Efficient algorithm for the k-means problem with Must-Link and Cannot-Link constraints

Constrained clustering, such as k -means with instance-level Must-Link (ML) and Cannot-Link (CL) auxiliary information as the constraints, has been extensively studied recently, due to its broad applications in data science and AI. Despite some heuristic approaches, there has not been any algorithm providing a non-trivial approximation ratio to the constrained k -means problem. To address this issue, we propose an algorithm with a provable approximation ratio of O(logk) when only ML constraints are considered. We also empirically evaluate the performance of our algorithm on real-world datasets having artificial ML and disjoint CL constraints. The experimental results show that our algorithm outperforms the existing greedy-based heuristic methods in clustering accuracy

A web workbench system for the Slurm cluster at IHEP

Slurm REST APIs are released since version 20.02. With those REST APIs one can interact with slurmctld and slurmdbd daemons in a REST- ful way. As a result, job submission and cluster status query can be achieved with a web system. To take advantage of Slurm REST APIs, a web workbench system is developed for the Slurm cluster at IHEP. The workbench system con- sists with four subsystems including dashboard, tomato, jasmine and cosmos. The dashboard subsystem is used to display cluster status including nodes and jobs. The tomato subsystem is developed to submit special HTCondor glidein jobs in the Slurm cluster. The jasmine system is used to generate and submit batch jobs based on workload parameters. The cosmos subsystem is an ac- counting system, which not only generates statistical charts but also provides REST APIs to query jobs. This paper presents design and implementation de- tails of the Slurm workbench. With the help of workbench, administrators and researchers can get their work done in an effective way

Porting LHAASO WFCTA simulation job to ARM computing cluster

With the advancement of many large-scale high-energy physics experiments, the amount of data to be processed and analyzed has significantly increased. For example, since the start of the Large High Altitude Air Shower Observatory (LHAASO) experiment in 2020, their simulation jobs have been running on an Intel X86 cluster, producing only a fraction of the planned data for the first phase due to limited CPU resources. Therefore, it is necessary to explore and expand other computing service devices. We built an application ecosystem based on the ARM architecture to support offline data processing for high-energy physics. The main work includes porting the offline software based on LHAASO experiments to run on ARM machines, formulating data transfer and job scheduling strategies in the ARM cluster, and evaluating performance and power consumption in both Intel X86 and ARM clusters. The results show that the LHAASO simulation jobs can run correctly on the ARM computing cluster. The singlecore performance of Intel X86 CPUs is better than ARM CPUs, but for the entire server with a multicore architecture, ARM servers perform better

Using Kerberos Tokens in Distributed Computing System at IHEP

The token-based certification method is spreading in the distributed computing system of high energy physics. More and more software and middleware are supporting tokens as one of the certification methods. As an example, WLCG has upgraded all the services to support WLCG tokens [1]. In IHEP (Institute of High Energy Physics in China), the Kerberos [2] token has been used as the main certification method in the local cluster. Naturally, it is selected as the certification method in the distributed computing system. In this case, a set of toolkits were developed or introduced to use Kerberos tokens in the distributed computing system, including token producer, token repository, token transfer and token client engine. The token producer is responsible for creating a token and publishing the token file to the token repository. The token repository stores all the latest token files and a refresh service periodically renews the lifetime of those tokens stored in the token repository. The token transfer brings the token file to the worker node. The token client engine initializes the token environment and renews the token’s lifetime on the worker node. With these toolkits, the jobs can run in any worker node in any site and use the Kerberos token to access other services, such as EOS [3] and the XRootd [4] proxy service. In IHEP, the Kerberos toolkit has been deployed in the distributed computing system. Currently, three experiments (LHAASO [5], BES [6] and HERD [7]) are using Kerberos tokens to remotely access the data in EOS or Lustre [8]

Enhanced luminescence properties of monodisperse trioctylphosphine oxide-capped Nd3+-doped LaF3 nanorods without OH groups

<p> Nd3+-doped LaF3 nanorods without -OH groups were synthesized via a simple thermolysis method in trioctylphosphine oxide (TOPO) solvent. FTIR spectrum indicates that TOPO molecules have been coordinated to LaF3:Nd nanorods surface, which reduce the number of -OH groups on the nanoparticles surface effectively. The structure and morphology of as-synthesized nanorods were characterized. The possible grow mechanism of LaF3:Nd nanorods has been also discussed in detail. The TOPO capped LaF3:Nd nanoparticles preferentially grow along the &lt;0 0 0 1&gt; orientation under high temperature. Based on the absorption spectra and Judd-Ofelt theory, higher value of emission cross-section for F-4(3/2)-&gt; I-4(11/2) transition of Nd3+ was calculated to be 3.22 x 10(-20) cm(2). The strong fluorescence intensity of LaF3:Nd nanorods in chloroform demonstrates that these nanorods are promising luminescence materials. (C) 2013 Elsevier B.V. All rights reserved.</p

Enhanced near infrared luminescence efficiency of ligand-free LaF3:Nd/LaF3 core/shell nanocrystals in solvent dispersion

<p> A variety of ligand-free LaF3:Nd/LaF3 core/shell nanocrystals with high quantum efficiency, great dispersibility and low quench ratio was prepared by a simple solvothermal method. Their phase and morphologies were characterized by X-ray diffraction (XRD) and transmission electron microscope (TEM). The optical properties of the samples prepared under different times were investigated. The core/shell nanocrystals have great dispersibility concentration (312 mg/mL) in dimethyl sulfoxide (DMSO)/tetrabromoethane solvents. These transparent colloidal solution exhibits enhanced high quantum efficiency (61.2%) at 1057 um. Therefore, the LaF3:Nd/LaF3 core/shell nanocrystals with excellent near infrared to near infrared (NIR-to-NIR) fluorescence are a promising candidate for luminescence material in liquid media. (C) 2014 Elsevier B.V. All rights reserved.</p

Luminescence in the fluoride-containing phosphate-based glasses: A possible origin of their high resistance to nanosecond pulse laser-induced damage

Fusion power offers the prospect of an almost inexhaustible source of energy for future generations. It was reported that fusion fuel gains exceeding unity on the National Ignition Facility (NIF) were achieved, but so far great deal of scientific and engineering challenges have to be overcome for realizing fusion power generation. There is a bottleneck for color-separation gratings in NIF and other similar inertial confinement fusion (ICF) lasers. Here we show a series of high performance phosphate-based glasses that can transmit the third harmonic frequency (3 omega) laser light with high efficiency meanwhile filter the fundamental (1 omega) and the second harmonic frequency (2 omega) laser lights through direct absorption, and especially they exhibit excellent damage threshold induced by nanosecond pulse laser compared with that of the fused silica used in NIF. Yellowish-orange fluorescence emits during the laser-material interaction process, and it can be tailored through regulating the glass structure. Study on its structural origin suggests that the fluorescence emission is a key factor that conduces to the high laser-induced damage resistance of these glasses. The results also indicated the feasibility of utilizing these high performance glasses in novel color separation optics, allowing novel design for the final optics assembly in ICF lasers

Forecasting Crude Oil Price Using EEMD and RVM with Adaptive PSO-Based Kernels

Crude oil, as one of the most important energy sources in the world, plays a crucial role in global economic events. An accurate prediction for crude oil price is an interesting and challenging task for enterprises, governments, investors, and researchers. To cope with this issue, in this paper, we proposed a method integrating ensemble empirical mode decomposition (EEMD), adaptive particle swarm optimization (APSO), and relevance vector machine (RVM)—namely, EEMD-APSO-RVM—to predict crude oil price based on the “decomposition and ensemble” framework. Specifically, the raw time series of crude oil price were firstly decomposed into several intrinsic mode functions (IMFs) and one residue by EEMD. Then, RVM with combined kernels was applied to predict target value for the residue and each IMF individually. To improve the prediction performance of each component, an extended particle swarm optimization (PSO) was utilized to simultaneously optimize the weights and parameters of single kernels for the combined kernel of RVM. Finally, simple addition was used to aggregate all the predicted results of components into an ensemble result as the final result. Extensive experiments were conducted on the crude oil spot price of the West Texas Intermediate (WTI) to illustrate and evaluate the proposed method. The experimental results are superior to those by several state-of-the-art benchmark methods in terms of root mean squared error (RMSE), mean absolute percent error (MAPE), and directional statistic (Dstat), showing that the proposed EEMD-APSO-RVM is promising for forecasting crude oil price