Miracle-dz/UniFAR-FTUs huggingface dataset:
A Fully Dynamic Algorithm for k-Regret Minimizing Sets

Selecting a small set of representatives from a large database is important in many applications such as multi-criteria decision making, web search, and recommendation. The k-regret minimizing set (k-RMS) problem was recently proposed for representative tuple discovery. Specifically, for a large database P of tuples with multiple numerical attributes, the k-RMS problem returns a size-r subset Q of P such that, for any possible ranking function, the score of the top-ranked tuple in Q is not much worse than the score of the kth-ranked tuple in P. Although the k-RMS problem has been extensively studied in the literature, existing methods are designed for the static setting and cannot maintain the result efficiently when the database is updated. To address this issue, we propose the first fully-dynamic algorithm for the k-RMS problem that can efficiently provide the up-to-date result w.r. t. any tuple insertion and deletion in the database with a provable guarantee. Experimental results on several real-world and synthetic datasets demonstrate that our algorithm runs up to four orders of magnitude faster than existing k-RMS algorithms while providing results of nearly equal quality.
Sensitivity of 44Ti and 56Ni Production in Core-collapse Supernova Shock-driven Nucleosynthesis to Nuclear Reaction Rate Variations

Recent observational advances have enabled high resolution mapping of 44Ti in core-collapse supernova (CCSN) remnants. Comparisons between observations and models provide stringent constraints on the CCSN mechanism. However, past work has identified several uncertain nuclear reaction rates that influence 44Ti and 56Ni production in postprocessing model calculations. We evolved one-dimensional models of 15 M⊙, 18 M⊙, 22 M⊙, and 25 M⊙ stars from zero age main sequence through CCSN using Modules for Experiments in Stellar Astrophysics and investigated the previously identified reaction rate sensitivities of 44Ti and 56Ni production. We tested the robustness of our results by making various assumptions about the CCSN explosion energy and mass cut. We found a number of reactions that have a significant impact on the nucleosynthesis of 44Ti and 56Ni, particularly for lower progenitor masses. Notably, the reaction rates , , , , , , , , , , and are influential for a large number of model conditions. Furthermore, we found the list of influential reactions identified by previous postprocessing studies of CCSN shock-driven nucleosynthesis is likely incomplete, motivating future larger-scale sensitivity studies.
Characteristics of aquatic biospheres on temperate planets around Sun-like stars and M dwarfs

Aquatic biospheres reliant on oxygenic photosynthesis are expected to play an important role on Earth-like planets with large-scale oceans insofar as carbon fixation (i.e., biosynthesis of organic compounds) is concerned. We investigate the properties of aquatic biospheres comprising Earth-like biota for habitable rocky planets orbiting Sun-like stars and late-type M-dwarfs such as TRAPPIST-1. In particular, we estimate how these characteristics evolve with the ambient ocean temperature ($T_W$), which is a key environmental variable. We show that many salient properties, such as the depth of the photosynthesis zone and the net primary productivity (i.e., the effective rate of carbon fixation), are sensitive to $T_W$, and eventually decline substantially as the ocean temperature is increased. We conclude by discussing the implications of our analysis for the past and future Earth, and exoplanets orbiting M-dwarfs.
Efficient Sparse-Dense Matrix-Matrix Multiplication on GPUs Using the Customized Sparse Storage Format

Multiplication of a sparse matrix to a dense matrix (SpDM) is widely used in many areas like scientific computing and machine learning. However, existing work under-looks the performance optimization of SpDM on modern manycore architectures like GPUs. The storage data structures help sparse matrices store in a memory-saving format, but they bring difficulties in optimizing the performance of SpDM on modern GPUs due to irregular data access of the sparse structure, which results in lower resource utilization and poorer performance. In this paper, we refer to the roofline performance model of GPUs to design an efficient SpDM algorithm called GCOOSpDM, in which we exploit coalescent global memory access, fast shared memory reuse, and more operations per byte of global memory traffic. Experiments are evaluated on three Nvidia GPUs (i.e., GTX 980, GTX Titan X Pascal, and Tesla P100) using a large number of matrices including a public dataset and randomly generated matrices. Experimental results show that GCOOSpDM achieves 1.5-8x speedup over Nvidia's library cuSPARSE in many matrices.
The landscape of disc outflows from black hole–neutron star mergers

We investigate mass ejection from accretion disks formed in mergers of black holes (BHs) and neutron stars (NSs). The third observing run of the LIGO/Virgo interferometers provided BH-NS candidate events that yielded no electromagnetic (EM) counterparts. The broad range of disk configurations expected from BH-NS mergers motivates a thorough exploration of parameter space to improve EM signal predictions. Here we conduct 27 high-resolution, axisymmetric, long-term hydrodynamic simulations of the viscous evolution of BH accretion disks that include neutrino emission/absorption effects and post-processing with a nuclear reaction network. In the absence of magnetic fields, these simulations provide a lower-limit to the fraction of the initial disk mass ejected. We find a nearly linear inverse dependence of this fraction on disk compactness (BH mass over initial disk radius). The dependence is related to the fraction of the disk mass accreted before the outflow is launched, which depends on the disk position relative to the innermost stable circular orbit. We also characterize a trend of decreasing ejected fraction and decreasing lanthanide/actinide content with increasing disk mass at fixed BH mass. This trend results from a longer time to reach weak freezout and an increasingly dominant role of neutrino absorption at higher disk masses. We estimate the radioactive luminosity from the disk outflow alone available to power kilonovae over the range of configurations studied, finding a spread of two orders of magnitude. For most of the BH-NS parameter space, the disk outflow contribution is well below the kilonova mass upper limits for GW190814.
The Association of Planting Density and Plant Type in French Bean (Phaseolus vulgaris)

SUMMARY Five varieties of French bean of contrasting growth habit were grown at a range of plant densities. Most varieties yielded best at 30 plants m−2, but the bush type variety Contender yielded best at 50 plants m−5. The best yield was obtained from the semi-spreading variety Rufus.
Goal-Directed Planning for Habituated Agents by Active Inference Using a Variational Recurrent Neural Network

It is crucial to ask how agents can achieve goals by generating action plans using only partial models of the world acquired through habituated sensory-motor experiences. Although many existing robotics studies use a forward model framework, there are generalization issues with high degrees of freedom. The current study shows that the predictive coding (PC) and active inference (AIF) frameworks, which employ a generative model, can develop better generalization by learning a prior distribution in a low dimensional latent state space representing probabilistic structures extracted from well habituated sensory-motor trajectories. In our proposed model, learning is carried out by inferring optimal latent variables as well as synaptic weights for maximizing the evidence lower bound, while goal-directed planning is accomplished by inferring latent variables for maximizing the estimated lower bound. Our proposed model was evaluated with both simple and complex robotic tasks in simulation, which demonstrated sufficient generalization in learning with limited training data by setting an intermediate value for a regularization coefficient. Furthermore, comparative simulation results show that the proposed model outperforms a conventional forward model in goal-directed planning, due to the learned prior confining the search of motor plans within the range of habituated trajectories.
Effect of Endosperm Type on Incremental Energy Value of Corn Grain for Dairy Cows

Abstract The incremental energy value of corn grain representing two contrasting endosperm types was measured with lactating and nonlactating cows in energy balance experiments using indirect calorimetry. The energy values of increments of hard and soft types of corn were not different, but the energy values of both endosperm types were substantially lower for lactating than for nonlactating cows at maintenance intake. The average values for lactating and nonlactating cows were 2.32 and 3.38 Meal metabolizable energy/kg dry matter, 2.28 and 3.51 Meal digestible energy/kg dry matter, and . 56 and . 84kg total digestible nutrients/kg dry matter. The average net energy value of the increment of corn grain for lactating cows was 1.34 Mcal/kg dry matter. Recommended energy values for corn grain are given as a function of intake and physical form.
CORRELATION BETWEEN CELL DENSITY AND DOMINANT GROWTH FORM OF EPILITHIC DIATOM ASSEMBLAGES

The correlation between the algal cell density and the growth form of freshwater epilithic diatoms was investigated in field observations. Three sampling stations were set in the Asa-Kawa River, Japan to examine the possible effects of water pollution on the correlation. The developmental process of epilithic algal communities reported by some preceding workers was reflected by the relationship in the intermediately polluted station: relative abundance of adnate diatoms decreased and that of rosette forming diatoms increased with increase in the algal cell density. Relative abundance of stalked diatoms increased with increase in the algal cell density at low density, but it began to decrease at densities above 105 cells.mm-″2. However, in the less polluted and in the considerably polluted stations, no significant correlation was found. Slow growth and grazing pressure in the less polluted station, and water pollution in the considerably polluted station were the possible driving forces for the establishme.
Distribution and breeding performance of a high-density Eagle Owl Bubo bubo population in southeast Spain

Capsule Despite very high breeding density, no density-dependent effects on reproductive parameters were detected. Aims To describe the distribution, abundance and breeding performance of Eagle Owls and to analyse density-dependent effects on breeding parameters. Methods We censused a high-density population of Eagle Owls in southeast Spain between 2003 and 2010. To census the population we employed acoustic signals and searched for field signs. Breeding performance was determined by nest monitoring. Results The population's density, productivity and fledgling rate were the highest recorded for this species. We detected a negative relationship between the laying date and productivity. Despite breeding pairs' high density, no density-dependent effects on reproductive parameters were detected. Conclusions Our results suggest that resources in the study area (mainly the availability of Rabbits Oryctolagus cuniculus) and adult turnover might be responsible for this population's high density and breeding success.
Specificity of the adhesion of some plant pathogenic micro-organisms to the cuticle of nematodes in the genus Anguina (Nematoda: Anguinidae).

The adhesion of Dilophospora alopecuri conidia from two host species and 35 strains of plant pathogenic corynebacteria to seven populations of seed-gall nematodes, Anguina spp. , was investigated. The nematodes included Anguina agrostis from Agrostis capillaris, Anguina funesta from Lolium rigidum, Anguina tritici from Triticum aestivum, Anguina sp. from Stipa scabra ssp. falcata and Anguina sp. from Holcus lanatus. Only corynebacteria that cause gummosis diseases of grasses (Clavibacter iranicum, Clavibacter rathayi, Clavibacter tritici, Clavibacter sp. associated with annual ryegrass toxicity and ''Corynebacterium agropyri'') and mostly known to be associated with an Anguina vector adhered to the nematodes. Adhesion was not restricted to the nematode species normally associated with the particular bacterial strain. The patterns of adhesion observed suggested that a range of different adhesin-receptor interactions may be involved. Both D. alopecuri spore samples adhered to varying degrees to all the nematode populations. The observed adhesion patterns showed some relationship to the taxonomic and geographic groupings of the organisms. The specificity of the associations of these pathogens in vivo seems only in part due to specificity of adhesion to the vector nematodes but may also be influenced by other factors such as the ability to colonize alternate hosts and/or lack of geographical overlap with other potential vectors.
A Process for the Evaluation of Node Embedding Methods in the Context of Node Classification

Node embedding methods find latent lower-dimensional representations which are used as features in machine learning models. In the last few years, these methods have become extremely popular as a replacement for manual feature engineering. Since authors use various approaches for the evaluation of node embedding methods, existing studies can rarely be efficiently and accurately compared. We address this issue by developing a process for a fair and objective evaluation of node embedding procedures w.r. t. node classification. This process supports researchers and practitioners to compare new and existing methods in a reproducible way. We apply this process to four popular node embedding methods and make valuable observations. With an appropriate combination of hyperparameters, good performance can be achieved even with embeddings of lower dimensions, which is positive for the run times of the downstream machine learning task and the embedding algorithm. Multiple hyperparameter combinations yield similar performance. Thus, no extensive, time-consuming search is required to achieve reasonable performance in most cases.
Isolation and characterization of EST-derived microsatellite loci from the fungal wheat pathogen Phaeosphaeria nodorum

Eleven polymorphic microsatellite loci and one minisatellite locus originating from expressed sequence tag (EST) libraries of Phaeosphaeria (syn. Stagonospora) nodorum were isolated and characterized. The satellite markers were used to genotype isolates from field populations collected in China, North America and South Africa. The number of alleles per locus ranged from two to 15. Genotype diversity ranged from 87.5 to 95.3 and gene diversity from 0.1 to 0.8. The variable levels of polymorphism within and among populations of P. nodorum renders these 12 satellite loci ideal markers for population genetic analysis of P. nodorum.
An Analytical Model for Molecular Communication Over a Non-Linear Reaction-Diffusion Medium

One of the main challenges in diffusion-based molecular communication is dealing with the non-linearity of reaction-diffusion chemical equations. While numerical methods can be used to solve these equations, a change in the input signals or the parameters of the medium requires one to redo the simulations. This makes it difficult to design modulation schemes and practically impossible to prove the optimality of a given transmission strategy. In this paper, we provide an analytical technique for modeling the non-linearity of chemical reaction equations based on the perturbation method. The perturbation method expresses the solution in terms of an infinite power series. An approximate solution can be found by keeping the leading terms of the power series. The approximate solution is shown to track the true solution if either the simulation time interval or the reaction rate is sufficiently small. Approximate solutions for long time intervals are also discussed. An illustrative example is given. For this example, it is shown that when the reaction rate (or the total time interval) is low, instead of using a continuous release waveform, it is optimal for the transmitters to release molecules at two time instances.
Effects of intersowing with beans on the spread of groundnut rosette virus by Aphis craccivora Koch (Hemiptera, Aphididae) in Malawi

In two trials, at Chitedze, Malawi, in 1966–1967, the spread of rosette virus disease in groundnuts ( Arachis hypogaea ) intersown with field beans ( Phaseolus vulgaris ) was less than the spread in groundnut monocultures comprising plant populations equivalent to the number of groundnut plants and to the total number of plants in the intersown crop. In one trial, numbers of early rosette transmissions were related to numbers of alate Aphis craccivora Koch, the vector of the virus, found on groundnut plants. Immigrant alate A. craccivora settling on the intersown crop were trapped by the hooked epidermal hairs of the bean plants and the observed reduction of rosette infection in the intersown crop was attributed to this effect. Inter-sowing with beans is considered less effective than early-sown, high-density monoculture as a method of reducing groundnut rosette infections and yields of groundnuts from inter-sown crops may be lower than those obtained from high-density monoculture.
Hyperbolic quotients of projection complexes

This paper is a continuation of our previous work with Margalit where we studied group actions on projection complexes. In that paper, we demonstrated sufficient conditions so that the normal closure of a family of subgroups of vertex stabilizers is a free product of certain conjugates of these subgroups. In this paper, we study both the quotient of the projection complex by this normal subgroup and the action of the quotient group on the quotient of the projection complex. We show that under certain conditions that the quotient complex is $\delta$-hyperbolic. Additionally, under certain circumstances, we show that if the original action on the projection complex was a non-elementary WPD action, then so is the action of the quotient group on the quotient of the projection complex. This implies that the quotient group is acylindrically hyperbolic.
Procerusternarchus pixuna, a new genus and species of electric knifefish (Gymnotiformes: Hypopomidae, Microsternarchini) from the Negro River, South America

ABSTRACT. Procerusternarchus pixuna, a new genus and species of hypopomid gymnotiform, is described from several tributaries of Negro River, Amazonas State, Brazil. The new genus is included in the tribe Microsternarchini with the genera Microsternarchus and Racenisia based on a phylogenetic analysis of 41 anatomical, morphometric, meristic, and electric organ discharge variable characters. The new taxon is distinguished from Microsternarchus and Racenisia by the apomorphic presence of a deep, enlarged myogenic electric organ, an electric organ discharge with a greatly reduced negative phase and the plesiomorphic condition of a fully-scaled body, whereas scales are absent from the upper back of Microsternarchus and Racenisia.
A Causal Machine Learning Framework for Predicting Preventable Hospital Readmissions

Clinical predictive algorithms are increasingly being used to form the basis for optimal treatment policies--that is, to enable interventions to be targeted to the patients who will presumably benefit most. Despite taking advantage of recent advances in supervised machine learning, these algorithms remain, in a sense, blunt instruments--often being developed and deployed without a full accounting of the causal aspects of the prediction problems they are intended to solve. Indeed, in many settings, including among patients at risk of readmission, the riskiest patients may derive less benefit from a preventative intervention compared to those at lower risk. Moreover, targeting an intervention to a population, rather than limiting it to a small group of high-risk patients, may lead to far greater overall utility if the patients with the most modifiable (or preventable) outcomes across the population could be identified. Based on these insights, we introduce a causal machine learning framework that decouples this prediction problem into causal and predictive parts, which clearly delineates the complementary roles of causal inference and prediction in this problem. We estimate treatment effects using causal forests, and characterize treatment effect heterogeneity across levels of predicted risk using these estimates. Furthermore, we show how these effect estimates could be used in concert with the modeled "payoffs" associated with successful prevention of individual readmissions to maximize overall utility. Based on data taken from before and after the implementation of a readmissions prevention intervention at Kaiser Permanente Northern California, our results suggest that nearly four times as many readmissions could be prevented annually with this approach compared to targeting this intervention using predicted risk.
Reduced order modeling of fluid flows: Machine learning, Kolmogorov barrier, closure modeling, and partitioning (Invited)

In this paper, we put forth a long short-term memory (LSTM) nudging framework for the enhancement of reduced order models (ROMs) of fluid flows utilizing noisy measurements. We build on the fact that in a realistic application, there are uncertainties in initial conditions, boundary conditions, model parameters, and/or field measurements. Moreover, conventional nonlinear ROMs based on Galerkin projection (GROMs) suffer from imperfection and solution instabilities due to the modal truncation, especially for advection-dominated flows with slow decay in the Kolmogorov width. In the presented LSTM-Nudge approach, we fuse forecasts from a combination of imperfect GROM and uncertain state estimates, with sparse Eulerian sensor measurements to provide more reliable predictions in a dynamical data assimilation framework. We illustrate the idea with the viscous Burgers problem, as a benchmark test bed with quadratic nonlinearity and Laplacian dissipation. We investigate the effects of measurements noise and state estimate uncertainty on the performance of the LSTM-Nudge behavior. We also demonstrate that it can sufficiently handle different levels of temporal and spatial measurement sparsity. This first step in our assessment of the proposed model shows that the LSTM nudging could represent a viable realtime predictive tool in emerging digital twin systems.
Proteomic analysis of Arabidopsis root gravitropism

In gravitropism of Arabidopsis root, the Arabidopsis root proteins induced and/or changed by gravitational stimulation were analyzed using a proteomic method. In this study, it was found that the levels of expression of some proteins related to the cytoskeleton and the calcium signal transmission system had changed during gravitational stimulation. In addition, the β subunit of the E1 component of pyruvate dehydrogenase, fructose bisphosphate aldolase, and 20S proteasome β subunit E1 protein appeared in two different molecular weight types during gravitational stimulation. The transitory changes in molecular weight in these three identified proteins were important findings although their functions in the gravity response in the roots of Arabidopsis seedlings remain uncertain. As far as we know, this is the first study involving the molecular weight shift-change in the same protein during the gravitropism response in plants using proteomic analysis.