Miracle-dz/UniFAR-FTUs huggingface dataset:
Identification of Auroral Electron Precipitation Mechanism Combinations and Their Relationships to Net Downgoing Energy and Number Flux

We present initial statistical results of a new methodology for identifying electron precipitation mechanisms in Earth's auroral zone. Unlike previous methodologies, it identifies multiple mechanisms observed in the same event, utilizing Fast Auroral Snapshot measurements of upward energy and pitch angle spectra in addition to downward energy spectra. For intense precipitation (peak downgoing differential energy flux >108 eV/cm2‐s‐sr‐eV) our method separately identifies the three main precipitation mechanisms: quasi‐static potential structure (inverted‐V, QSPS) acceleration, Alfvénic acceleration, and wave scattering or other nonaccelerated isotropic (diffuse) precipitation. Intense precipitation (~14% of all Fast Auroral Snapshot coverage) accounts for ~80–90% of electron number flux into the ionosphere globally and ~65% of the energy flux on the nightside. It is found that two or more different mechanisms occur in the same event ~60–75% of the time. Alfvénic and QSPS acceleration and the combination of the two contribute substantially. Each of the three primary precipitation mechanisms (alone or in combination) occur >~35% of the time with QSPS and Alfvénic acceleration observed together being the dominant identifiable energy precipitation mechanism/combination. This combination also significantly contributes to the net number flux. QSPS acceleration is the most prevalently observed mechanism (50–60%). The mechanism inferred from classification by downgoing spectral characteristics alone (i.e., monoenergetic = QSPS, broadband = Alfvénic, and diffuse = nonaccelerated isotropic) is not observed in the classification using our method ~20–65% of the time. The results do not confirm and may be inconsistent with wave scattering of electrons (diffuse auroral precipitation) being the dominant mechanism for electron energy and number flux into the ionosphere.
Credit-Market Sentiment and the Business Cycle

Using U.S. data from 1929 to 2015, we show that elevated credit-market sentiment in year t − 2 is associated with a decline in economic activity in years t and t + 1. Underlying this result is the existence of predictable mean reversion in credit-market conditions. When credit risk is aggressively priced, spreads subsequently widen. The timing of this widening is, in turn, closely tied to the onset of a contraction in economic activity. Exploring the mechanism, we find that buoyant credit-market sentiment in year t − 2 also forecasts a change in the composition of external finance: net debt issuance falls in year t, while net equity issuance increases, consistent with the reversal in credit-market conditions leading to an inward shift in credit supply. Unlike much of the current literature on the role of financial frictions in macroeconomics, this article suggests that investor sentiment in credit markets can be an important driver of economic fluctuations.
Connectedness in times of ecological overshoot: a quantitative analysis of the sustainability of globalization

This thesis sets out on a research journey to quantitatively assess the sustainability of globalisation and to gain insights into the main research question ‘to what extent is globalisation as a process and policy discourse sustainable? ’ We find “a world out of balance” as the emerging qualification for all four issues of globalisation: definition and measurement, recent trends, its consequences and the social robustness of the policy discourse. Hence, we conclude that globalisation is not sustainable in its recent form and that it needs to go into new directions if it is to contribute towards all aspects of sustainable development.
Digital Commons @ Trinity Digital Commons @ Trinity

: Understanding individual threat avoidance motivation and behavior is a critical component in designing effective cyber security solutions for both users and organizations. Technology threat avoidance theory (TTAT) asserts that individuals’ perceptions regarding their susceptibility to and the resulting severity of technology threats influence their awareness of the threats, which, in turn, influences their motivation and behavior to avoid them. While TTAT provides cogently and logically explains individuals’ technology threat motivations and behaviors, empirical tests have produced equivocal results particularly in terms of the influence of susceptibility and severity on threat perceptions. Due to these inconsistencies in the threat calculus involving susceptibility, severity, and threat, we need more work to improve and understand individual threat motivations. Additionally, TTAT does not account for individual differences such as risk propensity, distrust propensity, and impulsivity that have been shown to affect cyber security behavior. To address these gaps, we present an empirical assessment of a refined TTAT model, which includes individual differences and models the influence of susceptibility on threat perceptions as partially mediated by severity. Results indicate that, while perceived susceptibility is a significant predictor of threat perceptions, severity perceptions partially mediates its effect. Our results also support the inclusion of risk propensity and distrust propensity in the TTAT model as personal characteristics that significantly affect overall threat perceptions.
Noise Contrastive Priors for Functional Uncertainty

Obtaining reliable uncertainty estimates of neural network predictions is a long standing challenge. Bayesian neural networks have been proposed as a solution, but it remains open how to specify their prior. In particular, the common practice of a standard normal prior in weight space imposes only weak regularities, causing the function posterior to possibly generalize in unforeseen ways on inputs outside of the training distribution. We propose noise contrastive priors (NCPs) to obtain reliable uncertainty estimates. The key idea is to train the model to output high uncertainty for data points outside of the training distribution. NCPs do so using an input prior, which adds noise to the inputs of the current mini batch, and an output prior, which is a wide distribution given these inputs. NCPs are compatible with any model that can output uncertainty estimates, are easy to scale, and yield reliable uncertainty estimates throughout training. Empirically, we show that NCPs prevent overﬁtting outside of the training distribution and result in uncertainty estimates that are useful for active learning. We demonstrate the scalability of our method on the ﬂight delays data set, where we signiﬁcantly improve upon previously published results.
I NTERPRETING D ISTRIBUTIONAL R EINFORCEMENT L EARNING : A R EGULARIZATION P ERSPECTIVE

Distributional reinforcement learning (RL) is a class of state-of-the-art algorithms that estimate the whole distribution of the total return rather than only its expectation. Despite the remarkable performance of distributional RL, a theoretical understanding of its advantages over expectation-based RL remains elusive. In this paper, we attribute the superiority of distributional RL to its regularization effect in terms of the value distribution information regardless of its expectation. Firstly, by leverage of a variant of the gross error model in robust statistics, we decompose the value distribution into its expectation and the remaining distribution part. As such, the extra benefit of distributional RL compared with expectationbased RL is mainly interpreted as the impact of a risk-sensitive entropy regularization within the Neural Fitted Z-Iteration framework. Meanwhile, we establish a bridge between the risk-sensitive entropy regularization of distributional RL and the vanilla entropy in maximum entropy RL, focusing specifically on actor-critic algorithms. It reveals that distributional RL induces a corrected reward function and thus promotes a risk-sensitive exploration against the intrinsic uncertainty of the environment. Finally, extensive experiments corroborate the role of regularization effect of distributional RL and uncover mutual impacts of different entropy regularization. Our research paves a way towards better interpreting the efficacy of distributional RL algorithms, especially through the lens of regularization.
Syntaxonomical analysis of the Kobresio myosuroidis-Seslerietea caeruleae and Carici rupestris-Kobresietea bellardii classes in the central southern Apennines.

The aim of this study is a critical analysis of the syntaxonomic classification of the primary grasslands of the central-southern Apennines with a dominance of Sesleria juncifolia ssp. juncifolia and of those with a predominance of Kobresia myosuroides. The hypothesis considered is the coexistence in the Apennines of the classes Kobresio myosuroidis-Seslerietea caeruleae (=Elyno myosuroidis-Seslerietea caeruleae) and Carici rupestris-Kobresietea bellardii. 277 phytosociological releves were analysed, which relate to seven different plant associations of the central-southern Appenines; 80 of these releves were unpublished. A syntaxonomic revision is proposed, according to which the coenoses with a dominance of Sesleria juncifolia ssp. juncifolia, attributed to the class Kobresio-Seslerietea, to the order Seslerietalia tenuifoliae, to the suborder Seslerienalia apenninae and to the alliance Seslerion apenninae, are distinct from those with a prevalence of Kobresia myosuroides, ascribed to the class Carici-Kobresietea, to the order Oxytropido-Kobresietalia, to the alliance Oxytropido-Elynion and to the suballiance Leontopodio-Elynenion. The proposal of Bruno & Furnari (1966) is reconsidered, with regard to the Apennine order Seslerietalia apenninae, which is included as a suborder (Seslerienalia apenninae) in the Balcan-Apennine order Seslerietalia tenuifoliae. The syntaxonomic collocation of the alliance LeontopodioElynenion is re-examined, which is assigned to the class Carici-Kobresietea, as a modification of the previous interpretation that assigned it to the class Kobresio-Seslerietea (Blasi et al., 2003). Seven new subassociations are proposed: hieracietosum cymosi and anthylletosum pulchellae, referred to the association Carici humilis-Seslerietum apenninae; festucetosum alfredianae, referred to the association Seslerietum apenninae; polygaletosum alpestris, referred to the association Seslerio apenninae-Dryadetum octopetalae; anthylletosum weldenianae and helianthemetosum grandiflorii , referred to the association Helianthemo alpestris-Festucetum italicae; and saxifragetosum speciosae, referred to the association Galio magellensis-Silenetum acaulis.
A Review of Parallel Processing Approaches to Robot Kinematics and Jacobian

Due to continuously increasing demands in the area of advanced robot control, it became necessary to speed up the computation. One way to reduce the computation time is to distribute the computation onto several processing units. In this survey we present different approaches to parallel computation of robot kinematics and Jacobian. Thereby, we discuss both the forward and the reverse problem. We introduce a classification scheme and classify the references by this scheme.
Preserving Incumbent Users’ Privacy in Exclusion-Zone-Based Spectrum Access Systems

Dynamic spectrum access (DSA) technique has emerged as a fundamental approach to mitigate the spectrum scarcity problem. As a key form of DSA, the government is proposing to release more federal spectrum for sharing with commercial wireless users. However, the flourish of federal-commercial sharing hinges upon how the federal privacy is managed. In current DSA proposals, the sensitive exclusion zone (E-Zone) information of federal incumbent users (IUs) needs to be shared with a spectrum access system (SAS) to realize spectrum allocation. However, SAS is not necessarily trust-worthy for holding the sensitive IU E-Zone data, especially considering that FCC allows some industry third parties (e.g., Google) to operate SAS for better efficiency and scalability. Therefore, the current proposals dissatisfy the IUs' privacy requirement. To address the privacy issue, this paper presents an IU-privacy-preserving SAS (IP-SAS) design, which realizes the spectrum allocation process through secure computation over ciphertext based on homomorphic encryption so that none of the IU EZone information is exposed to SAS. This paper also proposes mechanisms to prevent malicious parties from compromising IP-SAS. We prove the privacy-preserving properties of IP-SAS and demonstrate the scalability and practicality of IP-SAS using experiments based on real-world data. Evaluation results show that IP-SAS can respond an SU's spectrum request in 1.25 seconds with communication overhead of 17.8 KB.
Malawi - Karonga HDSS INDEPTH Core Dataset 2003 - 2012 Release 2015

The Karonga Health and Demographic Surveillance System (Karonga HDSS) in northern Malawi currently has a population of more than 35 000 individuals under continuous demographic surveillance since completion of a baseline census (2002-2004). The surveillance system collects data on vital events and migration for individuals and for households. It also provides data on cause-specific mortality obtained by verbal autopsy for all age groups, and estimates rates of disease for specific presentations via linkage to clinical facility data. The Karonga HDSS provides a structure for surveys of socio-economic status, HIV zero-prevalence and incidence, sexual behavior, fertility intentions and a sampling frame for other studies, as well as evaluating the impact of interventions, such as antiretroviral therapy and vaccination programs. Uniquely, it relies on a network of village informants to report vital events and household moves, and furthermore is linked to an archive of biological samples and data from population surveys and other studies dating back three decades.
Exhaustive Testing of Trader-agents in Realistically Dynamic Continuous Double Auction Markets: AA Does Not Dominate.

: We analyse results from over 3.4million detailed market-trading simulation sessions which collectively confirm an unexpected result: in markets with dynamically varying supply and demand, the best-performing automated adaptive auction-market trading-agent currently known in the AI/Agents literature, i.e. Vytelingum’s Adaptive-Aggressive (AA) strategy, can be routinely out-performed by simpler trading strategies. AA is the most recent in a series of AI trading-agent strategies proposed by various researchers over the past twenty years: research papers contributing major steps in this evolution of strategies have been published at IJCAI , in the Artificial Intelligence journal, and at AAMAS . The innovative step taken here is to brute-force exhaustively evaluate AA in market environments that are in various ways more realistic, closer to real-world financial markets, than the simple constrained abstract experimental evaluations routinely used in the prior academic AI/Agents research literature. We conclude that AA can indeed appear dominant when tested only against other AI-based trading agents in the highly simplified market scenarios that have become the methodological norm in the trading-agents academic research literature, but much of that success seems to be because AA was designed with exactly those simplified experimental markets in mind. As soon as we put AA in scenarios closer to real-world markets, modify it to fit those markets accordingly, and exhaustively test it against simpler trading agents, AA’s dominance simply disappears.
Wider application of additions in self-compacting concrete

Compared to normally vibrated concrete (NVC), self-compacting concrete (SCC) possesses enhanced qualities and improves productivity and working conditions due to the elimination of compaction. SCC generally has a higher powder content than NVC and thus it is necessary to replace some of the cement by additions to achieve an economical and durable concrete. The established benefits of using low volumes of fly ash in SCC, high volumes of fly ash in NVC and the search for uses of waste glass led to the research on the possibilities of use higher fly ash contents than hitherto and ground glass as an addition in SCC whilst maintaining satisfactory properties. Mix design methods, tests, target properties and constituent materials were selected. This was followed by investigating the influence of fly ash and ground glass on the mortar fraction of the SCC and then using these results to produce concrete mixes with the target fresh properties. Hardened concrete of these mixes were measured and the relationships between these investigated. The results show that for constant filling ability of the SCC, replacement of cement with fly ash or ground glass requires an increase in water/powder ratio and a reduction in superplasticiser dosage. Both additions degraded the passing ability, consistence retention and hardened properties but not to a prohibitive extent. SCC with up 80% cement replaced by fly ash or glass volume ratio of 6.4% is possible and the material properties of SCC are similar to those of NVC. Also the UCL method of mix design was extended to higher coarse aggregate contents and different additions. The project can lead to the use of higher volume fly ash and ground glass in SCC, thus widening the types of additions available for SCC, saving landfill and reduce CO2 emissions by the use of less cement.
Climate variability in the subarctic area for the last 2 millennia

Abstract. To put recent climate change in perspective, it is necessary to extend the instrumental climate records with proxy data from paleoclimate archives. Arctic climate variability for the last 2 millennia has been investigated using statistical and signal analyses from three regionally averaged records from the North Atlantic, Siberia and Alaska based on many types of proxy data archived in the Arctic 2k database v1.1. 1. In the North Atlantic and Alaska, the major climatic trend is characterized by long-term cooling interrupted by recent warming that started at the beginning of the 19th century. This cooling is visible in the Siberian region at two sites, warming at the others. The cooling of the Little Ice Age (LIA) was identified from the individual series, but it is characterized by wide-range spatial and temporal expression of climate variability, in contrary to the Medieval Climate Anomaly. The LIA started at the earliest by around AD 1200 and ended at the latest in the middle of the 20th century. The widespread temporal coverage of the LIA did not show regional consistency or particular spatial distribution and did not show a relationship with archive or proxy type either. A focus on the last 2 centuries shows a recent warming characterized by a well-marked warming trend parallel with increasing greenhouse gas emissions. It also shows a multidecadal variability likely due to natural processes acting on the internal climate system on a regional scale. A ∼ 16–30-year cycle is found in Alaska and seems to be linked to the Pacific Decadal Oscillation, whereas ∼ 20–30- and ∼ 50–90-year periodicities characterize the North Atlantic climate variability, likely in relation with the Atlantic Multidecadal Oscillation. These regional features are probably linked to the sea ice cover fluctuations through ice–temperature positive feedback.
Rhéologie des mousses de fluides complexes

L’objet de cette these experimentale est l’etude de la rheologie des mousses de fluides complexes. L’utilisation de materiaux modeles permet de decoupler les effets des bulles et du fluide complexe sur le comportement rheologique des mousses. Nous caracterisons notamment le module elastique et la contrainte seuil des mousses de suspension de particules et des mousses d’emulsion. La rheologie des mousses de particules est fortement dependante du rapport entre la taille des particules et la taille du reseau interstitiel de la mousse (nœud et bords de Plateau). Lorsque les particules sont suffisamment petites pour etre inclues dans le reseau de la mousse, elles peuvent former un squelette granulaire compact. Cette structure mise en place par le drainage de la suspension augmente sensiblement la valeur du module elastique des mousses de particules. En effet, le confinement exerce par les bulles sur le squelette granulaire est a l’origine de l’elasticite de la matrice granulaire. Lorsque taille des particules augmente, ces dernieres sont exclues du reseau de la mousse et le module elastique des mousses de particules diminue avec la disparition de la matrice. Les mousses de particules avec un squelette granulaire possedent l’avantage d’avoir deux sources d’elasticite distinctes : l’elasticite capillaire des bulles et elasticite du squelette granulaire. On peut sommer ces deux contributions pour modeliser le module elastique macroscopique de la mousse, ce n’est pas le cas des mousses d’emulsion. En effet, il existe un couplage entre bulles et la matrice d’emulsion. L’introduction des nombres capillaire elastique et capillaire de Bingham permet de decrire l’evolution respective du module elastique et de la contrainte seuil des mousses d’emulsion. Enfin, l’analyse de la contrainte seuil de ces deux types de mousses permet d’identifier la matrice de particules comme etant un fluide a seuil, et forme un parallele interessant entre ces deux mousses a priori dissemblables
A robust shape model for blood vessels analysis

The availability of digital twins for the cardiovascular system will enable insightful computational tools both for research and clinical practice. This, however, demands robust and well defined models and methods for the different steps involved in the process. We present a vessel coordinate system (VCS) that enables the unanbiguous definition of locations in a vessel section, by adapting the idea of cylindrical coordinates to the vessel geometry. Using the VCS model, point correspondence can be defined among different samples of a cohort, allowing data transfer, quantitative comparison, shape coregistration or population analysis. Furthermore, the VCS model allows for the generation of specific meshes (e.g. cylindrical grids, ogrids) necessary for an accurate reconstruction of the geometries used in fluid simulations. We provide the technical details for coordinates computation and discuss the assumptions taken to guarantee that they are well defined. The VCS model is tested in a series of applications. We present a robust, low dimensional, patient specific vascular model and use it to study phenotype variability analysis of the thoracic aorta within a cohort of patients. Point correspondence is exploited to build an haemodynamics atlas of the aorta for the same cohort. The atlas originates from fluid simulations (Navier-Stokes with Finite Volume Method) conducted using OpenFOAMv10. We finally present a relevant discussion on the VCS model, which covers its impact in important areas such as shape modeling and computer fluids dynamics (CFD).
Scaling Limit of Kuramoto Model on Random Geometric Graphs

We consider the Kuramoto model on a graph with nodes given by $n$ i.i. d. points uniformly distributed on the $d$ dimensional torus. Two nodes are declared neighbors if they are at distance less than $\epsilon$. We prove a scaling limit for this model in compact time intervals as $n\to\infty$ and $\epsilon \to 0$ such that $\epsilon^{d+2}n/\log n \to \infty$. The limiting object is given by the heat equation. On the one hand this shows that the nonlinearity given by the sine function disappears under this scaling and on the other hand, provides evidence that stable equilibria of the Kuramoto model on these graphs are, as $n\to\infty$, in correspondence with those of the heat equation, which are explicit and given by twisted states. In view of this, we conjecture the existence of twisted stable equilibria with high probability as $n\to \infty$.