Miracle-dz/UniFAR-FTUs huggingface dataset:
Sampling and Update Frequencies in Proximal Variance Reduced Stochastic Gradient Methods

Variance reduced stochastic gradient methods have gained popularity in recent times. Several variants exist with different strategies for the storing and sampling of gradients. In this work we focus on the analysis of the interaction of these two aspects. We present and analyze a general proximal variance reduced gradient method under strong convexity assumptions. Special cases of the algorithm include SAGA, L-SVRG and their proximal variants. Our analysis sheds light on epoch-length selection and the need to balance the convergence of the iterates and how often gradients are stored. The analysis improves on other convergence rates found in literature and produces a new and faster converging sampling strategy for SAGA. Problem instances for which the predicted rates are the same as the practical rates are presented together with problems based on real world data.
Lowest Degree Decomposition of Complex Networks

The heterogeneous structure implies that a very few nodes may play the critical role in maintaining structural and functional properties of a large-scale network. Identifying these vital nodes is one of the most important tasks in network science, which allow us to better conduct successful social advertisements, immunize a network against epidemics, discover drug target candidates and essential proteins, and prevent cascading breakdowns in power grids, financial markets and ecological systems. Inspired by the nested nature of real networks, we propose a decomposition method where at each step the nodes with the lowest degree are pruned. We have strictly proved that this so-called lowest degree decomposition (LDD) is a subdivision of the famous k-core decomposition. Extensive numerical analyses on epidemic spreading, synchronization and nonlinear mutualistic dynamics show that the LDD can more accurately find out the most influential spreaders, the most efficient controllers and the most vulnerable species than k-core decomposition and other well-known indices. The present method only makes use of local topological information, and thus has high potential to become a powerful tool for network analysis.
Generalized LDPC Codes with Convolutional Code Constraints

Braided convolutional codes (BCCs) are a class of spatially coupled turbo-like codes that can be described by a (2), (3)-regular compact graph. In this paper, we introduce a family of (dv, dc)-regular GLDPC codes with convolutional code constraints (CC-GLDPC codes), which form an extension of classical BCCs to arbitrary regular graphs. In order to characterize the performance in the waterfall and error floor regions, we perform an analysis of the density evolution thresholds as well as the finitelength ensemble weight enumerators and minimum distances of the ensembles. In particular, we consider various ensembles of overall rate R = 1/3 and R = 1/2 and study the trade-off between variable node degree and strength of the component codes. We also compare the results to corresponding classical LDPC codes with equal degrees and rates. It is observed that for the considered LDPC codes with variable node degree dv > 2, we can find a CC-GLDPC code with smaller dv that offers similar or better performance in terms of BP and MAP thresholds at the expense of a negligible loss in the minimum distance.
Genetic algorithms for redundancy in interaction testing

It is imperative for testing to determine if the components within large-scale software systems operate functionally. Interaction testing involves designing a suite of tests, which guarantees to detect a fault if one exists among a small number of components interacting together. The cost of this testing is typically modeled by the number of tests, and thus much effort has been taken in reducing this number. Here, we incorporate redundancy into the model, which allows for testing in non-deterministic environments. Existing algorithms for constructing these test suites usually involve one "fast" algorithm for generating most of the tests, and another "slower" algorithm to "complete" the test suite. We employ a genetic algorithm that generalizes these approaches that also incorporates redundancy by increasing the number of algorithms chosen, which we call "stages. " By increasing the number of stages, we show that not only can the number of tests be reduced compared to existing techniques, but the computational time in generating them is also greatly reduced.
LOGAN: High-Performance GPU-Based X-Drop Long-Read Alignment

Pairwise sequence alignment is one of the most computationally intensive kernels in genomic data analysis, accounting for more than 90% of the runtime for key bioinformatics applications. This method is particularly expensive for third-generation sequences due to the high computational cost of analyzing sequences of length between 1Kb and 1Mb. Given the quadratic overhead of exact pairwise algorithms for long alignments, the community primarily relies on approximate algorithms that search only for high-quality alignments and stop early when one is not found. In this work, we present the first GPU optimization of the popular X-drop alignment algorithm, that we named LOGAN. Results show that our high-performance multi-GPU implementation achieves up to 181.6 GCUPS and speed-ups up to 6.6× and 30.7× using 1 and 6 NVIDIA Tesla V100, respectively, over the state-of-the-art software running on two IBM Power9 processors using 168 CPU threads, with equivalent accuracy. We also demonstrate a 2.3× LOGAN speed-up versus ksw2, a state-of-art vectorized algorithm for sequence alignment implemented in minimap2, a long-read mapping software. To highlight the impact of our work on a real-world application, we couple LOGAN with a many-to-many long-read alignment software called BELLA, and demonstrate that our implementation improves the overall BELLA runtime by up to 10.6×. Finally, we adapt the Roofline model for LOGAN and demonstrate that our implementation is near optimal on the NVIDIA Tesla V100s.
Efficient Distance Approximation for Structured High-Dimensional Distributions via Learning

We design efficient distance approximation algorithms for several classes of structured high-dimensional distributions. Specifically, we show algorithms for the following problems: - Given sample access to two Bayesian networks $P_1$ and $P_2$ over known directed acyclic graphs $G_1$ and $G_2$ having $n$ nodes and bounded in-degree, approximate $d_{tv}(P_1,P_2)$ to within additive error $\epsilon$ using $poly(n,\epsilon)$ samples and time - Given sample access to two ferromagnetic Ising models $P_1$ and $P_2$ on $n$ variables with bounded width, approximate $d_{tv}(P_1, P_2)$ to within additive error $\epsilon$ using $poly(n,\epsilon)$ samples and time - Given sample access to two $n$-dimensional Gaussians $P_1$ and $P_2$, approximate $d_{tv}(P_1, P_2)$ to within additive error $\epsilon$ using $poly(n,\epsilon)$ samples and time - Given access to observations from two causal models $P$ and $Q$ on $n$ variables that are defined over known causal graphs, approximate $d_{tv}(P_a, Q_a)$ to within additive error $\epsilon$ using $poly(n,\epsilon)$ samples, where $P_a$ and $Q_a$ are the interventional distributions obtained by the intervention $do(A=a)$ on $P$ and $Q$ respectively for a particular variable $A$. Our results are the first efficient distance approximation algorithms for these well-studied problems. They are derived using a simple and general connection to distribution learning algorithms. The distance approximation algorithms imply new efficient algorithms for {\em tolerant} testing of closeness of the above-mentioned structured high-dimensional distributions.
The origin of dust in galaxies across cosmic time

We study the dust evolution in galaxies by implementing a detailed dust prescription in the SAGE semi-analytical model (SAM) for galaxy formation. The new model, called Dusty SAGE, follows the condensation of dust in the ejecta of Type II supernovae and asymptotic giant branch stars, grain growth in the dense molecular clouds, destruction by supernovae shocks, and the removal of dust from the interstellar medium (ISM) by star formation, reheating, inflows, and outflows. Our model successfully reproduces the observed dust mass function at redshift z = 0 and the observed scaling relations for dust across a wide range of redshifts. We find that the dust mass content in the present Universe is mainly produced via grain growth in the ISM. By contrast, in the early Universe, the primary production mechanism for dust is the condensation in stellar ejecta. The shift of the significant production channel for dust characterizes the scaling relations of dust-to-gas (DTG) and dust-to-metal (DTM) ratios. In galaxies where the grain growth dominates, we find positive correlations for DTG and DTM ratios with both metallicity and stellar mass. On the other hand, in galaxies where dust is produced primarily via condensation, we find negative or no correlation for DTM and DTG ratios with either metallicity or stellar mass. In agreement with observation showing that the circumgalactic medium contains more dust than the ISM, our model also shows the same trend for z < 4. Our SAM is publicly available at https://github.com/dptriani/dusty-sage.
Giant low-surface-brightness dwarf galaxy as a test bench for MOdified Gravity

The lack of detection of supersymmetric particles is leading to look at alternative avenues for explaining dark matter’s effects. Among them, modified theories of gravity may play an important role accounting even for both dark components needed in the standard cosmological model. Scalar–tensor–vector gravity theory has been proposed to resolve the dark matter puzzle. Such a modified gravity model introduces, in its weak field limit, a Yukawa-like correction to the Newtonian potential, and is capable to explain most of the phenomenology related to dark matter at scale of galaxies and galaxy clusters. Nevertheless, some inconsistencies appear when studying systems that are supposed to be dark matter dominated such as dwarf galaxies. In this sense, Antlia II, an extremely diffuse galaxy which has been recently discovered in Gaia’s second data release, may serve to probe the aforementioned theory against the need for invoking dark matter. Our analysis shows several inconsistencies and leads to argue that MOdified Gravity may not be able to shed light on the intriguing nature of dark matter.
Conserved Small Nucleotidic Elements at the Origin of Concerted piRNA Biogenesis from Genes and lncRNAs

PIWI-interacting RNAs (piRNAs) target transcripts by sequence complementarity serving as guides for RNA slicing in animal germ cells. The piRNA pathway is increasingly recognized as critical for essential cellular functions such as germline development and reproduction. In the Anopheles gambiae ovary, as much as 11% of piRNAs map to protein-coding genes. Here we show that ovarian mRNAs and long non-coding RNAs (lncRNAs) are processed into piRNAs that can direct other transcripts into the piRNA biogenesis pathway. Targeting piRNAs fuel transcripts either into the ping-pong cycle of piRNA amplification or into the machinery of phased piRNA biogenesis, thereby creating networks of inter-regulating transcripts. RNAs of the same network share related genomic repeats. These repeats give rise to piRNAs, which target other transcripts and lead to a cascade of concerted RNA slicing. While ping-pong networks are based on repeats of several hundred nucleotides, networks that rely on phased piRNA biogenesis operate through short ∼40-nucleotides long repeats, which we named snetDNAs. Interestingly, snetDNAs are recurring in evolution from insects to mammals. Our study brings to light a new type of a conserved regulatory pathway, the snetDNA-pathway, by which short sequences can include independent genes and lncRNAs in the same biological pathway. AUTHOR SUMMARY Small RNA molecules are essential actors in silencing mobile genetic elements in animal germ cells. The 24-29-nucleotide-long Piwi-interacting RNAs (piRNAs) target transcripts by sequence complementarity serving as guides for RNA slicing. Mosquitoes of the Anopheles gambiae species complex are the principal vectors of malaria, and research on their germline is essential to develop new strategies of vector control by acting on reproduction. In the Anopheles gambiae ovary as much as 11% of piRNAs originate from protein-coding genes. We identified piRNAs which are able to target transcripts from several distinct genes or long non-coding RNAs (lncRNAs), bringing together genic transcripts and lncRNAs in a same regulation network. piRNA targeting induces transcript slicing and production of novel piRNAs, which then target other mRNAs and lncRNAs leading again to piRNA processing, thus resulting in a cascade of RNA slicing and piRNA production. Each network relies on piRNAs originating from repeated genetic elements, present in all transcripts of the same network. Some of these repeats are very short, only ∼40-nucleotides long. We identified similar repeats in all 43 animal species that we analysed, including mosquitoes, flies, arachnidae, snail, mouse, rat and human, suggesting that such regulation networks are recurrent, possibly conserved, in evolutionary history.
Improved Winter Wheat Spatial Distribution Extraction from High-Resolution Remote Sensing Imagery Using Semantic Features and Statistical Analysis

Improving the accuracy of edge pixel classification is an important aspect of using convolutional neural networks (CNNs) to extract winter wheat spatial distribution information from remote sensing imagery. In this study, we established a method using prior knowledge obtained from statistical analysis to refine CNN classification results, named post-processing CNN (PP-CNN). First, we used an improved RefineNet model to roughly segment remote sensing imagery in order to obtain the initial winter wheat area and the category probability vector for each pixel. Second, we used manual labels as references and performed statistical analysis on the class probability vectors to determine the filtering conditions and select the pixels that required optimization. Third, based on the prior knowledge that winter wheat pixels were internally similar in color, texture, and other aspects, but different from other neighboring land-use types, the filtered pixels were post-processed to improve the classification accuracy. We used 63 Gaofen-2 images obtained from 2017 to 2019 of a representative Chinese winter wheat region (Feicheng, Shandong Province) to create the dataset and employed RefineNet and SegNet as standard CNN and conditional random field (CRF) as post-process methods, respectively, to conduct comparison experiments. PP-CNN’s accuracy (94.4%), precision (93.9%), and recall (94.4%) were clearly superior, demonstrating its advantages for the improved refinement of edge areas during image classification.
microRNA-seq of cartilage reveals an overabundance of miR-140-3p which contains functional isomiRs

MiR-140 is selectively expressed in cartilage. Deletion of the entire miR-140 locus in mice results in growth retardation and early-onset osteoarthritis-like pathology, however the relative contribution of miR-140-5p or miR-140-3p to the phenotype remains to be determined. An unbiased small RNA sequencing approach identified miR-140-3p as significantly more abundant (>10-fold) than miR-140-5p in human cartilage. Analysis of these data identified multiple miR-140-3p isomiRs differing from the miRBase annotation at both the 5’ and 3’ end, with >99% having one of two seed sequences (5 ‘ bases 2-8). Canonical (miR-140-3p.2) and shifted (miR-140-3p.1) seed isomiRs were overexpressed in chondrocytes and transcriptomics performed to identify targets. miR-140-3p.1 and miR-140-3p.2 significantly down-regulated 694 and 238 genes respectively, of which only 162 genes were commonly down-regulated. IsomiR targets were validated using 3 ‘UTR luciferase assays. miR-140-3p.1 targets were enriched within up-regulated genes in rib chondrocytes of Mir140-null mice and within down-regulated genes during human chondrogenesis. Finally, through imputing the expression of miR-140 from the expression of the host gene WWP2 in 124 previously published datasets, an inverse correlation with miR-140-3p.1 predicted targets was identified. Together these data suggest the novel seed containing isomiR miR-140-3p.1 is more functional than original consensus miR-140-3p seed containing isomiR.
Comprehensive analysis of long non-coding RNAs and mRNAs in skeletal muscle of diabetic Goto-Kakizaki rats during the early stage of type 2 diabetes

Skeletal muscle long non-coding RNAs (lncRNAs) were reported to be involved in the development of type 2 diabetes (T2D). However, little is known about the mechanism of skeletal muscle lncRNAs on hyperglycemia of diabetic Goto-Kakizaki (GK) rats at the age of 3 and 4 weeks. To elucidate this, we used RNA-sequencing to profile the skeletal muscle transcriptomes including lncRNAs and mRNAs, in diabetic GK and control Wistar rats at the age of 3 and 4 weeks. In total, there were 438 differentially expressed mRNAs (DEGs) and 401 differentially expressed lncRNAs (DELs) in skeletal muscle of 3-week-old GK rats compared with age-matched Wistar rats, and 1000 DEGs and 726 DELs between GK rats and Wistar rats at 4 weeks of age. The protein–protein interaction analysis of overlapping DEGs between 3 and 4 weeks, the correlation analysis of DELs and DEGs, as well as the prediction of target DEGs of DELs showed that these DEGs (Pdk4, Stc2, Il15, Fbxw7 and Ucp3) might play key roles in hyperglycemia, glucose intolerance, and increased fatty acid oxidation. Considering the corresponding co-expressed DELs with high correlation coefficients or targeted DELs of these DEGs, our study indicated that these dysregulated lncRNA-mRNA pairs (NONRATG017315.2-Pdk4, NONRATG003318.2-Stc2, NONRATG011882.2-Il15, NONRATG013497.2-Fbxw7, MSTRG.1662-Ucp3) might be related to above biological processes in GK rats at the age of 3 and 4 weeks. Our study could provide more comprehensive knowledge of mRNAs and lncRNAs in skeletal muscle of GK rats at 3 and 4 weeks of age. And our study may provide deeper understanding of the underlying mechanism in T2D of GK rats at the age of 3 and 4 weeks.
Modelling, simulation and control of multiphase steel production

Multiphase steels combine good formability properties with high strength and have therefore become important construction materials, especially in automotive industry. The standard process route is hot rolling with subsequent controlled cooling to adjust the desired phase mixture. In the first part of the paper a phenomenological model for the austenite ferrite phase transition is developed in terms of a nucleation and growth process, where the growth rate depends on the carbon concentration in austenite. The approach allows for further extensions, e.g., to account for a speed up of nucleation due to deformation of austenite grains. The model is coupled with an energy balance to describe the phase transitions on a run-out table after hot rolling. Here, the most important control parameters are the amount of water flowing per time and the feed velocity of the strip. The spatial flux profile of the water nozzles has been identified from experiments. Since the process window for the adjustment of the phase composition is very tight the computation of optimal process parameters is an important task also in practice. This is discussed in the last part of the paper using a classical optimal control approach. To this end a cost functional is defined, which amounts to demanding a desired ferrite fraction and temperature at the end time. The functional is minimized in the set of solutions to the state system such that the control satisfies additional inequality constraints. Numerical results and the convergence history of the developed algorithm are presented for the production of a real dualphase steel with desired phase composition.
Discrimination of Biological Scatterers in Polarimetric Weather Radar Data: Opportunities and Challenges

For radar aeroecology studies, the identification of the type of scatterer is critically important. Here, we used a random forest (RF) algorithm to develop a variety of scatterer classification models based on the backscatter values in radar resolution volumes of six radar variables (reflectivity, radial velocity, spectrum width, differential reflectivity, correlation coefficient, and differential phase) from seven types of biological scatterers and one type of meteorological scatterer (rain). Models that discriminated among fewer classes and/or aggregated similar types into more inclusive classes classified with greater accuracy and higher probability. Bioscatterers that shared similarities in phenotype tended to misclassify against one another more frequently than against more dissimilar types, with the greatest degree of misclassification occurring among vertebrates. Polarimetric variables proved critical to classification performance and individual polarimetric variables played central roles in the discrimination of specific scatterers. Not surprisingly, purposely overfit RF models (in one case study) were our highest performing. Such models have a role to play in situations where the inclusion of natural history can play an outsized role in model performance. In the future, bioscatter classification will become more nuanced, pushing machine-learning model development to increasingly rely on independent validation of scatterer types and more precise knowledge of the physical and behavioral properties of the scatterer.
INVERSE IDENTIFICATION OF BUFFETING AND SELF-EXCITED WIND LOADS ON THE HARDANGER BRIDGE FROM ACCELERATION DATA

The traditional wind load assessment for long-span bridges rely on assumed models for the wind field and aerodynamic coefficients from wind tunnel tests, which usually introduces some uncertainties. It is therefore desired to develop tools that can utilize full-scale vibration response data from existing bridges in order to study the wind loading in detail for in-situ conditions. This paper presents a novel case study of inverse identification of dynamic wind loads on the 1310 m long Hardanger bridge, a suspension bridge equipped with a network of accelerometers. The identification method used is recent a unscented Kalman-type filter for joint input, state, and parameter estimation. A system model considering the still-air modes in addition to a quasi-steady submodel for the self-excited forces of the bridge is presented. Herein, the aerodynamic lift and pitch parameters are considered unknown and are jointly estimated with the buffeting forces.
Characterization of Tanzanian Avocado Using Morphological Traits

Two-hundred and twenty-six old avocado trees (Persea americana Mill) derived from seeds were selected from eight districts of the Mbeya, Njombe and Songwe regions in Tanzania. The tree, leaf, fruit and seed characteristics were studied using the descriptors for avocado (Persea spp.) from the International Plant Genetic Resources Institute. Cross tabulation and Chi-square tests were conducted in order to assess the distribution of traits between districts and altitude ranges. Principle coordinate analysis (PCoA) and hierarchical cluster analysis (HCA) were used to assess variation of traits within and among districts. Various morphological features were observed among the samples which point to the existence of the Mexican, Guatemalan and West Indian avocado races in Tanzania. The biplot from PCoA revealed extensive variation between the sampled trees at the district level but no clear groupings of the samples based on geographic location. Likewise, dendrograms ensuing from the neighbor–joining and Wards methods displayed that the avocado samples from the same district and even region differed considerably. This morphological trait variation suggests high diversity that may help in planning germplasm management and conservation, as well as breeding strategies in the future.