Rotating binary Bose-Einstein condensates and vortex clusters in quantum droplets

Quantum droplets may form out of a gaseous Bose-Einstein condensate, stabilized by quantum fluctuations beyond mean field. We show that multiple singly-quantized vortices may form in these droplets at moderate angular momenta in two dimensions. Droplets carrying these precursors of an Abrikosov lattice remain self-bound for certain timescales after switching off an initial harmonic confinement. Furthermore, we examine how these vortex-carrying droplets can be formed in a more pertubation-resistant setting, by starting from a rotating binary Bose-Einstein condensate and inducing a metastable persistent current via a non-monotonic trapping potential.Comment: 5 page, 4 figure

MultiLegalPile: A 689GB Multilingual Legal Corpus

Large, high-quality datasets are crucial for training Large Language Models (LLMs). However, so far, there are few datasets available for specialized critical domains such as law and the available ones are often only for the English language. We curate and release MULTILEGALPILE, a 689GB corpus in 24 languages from 17 jurisdictions. The MULTILEGALPILE corpus, which includes diverse legal data sources with varying licenses, allows for pretraining NLP models under fair use, with more permissive licenses for the Eurlex Resources and Legal mC4 subsets. We pretrain two RoBERTa models and one Longformer multilingually, and 24 monolingual models on each of the language-specific subsets and evaluate them on LEXTREME. Additionally, we evaluate the English and multilingual models on LexGLUE. Our multilingual models set a new SotA on LEXTREME and our English models on LexGLUE. We release the dataset, the trained models, and all of the code under the most open possible licenses

Testing M2T/T2M Transformations

Presentado en: 16th International Conference on Model Driven Engineering Languages and Systems (MODELS 2013). Del 29 de septiembre al 4 de octubre. Miami, EEUU.Testing model-to-model (M2M) transformations is becoming a prominent topic in the current Model-driven Engineering landscape. Current approaches for transformation testing, however, assume having explicit model representations for the input domain and for the output domain of the transformation. This excludes other important transformation kinds, such as model-to-text (M2T) and text-to-model (T2M) transformations, from being properly tested since adequate model representations are missing either for the input domain or for the output domain. The contribution of this paper to overcome this gap is extending Tracts, a M2M transformation testing approach, for M2T/T2M transformation testing. The main mechanism we employ for reusing Tracts is to represent text within a generic metamodel. By this, we transform the M2T/T2M transformation specification problems into equivalent M2M transformation specification problems. We demonstrate the applicability of the approach by two examples and present how the approach is implemented for the Eclipse Modeling Framework (EMF). Finally, we apply the approach to evaluate code generation capabilities of several existing UML tools.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. Proyecto TIN2011-2379

SCALE: Scaling up the Complexity for Advanced Language Model Evaluation

Recent strides in Large Language Models (LLMs) have saturated many NLP benchmarks (even professional domain-specific ones), emphasizing the need for novel, more challenging novel ones to properly assess LLM capabilities. In this paper, we introduce a novel NLP benchmark that poses challenges to current LLMs across four key dimensions: processing long documents (up to 50K tokens), utilizing domain specific knowledge (embodied in legal texts), multilingual understanding (covering five languages), and multitasking (comprising legal document to document Information Retrieval, Court View Generation, Leading Decision Summarization, Citation Extraction, and eight challenging Text Classification tasks). Our benchmark comprises diverse legal NLP datasets from the Swiss legal system, allowing for a comprehensive study of the underlying Non-English, inherently multilingual, federal legal system. Despite recent advances, efficiently processing long documents for intense review/analysis tasks remains an open challenge for language models. Also, comprehensive, domain-specific benchmarks requiring high expertise to develop are rare, as are multilingual benchmarks. This scarcity underscores our contribution’s value, considering most public models are trained predominantly on English corpora, while other languages remain understudied, particularly for practical domain-specific NLP tasks. Our benchmark allows for testing and advancing the state-of-the-art LLMs. As part of our study, we evaluate several pre-trained multilingual language models on our benchmark to establish strong baselines as a point of reference. Despite the large size of our datasets ∗ Equal contribution. (tens to hundreds of thousands of examples), existing publicly available models struggle with most tasks, even after in-domain pretraining. We publish all resources (benchmark suite, pre-trained models, code) under a fully permissive open CC BY-SA license

SCALE: Scaling up the Complexity for Advanced Language Model Evaluation

Recent strides in Large Language Models (LLMs) have saturated many NLP benchmarks (even professional domain-specific ones), emphasizing the need for novel, more challenging novel ones to properly assess LLM capabilities. In this paper, we introduce a novel NLP benchmark that poses challenges to current LLMs across four key dimensions: processing long documents (up to 50K tokens), utilizing domain specific knowledge (embodied in legal texts), multilingual understanding (covering five languages), and multitasking (comprising legal document to document Information Retrieval, Court View Generation, Leading Decision Summarization, Citation Extraction, and eight challenging Text Classification tasks). Our benchmark comprises diverse legal NLP datasets from the Swiss legal system, allowing for a comprehensive study of the underlying Non-English, inherently multilingual, federal legal system. Despite recent advances, efficiently processing long documents for intense review/analysis tasks remains an open challenge for language models. Also, comprehensive, domain-specific benchmarks requiring high expertise to develop are rare, as are multilingual benchmarks. This scarcity underscores our contribution's value, considering most public models are trained predominantly on English corpora, while other languages remain understudied, particularly for practical domain-specific NLP tasks. Our benchmark allows for testing and advancing the state-of-the-art LLMs. As part of our study, we evaluate several pre-trained multilingual language models on our benchmark to establish strong baselines as a point of reference. Despite the large size of our datasets (tens to hundreds of thousands of examples), existing publicly available models struggle with most tasks, even after in-domain pretraining. We publish all resources (benchmark suite, pre-trained models, code) under a fully permissive open CC BY-SA license

Bend it like Beckham: embodying the motor skills of famous athletes.

Observing an action activates the same representations as does the actual performance of the action. Here we show for the first time that the action system can also be activated in the complete absence of action perception. When the participants had to identify the faces of famous athletes, the responses were influenced by their similarity to the motor skills of the athletes. Thus, the motor skills of the viewed athletes were retrieved automatically during person identification and had a direct influence on the action system of the observer. However, our results also indicated that motor behaviours that are implicit characteristics of other people are represented differently from when actions are directly observed. That is, unlike the facilitatory effects reported when actions were seen, the embodiment of the motor behaviour that is not concurrently perceived gave rise to contrast effects where responses similar to the behaviour of the athletes were inhibited

Lumbar spine radiographic features and demographic, clinical, and radiographic knee, hip and hand osteoarthritis: The Johnston County Osteoarthritis Project

Objective—1) To determine the prevalence of lumbar spine individual radiographic features (IRF) of disc space narrowing (DSN), osteophytes (OST) and facet joint osteoarthritis (FOA). 2) To describe the frequencies of demographic, clinic and radiographic knee, hip and hand osteoarthritis (OA) across lumbar spine IRF. 3) To determine factors associated with lumbar spine IRF. Methods—A cross-sectional study of 840 participants enrolled in the Johnston County OA Project (2003-4). Sample-based prevalence estimates were generated for each lumbar spine IRF. Associations between lumbar spine IRF and demographic, clinical and peripheral joint OA were determined with logistic regression models. Results—Sample-based prevalence estimates were similar for DSN (57.6%) and FOA (57.9%) but higher for OST (88.1%) with significant differences across race and gender. Hand and knee OA frequencies increased across IRF whereas the effect was absent for hip OA. African Americans had lower odds of FOA (adjusted odds ratio [aOR]=0.45 (95% CI 0.32, 0.62)) while there was no racial association with DSN and OST. Low back symptoms were associated with DSN (aOR=1.37 (95% CI 1.04, 1.80)) but not OST or FOA. Knee OA was associated with OST (aOR=1.62 (95% CI 1.16, 2.27)) and FOA (aOR=1.69 (95% CI 1.15, 2.49)) but not DSN. Hand OA was associated with FOA (aOR=1.67 (95% CI 1.20, 2.28)) but not with DSN or OST. No associations were found with hip OA. Conclusion—These findings underscore the importance of analyzing lumbar spine IRF separately as the associations with demographic, clinic and radiographic knee, hip and hand OA differ widely

Impact of drug interactions, dosage, and duration of therapy on the risk of hip fracture associated with benzodiazepine use in older adults

To determine how concomitant use of potentially interacting drugs, drug dosage, and duration of therapy modify the risk of hip fracture associated with use of benzodiazepines and related drugs (BDZ) in older adults

Outgroup helping as a tool to communicate ingroup warmth

Item does not contain fulltextThe authors extend previous research on the effects of metastereotype activation on outgroup helping by examining in more detail the role of group impression management motives and by studying direct helping (i.e., helping the outgroup believed to hold a negative view of the ingroup). Data from three experiments provided full support for the communicative nature of direct outgroup helping by demonstrating that outgroup helping in response to a negative metastereotype was predicted by participants' concern for the image of their ingroup, but not by their self-image concerns. Moreover, group image concerns predicted outgroup helping but not ingroup helping and predicted outgroup helping only when a negative metastereotype was activated, compared with a positive metastereotype, or a (negative or positive) autostereotype. The results also ruled out an alternative explanation in terms of denying the self-relevance of the metastereotype.1 juni 201

Association between Postoperative Delirium and Long-term Cognitive Function after Major Nonemergent Surgery

Importance: Postoperative delirium is associated with decreases in long-term cognitive function in elderly populations. Objective: To determine whether postoperative delirium is associated with decreased long-term cognition in a younger, more heterogeneous population. Design, Setting, and Participants: A prospective cohort study was conducted at a single academic medical center (≥800 beds) in the southeastern United States from September 5, 2017, through January 15, 2018. A total of 191 patients aged 18 years or older who were English-speaking and were anticipated to require at least 1 night of hospital admission after a scheduled major nonemergent surgery were included. Prisoners, individuals without baseline cognitive assessments, and those who could not provide informed consent were excluded. Ninety-day follow-up assessments were performed on 135 patients (70.7%). Exposures: The primary exposure was postoperative delirium defined as any instance of delirium occurring 24 to 72 hours after an operation. Delirium was diagnosed by the research team using the Confusion Assessment Method (CAM). Main Outcomes and Measures: The primary outcome was change in cognition at 90 days after surgery compared with baseline, preoperative cognition. Cognition was measured using a telephone version of the Montreal Cognitive Assessment (T-MoCA) with cognitive impairment defined as a score less than 18 on a scale of 0 to 22. Results: Of the 191 patients included in the study, 110 (57.6%) were women; the mean (SD) age was 56.8 (16.7) years. For the primary outcome of interest, patients with and without delirium had a small increase in T-MoCA scores at 90 days compared with baseline on unadjusted analysis (with delirium, 0.69; 95% CI, -0.34 to 1.73 vs without delirium, 0.67; 95% CI, 0.17-1.16). The initial multivariate linear regression model included age, preoperative American Society of Anesthesiologists Physical Status Classification System score, preoperative cognitive impairment, and duration of anesthesia. Preoperative cognitive impairment proved to be the only notable confounder: when adjusted for preoperative cognitive impairment, patients with delirium had a 0.70-point greater decrease in 90-day T-MoCA scores than those without delirium compared with their respective baseline scores (with delirium, 0.16; 95% CI, -0.63 to 0.94 vs without delirium, 0.86; 95% CI, 0.40-1.33). Conclusions and Relevance: Although a statistically significant association between 90-day cognition and postoperative delirium was not noted, patients with preoperative cognitive impairment appeared to have improvements in cognition 90 days after surgery; however, this finding was attenuated if they became delirious. Preoperative cognitive impairment alone should not preclude patients from undergoing indicated surgical procedures