at Multiple Timescales with a Single Long Short-Term Memory Network

Abstract. Long Short-Term Memory (LSTM) networks have been applied to daily discharge prediction with remarkable success. Many practical applications, however, require predictions at more granular timescales. For instance, accurate prediction of short but extreme flood peaks can make a lifesaving difference, yet such peaks may escape the coarse temporal resolution of daily predictions. Naively training an LSTM on hourly data, however, entails very long input sequences that make learning difficult and computationally expensive. In this study, we propose two multi-timescale LSTM (MTS-LSTM) architectures that jointly predict multiple timescales within one model, as they process long-past inputs at a different temporal resolution than more recent inputs. In a benchmark on 516 basins across the continental United States, these models achieved significantly higher Nash–Sutcliffe efficiency (NSE) values than the US National Water Model. Compared to naive prediction with distinct LSTMs per timescale, the multi-timescale architectures are computationally more efficient with no loss in accuracy. Beyond prediction quality, the multi-timescale LSTM can process different input variables at different timescales, which is especially relevant to operational applications where the lead time of meteorological forcings depends on their temporal resolution.
Distributional Null Hypothesis Testing with the T distribution

Null Hypothesis Significance Testing (NHST) has long been central to the scientific project, guiding theory development and supporting evidence-based intervention and decision-making. Recent years, however, have seen growing awareness of serious problems with NHST as it is typically used, and hence to proposals to limit the use of NHST techniques, to abandon these techniques and move to alternative statistical approaches, or even to ban the use of NHST entirely. These proposals are premature, because the observed problems with NHST all arise as a consequence of a contingent and in many cases incorrect choice: that of NHST testing against point-form nulls. We show that testing against distributional, rather than point-form, nulls is better motivated mathematically and experimentally, and that the use of distributional nulls addresses many problems with the standard point-form NHST approach. We also show that use of distributional nulls allows a form of null hypothesis testing that takes into account both the statistical significance of a given result and the probability of replication of that result in a new experiment. Rather than abandoning NHST, we should use the NHST approach in its more general form, with distributional rather than point-form nulls.
Charge conservation, entropy current and gravitation

We propose a new class of vector fields to construct a conserved charge in a general field theory whose energy–momentum tensor is covariantly conserved. We show that there always exists such a vector field in a given field theory even without global symmetry. We also argue that the conserved current constructed from the (asymptotically) timelike vector field can be identified with the entropy current of the system. As a piece of evidence we show that the conserved charge defined therefrom satisfies the first law of thermodynamics for an isotropic system with a suitable definition of temperature. We apply our formulation to several gravitational systems such as the expanding universe, Schwarzschild and Banãdos, Teitelboim and Zanelli (BTZ) black holes, and gravitational plane waves. We confirm the conservation of the proposed entropy density under any homogeneous and isotropic expansion of the universe, the precise reproduction of the Bekenstein–Hawking entropy incorporating the first law of thermodynamics, and the existence of gravitational plane wave carrying no charge, respectively. We also comment on the energy conservation during gravitational collapse in simple models.
Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AI

Trust is a central component of the interaction between people and AI, in that 'incorrect' levels of trust may cause misuse, abuse or disuse of the technology. But what, precisely, is the nature of trust in AI? What are the prerequisites and goals of the cognitive mechanism of trust, and how can we promote them, or assess whether they are being satisfied in a given interaction? This work aims to answer these questions. We discuss a model of trust inspired by, but not identical to, interpersonal trust (i.e., trust between people) as defined by sociologists. This model rests on two key properties: the vulnerability of the user; and the ability to anticipate the impact of the AI model's decisions. We incorporate a formalization of 'contractual trust', such that trust between a user and an AI model is trust that some implicit or explicit contract will hold, and a formalization of 'trustworthiness' (that detaches from the notion of trustworthiness in sociology), and with it concepts of 'warranted' and 'unwarranted' trust. We present the possible causes of warranted trust as intrinsic reasoning and extrinsic behavior, and discuss how to design trustworthy AI, how to evaluate whether trust has manifested, and whether it is warranted. Finally, we elucidate the connection between trust and XAI using our formalization.
Enriched Derivators

The theory of derivators provides a convenient abstract setting for computing with homotopy limits and colimits. In enriched homotopy theory, the analogues of homotopy (co)limits are weighted homotopy (co)limits. In this thesis, we develop a theory of derivators and, more generally, prederivators enriched over a monoidal derivator E. In parallel to the unenriched case, these E-prederivators provide a framework for studying the constructions of enriched homotopy theory, in particular weighted homotopy (co)limits. As a precursor to E-(pre)derivators, we study E-categories, which are categories enriched over a bicategory Prof(E) associated to E. We prove a number of fundamental results about E-categories, which parallel classical results for enriched categories. In particular, we prove an E-category Yoneda lemma, and study representable maps of E-categories. In any E-category, we define notions of weighted homotopy limits and colimits. We define E-derivators to be E-categories with a number of further properties; in particular, they admit all weighted homotopy (co)limits. We show that the closed E-modules studied by Groth, Ponto and Shulman give rise to associated E-derivators, so that the theory of E-(pre)derivators captures these examples. However, by working in the more general context of E-prederivators, we can study weighted homotopy (co)limits in other settings, in particular in settings where not all weighted homotopy (co)limits exist. Using the E-category Yoneda lemma, we prove a representability theorem for E-prederivators. We show that we can use this result to deduce representability theorems for closed E-modules from representability results for their underlying categories.
Multi-Resolution 3D Mapping With Explicit Free Space Representation for Fast and Accurate Mobile Robot Motion Planning

With the aim of bridging the gap between high quality reconstruction and robot motion planning, we propose an efficient system that leverages the concept of adaptive-resolution volumetric mapping, which naturally integrates with the hierarchical decomposition of space in an octree data structure. Instead of a Truncated Signed Distance Function (TSDF), we adopt mapping of occupancy probabilities in log-odds representation, which allows to represent both surfaces, as well as the entire free, i.e. observed space, as opposed to unobserved space. We introduce a method for choosing resolution -on the fly- in real-time by means of a multi-scale max-min pooling of the input depth image. The notion of explicit free space mapping paired with the spatial hierarchy in the data structure, as well as map resolution, allows for collision queries, as needed for robot motion planning, at unprecedented speed. We quantitatively evaluate mapping accuracy, memory, runtime performance, and planning performance showing improvements over the state of the art, particularly in cases requiring high resolution maps.
Empty Cities: A Dynamic-Object-Invariant Space for Visual SLAM

In this article, we present a data-driven approach to obtain the static image of a scene, eliminating dynamic objects that might have been present at the time of traversing the scene with a camera. The general objective is to improve vision-based localization and mapping tasks in dynamic environments, where the presence (or absence) of different dynamic objects in different moments makes these tasks less robust. We introduce an end-to-end deep learning framework to turn images of an urban environment that include dynamic content, such as vehicles or pedestrians, into realistic static frames suitable for localization and mapping. This objective faces two main challenges: detecting the dynamic objects, and inpainting the static occluded background. The first challenge is addressed by the use of a convolutional network that learns a multiclass semantic segmentation of the image. The second challenge is approached with a generative adversarial model that, taking as input the original dynamic image and the computed dynamic/static binary mask, is capable of generating the final static image. This framework makes use of two new losses, one based on image steganalysis techniques, useful to improve the inpainting quality, and another one based on ORB features, designed to enhance feature matching between real and hallucinated image regions. To validate our approach, we perform an extensive evaluation on different tasks that are affected by dynamic entities, i.e.,visual odometry, place recognition, and multiview stereo, with the hallucinated images. Code has been made available on https://github.com/bertabescos/EmptyCities_SLAM.
The plateau experience: A. H. Maslow and others.

A. H. Maslow:There are two matters to bring up. One of them is something I'm interested in, and the other is something I have less interest in, but would like to bring up anyway. The second matter concerns a preface to a new edition of a little book that I did some time ago called Religions,Values, and Peak-Experiences.In it, I make varlOU! 1warnings about misinterpretations of my thesis. In the first edition of the book I had warned about the dangers in overemphasizing the legalistic, organizational, institutional, and traditional aspects of religion. In the preface to the new edition, I speak of the dangers of overemphasizing the mystical aspects of religion; some people ron the danger of turning away from the world and from other people to search for anything that will trigger peak experiences. This type of person represents the mystic gone wild.
Does Data Augmentation Benefit from Split BatchNorms

Data augmentation has emerged as a powerful technique for improving the performance of deep neural networks and led to state-of-the-art results in computer vision. However, state-of-the-art data augmentation strongly distorts training images, leading to a disparity between examples seen during training and inference. In this work, we explore a recently proposed training paradigm in order to correct for this disparity: using an auxiliary BatchNorm for the potentially out-of-distribution, strongly augmented images. Our experiments then focus on how to define the BatchNorm parameters that are used at evaluation. To eliminate the train-test disparity, we experiment with using the batch statistics defined by clean training images only, yet surprisingly find that this does not yield improvements in model performance. Instead, we investigate using BatchNorm parameters defined by weak augmentations and find that this method significantly improves the performance of common image classification benchmarks such as CIFAR-10, CIFAR-100, and ImageNet. We then explore a fundamental trade-off between accuracy and robustness coming from using different BatchNorm parameters, providing greater insight into the benefits of data augmentation on model performance.
Intermittency of Velocity Circulation in Quantum Turbulence

The velocity circulation, a measure of the rotation of a fluid within a closed path, is a fundamental observable in classical and quantum flows. It is indeed a Lagrangian invariant in inviscid classical fluids. In quantum flows, circulation is quantized, taking discrete values that are directly related to the number and the orientation of thin vortex filaments enclosed by the path. By varying the size of such closed loop, the circulation provides a measure of the dependence of the flow structure on the considered scale. Here we consider the scale dependence of circulation statistics in quantum turbulence, using high resolution direct numerical simulations of a generalized Gross-Pitaevskii model. Results are compared to the circulation statistics obtained from simulations of the incompressible Navier-Stokes equations. When the integration path is smaller than the mean inter-vortex distance, the statistics of circulation in quantum turbulence displays extreme intermittent behavior due to the quantization of circulation, in stark contrast with the viscous scales of classical flows. In contrast, at larger scales, circulation moments display striking similarities with the statistics probed in the inertial range of classical turbulence. This includes the emergence of the power law scalings predicted from Kolmogorov's 1941 theory, as well as intermittency deviations that closely follow the recently proposed bifractal model for circulation moments in classical flows. To date, this is the most convincing evidence of intermittency in the large scales of quantum turbulence. Moreover, our results strongly reinforce the resemblance between classical and quantum turbulence, highlighting the universality of inertial range dynamics, including intermittency, across these two a priori very different systems.
Species invasion in the marine fouling communities of British Columbia : factors that influence invasion dynamics and how they may affect Botrylloides violaceus

Species invasion has been recognized as a major threat to biodiversity. Knowledge of the factors that limit the establishment and spread of non-indigenous species (NIS), such as biotic resistance and unfavourable environmental conditions, are important to their effective management. To test the biotic resistance and environmental favourability hypotheses in the fouling communities of British Columbia (BC), 22 locations were compared using settlement tiles in a large-scale survey. Biotic resistance is believed to be stronger in more diverse communities, therefore NIS richness and abundance were compared to native species richness and environmental conditions to investigate their importance using generalized and linear mixed models. Invader taxonomic group may influence biotic resistance, and environmental tolerances vary by species, therefore factors that affected Botrylloides violaceus presence and abundance were investigated as a case study. The biotic resistance hypothesis was not supported for NIS richness or NIS abundance, but could not be fully discounted due to a trend toward a negative slope between native species richness and B. violaceus presence and abundance, and the absence of predator data. Environmental variables affected NIS: salinity had a positive influence on NIS richness, NIS abundance, and B. violaceus presence, and temperature had a positive effect on B. violaceus presence and abundance. Salinity had a positive impact on native species richness as well, supporting the environmental favourability hypothesis. This suggests that knowledge of relevant environmental conditions is more important for the management of invasive species than
Amplified predation after fire suppresses rodent populations in Australia’s tropical savannas

Abstract Context. Changes in abundance following fire are commonly reported for vertebrate species, but the mechanisms causing these changes are rarely tested. Currently, many species of small mammals are declining in the savannas of northern Australia. These declines have been linked to intense and frequent fires in the late dry season; however, why such fires cause declines of small mammals is unknown. Aims. We aimed to discover the mechanisms causing decline in abundance of two species of small mammals, the pale field rat, Rattus tunneyi, and the western chestnut mouse, Pseudomys nanus, in response to fire. Candidate mechanisms were (1) direct mortality because of fire itself, (2) mortality after fire because of removal of food by fire, (3) reduced reproductive success, (4) emigration, and (5) increased mortality because of predation following fire. Methods. We used live trapping to monitor populations of these two species under the following three experimental fire treatments: high-intensity fire that removed all ground vegetation, low-intensity fire that produced a patchy burn, and an unburnt control. We also radio-tracked 38 R. tunneyi individuals to discover the fates of individual animals. Key results. Abundance of both species declined after fire, and especially following the high-intensity burn. There was no support for any of the first four mechanisms of population decline, but mortality owing to predation increased after fire. This was related to loss of ground cover (which was greater in the high-intensity fire treatment), which evidently left animals exposed to predators. Also, local activity of two predators, feral cats and dingoes, increased after the burns, and we found direct evidence of predation by feral cats and snakes. Conclusions. Fire in the northern savannas has little direct effect on populations of these small mammals, but it causes declines by amplifying the impacts of predators. These effects are most severe for high-intensity burns that remove a high proportion of vegetation cover. Implications. To prevent further declines in northern Australia, fire should be managed in ways that limit the effects of increased predation. This could be achieved by setting cool fires that produce patchy burns, avoiding hot fires, and minimising the total area burnt.
Les rongeurs (Mammalia) de l’Oligocène supérieur de Coderet-Bransat (Allier)

De nouvelles fouilles dans le gisement de Coderet, gisement-type d'une zone de Mammiferes de l'Oligocene superieur, le lavage et le tri de 10 tonnes de marnes fossiliferes ont fourni un materiel extremement abondant en petits vertebres de tous ordres. Parmi les Mammiferes, les Rongeurs dominent par le nombre des especes et des individus ; l'examen et les mesures de plusieurs milliers de dents, leur comparaison avec les populations d'autres gisements d'âge voisin ont permis de porter a 22 le nombre d'especes reconnues dans ce gisement alors que 8 seulement etaient signalees. Certaines formes sont nouvelles : Eucricetodon collatus longidens nov. subsp. ; Eucricetodon ? thaleri nov. sp. ; Adelomyarion vireti nov. gen., nov. sp. ; Rhodanomys schlosseri transiens nov. subsp. ; Heteroxerus lavocati nov. sp. ; "Sciurus" solitarius nov. sp. Les Glirides Gliravus bruijni Hugueney, Pseudodryomys fugax Hugueney et Bransatoglis concavidens Hugueney, decrits de facon sommaire dans une note preliminaire, sont etudies plus en detail. L'etude des Eomyides dans une succession de petits gisements allant de l'Oligocene superieur au Miocene moyen a montre que ce groupe, encore mal connu, n'est pas denue d'interet biostratigraphique, comme d'ailleurs les Cricetides, les Sciurides et les Glirides. La description du seul avant-crâne actuellement connu de Rhizospalax a conduit a rapprocher cette forme, d'affinites jusqu'alors incertaines, des Castorides. La riche faune de Rongeurs de Coderet apporte une importante contribution a notre connaissance de l'Oligocene superieur.
Tracking Results and Utilization of Artificial Intelligence (tru-AI) in Radiology: Early-Stage COVID-19 Pandemic Observations

Objective: To introduce a method for tracking results and utilization of Artificial Intelligence (tru-AI) in radiology. By tracking both large-scale utilization and AI results data, the tru-AI approach is designed to calculate surrogates for measuring important disease-related observational quantities over time, such as the prevalence of intracranial hemorrhage during the COVID-19 pandemic outbreak. Methods: To quantitatively investigate the clinical applicability of the tru-AI approach, we analyzed service requests for automatically identifying intracranial hemorrhage (ICH) on head CT using a commercial AI solution. This software is typically used for AI-based prioritization of radiologists' reading lists for reducing turnaround times in patients with emergent clinical findings, such as ICH or pulmonary embolism.We analyzed data of N=9,421 emergency-setting non-contrast head CT studies at a major US healthcare system acquired from November 1, 2019 through June 2, 2020, and compared two observation periods, namely (i) a pre-pandemic epoch from November 1, 2019 through February 29, 2020, and (ii) a period during the COVID-19 pandemic outbreak, April 1-30, 2020. Results: Although daily CT scan counts were significantly lower during (40.1 +/- 7.9) than before (44.4 +/- 7.6) the COVID-19 outbreak, we found that ICH was more likely to be observed by AI during than before the COVID-19 outbreak (p<0.05), with approximately one daily ICH+ case more than statistically expected. Conclusion: Our results suggest that, by tracking both large-scale utilization and AI results data in radiology, the tru-AI approach can contribute clinical value as a versatile exploratory tool, aiming at a better understanding of pandemic-related effects on healthcare.
Revisiting Burton and Likens (1975): Nutrient Standing Stock and Biomass of a Terrestrial Salamander in the Midwestern United States

Animals found in high densities can have significant influence in nutrient cycles of ecosystems. For example, frogs have been known to influence nutrient cycles in tropical forests. However, research understanding the influence of vertebrates in nutrient cycles of North American forest is limited. It has been found that the biomass of terrestrial salamanders (family Plethodontidae) is higher than that of birds, small mammals, and deer in a New Hampshire forest, and recent studies have found prior estimates of terrestrial salamander densities are likely lower than current estimates using sampling and analysis frameworks to account for imperfect detection. A re-evaluation of the impact plethodontid salamanders could have on forest nutrient cycles is therefore justified. We quantified the degree to which a completely terrestrial, lungless salamander (Plethodon albagula; Western Slimy Salamander) constitutes a standing stock of limiting nutrients in a Missouri, USA forest ecosystem. We utilized values of whole-body nutrient composition of carbon (C), nitrogen (N), phosphorus (P), calcium (Ca), and magnesium (Mg) of P. albagula and spatially explicit density estimates to approximate the contribution of P. albagula to forest nutrient cycles. We found that estimates of the standing crop nutrients varied spatially across the landscape and were dictated by density of P. albagula. Standing crop estimates were lower than measures for leaf litter, but often were greater than those previously reported for plethodontid salamanders, birds, and in some cases small mammals and deer in North American forests.
New action for cosmology

We present a new action which reproduces the cosmological sector of general relativity in both the Friedmann-Lemaitre-Robertson-Walker (FLRW) and Bianchi models. This action makes no reference to the scale factor, and is of a frictional type first examined by Herglotz. We demonstrate that the extremization of this action reproduces the usual dynamics of physical observables, and the symplectification of this action is the Einstein-Hilbert action for cosmological models. We end by discussing some of the increased explanatory power produced by considering the reduced physical ontology resulting from eliminating scale.