Miracle-dz/UniFAR-FTUs huggingface dataset:
Multiple Regression Approach in Predicting Highway Runoff Pollutants Concentration

Multiple linear regression (MLR) analysis was performed to develop mathematical relationships to predict highway runoff pollutant concentrations from various site and storm event predictor variables. Partial correlations were used to select the independent variables for the MLR models. The final “optimized” MLR model was used to generate a new fitted variable calculated as the cumulative effects of the significant predictor variables for each constituent. This fitted variable was then included as the single covariate and final MLR models were developed for each constituent. Predictor variables found to have significant impacts on highway runoff pollutant concentration include: total event rainfall (TER), cumulative seasonal rainfall (CSR), antecedent dry peri od (ADP), contributing drainage area (DA), and annual average daily traffic (AADT). The MLR models then were validated using actual highway runoff measurements that were not included in model development.
Inference of dynamic systems from noisy and sparse data via manifold-constrained Gaussian processes

Significance Ordinary differential equations are a ubiquitous tool for modeling behaviors in science, such as gene regulation, biological rhythms, epidemics, and ecology. An important problem is to infer and characterize the uncertainty of parameters that govern equations. We present an accurate and fast inference method using manifold-constrained Gaussian processes, such that derivatives of the Gaussian process must satisfy the dynamics of the differential equations. Our method completely avoids the use of numerical integration and is thus fast to compute. Our construction is embedded in a principled statistical framework and is demonstrated to yield fast and reliable inference in a variety of practical problems. Our method works even when some system components are unobserved, which is a significant challenge for previous methods. Parameter estimation for nonlinear dynamic system models, represented by ordinary differential equations (ODEs), using noisy and sparse data, is a vital task in many fields. We propose a fast and accurate method, manifold-constrained Gaussian process inference (MAGI), for this task. MAGI uses a Gaussian process model over time series data, explicitly conditioned on the manifold constraint that derivatives of the Gaussian process must satisfy the ODE system. By doing so, we completely bypass the need for numerical integration and achieve substantial savings in computational time. MAGI is also suitable for inference with unobserved system components, which often occur in real experiments. MAGI is distinct from existing approaches as we provide a principled statistical construction under a Bayesian framework, which incorporates the ODE system through the manifold constraint. We demonstrate the accuracy and speed of MAGI using realistic examples based on physical experiments.
Accurate determination of the bulk electrical conductivity with the TDR100 cable tester.

Time domain reflectometry (TDR) is commonly used to determine the soil bulk electrical conductivity. To obtain accurate measurements, the three parameters of a series resistor model (probe constant, K p ; cable resistance, R c ; and remaining resistance, R 0 ) are typically calibrated using liquids with known electrical conductivity. Several studies have reported discrepancies between calibrated and directly measured parameters of the series resistor model. In this study, we examined the possibility that a technical issue with the TDR100 cable tester contributed to part of these inconsistencies. Our results showed that with an increasing level of waveform averaging, the reflection coefficient, as well as K p , R C , and R 0 , approached a maximum value. A comparison with independently determined values indicated that a high level of waveform averaging provided the physically most plausible results. Based on our results, we propose averaging at least 16 waveforms, each consisting of at least 250 points. An oscilloscope-based signal analysis showed that the increase in the reflection coefficient with increasing waveform averaging in saline media is related to a capacitance associated with electrode polarization in combination with a change in the pulse period of the pulse train when the TDR100 starts collecting data points. This capacitance resulted in a slow change of the average voltage in the TDR pulse train until a stable average voltage was reached. Higher levels of waveform averaging cancel the impact of the first erroneous voltage measurements out. In practical applications, the errors in the determination of the bulk electrical conductivity can be as high as 5% for the low-conductivity range (<0.1 S m -1 ) and up to 370% in saline media (1.4 S m -1 ) when waveform averaging is changed after calibration.
Two-scale gravitational collapse in the Amazon Fan: a coupled system of gravity tectonics and mass-transport processes

Abstract: Gravity tectonics affects the entire marine sedimentary sequence of the Foz do Amazonas basin, including the topmost Amazon Fan. During the various stages of the margin's evolution, the sedimentary loading and bathymetric slope induced the formation of a linked extensional–compressional system in which gravitational fold-and-thrust belts form significant structural features on the continental slope. Seismic analysis carried out in this study shows that sea-floor relief created along these thrust belts can trigger recurrent mass-transport deposits in the upper–middle Amazon Fan, suggesting a long-lasting linkage between gravity tectonics and mass-transport processes throughout the sedimentary evolution of the Amazon Fan. Supplementary material: Uninterpreted seismic sections are available at http://www.geolsoc. org.uk/SUP18393.
Monitoring river pollution with high-resolution satellite images.

The Bormida river basin is a strongly polluted rural area in North West Italy. The contamination has been caused by a chemical plant operating locally from 1892 to 1997 with ensuing heavy consequences on agriculture, animal life and human health. In the last few years, there has been an attempt to remove hazardous waste and improve the conditions of the site. We have employed a method based on high-resolution visible and infrared satellite images, which is able to assess the environmental impact of water contamination, with the aim of monitoring river banks. This method quantifies the Normalised Difference Vegetation Index (NDVI) on crops located close to the river using remote-sensed data. In this way it is possible to assess the spatial and temporal progression of land contamination and thus to be able to have an understanding of how much the interventions aimed at improving the situation were effective. The results obtained show that it is possible to assess the influence of water pollution along the entire valley: consequences are clearly visible up to 60 km downstream because of the great amounts of pollutants having been discharged. This method has been tested using Quickbird imagery (spatial resolution ~ 2.4 m/pixel) and has been validated comparing the results with chemical measurements.
Recent decrease in DOC concentrations in Arctic lakes of southwest Greenland

A key indicator of changes in the terrestrial carbon cycle is shifting dissolved organic carbon (DOC) concentrations in surface waters. Arctic permafrost holds twice as much C as the atmosphere, thus recent warming and changes in atmospheric deposition to the region raise the need for a better understanding of how DOC is changing in arctic surface waters. In Kangerlussuaq, Greenland, lakewater DOC concentrations declined by 14 to 55% (absolute changes of 1 to 24 mg L−1) between 2003 and 2013, without significant changes in quality. Lakewater sulfate concentrations, but not chloride or conductivity, increased. These results suggest that similar to processes that have occurred at northern midlatitudes, increases in soil ionic strength as a result of sulfate enrichment may be linked to declining surface water DOC concentrations. Such enrichment may be occurring with enhanced non‐sea‐salt sulfate deposition. Our results reveal that rapid changes are occurring in the carbon cycle of this region of southwest Greenland.
Models and theories of lambda calculus

In this paper we briefly summarize the contents of Manzonetto's PhD thesis which concerns denotational semantics and equational/order theories of the pure untyped lambda-calculus. The main research achievements include: (i) a general construction of lambda-models from reflexive objects in (possibly non-well-pointed) categories; (ii) a Stone-style representation theorem for combinatory algebras; (iii) a proof that no effective lambda-model can have lambda-beta or lambda-beta-eta as its equational theory (this can be seen as a partial answer to an open problem introduced by Honsell-Ronchi Della Rocca in 1984).
Reasoning About Strategies: On the Model-Checking Problem

In open systems verification, to formally check for reliability, one needs an appropriate formalism to model the interaction between agents and express the correctness of the system no matter how the environment behaves. An important contribution in this context is given by modal logics for strategic ability, in the setting of multi-agent games, such as ATL, ATL\star, and the like. Recently, Chatterjee, Henzinger, and Piterman introduced Strategy Logic, which we denote here by CHP-SL, with the aim of getting a powerful framework for reasoning explicitly about strategies. CHP-SL is obtained by using first-order quantifications over strategies and has been investigated in the very specific setting of two-agents turned-based games, where a non-elementary model-checking algorithm has been provided. While CHP-SL is a very expressive logic, we claim that it does not fully capture the strategic aspects of multi-agent systems. In this paper, we introduce and study a more general strategy logic, denoted SL, for reasoning about strategies in multi-agent concurrent games. We prove that SL includes CHP-SL, while maintaining a decidable model-checking problem. In particular, the algorithm we propose is computationally not harder than the best one known for CHP-SL. Moreover, we prove that such a problem for SL is NonElementarySpace-hard. This negative result has spurred us to investigate here syntactic fragments of SL, strictly subsuming ATL\star, with the hope of obtaining an elementary model-checking problem. Among the others, we study the sublogics SL[NG], SL[BG], and SL[1G]. They encompass formulas in a special prenex normal form having, respectively, nested temporal goals, Boolean combinations of goals and, a single goal at a time. About these logics, we prove that the model-checking problem for SL[1G] is 2ExpTime-complete, thus not harder than the one for ATL\star.
Impact assessment of rainfall-vegetation on sedimentation and predicting erosion-prone region by GIS and RS

Water reservoirs are facing universal sedimentation problems worldwide. Land covers, whether natural or manmade, eventually change, and the vegetation cover and rainfall have a great effect on the sediment load. Traditional techniques for analysing this problem are time-consuming and spatially limited. Remote sensing (RS) provides a convenient way to observe land cover changes, and geographic information system (GIS) provides tools for geographic analysis. This study demonstrates a GIS-based methodology for calculating the impact of vegetation and rainfall on the sediment load using remotely sensed data. Moderate resolution imaging spectroradiometer data were used to observe temporal changes in the vegetation-cover area of the watershed surface. The total drainage area for the reservoir was calculated from shuttle radar topographic mission data. The annual rainfall amount was used to compute the annual available rainwater for the watershed, and the impact of the annual available rainwater on the vegetation-covered area was determined. In addition, areas that were adding sedimentation to the reservoir were identified. An inverse relationship between the rainfall and vegetation cover was observed, clearly showing the triggering of erosion.
Statistical inference for Gaussian Whittle-Mat\'ern fields on metric graphs

Whittle-Mat\'ern fields are a recently introduced class of Gaussian processes on metric graphs, which are specified as solutions to a fractional-order stochastic differential equation. Unlike earlier covariance-based approaches for specifying Gaussian fields on metric graphs, the Whittle-Mat\'ern fields are well-defined for any compact metric graph and can provide Gaussian processes with differentiable sample paths. We derive the main statistical properties of the model class, particularly the consistency and asymptotic normality of maximum likelihood estimators of model parameters and the necessary and sufficient conditions for asymptotic optimality properties of linear prediction based on the model with misspecified parameters. The covariance function of the Whittle-Mat\'ern fields is generally unavailable in closed form, and they have therefore been challenging to use for statistical inference. However, we show that for specific values of the fractional exponent, when the fields have Markov properties, likelihood-based inference and spatial prediction can be performed exactly and computationally efficiently. This facilitates using the Whittle-Mat\'ern fields in statistical applications involving big datasets without the need for any approximations. The methods are illustrated via an application to modeling of traffic data, where allowing for differentiable processes dramatically improves the results.
Corrigendum to Improve Language Modelling for Code Completion through Learning General Token Repetition of Source Code

This paper is written because I receive several inquiry emails saying it is hard to achieve good results when applying token repetition learning techniques. If REP (proposed by me) or Pointer-Mixture (proposed by Jian Li) is directly applied to source code to decide all token repetitions, the performance will decrease sharply. Actually, as presented in Pointer-Mixture, there are many kinds of tokens that do not need to learn repetition patterns. For example, the tokens represent the grammar in Abstract Syntax Tree (AST) shows no obvious regularity of repetition. As I have also mentioned in the abstract section and experiment section, the REP model is only good at predicting unseen variables or unseen types in templates. In implementation, we concentrate on predicting unseen variables using REP. The variable-tokens and non-variable-tokens (grammar tokens or string literals) are treated differently. REP ignores tokens which are not variables. Because we predict token based on AST in pre-order, we can easily know the place currently being code-completed should be a Variable or a MethodInvocation or a StringLiteral. This important implementation trick is not clearly presented in the paper which may confuse readers when they reproduce the experiments. When computing accuracy, some kind of tokens such as grammar tokens are also ignored. Thus, in this paper, we correct some mistakes, clarify some confusing content, supplement the important implementation optimization details and provide a standard method for computing accuracy on Java benchmark for the already published papers.
Latt{\`e}s maps and the interior of the bifurcation locus

We show the existence of open sets of bifurcations near Latt{\`e}s maps of sufficiently high degree. In particular, every Latt{\`e}s map has an iterate which is in the closure of the interior of the bifurcation locus. To show this, we design a method to intersect the limit set of some particular type of IFS with a well-oriented curve. Then, we show that a Latt{\`e}s map of sufficiently high degree can be perturbed to exhibit this geometry.
Incremental computation of block triangular matrix exponentials with application to option pricing

We study the problem of computing the matrix exponential of a block triangular matrix in a peculiar way: Block column by block column, from left to right. The need for such an evaluation scheme arises naturally in the context of option pricing in polynomial diffusion models. In this setting a discretization process produces a sequence of nested block triangular matrices, and their exponentials are to be computed at each stage, until a dynamically evaluated criterion allows to stop. Our algorithm is based on scaling and squaring. By carefully reusing certain intermediate quantities from one step to the next, we can efficiently compute such a sequence of matrix exponentials.
Decadal climate variability of the North Sea during the last millennium reconstructed from bivalve shells (Arctica islandica)

Uninterrupted, annually resolved paleoclimate records are crucial to contextualize the current global change. Such information is particularly relevant for the Europe realm for which weather and climate projections are still very challenging if not virtually impossible. This study presents the first precisely dated, annually resolved, multiregional Arctica islandica chronologies from the North Sea which cover the time interval ad 1040–2010 and contain important information on supra-regional climatic conditions (sea surface temperature (SST), ocean productivity, wind stress). Shell growth varied periodically on timescales of 3–8, 12–16, 28–36, 50–80, and 120–240 years, possibly indicating a close association with the North Atlantic Oscillation, ocean-internal cycles of the North Atlantic controlled by ocean–atmosphere couplings, and the Atlantic Multi-Decadal Oscillation. Increased climatic instability, that is, stronger quasi-decadal variability, seems to be linked to the predominance of atmospheric forcings and some significantly decreased insolation phases (e.g. Spörer and Maunder Minima). Increased climatic variability of shorter timescales was also observed during some particularly warm phases or regime shifts (e.g. during the ‘Medieval Climate Anomaly’ and since c. 1970). More stable climatic conditions, that is, extended warm or cold periods (‘Medieval Climate Anomaly’, ‘Little Ice Age’), however, fell together with a predominance of multi-decadal oceanic cycles. Whether the sunspot number and the higher frequency climate variability are causally linked and which processes and mechanisms are required lie beyond this study.
Cyclomorphy

A main result is that, roughly, a dense set of the infinitesimal trace-preserving deformations of a semicircular system $s_1,. , s_n$ arise from one-parameter groups of automorphisms of the free-group factor $L(F(n))$ generated by $s_1,. , s_n$. More generally the paper studies cyclic gradients in von Neumann algebras, Lie algebras of noncommutative trace-preserving vector fields and the class of cyclomorphic maps which preserve the orthogonals of spaces of cyclic gradients.
Binaries discovered by the SPY project. II. HE 1414-0848: a double degenerate with a mass close to the Chandrasekhar limit

In the course of our search for double degenerate (DD) binaries as potential progenitors of type Ia supernovae with the UVES spectrograph at the ESO VLT (ESO S NI aProgenitor surveY { SPY) we discovered that the white dwarf HE 1414 0848 is a double-lined DA+DA binary with an orbital period of P =1 2 h 25 m 39 s . Semi-amplitudes of 128 km s 1 and 100 km s 1 are derived for the individual components. The amplitude ratio and the measured dierence in gravitational redshift is used to estimate the masses of the individual components: 0:55 M and 0:71 M. Hence the total mass of the HE 1414 0848 system is 1:26 M, only 10% below the Chandrasekhar limit. The results of a model atmosphere analysis are consistent with our mass estimated from the orbit. Temperatures of the individual components are also determined. Possible scenarios for the formation of this system are discussed. The system will merge due to loss of angular momentum via gravitational wave radiation after two Hubble times. HE 1414 0848 does not qualify as an SN Ia progenitor, but it is the most massive close DD known today.
Deciphering bottom current velocity and paleoclimate signals from contourite deposits in the Gulf of Cádiz during the last 140 kyr: An inorganic geochemical approach

Contourites in the Gulf of Cádiz (GC) preserve a unique archive of Mediterranean Outflow Water (MOW) variability over the past 5.3 Ma. In our study, we investigate the potential of geochemical data obtained by XRF scanning to decipher bottom current processes and paleoclimatic evolution at two different sites drilled during IODP Expedition 339 through contourites in the northern GC: Site U1387, which is bathed by the upper MOW core, and Site U1389, located more proximal to the Strait of Gibraltar. The lack of major downslope transport during the Pleistocene makes both locations ideally suited for our study. The results indicate that the Zr/Al ratio, representing the relative enrichment of heavy minerals (zircon) over less dense alumnosilicates under fast bottom current flow, is the most useful indicator for a semiquantitative assessment of current velocity. Although most elements are biased by current‐related processes, the bromine (Br) record, representing organic content, preserves the most pristine climate signal rather independent of grain‐size changes. Hence, Br can be used for chronostratigraphy and site‐to‐site correlation in addition to stable isotope stratigraphy. Based on these findings, we reconstructed MOW variability for Marine Isotope Stages (MIS) 1–5 using the Zr/Al ratio from Site U1387. The results reveal abrupt, millennial‐scale variations of MOW strength during Greenland Stadials (GS) and Interstadials (GI) with strong MOW during GS and glacial Terminations and a complex behavior during Heinrich Stadials. Millennial‐scale variability persisting during periods of poorly expressed GS/GI cyclicities implies a strong internal oscillation of the Mediterranean/North Atlantic climate system.
Wireless network-coded three-way relaying using Latin Cubes

The design of modulation schemes for the physical layer network-coded three-way wireless relaying scenario is considered. The protocol employs two phases: Multiple Access (MA) phase and Broadcast (BC) phase with each phase utilizing one channel use. For the two-way relaying scenario, it was observed by Koike-Akino et al. [4], that adaptively changing the network coding map used at the relay according to the channel conditions greatly reduces the impact of multiple access interference which occurs at the relay during the MA phase and all these network coding maps should satisfy a requirement called exclusive law. This paper does the equivalent for the three-way relaying scenario. We show that when the three users transmit points from the same 4-PSK constellation, every such network coding map that satisfies the exclusive law can be represented by a Latin Cube of Second Order. The network code map used by the relay for the BC phase is explicitly obtained and is aimed at reducing the effect of interference at the MA stage.
Deep learning approach to description and classification of fungi microscopic images

Diagnosis of fungal infections can rely on microscopic examination, however, in many cases, it does not allow unambiguous identification of the species due to their visual similarity. Therefore, it is usually necessary to use additional biochemical tests. That involves additional costs and extends the identification process up to 10 days. Such a delay in the implementation of targeted treatment is grave in consequences as the mortality rate for immunosuppressed patients is high. In this paper, we apply machine learning approach based on deep learning and bag-of-words to classify microscopic images of various fungi species. Our approach makes the last stage of biochemical identification redundant, shortening the identification process by 2-3 days and reducing the cost of the diagnostic examination.