Random matrix ensembles with random interactions: Results for EGUE(2)-SU(4)

We introduce in this paper embedded Gaussian unitary ensemble of random matrices, for $m$ fermions in $\Omega$ number of single particle orbits, generated by random two-body interactions that are SU(4) scalar, called EGUE(2)-SU(4). Here the SU(4) algebra corresponds to Wigner's supermultiplet SU(4) symmetry in nuclei. Formulation based on Wigner-Racah algebra of the embedding algebra $U(4\Omega) \supset U(\Omega) \otimes SU(4)$ allows for analytical treatment of this ensemble and using this analytical formulas are derived for the covariances in energy centroids and spectral variances. It is found that these covariances increase in magnitude as we go from EGUE(2) to EGUE(2)-\cs to EGUE(2)-SU(4) implying that symmetries may be responsible for chaos in finite interacting quantum systems.Comment: 11 pages, 2 figures, some formulas in Table 1 corrected, Table 1 changed to Table 1 and 2, Fig. 2 modifie

Multitask Prompted Training Enables Zero-Shot Task Generalization

International audienceLarge language models have recently been shown to attain reasonable zero-shot generalization on a diverse set of tasks (Brown et al., 2020). It has been hypothesized that this is a consequence of implicit multitask learning in language models’ pretraining (Radford et al., 2019). Can zero-shot generalization instead be directly induced by explicit multitask learning? To test this question at scale, we develop a system for easily mapping any natural language tasks into a human-readable prompted form. We convert a large set of supervised datasets, each with multiple prompts with diverse wording. These prompted datasets allow for benchmarking the ability of a model to perform completely held-out tasks. We fine-tune a pre-trained encoder-decoder model (Raffel et al., 2020; Lester et al., 2021) on this multitask mixture covering a wide variety of tasks. The model attains strong zero-shot performance on several standard datasets, often outperforming models up to 16x its size. Further, our approach attains strong performance on a subset of tasks from the BIG-bench benchmark, outperforming models up to 6x its size. All trained models are available at https://github.com/bigscience-workshop/t-zero, and all prompts are available at https://github.com/bigscience-workshop/promptsource

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License

Exploring “The Healthy Immigrant Effect” Among Elderly Asians with Cancer: A Nationwide Population-Based Assessment

Asians are the fastest-growing immigrant group in the U.S. Asian Americans are also, on average, the oldest immigrant group and so are at heightened risk of cancer and other diseases that are more prevalent in the elderly population. Although a health advantage among Latino immigrants is well documented, there has been less evidence of this “healthy immigrant effect” (HIE) among Asians as a whole or in specific subgroups of Asians, and almost none focused on those who are elderly or those with a cancer diagnosis. This study examines the evidence for a healthy immigrant effect in a large ethnically diverse sample of elderly persons with a cancer diagnosis. This is a retrospective observational study utilizing the Surveillance Epidemiology and End Results –Medicare Health Outcomes Survey (SEER-MHOS). Descriptive, bivariate, and multivariate analyses are used to examine HIE among Latinos and Asians in the aggregate and among subgroups of Asians for five health outcomes: smoking, cancer type, stage of cancer, body mass index (BMI), and self-reported health (SRH). Nativity effects were estimated using survey language and ethnic concentration as proxies. Asians living in an ethnic enclave had lower smoking prevalence, lower BMI, a lower likelihood of a non-endocrine diagnosis, and late-stage cancer diagnosis. A parallel analysis among Latinos living in ethnic enclaves and those who answered surveys in Spanish had lower odds of smoking, non-endocrine cancer, and late-stage diagnosis, consistent with the HIE. No differences consistent with the HIE were found for Chinese respondents in relation to smoking prevalence, cancer type, or stage; however, Chinese respondents opting to complete surveys in their native language or living in ethnic enclaves were less likely to be overweight. The HIE prediction was contradicted by the poorer SRH of Latinos, Asians respondents who were more likely to be first-generation immigrants. Thus, support for the HIE was largely consistent for Asians and Latinos with respect to both language and ethnic concentration. However, there was little evidence of an HIE among Chinese immigrants. Future research to identify the HEI among Asians should use multiple health outcomes and take account of subgroup differences

Backgrounds and Trainings in Cannabis Therapeutics of Dispensary Personnel.

PURPOSE: A growing body of scientific research indicates that oncology teams tend to offer individuals with cancer little clinical advice regarding medicinal cannabis (MC) and that individuals with cancer instead turn to cannabis dispensaries for MC guidance. Our objective was to investigate dispensary personnels backgrounds and trainings in MC advising. METHODS: The study design was semistructured interviews across 13 states with cannabis dispensary personnel in managerial or client-facing positions. Of 38 recruited, 26 (68%) completed interview. The primary outcome was training in MC advising. Researchers targeted thematic saturation and adhered to Consolidated Criteria for Reporting Qualitative Research. RESULTS: Of 26 participants, 54% were female, with an average age of 40 (range: 22-64) years. Half worked in client-facing roles; half worked in managerial ones. Study participants endorsed passionate commitment to their profession, often motivated by personal experience with MC therapeutics. Cannabis dispensaries often privileged sales skills over cannabis therapeutics knowledge when hiring, resulting in uneven baseline levels of cannabis therapeutics expertise among staff. Most participants reported workplace cannabis therapeutics training to be unstandardized and weak. They described dispensary personnel as resourceful in pursuing cannabis knowledge, self-financing learning in off-hours, sampling dispensary products, and exchanging knowledge. Nearly half the participants called for quality, standardized cannabis therapeutics training for dispensary personnel. CONCLUSION: The many oncology teams who defer to dispensary personnel regarding MC advising rely on a workforce who views themselves as unevenly trained. Further research should include a national survey of cannabis dispensary personnel to learn whether these findings hold true in a larger sample. If so, the oncology community must determine the best approach to clinically advising individuals with cancer about MC

Medical cannabis-related stigma: cancer survivors’ perspectives

Abstract Background Although the vast majority of medical cannabis laws in the USA includes cancer as a qualifying condition and medical cannabis-related stigma influences decision-making regarding the botanical, few studies have explored the phenomenon in oncology. Early findings indicated oncologic cannabis-related stigma to be quite widespread. Methods Semi-structured interviews with 24 adults with cancer histories using medical cannabis were analyzed using the Health Stigma and Discrimination Framework. Results Sixteen out of 24 participants discussed medical cannabis-related stigma in some depth. The phenomena emerged as more pervasive in medical than personal/professional domains and was internalized as well as experienced directly. It led some participants, but not others, to practice partial or complete secrecy. Discussion Taken together, our findings suggest that, while medical cannabis-related stigma remains widespread and led some study participants to alter behavior, an early shift in ethos towards greater medical cannabis acceptance could be underway. If so, this transition may be occurring more rapidly in non-medical than in clinical settings. Conclusion Cancer survivors may experience heightened medical cannabis-related stigma in the clinic as compared to their personal/professional lives. Healthcare providers who depend on patient transparency when gathering medical histories and devising care plans may wish to neutralize perceptions of medical cannabis-related stigma

Algorithm-based decision support for symptom self-management among adults with Cancer: results of usability testing

Abstract Background It is essential that cancer patients understand anticipated symptoms, how to self-manage these symptoms, and when to call their clinicians. However, patients are often ill-prepared to manage symptoms at home. Clinical decision support (CDS) is a potentially innovative way to provide information to patients where and when they need it. The purpose of this project was to design and evaluate a simulated model of an algorithm-based CDS program for self-management of cancer symptoms. Methods This study consisted of three phases; development of computable algorithms for self-management of cancer symptoms using a modified ADAPTE process, evaluation of a simulated model of the CDS program, and identification of design objectives and lessons learned from the evaluation of patient-centered CDS. In phase 1, algorithms for pain, constipation and nausea/vomiting were developed by an expert panel. In phase 2, we conducted usability testing of a simulated symptom assessment and management intervention for self-care (SAMI-Self-Care) CDS program involving focus groups, interviews and surveys with cancer patients, their caregivers and clinicians. The Acceptability E-scale measured acceptability of the program. In phase 3, we developed design objectives and identified barriers to uptake of patient-centered CDS based on the data gathered from stakeholders. Results In phase 1, algorithms were reviewed and approved through a consensus meeting and majority vote. In phase 2, 24 patients & caregivers and 13 clinicians participated in the formative evaluation. Iterative changes were made in a simulated SAMI-Self-Care CDS program. Acceptability scores were high among patients, caregivers and clinicians. In phase 3, we formulated CDS design objectives, which included: 1) ensure patient safety, 2) communicate clinical concepts effectively, 3) promote communication with clinicians, 4) support patient activation, and 5) facilitate navigation and use. We identified patient barriers and clinician concerns to using CDS for symptom self-management, which were consistent with the chronic care model, a theoretical framework used to enhance patient-clinician communication and patient self-management. Conclusion Patient safety and tool navigation were critical features of CDS for patient self-management. Insights gleaned from this study may be used to inform the development of CDS resources for symptom self-management in patients with other chronic conditions