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In this paper, we argue that database systems be augmented with an automated data exploration service that methodically steers users through the data in a meaningful way. Such an automated system is crucial for deriving insights from…
Since the beginning of 2010 the LHC provides p+p collisions at the highest center of mass energies to date, allowing to study high $\pT$ particle production and jet properties in a new energy regime. For a clear interpretation and the…
During the second LHC long shutdown (LS2) the Inner Tracking System (ITS) of ALICE (A Large Ion Collider Experiment) will be replaced by seven layers of CMOS Monolithic Active Pixel Sensors (MAPS). The latest innovations in silicon imaging…
ALICE, one of the four main CERN LHC experiments, studies heavy-ion collisions at ultra-relativistic energies. For LHC Run 4 (starting 2030), ALICE will replace the three innermost cylindrical layers of its inner tracking system during Long…
As Retrieval-Augmented Generation (RAG) systems evolve toward more sophisticated architectures, ensuring their trustworthiness through explainable and robust evaluation becomes critical. Existing scalar metrics suffer from limited…
This work aims at the performance of the ALICE detector for the measurement of high-energy jets at mid-pseudo-rapidity in ultra-relativistic nucleus--nucleus collisions at LHC and their potential for the characterization of the partonic…
The physics programme of the ALICE experiment at CERN-LHC comprises besides studies of high-energy heavy-ion collisions measurements of proton-proton interactions at unprecedented energies, too. This paper focuses on the global event…
Detecting arousals in sleep is essential for diagnosing sleep disorders. However, using Machine Learning (ML) in clinical practice is impeded by fundamental issues, primarily due to mismatches between clinical protocols and ML methods.…
The rapid accumulation of Earth observation data presents a formidable challenge for the processing capabilities of traditional remote sensing desktop software, particularly when it comes to analyzing expansive geographical areas and…
The design, construction, and commissioning of the ALICE Time-Projection Chamber (TPC) is described. It is the main device for pattern recognition, tracking, and identification of charged particles in the ALICE experiment at the CERN LHC.…
The ALICE experiment has several unique features which makes it an important contributor to proton-proton physics at the LHC, in addition to its specific design goal of studying the physics of strongly interacting matter in heavy-ion…
ALICE, a general purpose experiment designed to investigate nucleus-nucleus collisions at the CERN Large Hadron Collider (LHC), has also been used to detect atmospheric muons produced by cosmic-ray interactions in the atmosphere. In this…
While reasoning models (e.g., DeepSeek R1) trained with reinforcement learning (RL), excel in textual reasoning, they struggle in scenarios requiring structured problem-solving, such as geometric reasoning, concise computation, or complex…
Many critical applications, from autonomous scientific discovery to personalized medicine, demand systems that can both strategically acquire the most informative data and instantaneously perform inference based upon it. While amortized…
Accident severity prediction plays a critical role in transportation safety systems but is a persistently difficult task due to incomplete data, strong feature dependencies, and severe class imbalance in which rare but high-severity cases…
MONC is a highly scalable modelling tool for the investigation of atmospheric flows, turbulence and cloud microphysics. Typical simulations produce very large amounts of raw data which must then be analysed for scientific investigation. For…
ALICE has been specifically optimized to study heavy-ion collisions at the LHC, up to a charged particle density of 8000 per unit of rapidity in central heavy-ion collisions at $\sqrt{s_{NN}}$ = 5.5 TeV. The High Momentum Particle…
Recent progress in large language models (LLMs) has enabled tool-augmented agents capable of solving complex real-world tasks through step-by-step reasoning. However, existing evaluations often focus on general-purpose or multimodal…
Data-driven scientific discovery requires the iterative integration of scientific domain knowledge, statistical expertise, and an understanding of data semantics to make nuanced analytical decisions, e.g., about which variables,…
Recent advances in robot learning have shown promise in enabling robots to perform a variety of manipulation tasks and generalize to novel scenarios. One of the key contributing factors to this progress is the scale of robot data used to…