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

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

Feasibility and acceptability of healthy directions a lifestyle intervention for adults with lung cancer

OBJECTIVE: The aims of this feasibility study of an adapted lifestyle intervention for adults with lung cancer were to (1) determine rates of enrollment, attrition, and completion of 5 nurse-patient contacts; (2) examine demographic characteristics of those more likely to enroll into the program; (3) determine acceptability of the intervention; and (4) identify patient preferences for the format of supplemental educational intervention materials. METHODS: This study used a single-arm, pretest and posttest design. Feasibility was defined as \u3e /=20% enrollment and a completion rate of 70% for 5 nurse-patient contact sessions. Acceptability was defined as 80% of patients recommending the program to others. Data was collected through electronic data bases and phone interviews. Descriptive statistics, Fisher\u27s exact test and Wilcoxon rank sum test were used for analyses. RESULTS: Of 147 eligible patients, 42 (28.6%) enrolled and of these, 32 (76.2%) started the intervention and 27 (N = 27/32; 84.4%; 95% CI, 67.2%-94.7%) completed the intervention. Patients who were younger were more likely to enroll in the study (P = .04) whereas there were no significant differences by gender (P = .35). Twenty-three of the 24 (95.8%) participants\u27 contacted posttest recommended the intervention for others. Nearly equal numbers of participants chose the website (n = 16, 50%) vs print (n = 14, 44%). CONCLUSION: The intervention was feasible and acceptable in patients with lung cancer. Recruitment rates were higher and completion rates were similar as compared to previous home-based lifestyle interventions for patients with other types of cancer. Strategies to enhance recruitment of older adults are important for future research

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 model training (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 general natural language tasks into a human-readable prompted form. We convert a large set of supervised datasets, each with multiple prompts using varying natural language. These prompted datasets allow for benchmarking the ability of a model to perform completely unseen tasks specified in natural language. We fine-tune a pretrained 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 onseveral datasets, often outperforming models 16× its size. Further, our model attains strong performance on a subset of tasks from the BIG-Bench benchmark, out-performing models 6× its size. All prompts and trained models are available at https://github.com/bigscience-workshop/promptsource/ and https://huggingface.co/bigscience/T0pp