Ultra-low-threshold InGaN/GaN quantum dot micro-ring lasers.

In this work, we demonstrate ultra-low-threshold, optically pumped, room-temperature lasing in GaN microdisk and micro-ring cavities containing InGaN quantum dots and fragmented quantum wells, with the lowest measured threshold at a record low of 6.2  μJ/cm2. When pump volume decreases, we observe a systematic decrease in the lasing threshold of micro-rings. The photon loss rate, γ, increases with increasing inner ring diameter, leading to a systematic decrease in the post-threshold slope efficiency, while the quality factor of the lasing mode remains largely unchanged. A careful analysis using finite-difference time-domain simulations attributes the increased γ to the loss of photons from lower-quality higher-order modes during amplified spontaneous emission

Zero-shot information extraction from radiological reports using ChatGPT

Electronic health records contain an enormous amount of valuable information, but many are recorded in free text. Information extraction is the strategy to transform the sequence of characters into structured data, which can be employed for secondary analysis. However, the traditional information extraction components, such as named entity recognition and relation extraction, require annotated data to optimize the model parameters, which has become one of the major bottlenecks in building information extraction systems. With the large language models achieving good performances on various downstream NLP tasks without parameter tuning, it becomes possible to use large language models for zero-shot information extraction. In this study, we aim to explore whether the most popular large language model, ChatGPT, can extract useful information from the radiological reports. We first design the prompt template for the interested information in the CT reports. Then, we generate the prompts by combining the prompt template with the CT reports as the inputs of ChatGPT to obtain the responses. A post-processing module is developed to transform the responses into structured extraction results. We conducted the experiments with 847 CT reports collected from Peking University Cancer Hospital. The experimental results indicate that ChatGPT can achieve competitive performances for some extraction tasks compared with the baseline information extraction system, but some limitations need to be further improved

Synthesis and Characterization of Nanostructured WC-Co/Al Powder Prepared by Mechanical Alloying

Nanostructured WC-Co/Al powder was synthesized from WC-12Co powder and pure Al powder by mechanical alloying (MA). The morphology and microstructural evolution of WC-Co/Al powder were investigated by a series of characterization methods. The results showed that the β-Co phase in the initial WC-12Co powder was replaced by the AlxCo phases (such as Al9Co2 and Al13Co4). As the ball milling time increased, the average grain size of WC in the WC-Co/Al powder decreased firstly and then remained at a constant value of around 40 nm. The deposition behavior of powders sprayed by high velocity oxygen fuel (HVOF) spraying was investigated. During spraying, the WC-Co/Al powder had a better flattening than the WC-12Co powder without ball milling, which is beneficial to fabricate compact coatings with lower porosity

Learning Personalized Story Evaluation

While large language models (LLMs) have shown impressive results for more objective tasks such as QA and retrieval, it remains nontrivial to evaluate their performance on open-ended text generation for reasons including (1) data contamination; (2) multi-dimensional evaluation criteria; and (3) subjectiveness stemming from reviewers' personal preferences. To address such issues, we propose to model personalization in an uncontaminated open-ended generation assessment. We create two new datasets Per-MPST and Per-DOC for personalized story evaluation, by re-purposing existing datasets with proper anonymization and new personalized labels. We further develop a personalized story evaluation model PERSE to infer reviewer preferences and provide a personalized evaluation. Specifically, given a few exemplary reviews from a particular reviewer, PERSE predicts either a detailed review or fine-grained comparison in several aspects (such as interestingness and surprise) for that reviewer on a new text input. Experimental results show that PERSE outperforms GPT-4 by 15.8% on Kendall correlation of story ratings, and by 13.7% on pairwise preference prediction accuracy. Both datasets and code will be released.Comment: 19 page

End-to-end Story Plot Generator

Story plots, while short, carry most of the essential information of a full story that may contain tens of thousands of words. We study the problem of automatic generation of story plots, which includes story premise, character descriptions, plot outlines, etc. To generate a single engaging plot, existing plot generators (e.g., DOC (Yang et al., 2022a)) require hundreds to thousands of calls to LLMs (e.g., OpenAI API) in the planning stage of the story plot, which is costly and takes at least several minutes. Moreover, the hard-wired nature of the method makes the pipeline non-differentiable, blocking fast specialization and personalization of the plot generator. In this paper, we propose three models, $\texttt{OpenPlot}$, $\texttt{E2EPlot}$ and $\texttt{RLPlot}$, to address these challenges. $\texttt{OpenPlot}$ replaces expensive OpenAI API calls with LLaMA2 (Touvron et al., 2023) calls via careful prompt designs, which leads to inexpensive generation of high-quality training datasets of story plots. We then train an end-to-end story plot generator, $\texttt{E2EPlot}$, by supervised fine-tuning (SFT) using approximately 13000 story plots generated by $\texttt{OpenPlot}$. $\texttt{E2EPlot}$ generates story plots of comparable quality to $\texttt{OpenPlot}$, and is > 10$\times$ faster (1k tokens in only 30 seconds on average). Finally, we obtain $\texttt{RLPlot}$ that is further fine-tuned with RLHF on several different reward models for different aspects of story quality, which yields 60.0$\%$ winning rate against $\texttt{E2EPlot}$ along the aspect of suspense and surprise.Comment: 17 page

Distinctive signature of indium gallium nitride quantum dot lasing in microdisk cavities.

Low-threshold lasers realized within compact, high-quality optical cavities enable a variety of nanophotonics applications. Gallium nitride materials containing indium gallium nitride (InGaN) quantum dots and quantum wells offer an outstanding platform to study light-matter interactions and realize practical devices such as efficient light-emitting diodes and nanolasers. Despite progress in the growth and characterization of InGaN quantum dots, their advantages as the gain medium in low-threshold lasers have not been clearly demonstrated. This work seeks to better understand the reasons for these limitations by focusing on the simpler, limited-mode microdisk cavities, and by carrying out comparisons of lasing dynamics in those cavities using varying gain media including InGaN quantum wells, fragmented quantum wells, and a combination of fragmented quantum wells with quantum dots. For each gain medium, we use the distinctive, high-quality (Q ∼ 5,500) modes of the cavities, and the change in the highest-intensity mode as a function of pump power to better understand the dominant radiative processes. The variations of threshold power and lasing wavelength as a function of gain medium help us identify the possible limitations to lower-threshold lasing with quantum dot active medium. In addition, we have identified a distinctive lasing signature for quantum dot materials, which consistently lase at wavelengths shorter than the peak of the room temperature gain emission. These findings not only provide better understanding of lasing in nitride-based quantum dot cavity systems but also shed insight into the more fundamental issues of light-matter coupling in such systems.This is the author's accepted manuscript. The final version is available from PNAS at http://www.pnas.org/content/111/39/14042.abstract

Ultra-low threshold gallium nitride photonic crystal nanobeam laser

We report exceptionally low thresholds (9.1 μJ/cm2) for room temperature lasing at ∼450 nm in optically pumped Gallium Nitride (GaN) nanobeam cavity structures. The nanobeam cavity geometry provides high theoretical Q (>100 000) with small modal volume, leading to a high spontaneous emission factor, β = 0.94. The active layer materials are Indium Gallium Nitride (InGaN) fragmented quantum wells (fQWs), a critical factor in achieving the low thresholds, which are an order-of-magnitude lower than obtainable with continuous QW active layers. We suggest that the extra confinement of photo-generated carriers for fQWs (compared to QWs) is responsible for the excellent performance.This work was enabled by facilities available at the Center for Nanoscale Systems (CNS), a member of the National Nanotechnology Infrastructure Network (NNIN), which was supported by the National Science Foundation under NSF Award No. ECS- 0335765. This work was also supported in part by the NSF Materials World Network (Award No. 1008480), the Engineering and Physical Sciences Research Council (Award No. EP/H047816/1), and the Royal Academy of Engineering.This is the author accepted manuscript. The final version is available from AIP at http://scitation.aip.org/content/aip/journal/apl/106/23/10.1063/1.4922211

Evaluation of myocardial work in patients with hypertrophic cardiomyopathy and hypertensive left ventricular hypertrophy based on non-invasive pressure-strain loops

BackgroundThe capacity to distinguish hypertrophic cardiomyopathy (HCM) from hypertensive left ventricular hypertrophy (H-LVH) based on morphological features obtained by conventional echocardiography is limited. We investigated the global myocardial work of the left ventricle in two types of hypertrophies using the non-invasive myocardial work index (NMWI).MethodsConventional echocardiography was performed on 107 subjects with preserved left ventricular ejection fraction (LVEF ≥ 50%), who comprised patients with HCM (n = 40), H-LVH (n = 35), and healthy people with normal blood pressure and left ventricular structure (n = 32). Except for the conventional echocardiographic parameters, the left ventricular myocardial work parameters based on pressure-strain loops, including global myocardial work index (GWI), global constructive work (GCW), global wasted work (GWW), and global work efficiency (GWE), were evaluated in three groups. Multivariate discriminant analysis and receiver operating characteristic (ROC) curve were used to evaluate the incremental value of NMWI for distinguishing HCM from H-LVH.ResultsCompared to the control group, GWI and GCW were significantly lower in HCM patients (P < 0.05), whereas GWI was significantly higher in H-LVH patients. GWW was higher and GWE was significantly decreased in both HCM and H-LVH patients than in the control group (P < 0.05). Multivariate discriminant analysis and ROC curve revealed that the inter-ventricular septum thickness (IVST)/left ventricular posterior wall thickness (LVPWT) and GCW were each able to distinguish HCM from H-LVH. The combination of IVST/LVPWT and GCW discriminated HCM and H-LVH with a higher predictive accuracy of 94.7%.ConclusionNMWI may provide additional information in evaluating the myocardial function in patients with HCM and H-LVH. Myocardial work combined with conventional echocardiography could improve the clinical diagnostic accuracy of distinguishing HCM and H-LVH