Related papers: Online Pattern Recognition for the ALICE High Leve…

We propose a software framework based on the ideas of the Learning-Compression (LC) algorithm, that allows a user to compress a neural network or other machine learning model using different compression schemes with minimal effort.…

We present a novel deep-learning-based method to cluster words in documents which we apply to detect and recognize tables given the OCR output. We interpret table structure bottom-up as a graph of relations between pairs of words (belonging…

P300 is an Event-Related Potential widely used in Brain-Computer Interfaces, but its detection is challenging due to inter-subject and temporal variability. This work introduces a clustering methodology based on Normalized Compression…

The ALICE collaboration at the CERN LHC reports novel measurements of jet substructure in pp collisions at $\sqrt{s}$= 7 TeV and central Pb-Pb collisions at $\sqrt{s_{\rm{NN}}}$ = 2.76 TeV. Jet substructure of track-based jets is explored…

First results of ALICE on the production of nuclei and antinuclei in pp collisions at \surd s = 7 TeV are presented. These particles are identified using the energy loss (dE/dx) measurements in the Time Projection Chamber. The Inner…

With the aggressive scaling of VLSI technology, the explosion of layout patterns creates a critical bottleneck for DFM applications like OPC. Pattern clustering is essential to reduce data complexity, yet existing methods struggle with…

Data classification techniques partition the data or feature space into smaller sub-spaces, each corresponding to a specific class. To classify into subspaces, physical features e.g., distance and distributions are utilized. This approach…

For LHC Run 3, the ALICE Time Projection Chamber was upgraded to operate in continuous readout mode. Interaction rates of up to 50 kHz in Pb-Pb collisions require real-time processing of more than 3 TB/s of raw detector data. This…

In this paper we propose an algorithm to classify tensor data. Our methodology is built on recent studies about matrix classification with the trace norm constrained weight matrix and the tensor trace norm. Similar to matrix classification,…

The forward muon spectrometer of ALICE (A Large Ion Collider Experiment) is equipped with a trigger system made of four planes of Resistive Plate Chambers (RPC), arranged in two stations with two planes each, for a total area of about 140…

Subsequence-based time series classification algorithms provide accurate and interpretable models, but training these models is extremely computation intensive. The asymptotic time complexity of subsequence-based algorithms remains a…

The main focus of the ALICE experiment, quark--gluon plasma measurements, requires accurate particle identification (PID). The ALICE subdetectors allow identifying particles over a broad momentum interval ranging from about 100 MeV/c up to…

Deep learning models have become state of the art for natural language processing (NLP) tasks, however deploying these models in production system poses significant memory constraints. Existing compression methods are either lossy or…

Machine learning is penetrating various domains virtually, thereby proliferating excellent results. It has also found an outlet in digital forensics, wherein it is becoming the prime driver of computational efficiency. A prominent feature…

The ALICE experiment will run with continuous readout at interaction rates of up to 50 kHz in Pb-Pb collisions during Run 3 of the LHC. In order to achieve this goal, a new data processing scheme and software are developed. This scheme…

In this work, we introduce a novel method for Particle Identification (PID) within the scope of the ALICE experiment at the Large Hadron Collider at CERN. Identifying products of ultrarelativisitc collisions delivered by the LHC is one of…

Tracking requires building a discriminative model for the target in the inference stage. An effective way to achieve this is online learning, which can comfortably outperform models that are only trained offline. Recent research shows that…

The ALICE collaboration consolidated and completed the installation of current detectors during LS1 with the aim to accumulate 1 nb$^{-1}$ of Pb-Pb collisions during Run 2 corresponding to about 10 times the Run 1 integrated luminosity. In…

The ALICE experiment at the Large Hadron Collider (LHC) proposes major detector upgrades to fully exploit the increase of the luminosity of the LHC in RUN~3 and to extend the physics reach for rare probes at low transverse momentum. The…

This study introduces a predictive maintenance strategy for high pressure industrial compressors using sensor data and features derived from unsupervised clustering integrated into classification models. The goal is to enhance model…