Miracle-dz/UniFAR-FTUs huggingface dataset:
Bitcoin Transaction Networks: An Overview of Recent Results

Cryptocurrencies are distributed systems that allow exchanges of native (and non-) tokens between participants. The availability of the complete historical bookkeeping opens up an unprecedented possibility: that of understanding the evolution of a cryptocurrency's network structure while gaining useful insights into the relationships between users' behavior and cryptocurrency pricing in exchange markets. In this article we review some recent results concerning the structural properties of the Bitcoin Transaction Networks, a generic name referring to a set of three different constructs: the Bitcoin Address Network, the Bitcoin User Network, and the Bitcoin Lightning Network. The picture that emerges is of a system growing over time, which becomes increasingly sparse and whose mesoscopic structural organization is characterized by the presence of an increasingly significant core-periphery structure. Such a peculiar topology is accompanied by a highly uneven distribution of bitcoins, a result suggesting that Bitcoin is becoming an increasingly centralized system at different levels.
Forms of Soil Potassium as Influenced by Long Term Application of Chemical Fertilizers and Organics in Rainfed Maize-Wheat Cropping System

A study on potassium dynamics under long term use of variable doses of imbalanced chemical fertilizers and organics in rainfed maize-wheat system was carried out in Typic Ustipsamments of low hill zone of Himachal Pradesh between rabi (2007–08) and kharif (2008) seasons in the ongoing long-term fertilizer experiment at the research farm of CSKHPKV, HAREC, Dhaulakuan. Surface soil (0-15cm) after sixteen cropping cycles was analyzed for different forms of potassium. All the fractions of potassium viz. water soluble-K, exchangeable-K and non exchangeable-K decreased from its initial status in most of the treatments. Treatments receiving K fertilizer (100% NPK) showed increase in water soluble-K and exchangeable-K fraction over the initial status. Application of FYM (Farmyard manure) in both the seasons also increased the content of water soluble-K and exchangeable-K over the initial value. In general application of FYM with chemical fertilizers showed higher content of all the fractions compared to treatments where imbalanced inorganic fertilizers were applied (100% N and 100% NP). Maximum decrease in all the fractions of K was recorded in treatment 100 per cent N followed by 100 per cent NP. Use of FYM to both crops has resulted in higher yield in case of wheat crop. Water soluble-K was the important fraction of potassium contributing towards crop yields and total K uptake.
TESS Hunt for Young and Maturing Exoplanets (THYME). III. A Two-planet System in the 400 Myr Ursa Major Group

Exoplanets can evolve significantly between birth and maturity, as their atmospheres, orbits, and structures are shaped by their environment. Young planets (<1 Gyr) offer an opportunity to probe the critical early stages of this evolution, where planets evolve the fastest. However, most of the known young planets orbit prohibitively faint stars. We present the discovery of two planets transiting HD 63433 (TOI 1726, TIC 130181866), a young Sun-like ( ) star. Through kinematics, lithium abundance, and rotation, we confirm that HD 63433 is a member of the Ursa Major moving group (τ = 414 ± 23 Myr). Based on the TESS light curve and updated stellar parameters, we estimate that the planet radii are 2.15 ± 0.10 R⊕ and 2.67 ± 0.12 R⊕, the orbital periods are 7.11 and 20.55 days, and the orbital eccentricities are lower than about 0.2. Using High Accuracy Radial velocity Planet Searcher for the Northern hemisphere velocities, we measure the Rossiter–McLaughlin signal of the inner planet, demonstrating that the orbit is prograde. Since the host star is bright (V = 6.9), both planets are amenable to transmission spectroscopy, radial velocity measurements of their masses, and more precise determination of the stellar obliquity. This system is therefore poised to play an important role in our understanding of planetary system evolution in the first billion years after formation.
44 GHz Methanol Masers: Observations toward 95 GHz Methanol Masers

We report a simultaneous 44 and 95 GHz class I methanol maser survey toward 144 sources from the 95 GHz class I methanol maser catalog. The observations were made with the three telescopes of the Korean very long baseline interferometry network operating in single-dish mode. The detection rates are 89% at 44 GHz and 77% at 95 GHz. There are 106 new discoveries at 44 GHz. Comparing the previous 95 GHz detections with new observations of the same transitions made using the Purple Mountain Observatory 13.7 m radio telescope shows no clear evidence of variability on a timescale of six years. Emission from the 44 and 95 GHz transitions shows strong correlations in peak velocity, peak flux density, and integrated flux density, indicating that they are likely cospatial. We found that the peak flux density ratio / decreases as the 44 GHz peak flux density increases. We found that some class I methanol masers in our sample might be associated with infrared dark clouds, while others are associated with H ii regions, indicating that some sources occur at an early stage of high-mass star formation, while others are located toward more evolved sources.
Do Neural Ranking Models Intensify Gender Bias?

Concerns regarding the footprint of societal biases in information retrieval (IR) systems have been raised in several previous studies. In this work, we examine various recent IR models from the perspective of the degree of gender bias in their retrieval results. To this end, we first provide a bias measurement framework which includes two metrics to quantify the degree of the unbalanced presence of gender-related concepts in a given IR model's ranking list. To examine IR models by means of the framework, we create a dataset of non-gendered queries, selected by human annotators. Applying these queries to the MS MARCO Passage retrieval collection, we then measure the gender bias of a BM25 model and several recent neural ranking models. The results show that while all models are strongly biased toward male, the neural models, and in particular the ones based on contextualized embedding models, significantly intensify gender bias. Our experiments also show an overall increase in the gender bias of neural models when they exploit transfer learning, namely when they use (already biased) pre-trained embeddings.
Magnetic Compass Orientation in the Eastern Red-Spotted Newt, Notophthalmus viridescens: Rapid Acquisition of the Shoreward Axis

Abstract Orientation toward and away from shore (y-axis orientation) plays an important role in the daily and seasonal movements of amphibians. A variety of studies have shown that amphibians use both celestial cues and the geomagnetic field for y-axis orientation. However, few studies have addressed how quickly amphibians are able to learn the direction of a new shore. In the present laboratory-based experiments, we show that the Eastern Red-Spotted Newt, Notophthalmus viridescens, is able to learn the direction of the y-axis with respect to the geomagnetic field within 12–16 h. In conjunction with previous findings, our results suggest that the geomagnetic field is an important and readily used cue for orientation in amphibians.
HipoRank: Incorporating Hierarchical and Positional Information into Graph-based Unsupervised Long Document Extractive Summarization

We propose a novel graph-based ranking model for unsupervised extractive summarization of long documents. Graph-based ranking models typically represent documents as undirected fully-connected graphs, where a node is a sentence, an edge is weighted based on sentence-pair similarity, and sentence importance is measured via node centrality. Our method leverages positional and hierarchical information grounded in discourse structure to augment a document's graph representation with hierarchy and directionality. Experimental results on PubMed and arXiv datasets show that our approach outperforms strong unsupervised baselines by wide margins and performs comparably to some of the state-of-the-art supervised models that are trained on hundreds of thousands of examples. In addition, we find that our method provides comparable improvements with various distributional sentence representations; including BERT and RoBERTa models fine-tuned on sentence similarity.
1-MCP treatment prolongs postharvest life of 'Santa Rosa' plums

: 'Santa Rosa’plums were treated with concentrations of 0- (control), 0.5-, or 0.75-μL/L 1-methylcyclopropene (1-MCP) for 24 h at 1°C and stored at 1 °C for up to 40 d. Ethylene and CO2 production, firmness, weight loss, Brix, acidity, color evolution, and ethanol and acetaldehyde concentrations were determined periodically after cold storage and after subsequent shelf life of 5 and 8 d at 20 °C. 1-MCP strongly inhibited ethylene and CO2 production. Higher values of firmness were observed in 1-MCP-treated plums compared with control. 1-MCP treatment delayed color evolution, reduced acidity loss, and inhibited ethanol and acetaldehyde production. 1-MCP did not affect weight loss or sugar content.
2-intersecting permutations

Abstract. In this paper we consider the Erdős-Ko-Rado property for both 2-pointwise and 2-setwise intersecting permutations. Two permutations σ, τ ∈ Sym(n) are t-setwise intersecting if there exists a t-subset S of {1, 2, . , n} such that Sσ = Sτ . If for each s ∈ S, sσ = sτ , then we say σ and τ are t-pointwise intersecting. We say that Sym(n) has the t-setwise (resp. t-pointwise) intersecting property if for any family F of t-setwise (resp. t-pointwise) intersecting permutations, |F| ≤ (n − t)! t! (resp. |F| ≤ (n − t)! ). Ellis ([“Setwise intersecting families of permutations”. Journal of Combinatorial Theory, Series A, 119(4):825849, 2012. ]), proved that for n sufficiently large relative to t, Sym(n) has the tsetwise intersecting property. Ellis also conjuctured that this result holds for all n ≥ t. Ellis, Friedgut and Pilpel [Ellis, David, Ehud Friedgut, and Haran Pilpel. “Intersecting families of permutations. ” Journal of the American Mathematical Society 24(3):649-682, 2011. ] also proved that for n sufficiently large relative to t, Sym(n) has the t-pointwise intersecting property. It is also conjectured that Sym(n) has the t-pointwise intersecting propoperty for n ≥ 2t+ 1. In this work, we prove these two conjectures for Sym(n) when t = 2.
Multilingual Unsupervised Sentence Simplification

Progress in Sentence Simplification has been hindered by the lack of supervised data, particularly in languages other than English. Previous work has aligned sentences from original and simplified corpora such as English Wikipedia and Simple English Wikipedia, but this limits corpus size, domain, and language. In this work, we propose using unsupervised mining techniques to automatically create training corpora for simplification in multiple languages from raw Common Crawl web data. When coupled with a controllable generation mechanism that can flexibly adjust attributes such as length and lexical complexity, these mined paraphrase corpora can be used to train simplification systems in any language. We further incorporate multilingual unsupervised pretraining methods to create even stronger models and show that by training on mined data rather than supervised corpora, we outperform the previous best results. We evaluate our approach on English, French, and Spanish simplification benchmarks and reach state-of-the-art performance with a totally unsupervised approach. We will release our models and code to mine the data in any language included in Common Crawl.
Study on a New Electromagnetic Flow Measurement Technology Based on Differential Correlation Detection

Under the conditions of low flow rate and strong noise, the current electromagnetic flowmeter (EMF) cannot satisfy the requirement for measurement or separate the actual flow signal and interference signal accurately. Correlation detection technology can reduce the bandwidth and suppress noise effectively using the periodic transmission of signal and noise randomness. As for the problem that the current anti-interference technology cannot suppress noise effectively, the noise and interference of the electromagnetic flowmeter were analyzed in this paper, and a design of the electromagnetic flowmeter based on differential correlation detection was proposed. Then, in order to verify the feasibility of the electromagnetic flow measurement system based on differential correlation, an experimental platform for the comparison between standard flow and measured flow was established and a verification experiment was carried out under special conditions and with flow calibration measurements. Finally, the data obtained in the experiment were analyzed. The research result showed that an electromagnetic flowmeter based on differential correlation detection satisfies the need for measurement completely. The lower limit of the flow rate of the electromagnetic flowmeter based on the differential correlation principle could reach 0.084 m/s. Under strong external interferences, the electromagnetic flowmeter based on differential correlation had a fluctuation range in output value of only 10 mV. This shows that the electromagnetic flowmeter based on the differential correlation principle has unique advantages in measurements taken under the conditions of strong noise, slurry flow, and low flow rate.
MOPS-Net: A Matrix Optimization-driven Network forTask-Oriented 3D Point Cloud Downsampling

Downsampling is a commonly-used technique in 3D point cloudprocessing for saving storage space, transmission bandwidth andcomputational complexity. The classic downsampling methods,such as farthest point sampling and Poisson disk sampling, thoughwidely used for decades, are independent of the subsequent applications, since the downsampled point cloud may compromisetheir performance severely. This paper explores the problem oftask-oriented downsampling, which aims to downsample a pointcloud and maintain the performance of subsequent applications asmuch as possible. We propose MOPS-Net, a novel end-to-end deepneural network which is designed from the perspective of matrixoptimization, making it fundamentally different from the existing deep learning-based methods. Due to its discrete and combinatorial nature, it is difficult to solve the downsampling problem directly. To tackle this challenge, we relax the binary constraint of each variableand lift 3D points to a higher dimensional feature space, in which a constrained and differentiable matrix optimization problem withan implicit objective function is formulated. We then propose a deep neural network architecture to mimic the matrix optimization problem by exploring both the local and the global structures of the input data. With a task network, MOPS-Net can be end-to-endtrained. Moreover, it is permutation-invariant, making it robust to input data. We also extend MOPS-Net such that a single network after one-time training is capable of handling arbitrary downsam-pling ratios. Extensive experimental results show that MOPS-Net can achieve favorable performance against state-of-the-art deeplearning-based method over various tasks, including point cloud classification, retrieval and reconstruction.
Macrofauna do solo em uma cronosequência de capoeiras, florestas e pastos no Centro de Endemismo Belém, Amazônia Oriental

The Belem Endemism Center (CEB; the biogeographic region located between the Tocantins and Pindare rivers) is the most deforested area of the Amazon. To study the soil biota of the remaining old-growth and second-growth forests is essential to promote more sustainable farming practices, and provide orientation for degraded land restoration. The aim of this study was to identify the main determinants of the composition and diversity of major taxa of soil macrofauna (current soil use, geographic centers, soil use history, fire frequency) in a chronosequence of fallows and forests in the CEB. Macrofauna was sampled in eight sites of young secondary forests (4-7 y), six sites of medium secondary forests (11-15 y), seven sites of "old" secondary forests (20-40 y), eight sites of old-growth forest remnants and three sites of pastures. Its composition and diversity were analysed through Principal Component and Between-class Analyses. A total of 9.225 individuals from 29 major taxa (orders or families) were collected in the 32 plots. Soil macrofauna composition was very sensitive to soil use history and identified different communities among geographic centers. The intensity of soil use decreased the abundance and richness of predators, indicating long-lasting impacts of soil degradation on macrofauna composition and diversity, and therefore on soil ecological functions.
Accurate and Efficient KIR Gene and Haplotype Inference From Genome Sequencing Reads With Novel K-mer Signatures

The killer-cell immunoglobulin-like receptor (KIR) proteins evolve to fight viruses and mediate the body’s reaction to pregnancy. These roles provide selection pressure for variation at both the structural/haplotype and base/allele levels. At the same time, the genes have evolved relatively recently by tandem duplication and therefore exhibit very high sequence similarity over thousands of bases. These variation-homology patterns make it impossible to interpret KIR haplotypes from abundant short-read genome sequencing data at population scale using existing methods. Here, we developed an efficient computational approach for in silico KIR probe interpretation (KPI) to accurately interpret individual’s KIR genes and haplotype-pairs from KIR sequencing reads. We designed synthetic 25-base sequence probes by analyzing previously reported haplotype sequences, and we developed a bioinformatics pipeline to interpret the probes in the context of 16 KIR genes and 16 haplotype structures. We demonstrated its accuracy on a synthetic data set as well as a real whole genome sequences from 748 individuals from The Genome of the Netherlands (GoNL). The GoNL predictions were compared with predictions from SNP-based predictions. Our results show 100% accuracy rate for the synthetic tests and a 99.6% family-consistency rate in the GoNL tests. Agreement with the SNP-based calls on KIR genes ranges from 72%–100% with a mean of 92%; most differences occur in genes KIR2DS2, KIR2DL2, KIR2DS3, and KIR2DL5 where KPI predicts presence and the SNP-based interpretation predicts absence. Overall, the evidence suggests that KPI’s accuracy is 97% or greater for both KIR gene and haplotype-pair predictions, and the presence/absence genotyping leads to ambiguous haplotype-pair predictions with 16 reference KIR haplotype structures. KPI is free, open, and easily executable as a Nextflow workflow supported by a Docker environment at https://github.com/droeatumn/kpi.
On the Benefits of Invariance in Neural Networks

Many real world data analysis problems exhibit invariant structure, and models that take advantage of this structure have shown impressive empirical performance, particularly in deep learning. While the literature contains a variety of methods to incorporate invariance into models, theoretical understanding is poor and there is no way to assess when one method should be preferred over another. In this work, we analyze the benefits and limitations of two widely used approaches in deep learning in the presence of invariance: data augmentation and feature averaging. We prove that training with data augmentation leads to better estimates of risk and gradients thereof, and we provide a PAC-Bayes generalization bound for models trained with data augmentation. We also show that compared to data augmentation, feature averaging reduces generalization error when used with convex losses, and tightens PAC-Bayes bounds. We provide empirical support of these theoretical results, including a demonstration of why generalization may not improve by training with data augmentation: the `learned invariance' fails outside of the training distribution.
Ecosystem multifunctionality of coastal marshes is determined by key plant traits.

Questions As biodiversity losses increase due to global change and human-induced habitat destruction, the relationships between plant traits and ecosystem properties can provide a new level of understanding ecosystem complexity. Using a functional response–effect approach, we show that multiple components of the carbon cycle are determined by a few plant traits, which in turn are strongly affected by environmental conditions. Location Salt marshes, northwest Germany. Methods We explored responses of morphological, chemical and biomass-related plant traits to environmental drivers and examined their effects on carbon cycle properties, i.e. above-ground biomass, above-ground net primary productivity and decomposition. The combined analysis between environmental parameters, functional traits and ecosystem properties used structural equation modelling (SEM). Results Important response and effect traits were leaf dry matter content (LDMC) and below-ground dry mass (BDM, responding to groundwater level and salinity) and leaf C:N ratio (responding to inundation frequency). Inundation and salinity led to increased allocation to below-ground biomass and salt stress adaptation in leaves, which translated into increased decomposition rates. Release from these abiotic controls resulted in standing biomass accumulation, which was controlled by LDMC and canopy height as key traits. Conclusions These findings demonstrate the interacting effects of non-consumable environmental factors and soil resources on morphological, chemical and biomass traits, which affected carbon cycle properties. Loss of species from the community has the potential to change the relationships between environment and vegetation-based ecosystem properties and therefore elicit effects on the multifunctionality of the entire and adjacent ecosystems.
Cell autonomous requirement of neurofibromin (Nf1) for postnatal muscle hypertrophic growth and metabolic homeostasis

Background Neurofibromatosis type 1 (NF1) is a multi-organ disease caused by mutations in Neurofibromin (NF1). Amongst other features, NF1 patients frequently show reduced muscle mass and strength, impairing patients’ mobility and increasing the risk of fall. The role of Nf1 in muscle and the cause for the NF1-associated myopathy is mostly unknown. Methods To dissect the function of Nf1 in muscle, we created muscle-specific knockout mouse models for Nf1, inactivating Nf1 in the prenatal myogenic lineage either under the Lbx1 promoter or under the Myf5 promoter. Mice were analyzed during pre-and postnatal myogenesis and muscle growth. Results Nf1Lbx1 and Nf1Myf5 animals showed only mild defects in prenatal myogenesis. Nf1Lbx1 animals were perinatally lethal, while Nf1Myf5 animals survived only up to approx. 25 weeks. A comprehensive phenotypic characterization of Nf1Myf5 animals showed decreased postnatal growth, reduced muscle size, and fast fiber atrophy. Proteome and transcriptome analysis of muscle tissue indicated decreased protein synthesis and increased proteasomal degradation, and decreased glycolytic and increased oxidative activity in muscle tissue. High-resolution respirometry confirmed enhanced oxidative metabolism in Nf1Myf5 muscles, which was concomitant to a fiber type shift from type 2B to type 2A and type 1. Moreover, Nf1Myf5 muscles showed hallmarks of decreased activation of mTORC1 and increased expression of atrogenes. Remarkably, loss of Nf1 promoted a robust activation of AMPK with a gene expression profile indicative of increased fatty acid catabolism. Additionally, we observed a strong induction of genes encoding catabolic cytokines in muscle Nf1Myf5 animals, in line with a drastic reduction of white, but not brown adipose tissue. Conclusions Our results demonstrate a cell-autonomous role for Nf1 in myogenic cells during postnatal muscle growth required for metabolic and proteostatic homeostasis. Furthermore, Nf1 deficiency in muscle drives cross-tissue communication and mobilization of lipid reserves.