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Since the Enlightenment, we've been making our own decisions. T his summer, I found myself battling through traffic in the sweltering streets of Marseille. At a crossing, my friend in the passenger seat told me to turn right toward a spot known for its fish soup. But the navigation app Waze instructed us to go straight. Tired, and with the Renault feeling like a sauna on wheels, I followed Waze's advice.

June 2022 Across the world, video cameras have become an accepted feature of urban life. Many cities in China now have dense networks of them, and London and New Delhi aren't far behind. Now France is playing catch-up. Concerns have been raised throughout the country. But the surveillance rollout has met special resistance in Marseille, France's second-biggest city. It's unsurprising, perhaps, that activists are fighting back against the cameras, highlighting the surveillance system's overreach and underperformance.

The disparity in the languages commonly studied in Natural Language Processing (NLP) is typically reflected by referring to languages as low vs high-resourced. However, there is limited consensus on what exactly qualifies as a `low-resource language.' To understand how NLP papers define and study `low resource' languages, we qualitatively analyzed 150 papers from the ACL Anthology and popular speech-processing conferences that mention the keyword `low-resource.' Based on our analysis, we show how several interacting axes contribute to `low-resourcedness' of a language and why that makes it difficult to track progress for each individual language. We hope our work (1) elicits explicit definitions of the terminology when it is used in papers and (2) provides grounding for the different axes to consider when connoting a language as low-resource.

When immigrating to a new country, it is easy to feel overwhelmed by the need to obtain information on financial support, housing, schooling, language courses, and other issues. If relocation is rushed or even forced, the necessity for high-quality answers to such questions is all the more urgent. Official immigration counselors are usually overbooked, and online systems could guide newcomers to the requested information or a suitable counseling service. To this end, we present OMoS-QA, a dataset of German and English questions paired with relevant trustworthy documents and manually annotated answers, specifically tailored to this scenario. Questions are automatically generated with an open-source large language model (LLM) and answer sentences are selected by crowd workers with high agreement. With our data, we conduct a comparison of 5 pretrained LLMs on the task of extractive question answering (QA) in German and English. Across all models and both languages, we find high precision and low-to-mid recall in selecting answer sentences, which is a favorable trade-off to avoid misleading users. This performance even holds up when the question language does not match the document language. When it comes to identifying unanswerable questions given a context, there are larger differences between the two languages.

Protecting privacy is essential when sharing data, particularly in the case of an online radicalization dataset that may contain personal information. In this paper, we explore the balance between preserving data usefulness and ensuring robust privacy safeguards, since regulations like the European GDPR shape how personal information must be handled. We share our method for manually pseudonymizing a multilingual radicalization dataset, ensuring performance comparable to the original data. Furthermore, we highlight the importance of establishing comprehensive guidelines for processing sensitive NLP data by sharing our complete pseudonymization process, our guidelines, the challenges we encountered as well as the resulting dataset.

Since Labov's (1964) foundational work on the social stratification of language, linguistics has dedicated concerted efforts towards understanding the relationships between socio-demographic factors and language production and perception. Despite the large body of evidence identifying significant relationships between socio-demographic factors and language production, relatively few of these factors have been investigated in the context of NLP technology. While age and gender are well covered, Labov's initial target, socio-economic class, is largely absent. We survey the existing Natural Language Processing (NLP) literature and find that only 20 papers even mention socio-economic status. However, the majority of those papers do not engage with class beyond collecting information of annotator-demographics. Given this research lacuna, we provide a definition of class that can be operationalised by NLP researchers, and argue for including socio-economic class in future language technologies.

Across the world, video cameras have become an accepted feature of urban life. Many cities in China now have dense networks of them, and London and New Delhi aren't far behind. Now France is playing catch-up. Since 2015, the year of the Bataclan terrorist attacks, the number of cameras in Paris has increased fourfold. The police have used such cameras to enforce pandemic lockdown measures and monitor protests.

The spinal cord (SC), which conveys information between the brain and the peripheral nervous system, plays a key role in various neurological disorders such as multiple sclerosis (MS) and amyotrophic lateral sclerosis (ALS), in which both gray matter (GM) and white matter (WM) may be impaired. While automated methods for WM/GM segmentation are now largely available, these techniques, developed for conventional systems (3T or lower) do not necessarily perform well on 7T MRI data, which feature finer details, contrasts, but also different artifacts or signal dropout. The primary goal of this study is thus to propose a new deep learning model that allows robust SC/GM multi-class segmentation based on ultra-high resolution 7T T2*-w MR images. The second objective is to highlight the relevance of implementing a specific data augmentation (DA) strategy, in particular to generate a generic model that could be used for multi-center studies at 7T.

This position will investigate the application of a range of supervised learning techniques, representations and data augmentation strategies to the discovery of bioactive molecules in ultra-large libraries for selected therapeutic targets. The project will exploit large volumes of protein structure data, including recently available Alphafold2 structures. Selection criteria - Essential • A PhD awarded in an area relevant to the project. The CRCM is also affiliated to the private cancer hospital Institut Paoli Calmettes, the CNRS and Aix-Marseille University. The successful candidate will have a 3-year contract with a gross monthly salary of up to €2,900 gross monthly (depending on experience).