相关论文: Online Pattern Recognition for the ALICE High Leve…

Scanning probe experiments such as scanning tunneling microscopy (STM) and atomic force microscopy (AFM) on strongly correlated electronic systems often reveal complex pattern formation on multiple length scales. By studying the universal…

The measurement of the identified charged hadron p_t spectra using the ITS energy loss signal in the p-p data at sqrt{s}=900 GeV collected by the ALICE experiment at LHC will be discussed. It is performed using the Inner Tracking System…

The Inner Tracking System (ITS) of the ALICE experiment at the CERN Large Hadron Collider (LHC) is the largest Monolithic Active Pixel Sensor technology application in high-energy physics. The upgraded version of the tracking system, called…

In this paper a novel biclustering algorithm based on artificial intelligence (AI) is introduced. The method called EBIC aims to detect biologically meaningful, order-preserving patterns in complex data. The proposed algorithm is probably…

Click-Through Rate prediction aims to predict the ratio of clicks to impressions of a specific link. This is a challenging task since (1) there are usually categorical features, and the inputs will be extremely high-dimensional if one-hot…

The ALICE Transition Radiation Detector (TRD) significantly enlarges the scope of physics observables studied in ALICE, because it allows due to its electron identification capability to measure open heavy-flavour production and quarkonium…

In the High-Level Trigger (HLT) of both electron-positron and hadron collision experiments, the tracking process for large-volume gaseous detectors typically consumes a latency of hundreds of milliseconds. Upgrades of existing experiments…

This paper describes a design that can be used for Explainable AI. The lower level is a nested ensemble of patterns created by self-organisation. The upper level is a hierarchical tree, where nodes are linked through individual concepts, so…

High-Energy Physics experiments are rapidly escalating in generated data volume, a trend that will intensify with the upcoming High-Luminosity LHC upgrade. This surge in data necessitates critical revisions across the data processing…

To cluster, classify and represent are three fundamental objectives of learning from high-dimensional data with intrinsic structure. To this end, this paper introduces three interpretable approaches, i.e., segmentation (clustering) via the…

This paper addresses the design of an event-triggered, data-based, and performance-oriented adaption method for model predictive control (MPC). The performance of such a strategy strongly depends on the accuracy of the prediction model,…

In high-dimensional classification problems, a commonly used approach is to first project the high-dimensional features into a lower dimensional space, and base the classification on the resulting lower dimensional projections. In this…

This paper describes a new approach for learning structures of large Bayesian networks based on blocks resulting from feature space clustering. This clustering is obtained using normalized mutual information. And the subsequent aggregation…

In this work several aspects of ATLAS RPC offline monitoring and data quality assessment are illustrated with cosmics data selected by RPC trigger. These correspond to trigger selection, front-end mapping, detection efficiency and…

The Inner Tracking System (ITS) of the ALICE experiment will be upgraded during the second long LHC shutdown in $\mathrm{2019}-\mathrm{2020}$. The main goal of the ALICE ITS Upgrade is to enable high precision measurements of low - momentum…

The ALICE muon spectrometer studies the production of quarkonia and open heavy- flavour particles. It is equipped with a Trigger System composed of Resistive Plate Chambers which, by applying a transverse-momentum-based muon selection,…

This article presents a sparse, low-memory footprint optimization algorithm for the implementation of the model predictive control (MPC) for tracking formulation in embedded systems. This MPC formulation has several advantages over standard…

In the context of Intelligent Transportation Systems (ITS), efficient data compression is crucial for managing large-scale point cloud data acquired by roadside LiDAR sensors. The demand for efficient storage, streaming, and real-time…

The ALICE detector is well suited to measure heavy-flavour (charm and beauty) production via hadronic and semi-leptonic decay channels of heavy-flavour particles. Here an overview of heavy-flavour measurements made with the ALICE detector…

We propose a scalable track-before-detect (TBD) tracking method based on a Poisson/multi-Bernoulli model. To limit computational complexity, we approximate the exact multi-Bernoulli mixture posterior probability density function (pdf) by a…