Miracle-dz/UniFAR-FTUs huggingface dataset:
Distance Encoding -- Design Provably More Powerful GNNs for Structural Representation Learning

Learning structural representations of node sets from graph-structured data is crucial for applications ranging from node-role discovery to link prediction and molecule classiﬁcation. Graph Neural Networks (GNNs) have achieved great success in structural representation learning. However, most GNNs are limited by the 1-Weisfeiler-Lehman (WL) test and thus possible to generate identical representation for structures and graphs that are actually different. More powerful GNNs, proposed recently by mimicking higher-order-WL tests, only focus on entire-graph representations and cannot utilize sparsity of the graph structure to be computationally efﬁcient. Here we propose a general class of structure-related features, termed Distance Encoding (DE), to assist GNNs in representing node sets with arbitrary sizes with strictly more expressive power than the 1-WL test. DE essentially captures the distance between the node set whose representation is to be learnt and each node in the graph, which includes important graph-related measures such as shortest-path-distance and generalized PageRank scores. We propose two general frameworks for GNNs to use DEs (1) as extra node attributes and (2) further as controllers of message aggregation in GNNs. Both frameworks may still utilize the sparse structure to keep scalability to process large graphs. In theory, we prove that these two frameworks can distinguish node sets embedded in almost all regular graphs where traditional GNNs always fail. We also rigorously analyze their limitations. Empirically, we evaluate these two frameworks on node structural roles prediction, link prediction and triangle prediction over six real networks. The results show that DE-assisted GNNs outperform GNNs without DEs by up-to 15% improvement in average accuracy and DE-assisted GNNs also signiﬁcantly outperform other
SPInS, a pipeline for massive stellar parameter inference

Context. Stellar parameters are required in a variety of contexts, ranging from the characterisation of exoplanets to Galactic archaeology. Among them, the age of stars cannot be directly measured, while the mass and radius can be measured in some particular cases (e.g. binary systems, interferometry). More generally, stellar ages, masses, and radii have to be inferred from stellar evolution models by appropriate techniques. Aims. We have designed a Python tool named SPInS. It takes a set of photometric, spectroscopic, interferometric, and/or asteroseismic observational constraints and, relying on a stellar model grid, provides the age, mass, and radius of a star, among others, as well as error bars and correlations. We make the tool available to the community via a dedicated website. Methods. SPInS uses a Bayesian approach to find the probability distribution function of stellar parameters from a set of classical constraints. At the heart of the code is a Markov chain Monte Carlo solver coupled with interpolation within a pre-computed stellar model grid. Priors can be considered, such as the initial mass function or stellar formation rate. SPInS can characterise single stars or coeval stars, such as members of binary systems or of stellar clusters. Results. We first illustrate the capabilities of SPInS by studying stars that are spread over the Hertzsprung-Russell diagram. We then validate the tool by inferring the ages and masses of stars in several catalogues and by comparing them with literature results. We show that in addition to the age and mass, SPInS can efficiently provide derived quantities, such as the radius, surface gravity, and seismic indices. We demonstrate that SPInS can age-date and characterise coeval stars that share a common age and chemical composition. Conclusions. The SPInS tool will be very helpful in preparing and interpreting the results of large-scale surveys, such as the wealth of data expected or already provided by space missions, such as Gaia, Kepler, TESS, and PLATO.
Machine Reasoning Explainability

As a field of AI, Machine Reasoning (MR) uses largely symbolic means to formalize and emulate abstract reasoning. Studies in early MR have notably started inquiries into Explainable AI (XAI) -- arguably one of the biggest concerns today for the AI community. Work on explainable MR as well as on MR approaches to explainability in other areas of AI has continued ever since. It is especially potent in modern MR branches, such as argumentation, constraint and logic programming, planning. We hereby aim to provide a selective overview of MR explainability techniques and studies in hopes that insights from this long track of research will complement well the current XAI landscape. This document reports our work in-progress on MR explainability.
Structural balance of alliance and rivalry networks in international relations

Does the enemy of my enemy become my friend? A growing literature on structural analysis of interstate relationships has tackled this old question from the network perspective. However, the mechanism of long-term change in the structure of cooperation and enmity has yet to be fully understood. In search for a general explanation for the long-term evolution of interstate structure, we empirically examine the structural balance theory which predicts that a signed network evolves toward a more "balanced" structure where in many triangular relationships (i.e., triads) two states tend to share a common enemy or three states cooperate with each other. We investigate the network of alliances (positive edges) and rivalries (negative edges) between sovereign states and examine whether its evolution from 1816 to 2009 can be explained by the structural balance theory. We find the consistency with the structural balance theory drastically changes over time. The empirical pattern follows the prediction by the theory before the German unification in the nineteenth century and after World War II while inconsistent in the middle period. This result reveals the impact of the two historical events on the underlying mechanism of network evolution. Moreover, the contrast with previous studies of signed social networks that generally support the structural balance theory indicates that international alliance and rivalry networks can be a promising material to study novel mechanisms behind the time evolution of signed networks.
Pressure-robust error estimate of optimal order for the Stokes equations on domains with edges

The velocity solution of the incompressible Stokes equations is not affected by changes of the right hand side data in form of gradient fields. Most mixed methods do not replicate this property in the discrete formulation due to a relaxation of the divergence constraint which means that they are not pressure-robust. A recent reconstruction approach for classical methods recovers this invariance property for the discrete solution, by mapping discretely divergence-free test functions to exactly divergence-free functions in the sense of $\boldsymbol{H}(\operatorname{div})$. Moreover, the Stokes solution has locally singular behavior in three-dimensional domains near concave edges, which degrades the convergence rates on quasi-uniform meshes and makes anisotropic mesh grading reasonable in order to regain optimal convergence characteristics. Finite element error estimates of optimal order on meshes of tensor-product type with appropriate anisotropic grading are shown for the pressure-robust modified Crouzeix--Raviart method using the reconstruction approach. Numerical examples support the theoretical results.
Molecular Detection of Blood Protozoa in Ticks Collected from Cattle in The Buffer Zone of Sai Yok National Park, Thailand

Ixodid ticks transmit many pathogens to both animals and humans. This study was carried out to detect tick-borne protozoa (Theileria and Babesia) in ticks collected from cattle in the buffer zone of Sai Yok National Park, Thailand. A total of 30 cattle were examined for tick infestation. Of these, 10 (33.3%) were infested with ticks. A total of 85 ticks were collected and identified as Rhipicephalus (Boophilus) microplus. The collected ticks were separated into 50 tick pools by sex and life stage. These pools were examined for Theileria and Babesia species by polymerase chain reaction (PCR) for amplification of a fragment of the 18S ribosomal RNA (rRNA) gene. Of the 50 tick pools examined, 9 (18%) were infected with the Theileria species; of this group, 8 (88.9%) shared 100% identity with the unidentified Theileria sp. , whereas 1 (11.1%) shared 99% identity with T. orientalis/sergenti/buffeli. In the phylogenic tree, unidentified Theileria sp. was closely related to T. sinensis and was clearly separated from other Theileria species. This study is the first report of R. microplus infected with the bovine Theileria species in Thailand conducted using molecular identification techniques.
Privacy-Aware Recommender Systems Challenge on Twitter's Home Timeline.

Recommender systems constitute the core engine of most social network platforms nowadays, aiming to maximize user satisfaction along with other key business objectives. Twitter is no exception. Despite the fact that Twitter data has been extensively used to understand socioeconomic and political phenomena and user behaviour, the implicit feedback provided by users on Tweets through their engagements on the Home Timeline has only been explored to a limited extent. At the same time, there is a lack of large-scale public social network datasets that would enable the scientific community to both benchmark and build more powerful and comprehensive models that tailor content to user interests. By releasing an original dataset of 160 million Tweets along with engagement information, Twitter aims to address exactly that. During this release, special attention is drawn on maintaining compliance with existing privacy laws. Apart from user privacy, this paper touches on the key challenges faced by researchers and professionals striving to predict user engagements. It further describes the key aspects of the RecSys 2020 Challenge that was organized by ACM RecSys in partnership with Twitter using this dataset.
Privacy‐cost trade‐offs in smart electricity metering systems

Trade-offs between privacy and cost are studied for a smart grid consumer, whose electricity consumption is monitored in almost real time by the utility provider (UP) through smart meter (SM) readings. It is assumed that an electrical battery is available to the consumer, which can be utilised both to achieve privacy and to reduce the energy cost by demand shaping. Privacy is measured via the mean squared distance between the SM readings and a target load profile, while time-of-use pricing is considered to compute the cost incurred. The consumer can also sell electricity back to the UP to further improve the privacy-cost trade-off. Two privacy-preserving energy management policies (EMPs) are proposed, which differ in the way the target load profile is characterised. A more practical EMP, which optimises the energy management less frequently, is also considered. Numerical results are presented to compare the privacy-cost trade-off of these EMPs, considering various privacy indicators.
Determination of Local Tobacco Cultivars Using ISSR Molecular Marker

In many tobacco-growing countries, imported and local tobacco cultivars are subject to different regulations in tariff collection. However, there is a major technical problem how to distinguish between the imported and local cultivars. No standard method has been established to judge whether the plant has been grown for long time, enough to be legally called “local cultivar”. In this study, an Inter-Simple Sequence Repeat (ISSR) method was introduced to study genetic relationships between imported and local tobacco cultivars grown in Thailand. Of 20 screened-primers, five ISSR primers showed polymorphic PCR amplified products suitable for further phylogenetic analysis. Among them, two primers generated specific PCR bands that could separate local tobacco cultivars (Chorlare 1 and Chorlare 2) from other imported cultivars. Genetic relationship trees revealed four pairs of tobacco cultivars having bootstrap supporting-values higher than 50%. Among all 13 local tobacco cultivars collected and examined, only the couple of Chorlare 1 and 2 was distantly separated from the imported cultivars, suggesting their long-history of being grown in Thailand. The other 11 local cultivars were found to be morphologically and genetically similar to some imported cultivars and could have originated from them. Therefore, the ISSR technique could be developed and implemented as a simple, effective molecular marker to recognise early-introduced local tobacco cultivars.
Low-Cost Outdoor Air Quality Monitoring and Sensor Calibration

The significance of air pollution and the problems associated with it are fueling deployments of air quality monitoring stations worldwide. The most common approach for air quality monitoring is to rely on environmental monitoring stations, which unfortunately are very expensive both to acquire and to maintain. Hence, environmental monitoring stations are typically sparsely deployed, resulting in limited spatial resolution for measurements. Recently, low-cost air quality sensors have emerged as an alternative that can improve the granularity of monitoring. The use of low-cost air quality sensors, however, presents several challenges: They suffer from cross-sensitivities between different ambient pollutants; they can be affected by external factors, such as traffic, weather changes, and human behavior; and their accuracy degrades over time. Periodic re-calibration can improve the accuracy of low-cost sensors, particularly with machine-learning-based calibration, which has shown great promise due to its capability to calibrate sensors in-field. In this article, we survey the rapidly growing research landscape of low-cost sensor technologies for air quality monitoring and their calibration using machine learning techniques. We also identify open research challenges and present directions for future research.
22 Translationally Coupled Degradation of mRNA in Eukaryotes

The abundance of cytoplasmic messenger RNA in a cell is determined by both the rate of synthesis of the mRNA (i.e., its transcription rate) and the rate at which the mRNA is degraded. Thus, both processes contribute equally to gene expression. If the cell is to change the level of an mRNA rapidly, then it would be advantageous for that mRNA to be short-lived, so that it could respond to rapid changes in transcription. Similarly, a long half-life would be useful if an mRNA was to encode a protein that is needed in great abundance. Therefore, it would seem likely that different mRNAs would have different stabilities, depending on their cellular role. It is becoming increasingly clear that protein synthesis may have a role in mRNA degradation. There are many examples in which the inhibition of protein synthesis stabilizes mRNA (see below). In principle, this increase in mRNA stability could be a result of the decay of a labile factor, or that the translation machinery itself is involved in the degradation of mRNA. We have limited this discussion to mechanisms of mRNA degradation in eukaryotes; for a recent review of the role of translation in prokaryotic mRNA degradation, see Peterson (1993). In the first part of this chapter, we focus on the degradation of specific mRNAs that carry message instability elements; the second part deals with a global mechanism for degradation of mRNA. EXAMPLES OF SPECIFIC mRNAS IN WHICH TRANSLATION IS COUPLED TO DEGRADATION β -Tubulin One of the best studied.
Genetic Transformation of Sweet Potato and Its Application in Molecular Breeding

Sweet potato is an important root crop for food production world-wide and serves as raw material for the processing of feeds,starches and bioethanol in various industries.Establishment of efficient genetic transformation system in sweet potato not only provides a platform for functional verification of important genes,but also is an essential tool for molecular breeding in sweet potato improvement.This review presents the development and application of genetic transformation in sweet potato.
A reply

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Constraints and trade-offs in climate dependent adaptation: energy budgets and growth in a latitudinal cline.

Characteristics of temperature-dependent metabolic adaptation as well as their implications for associated changes in energy budgets are analysed based on comparisons of fish and invertebrates from various latitudinal clines in northern and southern hemispheres and on integrated ecological and physiological approaches. To identify putative “bottlenecks” of adaptation and for a general cause and effect understanding, the temperature sensitivity of growth as a key energy budget component is investigated, considering underlying processes at population, whole animal and cellular levels. Available data support the hypothesis that natural selection favours individuals for energy efficiency and maximised growth, but is subject to constraints of limited energy availability and temperature. According to emerging relationships between energy turnover, temperature variability and thermal tolerance, the notion that selection should favour a certain metabolic rate according to mean temperature is too simplistic. Within the energy budget, savings in maintenance costs set free energy for growth, visible as growth increments at a low standard metabolic rate. Such energy savings are maximised at the permanently low temperature of the Antarctic. However, some variability persists as pelagic lifestyles in the Antarctic are fuelled by higher metabolic rates at the expense of reduced growth. Temperature variability in the cold, as in the Subarctic, causes a rise in maintenance costs at the expense of growth, but in favour of exercise and thus foraging capacity. Such transitions in energy cost between sub-polar and polar areas are not visible in the southern hemisphere, where there is less temperature variability. However, these patterns—as well as many of the underlying mechanisms—still remain incompletely investigated, especially with respect to the suggested hierarchy in energy allocation to energy budget components.
Comparative FISH-mapping of TTAGG telomeric sequences to the chromosomes of leafcutter ants (Formicidae, Myrmicinae): is the insect canonical sequence conserved?

Abstract Telomeric sequences are conserved across species. The most common sequence reported among insects is (TTAGG)n, but its universal occurrence is not a consensus because other canonical motifs have been reported. In the present study, we used fluorescence in situ hybridization (FISH) using telomeric probes with (TTAGG)6 repeats to describe the telomere composition of leafcutter ants. We performed the molecular cytogenetic characterization of six Acromyrmex Mayr, 1865 and one Atta Fabricius, 1804 species (Acromyrmex ambiguus (Emery, 1888), Ac. crassispinus (Forel, 1909), Ac. lundii (Guérin-Mèneville, 1838), Ac. nigrosetosus (Forel, 1908), Ac. rugosus (Smith, 1858), Ac. subterraneus subterraneus (Forel, 1893), and Atta sexdens (Linnaeus, 1758)) and described it using a karyomorphometric approach on their chromosomes. The diploid chromosome number 2n = 38 was found in all Acromyrmex species, and the karyotypic formulas were as follows: Ac. ambiguus 2K = 14M + 12SM + 8ST + 4A, Ac. crassispinus 2K = 12M + 20SM + 4ST + 2A, Ac. lundii 2K = 10M + 14SM + 10ST + 4A, Ac. nigrosetosus 2K = 12M + 14SM + 10ST + 2A, and Ac. subterraneus subterraneus 2K = 14M + 18SM + 4ST + 2A. The exact karyotypic formula was not established for Ac. rugosus. FISH analyses revealed the telomeric regions in all the chromosomes of the species studied in the present work were marked by the (TTAGG)6 sequence. These results reinforce the premise that Formicidae presents high homology between their genera for the presence of the canonical sequence (TTAGG)n.
Hyperglycemia Augments Endothelin-1–Induced Constriction of Human Retinal Venules

Purpose Endothelin-1 (ET-1) is a potent vasoactive factor implicated in development of diabetic retinopathy, which is commonly associated with retinal edema and hyperglycemia. Although the vasomotor activity of venules contributes to the regulation of tissue fluid homeostasis, responses of human retinal venules to ET-1 under euglycemia and hyperglycemia remain unknown and the ET-1 receptor subtype corresponding to vasomotor function has not been determined. Herein, we addressed these issues by examining the reactivity of isolated human retinal venules to ET-1, and results from porcine retinal venules were compared. Methods Retinal tissues were obtained from patients undergoing enucleation. Human and porcine retinal venules were isolated and pressurized to assess diameter changes in response to ET-1 after exposure to 5 mM control glucose or 25 mM high glucose for 2 hours. Results Both human and porcine retinal venules exposed to control glucose developed similar basal tone and constricted comparably to ET-1 in a concentration-dependent manner. ET-1–induced constrictions of human and porcine retinal venules were abolished by ETA receptor antagonist BQ123. During high glucose exposure, basal tone of human and porcine retinal venules was unaltered but ET-1–induced vasoconstrictions were enhanced. Conclusions ET-1 elicits comparable constriction of human and porcine retinal venules by activation of ETA receptors. In vitro hyperglycemia augments human and porcine retinal venular responses to ET-1. Translational Relevance Similarities in vasoconstriction to ET-1 between human and porcine retinal venules support the latter as an effective model of the human retinal microcirculation to help identify vascular targets for the treatment of retinal complications in patients with diabetes.