Reconstructing precise clinical timelines is essential for modeling patient trajectories and forecasting risk in complex, heterogeneous conditions like sepsis. While unstructured clinical narratives offer semantically rich and contextually complete descriptions of a patient's course, they often lack temporal precision and contain ambiguous event timing. Conversely, structured electronic health record (EHR) data provides precise temporal anchors but misses a substantial portion of clinically meaningful events. We introduce a retrieval-augmented multimodal alignment framework that bridges this gap to improve the temporal precision of absolute clinical timelines extracted from text. Our approach formulates timeline reconstruction as a graph-based multistep process: it first extracts central anchor events from narratives to build an initial temporal scaffold, places non-central events relative to this backbone, and then calibrates the timeline using retrieved structured EHR rows as external temporal evidence. Evaluated using instruction-tuned large language models on the i2m4 benchmark spanning MIMIC-III and MIMIC-IV, our multimodal pipeline consistently improves absolute timestamp accuracy (AULTC) and improves temporal concordance across nearly all evaluated models over unimodal text-only reconstruction, without compromising event match rates. Furthermore, our empirical gap analysis reveals that 34.8% of text-derived events are entirely absent from tabular records, demonstrating that aligning these modalities can produce a more temporally faithful and clinically informative reconstruction of patient trajectories than either source alone.

European Robotics Week 2020 (ERW2020) began on Thursday and hundreds of interactive robotics events for the public have been announced. These will take place in countries across Europe and beyond, to show how robots will impact the way we work, live, and learn. In a year when humanity has faced a global pandemic crisis, robotics companies and researchers across Europe have been able to demonstrate how robotics help societies and economies to keep operating in a world affected by Covid-19. With the opportunities arising from Europe's digital transformation driven by new technologies like artificial intelligence, robotics, cloud computing and blockchain, the demand for ICT specialists continues to grow. In the future, 9 out of 10 jobs will require digital skills (source). Yet fewer women than men take up ICT-related jobs and education: for every 1000 women, only 24 graduate in digital fields (source).

The European Robotics Week 2017 (ERW2017) Central Event organised in Brussels saw the "Robots Discovery" exhibition hosted by the European Committee of the Regions on 20-23 November, where robotics experts from 30 European and regionally-funded projects outlined the impact of their work on society. The exhibiting projects showed robots assisting during surgery or providing support for elderly care, how robots can help students develop digital skills, monitor the environment and apply agricultural chemicals with precision and less waste or how they can save lives after disasters. The #ERW2017 hashtag has reached over 1 million impressions on social media. Here's a look at how the "Robots Discovery" central event was portrayed. Essential to help young people & workers gain the right skills #digitalskills #SocialSummit17.

Centrality, as a geometrical property of the collision, is crucial for the physical interpretation of nucleus-nucleus and proton-nucleus experimental data. However, it cannot be directly accessed in event-by-event data analysis. Common methods for centrality estimation in AA and p-A collisions usually rely on a single detector (either on the signal in zero-degree calorimeters or on the multiplicity in some semi-central rapidity range). In the present work, we made an attempt to develop an approach for centrality determination that is based on machine-learning techniques and utilizes information from several detector subsystems simultaneously. Different event classifiers are suggested and evaluated for their selectivity power in terms of the number of nucleons-participants and the impact parameter of the collision. Finer centrality resolution may allow to reduce impact from so-called volume fluctuations on physical observables being studied in heavy-ion experiments like ALICE at the LHC and fixed target experiment NA61/SHINE on SPS. Machine-learning (ML) techniques have been used in High-Energy Physics (HEP) so far in a limited number of ways.