Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimation of land surface temperature from a Geostationary Operational Environmental Satellite (GOES‐8)

[1] Two algorithms are developed and applied to observations from the Geostationary Operational Environmental Satellite (GOES) to enable frequent estimate of Land Surface Temperature (LST) representing the diurnal cycle. The derived LSTs are evaluated against a wide range of ground observations. Both algorithms are based on radiative transfer theory; one is similar to the classical split window approach used for deriving Sea Surface Temperature (SST), while the other is a three-channel algorithm. The three-channel LST algorithm aims to improve atmospheric correction by utilizing the characteristics of the middle-infrared (MIR) band. Effects of both the atmosphere and the surface emissivity are accounted for. The simulations from the proposed algorithms are compared with previously developed generalized split window algorithm, and a split window algorithm with water vapor correction. During daytime, the proposed new split window algorithm gives the best LST retrievals, while during nighttime, the proposed three-channel algorithm gives the best retrievals, both within a Root Mean Square (RMS) error of less than 1 K and without a significant bias. Evaluations against the Atmospheric Radiation Measurement (ARM) observations of radiometric surface temperatures and Surface Radiation Network (SURFRAD) observations of outgoing long wave (LW) radiation indicate that LST can be determined from the actual GOES-8 observations within an RMS accuracy of about 1–2 K, standard error of about 1 K, and bias of less than 1 K. When evaluated against the North Carolina Agricultural Research Service (NCARS) soil temperature as observed at depth of 8 in. and against air temperature observations, the amplitude of the retrieved LST is found to be significantly greater than that of the observed soil temperature, lower than the nighttime air temperature, and higher than the daytime air temperature. When the soil observations are “corrected” to account for the depth difference, they are in good agreement with the LST retrieved from the satellite observations. This indicates that observations of soil temperature, which are more readily available than measurements of “skin” temperatures, can be useful in evaluating satellite-based estimates. The LST retrieved from both of the proposed algorithms and from a NOAA/NESDIS algorithm, are generally very close to the converted skin temperature from the SURFRAD surface outgoing LW radiation. In most cases, the newly proposed algorithm shows better agreement with ground observations.
Multiple Kernel Clustering with Local Kernel Alignment Maximization

Kernel alignment has recently been employed for multiple kernel clustering (MKC). However, we find that most of existing works implement this alignment in a global manner, which: i) indiscriminately forces all sample pairs to be equally aligned with the same ideal similarity; and ii) is inconsistent with a well-established concept that the similarity evaluated for two farther samples in a high dimensional space is less reliable. To address these issues, this paper proposes a novel MKC algorithm with a "local" kernel alignment, which only requires that the similarity of a sample to its k-nearest neighbours be aligned with the ideal similarity matrix. Such an alignment helps the clustering algorithm to focus on closer sample pairs that shall stay together and avoids involving unreliable similarity evaluation for farther sample pairs. We derive a new optimization problem to implement this idea, and design a two-step algorithm to efficiently solve it. As experimentally demonstrated on six challenging multiple kernel learning benchmark data sets, our algorithm significantly outperforms the state-of-the-art comparable methods in the recent literature, verifying the effectiveness and superiority of maximizing local kernel alignment.
Scene Graph Generation from Hierarchical Relationship Reasoning

This paper presents a novel approach for inferring relationships between objects in visual scenes. It explicitly exploits an informative hierarchical structure that can be imposed to divide the object and relationship categories into disjoint super-categories. Speciﬁcally, our proposed method incorporates a Bayes prediction head, enabling joint predictions of the super-category as the type of relationship between the two objects, along with the detailed relationship within that super-category. This design reduces the impact of class imbalance problems. Furthermore, we also modify the supervised contrastive learning to adapt our hierarchical classiﬁcation scheme. Experimental evaluations on the Visual Genome and OpenImage V6 datasets demonstrate that this factorized approach allows a relatively simple model to achieve competitive performance, particularly in predicate classiﬁcation and zero-shot tasks.
Unraveling the Age Estimation Puzzle: Comparative Analysis of Deep Learning Approaches for Facial Age Estimation

Comparing different age estimation methods poses a challenge due to the unreliability of published results, stemming from inconsistencies in the benchmarking process. Previous studies have reported continuous performance improvements over the past decade using specialized meth-ods; however, our ﬁndings challenge these claims. We argue that, for age estimation tasks outside of the low-data regime, designing specialized methods is unnecessary, and the standard approach of utilizing cross-entropy loss is sufﬁ-cient. This paper aims to address the benchmark shortcomings by evaluating state-of-the-art age estimation methods in a uniﬁed and comparable setting. We systematically analyze the impact of various factors, including facial alignment, facial coverage, image resolution, image representation, model architecture, and the amount of data on age estimation results. Surprisingly, these factors often exert a more signiﬁcant inﬂuence than the choice of the age estimation method itself. We assess the generalization capability of each method by evaluating the cross-dataset performance for publicly available age estimation datasets. The re-sults emphasize the importance of using consistent data pre-processing practices and establishing standardized benchmarks to ensure reliable and meaningful comparisons. The source code is available at Facial-Age-Benchmark.
Potassium adsorption characteristics and potassium forms in some New South Wales soils in relation to early senescence in cotton

The occurrence of premature senescence (PS) in cotton in Australia has been related to decreased potassium (K) concentration in the affected plants. Soil samples (0–120 cm) were taken from paired cotton fields, i.e. PS fields and similar soils not affected by PS (Non-PS fields), in northern New South Wales. The samples were analysed for different forms of K in soil, mineralogy of various size fractions, and K adsorption characteristics to evaluate differences in their K availability. Smectite was the dominant clay mineral in the studied soils. The K-bearing mineral illite was present in the clay fraction of all samples and its content was generally higher in soils from the Non-PS sites than the PS sites from Moree, Pilliga, and Warren. Water-soluble K (H2O-K) ranged from 0.03 to 2.64 mg/kg (median 0.35 mg/kg), exchangeable K (Exch-K) from 43 to 687 mg/kg (median 107 mg/kg), non-exchangeable K (Nonexch-K) from 164 to 1981 mg/kg (median 819 mg/kg), and total K (Total-K) from 16 811 to 23 207 mg/kg (median 14 740 mg/kg). The values of various K forms were generally higher in samples from the Non-PS fields than the PS fields from Pilliga and Warren sites and the reverse trend occurred for the samples from Trangie. Similar H2O-K and Exch-K values were found for the PS and Non-PS samples from Moree, whereas Nonexch-K and Total-K contents were higher in the top 60 cm soil depth from the Non-PS field than the PS field. The equilibrium activity ratio (ARKe) values were significantly higher for surface samples from the Non-PS sites than the PS sites from Pilliga and Warren and the reverse was true for the Trangie site. There was a sharp decrease in ARKe with depth for the studied samples. The potential buffering capacity (PBCK) for both surface and subsurface samples from the Non-PS site (mean 31.5 (mmol/kg)/(mol/L)1/2) from Trangie was substantially higher than the corresponding samples from the PS sites (mean 14.7 (mmol/kg)/(mol/L)1/2). There was a significant increase in K adsorption for the subsurface samples than the surface samples for all sites, as indicated by the higher values of Freundlich adsorption coefficient, k. At the Pilliga and Warren sites, the occurrence of premature senescence in cotton plants can be explained on the basis of differences in the levels of different forms of K, mineralogy, and K adsorption characteristics of soils from the PS and Non-PS fields. At the Moree site, higher illite content in the Non-PS than the PS soil may explain the difference in their K availability. For the paired Trangie samples, greater PBCK of the Non-PS soil than the PS soil may be responsible for increased K availability in the Non-PS soil. The occurrence of PS symptoms in cotton at Narrabri site cannot be directly contributed to K supplying parameters analysed in the study. The results also show that Exch-K alone may not be adequate to measure K availability to cotton in these soils.
A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Based Approaches

Classifier learning with data-sets that suffer from imbalanced class distributions is a challenging problem in data mining community. This issue occurs when the number of examples that represent one class is much lower than the ones of the other classes. Its presence in many real-world applications has brought along a growth of attention from researchers. In machine learning, the ensemble of classifiers are known to increase the accuracy of single classifiers by combining several of them, but neither of these learning techniques alone solve the class imbalance problem, to deal with this issue the ensemble learning algorithms have to be designed specifically. In this paper, our aim is to review the state of the art on ensemble techniques in the framework of imbalanced data-sets, with focus on two-class problems. We propose a taxonomy for ensemble-based methods to address the class imbalance where each proposal can be categorized depending on the inner ensemble methodology in which it is based. In addition, we develop a thorough empirical comparison by the consideration of the most significant published approaches, within the families of the taxonomy proposed, to show whether any of them makes a difference. This comparison has shown the good behavior of the simplest approaches which combine random undersampling techniques with bagging or boosting ensembles. In addition, the positive synergy between sampling techniques and bagging has stood out. Furthermore, our results show empirically that ensemble-based algorithms are worthwhile since they outperform the mere use of preprocessing techniques before learning the classifier, therefore justifying the increase of complexity by means of a significant enhancement of the results.
Tight upper bounds for the convergence of the randomized extended Kaczmarz and Gauss–Seidel algorithms

The randomized extended Kaczmarz and Gauss–Seidel algorithms have attracted much attention because of their ability to treat all types of linear systems (consistent or inconsistent, full rank or rank deficient). In this paper, we present tight upper bounds for the convergence of the randomized extended Kaczmarz and Gauss–Seidel algorithms. Numerical experiments are given to illustrate the theoretical results.
SNiPer-HD: improved genotype calling accuracy by an expectation-maximization algorithm for high-density SNP arrays

MOTIVATION The technology to genotype single nucleotide polymorphisms (SNPs) at extremely high densities provides for hypothesis-free genome-wide scans for common polymorphisms associated with complex disease. However, we find that some errors introduced by commonly employed genotyping algorithms may lead to inflation of false associations between markers and phenotype. RESULTS We have developed a novel SNP genotype calling program, SNiPer-High Density (SNiPer-HD), for highly accurate genotype calling across hundreds of thousands of SNPs. The program employs an expectation-maximization (EM) algorithm with parameters based on a training sample set. The algorithm choice allows for highly accurate genotyping for most SNPs. Also, we introduce a quality control metric for each assayed SNP, such that poor-behaving SNPs can be filtered using a metric correlating to genotype class separation in the calling algorithm. SNiPer-HD is superior to the standard dynamic modeling algorithm and is complementary and non-redundant to other algorithms, such as BRLMM. Implementing multiple algorithms together may provide highly accurate genotyping calls, without inflation of false positives due to systematically miss-called SNPs. A reliable and accurate set of SNP genotypes for increasingly dense panels will eliminate some false association signals and false negative signals, allowing for rapid identification of disease susceptibility loci for complex traits. AVAILABILITY SNiPer-HD is available at TGen's website: http://www.tgen. org/neurogenomics/data.
Timing of collision of the Sino-Korean and Yangtse cratons: U-Pb zircon dating of coesite-bearing eclogites

The suture zone between the Sino-Korean and Yangtse cratons in central China is marked by a series of fault-bounded belts composed of crustal protoliths. In the Dabie Mountains some of these blocks contain coesite and diamond, indicating subduction to minimum depths of ∼120 km as a result of continental collision. The blocks range from the structurally lowest ultrahigh-pressure metamorphic belt and decrease in metamorphic grade upsection to the structurally highest, low greenschist facies belt. Furthermore, the faults mark large contrasts in metamorphic grade. These features form a structure that is similar to metamorphic core complexes, indicating extension as part of the exhumation process. Timing of the continental collision has previously been inferred as Late Triassic, from regional geologic relations, or Caledonian, from 40 Ar/ 39 Ar dating of the adjacent and possibly related Qinling metamorphic belt. U-Pb dating of zircon from ultra-high-pressure eclogites yields a metamorphic age of 209 ±2 Ma. This age coincides with later stages of collision and is therefore a minimum estimate of the time of collision.
Overview of Genia Event Task in BioNLP Shared Task 2011

The Genia event task, a bio-molecular event extraction task, is arranged as one of the main tasks of BioNLP Shared Task 2011. As its second time to be arranged for community-wide focused efforts, it aimed to measure the advance of the community since 2009, and to evaluate generalization of the technology to full text papers. After a 3-month system development period, 15 teams submitted their performance results on test cases. The results show the community has made a significant advancement in terms of both performance improvement and generalization.
An Implementation of User-level Restartable Atomic Sequences on the NetBSD Operating System

This paper outlines an implementation of restartable atomic sequences on the NetBSD operating system as a mechanism for implementing atomic operations in a mutual-exclusion facility on uniprocessor systems. Kernel-level and user-level interfaces are discussed along with implementation details. Issues associated with protecting restartable atomic sequences from violation are considered. The performance of restartable atomic sequences is demonstrated to outperform syscall-based and emulation-based atomic operations. Performance comparisons with memoryinterlocked instructions demonstrate that restartable atomic sequences continue to provide performance advantages on modern hardware. Restartable atomic sequences are now the preferred mechanism for atomic operations in the NetBSD threads library on all uniprocessor systems.
The cation and silica chemistry of a Subandean river basin in western Amazonia

We sampled river water at 13 locations in the Pichis basin, a 10 500 km2 large rainforest‐covered drainage basin in Peru, to assess the influence of lithological variability and seasonality on water chemistry. The concentrations of major cations and silica show a strong seasonal dependence and a remarkable variability over short distances that is only weakly reduced in the wet season; cation concentrations in streams differ by up to 100% within a few kilometres. The lowest cation concentrations were associated with relatively cation‐depleted upper Tertiary and lower Quaternary formations, whereas relatively cation‐rich lower Tertiary and Jurassic formations left a clear calcium and sodium signal in the respective rivers. Cluster analysis, in conjunction with boxplots, suggests that the sampling locations can be segregated into three groups based on similarities of their geochemical signals. According to the previously defined criteria, one river is classified as a Group 2 river with 200 < TZ+ < 450 µeq/L, whereas all other rivers fall into Group 3 with 450 < TZ+ < 3000 µeq/L (where TZ+ refers to the total cation charge). Based on a comparison with other studies at different sections of the Amazon mainstem, the river chemistry of our study area is relatively enriched in K+, Mg2+ and Ca2+, and, consequently, has a higher TZ+ value, while being relatively depleted in silica. The influence of lithological variability on water chemistry must be considered in land‐use change studies even at watershed areas of 26–3382 km2. Copyright © 2002 John Wiley & Sons, Ltd.
On the Computation of Fixed Points in the Product Space of Unit Simplices and an Application to Noncooperative N Person Games

In this paper an algorithm based on the principle of simplicial approximation is introduced to compute fixed points of upper semicontinuous point to set mappings from the product space S of unit simplices into itself. The algorithm is a modification of an algorithm, introduced in an earlier paper. The main feature is that it starts with an arbitrary chosen point in S and that the triangulation of S depends on the starting point. Moreover, the algorithm can terminate with a non-full-dimensional subsimplex, yielding a good approximation. An application is given for non cooperative n person games, where S is the strategy space. Some computational experiences are given.
Iceberg Detection Using Simulated Dual-Polarized Radarsat Constellation Data

Abstract Iceberg monitoring is an important application of synthetic aperture radar (SAR), and is one of the stated objectives of the Radarsat Constellation, the next generation of Canada’s Radarsat satellites. In this paper, we simulate Radarsat Constellation data in a number of different imaging modes, using Radarsat-2 single-look complex data covering a study area in the Labrador Sea. We test the iceberg detection performance of both linear dual-pol data as well as compact polarimetry, a novel SAR architecture that transmits circular polarization rather than the traditional horizontal or vertical polarizations. We use the likelihood ratio test method to calculate a decision variable image for each of a number of different dual-pol and compact configurations, then analyze the detection performance using 25 validated iceberg locations spread across 12 different scenes. We found that compared to the linear data, the compact data missed fewer targets, and detected a greater number of pixels of detected targets, for most of the incidence angles and imaging modes tested. Compact polarimetry seems to be a promising choice for iceberg detection applications. Résumé La détection des icebergs est une application importante du radar à synthèse d’ouverture (RSO) et est un des objectifs déclarés de la Constellation RADARSAT, la prochaine génération de satellites RADARSAT du Canada. Dans cet article, nous simulons les données de la Constellation RADARSAT dans différents modes d’imagerie en utilisant des données singulières complexes de RADARSAT-2 couvrant une zone d’étude dans la mer du Labrador. Nous testons les performances de détection des icebergs des données linéaires en polarisation double ainsi que la polarimétrie compacte, une nouvelle architecture RSO qui transmet en polarisation circulaire plutôt qu’en polarisations horizontales ou verticales traditionnelles. Nous utilisons la méthode du test du rapport des vraisemblances pour calculer des images d’une variable de décision pour plusieurs configurations différentes en polarisation double et compacte, puis nous analysons les performances de détection en utilisant 25 positions d’iceberg validées réparties sur 12 scènes différentes. Nous avons constaté que, par rapport aux données linéaires, les données compactes ont manqué moins de cibles, et ont détecté un plus grand nombre de pixels sur les cibles détectées, pour la plupart des angles d’incidence et modes d’imagerie testés. La polarimétrie compacte semble être un choix prometteur pour des applications de détection d’iceberg.
Improved Distributed Approximation to Maximum Independent Set

We present improved results for approximating Maximum Independent Set ($\MaxIS$) in the standard LOCAL and CONGEST models of distributed computing. Let $n$ and $\Delta$ be the number of nodes and maximum degree in the input graph, respectively. Bar-Yehuda et al. [PODC 2017] showed that there is an algorithm in the CONGEST model that finds a $\Delta$-approximation to $\MaxIS$ in $O(\MIS(n,\Delta)\log W)$ rounds, where $\MIS(n,\Delta)$ is the running time for finding a \emph{maximal} independent set, and $W$ is the maximum weight of a node in the network. Whether their algorithm is randomized or deterministic depends on the $\MIS$ algorithm that they use as a black-box. Our results: (1) A deterministic $O(\MIS(n,\Delta))$ rounds algorithm for $O(\Delta)$-approximation to $\MaxIS$ in the CONGEST model. (2) A randomized $2^{O(\sqrt{\log \log n})}$ rounds algorithm that finds, with high probability, an $O(\Delta)$-approximation to $\MaxIS$ in the CONGEST model. (3) An $\Omega(\log^*n)$ lower bound for any randomized algorithm that finds an independent set of size $\Omega(n/\Delta)$ that succeeds with probability at least $1-1/\log n$, even for the LOCAL model. This hardness result applies for graphs of maximum degree $\Delta=O(n/\log^*n)$. One might wonder whether the same hardness result applies for low degree graphs. We rule out this possibility with our next result. (4) An $O(1)$ rounds algorithm that finds an independent set of size $\Omega(n/\Delta)$ in graphs with maximum degree $\Delta\leq n/\log n$, with high probability. Due to a lower bound of $\Omega(\sqrt{\log n/\log \log n})$ that was given by Kuhn, Moscibroda and Wattenhofer [JACM, 2016] on the number of rounds for finding a maximal independent set ($\MIS$) in the LOCAL model, even for randomized algorithms, our second result implies that finding an $O(\Delta)$-approximation to $\MaxIS$ is strictly easier than $\MIS$.
ARCH-COMP21 Category Report: Stochastic Models

This report presents the results of a friendly competition for formal verification and policy synthesis of stochastic models. It also introduces new benchmarks within this category, and recommends next steps for this category towards next year’s edition of the competition. The friendly competition took place as part of the workshop Applied Verification for Continuous and Hybrid Systems (ARCH) in Spring/Summer 2021.