Miracle-dz/UniFAR-FTUs huggingface dataset:
An Augmented Index-based Efficient Community Search for Large Directed Graphs

Given a graph G and a query vertex q, the topic of community search (CS), aiming to retrieve a dense subgraph of G containing q, has gained much attention. Most existing works focus on undirected graphs which overlooks the rich information carried by the edge directions. Recently, the problem of community search over directed graphs (or CSD problem) has been studied; it finds a connected subgraph containing q, where the in-degree and out-degree of each vertex within the subgraph are at least k and l, respectively. However, existing solutions are inefficient, especially on large graphs. To tackle this issue, in this paper, we propose a novel index called D-Forest, which allows a CSD query to be completed within the optimal time cost. We further propose efficient index construction methods. Extensive experiments on six real large graphs show that our index-based query algorithm is up to two orders of magnitude faster than existing solutions.
A 2D stochastic fluid-structure interaction problem in compliant arteries with non-zero longitudinal displacement

In this paper, we study a nonlinear fluid-structure interaction problem driven by a multiplicative, white-in-time noise. The problem consists of the Navier-Stokes equations describing the flow of an incompressible, viscous fluid in a 2D cylinder interacting with an elastic wall whose elastodynamics is described by membrane equations. The stochastic force is applied both to the fluid equations as a volumetric body force, and to the structure as an external forcing to the deformable fluid boundary. The fluid and the structure are nonlinearly coupled via the kinematic and dynamic conditions assumed at the moving interface, which is a random variable not known a priori. In particular, we consider the case where the structure is allowed to have non-zero longitudinal displacement.
Extremum Seeking for Stefan PDE with Moving Boundary

This paper presents the design and analysis of the extremum seeking for static maps with input passed through a partial differential equation (PDE) of the diffusion type defined on a time-varying spatial domain whose boundary position is governed by an ordinary differential equation (ODE). This is the first effort to pursue an extension of extremum seeking from the heat PDE to the Stefan PDE. We compensate the average-based actuation dynamics by a controller via backstepping transformation for the moving boundary, which is utilized to transform the original coupled PDE-ODE into a target system whose exponential stability of the average equilibrium of the average system is proved. The discussion for the delay-compensated extremum seeking control of the Stefan problem is also presented and illustrated with numerical simulations.
To Punish or Protect? Local Leaders and Economic Coercion in China

Abstract During foreign policy disputes involving China and some of its most important economic partners, why do local leaders punish or protect foreign commercial actors? Understanding this variation is important because how local leaders treat foreign businesses can influence the overall effectiveness of the Chinese government's use of economic coercion against foreign states. Two variables help determine whether local leaders participate in economic retaliation: (1) whether they regard commercial ties to the targeted state as essential to achieving the economic growth necessary for career advancement; and (2) whether they are politically vulnerable, either because they lack powerful patrons or because their locality recently experienced social unrest. Economic importance creates incentives to protect foreign commercial interests, and political vulnerability provides incentives for local leaders to demonstrate their patriotic credentials through economic retaliation. Drawing on interviews with Chinese, Korean, and Japanese sources, an examination of local leader behavior in Chinese cities illuminates why they responded differently during foreign policy disputes with Japan in 2012 and South Korea in 2017.
A systematic screening assay identifies efficient small guide RNAs for CRISPR activation

CRISPR-mediated gene activation (CRISPRa) encompasses a growing field of biotechnological approaches with exciting implications for gene therapy. However, there is a lack of experimental validation tools for selecting efficient sgRNAs for downstream applications. Here, we present a screening assay capable of identifying efficient single- and double sgRNAs through fluorescence quantification in vitro. In addition, we provide a tailored Golden Gate cloning workflow for streamlined incorporation of selected sgRNA candidates into lentiviral (LVs) or adeno-associated vectors (AAVs). The overall workflow was validated using therapeutically relevant genes for neurodegenerative diseases, such as Tfeb, Adam17, and Sirt1. The most efficient sgRNAs also demonstrated activation of endogenous gene expression at mRNA and protein levels. Further proof- of-principle assays using Tfeb indicated that gene activation was accompanied by increased levels of Lc3b. This data demonstrates the potential of the screening assay to identify functionally efficient sgRNA candidates across multiple genes along with streamlined cloning of viral vectors and may assist in accelerating future developments of CRISPRa-focused applications.
C3 and CD47 dependent mechanisms in the refinement of sensory-motor circuits during spinal cord development

Overground movement in mammals require the timely and appropriate assembly of spinal sensory-motor circuits. Within spinal cord, sensory neurons, spinal interneurons and motor neurons are key players forming intricate neuronal circuits to ensure proper motor control. The formation of neuronal circuits starts at embryonic period and continues into postnatal development. During this process supernumerary synapses are pruned and refinement of immature circuits occurs, making way for emergence of mature circuits. A major aspect of the sensory-motor circuits’ refinement involves the elimination of inappropriate synapses, the molecular mechanisms of which are relatively unknown. We have investigated the molecular mechanisms involved in the elimination of inappropriate proprioceptive synapses from motor neurons by focusing on the classical complement proteins C3 and C1q, as well as the integrin associated protein CD47. Using mouse genetics, viral-mediated neuronal map strategies together with physiological, morphological and molecular biology assays, we found that inappropriate synaptic elimination occurs during early development utilizing both C3 and C1q proteins, but importantly and totally unexpectedly, the CD47 protein. Taken together, our study demonstrates that refinement of immature sensory-motor circuits in the spinal cord is mediated by classical complement dependent mechanisms as well as CD47-dependent mechanisms, uncovering a new role for CD47 as a major player in synapse elimination.
CONSULT-II: Accurate taxonomic identification and profiling using locality-sensitive hashing

Taxonomic classification of short reads and taxonomic profiling of metagenomic samples are well-studied yet challenging problems. The presence of species belonging to ranks without close representation in a reference dataset is particularly challenging. While k-mer-based methods have performed well in terms of running time and accuracy, they tend to have reduced accuracy for such novel species. Here, we show that using locality-sensitive hashing (LSH) can increase the sensitivity of the k-mer-based search. Our method, which combines LSH with several heuristics techniques including soft LCA labeling and voting is, more accurate than alternatives in both taxonomic classification of individual reads and abundance profiling.
Transcriptional profiling of Drosophila male-specific P1 (pC1) neurons

In Drosophila melanogaster, the P1 (pC1) cluster of male-specific neurons both integrates sensory cues and drives or modulates behavioral programs such as courtship, in addition to contributing to a social arousal state. The behavioral function of these neurons is linked to the genes they express, which underpin their capacity for synaptic signaling, neuromodulation, and physiology. Yet, P1 (pC1) neurons have not been fully characterized at the transcriptome level. Moreover, it is unknown how the molecular landscape of P1 (pC1) neurons acutely changes after flies engage in social behaviors, where baseline P1 (pC1) neural activity is expected to increase. To address these two gaps, we use single cell-type RNA sequencing to profile and compare the transcriptomes of P1 (pC1) neurons harvested from socially paired versus solitary male flies. Compared to control transcriptome datasets, we find that P1 (pC1) neurons are enriched in 2,665 genes, including those encoding receptors, neuropeptides, and cell-adhesion molecules (dprs/DIPs). Furthermore, courtship is characterized by changes in ∼300 genes, including those previously implicated in regulating behavior (e.g. DopEcR, Octβ3R, Fife, kairos, rad). Finally, we identify a suite of genes that link conspecific courtship with the innate immune system. Together, these data serve as a molecular map for future studies of an important set of higher-order and sexually-dimorphic neurons.
Extra-Deep: Automated Empirical Performance Modeling for Distributed Deep Learning

With the rapidly increasing size and complexity of DNNs, equally sophisticated methods are needed to train them efficiently, including distributed training and various model/hybrid parallelism approaches. Even though developers heavily rely on state-of-the-art frameworks such as PyTorch and TensorFlow, these provide little insight into an application’s training behavior at scale, leading to latent performance bottlenecks and inefficient training configurations. We propose Extra-Deep, an automated empirical performance modeling approach for distributed deep learning to model performance metrics, such as the training time, as a function of the applications’ configuration parameters. We leverage the created models to analyze a training task’s performance, scalability, efficiency, and cost. Gathering empirical measurements of full training runs is very laborious and costly. Therefore, we employ an efficient sampling strategy that reduces the profiling time for the required empirical measurements by, on average, about 94.9%. Using our sampling strategy, we can analyze the performance behavior and identify cost-effective training configurations even for large-scale and long-running applications. We evaluated our approach on three parallelization strategies, with four DNN models and five datasets. The results show that Extra-Deep has an average prediction accuracy of 93.6% when compared to empirical results.
Ramble: A flexible, extensible, and composable experimentation framework

Reproducibility and replicability are extremely important components of scientific computing, and any computational research. The ability to replicate a set of experiments aids many other computational use cases, such as systems acceptance where a compute center requires the ability to create and execute the same experiment as a hardware vendor. Several test harnesses and frameworks currently exist, with the aim of improving experiment replicability. In this paper, we introduce Ramble, a new Python based experimentation framework. Ramble provides a domain specific language for abstracting how experiments can be created from application definitions, and a flexible templating engine for creating experiments. Ramble can be used for automating system tests, scientific parameter studies, performance focused benchmarking, and many other types of computational experiments. We will introduce Ramble, describe its architecture, and give some concrete use cases where it can be applied to HPC application experimentation.
TRANSFER AND BIOACCUMULATION OF HEAVY METAL IONS FROM SOIL INTO PLANTS

The soil is a thin surface layer of the Earth's crust formed in long-term, mutual interactions between rocks, climate and living beings. It is, conditionally, one of the renewable resources due to the long process of formation and development. The widespread contamination of soils, particularly with heavy metals, is currently one of the most serious environmental concerns. Although a small portion of heavy metals in soil is derived from natural processes, a much higher amount originates from anthropogenic sources such as industry (mining and smelting), agriculture (use of mineral fertilizers and pesticides) and transportation (automobiles and aircraft). The aim of this paper is to show the relation between the heavy metal ions content in soil and their bioaccumulation in plants. The accumulation of heavy metal ions depends on various biotic (plant species, genotype, developments stage, etc.) and abiotic factors (qualitative and quantitative characteristics of heavy metals, temperature, pH value, ion interactions, etc.). One of the abiotic factors which has recently been in focus of the research, and which is important for uptake and physiological effects of pollutants, is the interaction of ions which relate to antagonism and synergism.
Research on Keyword-Overlap Similarity Algorithm Optimization in Short English Text Based on Lexical Chunk Theory

Short-text similarity calculation is one of the hot issues in natural language processing research. The conventional keyword-overlap similarity algorithms merely consider the lexical item information and neglect the effect of the word order. And some of its optimized algorithms combine the word order, but the weights are hard to be determined. In the paper, viewing the keyword-overlap similarity algorithm, the short English text similarity algorithm based on lexical chunk theory (LC-SETSA) is proposed, which introduces the lexical chunk theory existing in cognitive psychology category into the short English text similarity calculation for the first time. The lexical chunks are applied to segment short English texts, and the segmentation results demonstrate the semantic connotation and the fixed word order of the lexical chunks, and then the overlap similarity of the lexical chunks is calculated accordingly. Finally, the comparative experiments are carried out, and the experimental results prove that the proposed algorithm of the paper is feasible, stable, and effective to a large extent.
CURRENT CONSERVATION STATUS OF LARGE MAMMALS IN SIME DARBY OIL PALM CONCESSION IN LIBERIA

The forest ecosystem of Liberia is part from the Upper Guinea Eco-region, and harbors an exceptional biodiversity in a rich mosaic of habitats serving as refuge for numerous endemic species. Unfortunately, many of these forests have been lost rapidly over the past decades, and the remaining are under various forms of anthropogenic pressure, subsistence farming, and large-scale industrial agriculture and mining. As part of a broader survey to generate information for conservation management strategies in the Gross Concession Area in preparation for its oil palm and rubber plantations in western Liberia, Sime Darby (Liberia) Inc., commissioned surveys on large mammals species in 2011. Through a combination of hunter interviews and foot surveys, we documented evidence of 46 and 32, respectively, of large mammals in the area. Fourteen of the confirmed species are fully protected at national level and three are partially protected. At the international level, 15 species are of conservation concern, including Zebra and Jentink’s duiker, Diana monkey, Sooty mangabey, Olive colobus, Elephant and Leopard.
Enabling Performant Thermal Conductivity Modeling with DeePMD and LAMMPS on CPUs

The limiting factor in the application of high-accuracy ab-initio molecular dynamics (AIMD) simulations for large systems has been the associated high computational costs. The ability to retain DFT-level accuracy while alleviating these computational costs has been made possible by the use of machine learning potentials, known as Deep Potential (DP) models, which allow accurate prediction of interatomic force and energy distributions for a system based on AIMD input data. DeePMD-kit is a Python/C++ package which implements this DP series model. It is interfaced with TensorFlow for model training and with LAMMPS when using the model for inference. In this paper, we extend DeePMD to accurately predict the thermal conductivity for crystalline Au and Ag systems of up to 2 million atoms. We demonstrate that both DeePMD training and DeePMD inference with LAMMPS can be run efficiently on CPU-based systems. On a single node of 4th generation Intel® Xeon® Scalable 9480 processors, we are able to train the model in less than 5 minutes1. Using this trained model with LAMMPS on 128 dual-socket nodes with Intel® Xeon® Scalable 8480+ processors, we are able to accurately determine the thermal conductivity of crystalline Au and Ag systems, within 5% of experimental results, in under one hour. 2.
Influence of Soil Series and Physico-chemical Properties on Weed Flora Distribution at Moor Plantation Ibadan, Southwestern Nigeria

Influence of soil series and some soil physico-chemical properties on weed flora composition, density and distribution were assessed. The main objective was to find relationship between soil series and the distribution of different weed species as well as identify those soil properties, which significantly influence the occurrences of specific weed species. Using a detailed soil map (1:10,000), weeds and soil samples were drawn from five soil mapping units – Apomu series, Mamu series, Ibadan series, Iregun series and Iwo series each covering 1.5 ha of the land. From the result of field study occurrences and distribution of different weed species were associated more or less with some soil series than others. Chi-square test of associations showed a high and significant association (χ 2 = 28-88, P = 0.01- 0.001), between soil series and weed occurrence. A stepwise multiple regression analysis (P < 0.05) showed that the soil property for most of the variation in the distribution of some of the major weed species included Mn, which accounted for 80% and 78% in the distribution of Digitaria horizontalis and Euphorbia heterophylla, respectively. Clay content contributed 68% to the distribution of Mariscus spp. and N contributed 59% to the variation in the distribution of Panicum maximum. Results showed that in the landscape of Ibadan, certain weed species were associated more with specific soil series. Also the occurrence of some weed species was influenced by specific soil physical and chemical properties.
Precision and Performance Analysis of C Standard Math Library Functions on GPUs

With the advent of GPU computing, executing large program sections on accelerators has become increasingly important. Efforts are being made to support the C standard library, LIBC, on GPUs via LLVM machinery. Therefore, the C standard math library, LIBM, must be supported on GPUs. So far, LLVM frontends, such as Clang, have relied on GPU vendor implementations of LIBM functionality wrapped into (mostly) LIBM-compatible forwarding functions. We propose a novel LIBM for GPUs reusing a collection of LLVM target-agnostic implementations and built-ins alongside vendor implementations of most single and double-precision floating point math functions. Our approach allows selecting between individual implementations based on the GPU target as opposed to the current approach, which serves only the single third-party library implementation. Our extensive numerical analysis highlights the various implementations’ differences in performance and precision. Our solution allows users to choose the implementation that maximizes speed while meeting their specific precision requirements.
Power Analysis of NERSC Production Workloads

Power has become a key limiting factor in supercomputing. Understanding the power signatures of current production workloads is essential to address this limit and continue to advance scientific computing at scale. This paper analyzes the power characteristics of NERSC production workloads at the system and application levels. Our system-level analysis revealed a large gap between the average and peak power usage distribution, indicating a significant power swing from running various applications on the system. On the application level, we select four workflow benchmarks representing NERSC’s production workloads to analyze the power characteristics of applications and attempt to correlate the observed power timeline patterns with GPU performance metrics and application profiling data. We found different applications have distinct power usage patterns and widespread average and peak power usage. We discuss how these findings may help improve the current system’s operational power efficiency and the implications for future system procurement.
Interpretable Edge Enhancement and Suppression Learning for 3D Point Cloud Segmentation

—3D point clouds can ﬂexibly represent continuous surfaces and can be used for various applications; however, the lack of structural information makes point cloud recognition challenging. Recent edge-aware methods mainly use edge information as an extra feature that describes local structures to facilitate learning. Although these methods show that incorporating edges into the network design is beneﬁcial, they generally lack interpretability, making users wonder how exactly edges help. To shed light on this issue, in this study, we propose the Diffusion Unit (DU) that handles edges in an interpretable manner while providing decent improvement. Our method is interpretable in three ways. First, we theoretically show that DU learns to perform task-beneﬁcial edge enhancement and suppression. Second, we experimentally observe and verify the edge enhancement and suppression behavior. Third, we empirically demonstrate that this behavior contributes to performance improvement. Extensive experiments performed on challenging benchmarks verify the superiority of DU in terms of both interpretability and performance gain. Speciﬁcally, our method achieves state-of-the-art performance in object part segmentation using ShapeNet part and scene segmentation using S3DIS. Our source code will be released at https://github.com/martianxiu/DiffusionUnit.
Disk Failure Trends in Alpine Storage System

Large-scale HPC systems demand extensive disk-based storage for data generated by HPC applications, necessitating scalable reliability, availability, and failure management. Extracted failure data from HPC storage offers valuable insights for preventing and managing failures, spanning understanding storage robustness, guiding system design and deployment, and creating durable data protection schemes. This paper introduces a failure dataset from OLCF’s Summit supercomputer’s file system, Alpine, encompassing 4000+ events over 2.75 years from 32000+ disks. Before analysis, we delve into Alpine’s components and introduce IBM Spectrum Scale technology, then assess collected data for failure distribution and burst correlations. We infer that, proximity to enclosure fan modules heightens disk failure rates. Also, burst failure analysis highlights 1/3rd of failures occurring in bursts, with 90% non-spatially correlated, impacting multiple racks.