相关论文: The POOL Data Storage, Cache and Conversion Mechan…

In high energy physics, the ability to reconstruct particles based on their detector signatures is essential for downstream data analyses. A particle reconstruction algorithm based on learning hypergraphs (HGPflow) has previously been…

We introduce a framework that integrates both analytical and machine-learning approaches for calculating observables optimal for EFT and broader applications at the LHC. A new metric for evaluating the performance of these approaches has…

Cloud computing enables users to process and store data remotely on high-performance computers and servers by sharing data over the Internet. However, transferring data to clouds causes unavoidable privacy concerns. Here, we present a…

Data access is key to science driven by distributed high-throughput computing (DHTC), an essential technology for many major research projects such as High Energy Physics (HEP) experiments. However, achieving efficient data access becomes…

In the upcoming upgrades for Run 3 and 4, the LHC will significantly increase Pb--Pb and pp interaction rates. This goes along with upgrades of all experiments, ALICE, ATLAS, CMS, and LHCb, related to both the detectors and the computing.…

Conventional physics-based modeling techniques involve high effort, e.g., time and expert knowledge, while data-driven methods often lack interpretability, structure, and sometimes reliability. To mitigate this, we present a data-driven…

The Detector Safety System (DSS), currently being developed at CERN under the auspices of the Joint Controls Project (JCOP), will be responsible for assuring the protection of equipment for the four LHC experiments. Thus, the DSS will…

Optimising use of the Web (WWW) for LHC data analysis is a complex problem and illustrates the challenges arising from the integration of and computation across massive amounts of information distributed worldwide. Finding the right piece…

The performance of machine learning algorithms heavily relies on the availability of a large amount of training data. However, in reality, data usually reside in distributed parties such as different institutions and may not be directly…

A multi-physics formulation for Data Driven Prognosis (DDP) is developed. Unlike traditional predictive strategies that require controlled off-line measurements or training for determination of constitutive parameters to derive the…

FleCSPH is a smoothed particle hydrodynamics simulation tool, based on the compile-time configurable framework FleCSI. The asynchronous distributed tree topology combined with a fast multipole method allows FleCSPH to efficiently compute…

Reservoir Computing is a relatively new framework created to allow the usage of powerful but complex systems as computational mediums. The basic approach consists in training only a readout layer, exploiting the innate separation and…

Proofs of Retrievability (PoRs) are protocols which allow a client to store data remotely and to efficiently ensure, via audits, that the entirety of that data is still intact. A dynamic PoR system also supports efficient retrieval and…

A distributed data warehouse system is one of the actual issues in the field of astroparticle physics. Famous experiments, such as TAIGA, KASCADE-Grande, produce tens of terabytes of data measured by their instruments. It is critical to…

Quantum data locking is a quantum phenomenon that allows us to encrypt a long message with a small secret key with information-theoretic security. This is in sharp contrast with classical information theory where, according to Shannon, the…

Machine learning recently proved efficient in learning differential equations and dynamical systems from data. However, the data is commonly assumed to originate from a single never-changing system. In contrast, when modeling real-world…

Many performance critical systems today must rely on performance enhancements, such as multi-port memories, to keep up with the increasing demand of memory-access capacity. However, the large area footprints and complexity of existing…

In this paper, we consider a class of sensor networks where the data is not required in real-time by an observer; for example, a sensor network monitoring a scientific phenomenon for later play back and analysis. In such networks, the data…

Extract-Transform-Load (ETL) handles large amount of data and manages workload through dataflows. ETL dataflows are widely regarded as complex and expensive operations in terms of time and system resources. In order to minimize the time and…

As an essential attribute of organic compounds, polarity has a profound influence on many molecular properties such as solubility and phase transition temperature. Thin layer chromatography (TLC) represents a commonly used technique for…