High-temperature magnetic anomaly in the Kitaev hyperhoneycomb compound β-Li2IrO3

We report the existence of a high-temperature magnetic anomaly in the three-dimensional Kitaev candidate material, β-Li2IrO3. Signatures of the anomaly appear in magnetization, heat capacity, and muon spin relaxation measurements. The onset coincides with a reordering of the principal axes of magnetization, which is thought to be connected to the onset of Kitaev-like correlations in the system. The anomaly also shows magnetic hysteresis with a spatially anisotropic magnitude that follows the spin-anisotropic exchange anisotropy of the underlying Kitaev Hamiltonian. We discuss possible scenarios for a bulk and impurity origin

STEVE-1: A Generative Model for Text-to-Behavior in Minecraft

Constructing AI models that respond to text instructions is challenging, especially for sequential decision-making tasks. This work introduces an instruction-tuned Video Pretraining (VPT) model for Minecraft called STEVE-1, demonstrating that the unCLIP approach, utilized in DALL-E 2, is also effective for creating instruction-following sequential decision-making agents. STEVE-1 is trained in two steps: adapting the pretrained VPT model to follow commands in MineCLIP's latent space, then training a prior to predict latent codes from text. This allows us to finetune VPT through self-supervised behavioral cloning and hindsight relabeling, bypassing the need for costly human text annotations. By leveraging pretrained models like VPT and MineCLIP and employing best practices from text-conditioned image generation, STEVE-1 costs just $60 to train and can follow a wide range of short-horizon open-ended text and visual instructions in Minecraft. STEVE-1 sets a new bar for open-ended instruction following in Minecraft with low-level controls (mouse and keyboard) and raw pixel inputs, far outperforming previous baselines. We provide experimental evidence highlighting key factors for downstream performance, including pretraining, classifier-free guidance, and data scaling. All resources, including our model weights, training scripts, and evaluation tools are made available for further research

Clinical outcomes of a treat and extend regimen with intravitreal aflibercept injections in patients with diabetic macular edema: Experience in clinical practice

Introduction: Treat-and-extend (T&E) and prore nata (PRN; ‘as needed’) regimens of intravitreal anti-vascular endothelial growth factor(VEGF) treatment have been found to reducethe injection burden on patients and improvethe cost effectiveness of the treatment of macular edema. The aim of this study was to assessthe effectiveness of a T&E regimen of aflibercept, in a clinical setting, in patients with diabetic macular edema (DME) who were either intravitreal anti-VEGF therapy naive or withminimal exposure to anti-VEGF (B 6 treatments) in the previous 12 months.Methods: This prospective, single arm, open labelstudy recruited patients with DME (macularthickness of C 300 lm) and best-corrected visualacuity (BCVA) between 28-78 ETDRS letters. Participants received five loading doses of intravitrealaflibercept at 4-weekly intervals. BCVA measurements and macular optical coherence tomographywere performed at each visit. If no disease activitywas detected, treatment intervals were increased by2 weeks to a maximum of 12 weeks. Outcomemeasures included: changes in BCVA and retinalanatomical measures (central foveal thickness[CFT] and central macular volume within 6 mm ofthe fovea [CSVol]) between baseline and 2 years,patient treatment intervals; and adverse events.Results: Of the 36 patients who providedinformed consent to participate in the studyand were screened, 26 patients (eyes) were eligible to participate in the study. After regressionanalysis, adjustment for repeated measures, andsignificant covariates, the mean BCVA increasedby 3.8 letters (95% confidence interval [CI] 1.1,6.4) and the CFT and CSVol decreased by127.2 lm (95% CI 91.7, 162.5) and 1.6 mm3 (95% CI 1.2, 2.0), respectively, over the courseof the study. In the second year, 16 of the 25patients still participating had their treatmentintervals extended to 12 weeks. There was noevidence of any new adverse events that wouldrequire changes to the aflibercept safety profile.Conclusion: For the majority of patients presenting with DME, a T&E regimen of afliberceptin the first 2 years of therapy is a practical alternative to PRN treatment with regular review

Mini-Cog for the diagnosis of Alzheimer's disease dementia and other dementias within a secondary care setting

© 2014 The Cochrane Collaboration. This is the protocol for a review and there is no abstract. The objectives are as follows: To determine the diagnostic accuracy of the Mini-Cog for detecting Alzheimer's disease dementia and related dementias in a secondary care setting. To investigate the heterogeneity of test accuracy in the included studies and potential sources of heterogeneity. These potential sources of heterogeneity will include the baseline prevalence of dementia in study samples, thresholds used to determine positive test results, the type of dementia (Alzheimer's disease dementia or all causes of dementia), and aspects of study design related to study quality. We will also identify gaps in the evidence where further research is required

Large Language Models Are Human-Level Prompt Engineers

By conditioning on natural language instructions, large language models (LLMs) have displayed impressive capabilities as general-purpose computers. However, task performance depends significantly on the quality of the prompt used to steer the model, and most effective prompts have been handcrafted by humans. Inspired by classical program synthesis and the human approach to prompt engineering, we propose Automatic Prompt Engineer (APE) for automatic instruction generation and selection. In our method, we treat the instruction as the "program," optimized by searching over a pool of instruction candidates proposed by an LLM in order to maximize a chosen score function. To evaluate the quality of the selected instruction, we evaluate the zero-shot performance of another LLM following the selected instruction. Experiments on 24 NLP tasks show that our automatically generated instructions outperform the prior LLM baseline by a large margin and achieve better or comparable performance to the instructions generated by human annotators on 19/24 tasks. We conduct extensive qualitative and quantitative analyses to explore the performance of APE. We show that APE-engineered prompts can be applied to steer models toward truthfulness and/or informativeness, as well as to improve few-shot learning performance by simply prepending them to standard in-context learning prompts. Please check out our webpage at https://sites.google.com/view/automatic-prompt-engineer

Collider Phenomenology with Split-UED

We investigate the collider implications of Split Universal Extra Dimensions. The non-vanishing fermion mass in the bulk, which is consistent with the KK-parity, largely modifies the phenomenology of Minimal Universal Exta Dimensions. We scrutinize the behavior of couplings and study the discovery reach of the Tevatron and the LHC for level-2 Kaluza-Klein modes in the dilepton channel, which would indicates the presence of the extra dimensions. Observation of large event rates for dilepton resonances can result from a nontrivial fermion mass profile along the extra dimensions, which, in turn, may corroborate extra dimensional explanation for the observation of the positron excess in cosmic rays.Comment: 23 pages, 15 figure