Occult thoraco-abdominal injuries from an airbag and seatbelt

AbstractA combination of a seatbelt and airbags decreases the risk of injury for most body regions in motor vehicle crashes. Although the severity of injuries decreases, injuries still occur. We report a case of occult thoraco-abdominal trauma in a patient who was wearing a three-point seatbelt and had airbag protection. A 59-year-old man presented to the emergency department in shock after a motor vehicle accident. He was protected by a three-point seatbelt and airbag. Chest radiographs and focused assessment with sonography for trauma showed no abnormalities. However, computed tomography revealed multiple injuries in the chest and abdomen. This case report highlights occult thoraco-abdominal trauma in a victim protected by a seatbelt and airbag, which may be a pitfall for emergency physicians. Emergency physicians should understand the limitations of chest radiographs in trauma evaluation and carry out a complete evaluation of patients

Detach and Adapt: Learning Cross-Domain Disentangled Deep Representation

While representation learning aims to derive interpretable features for describing visual data, representation disentanglement further results in such features so that particular image attributes can be identified and manipulated. However, one cannot easily address this task without observing ground truth annotation for the training data. To address this problem, we propose a novel deep learning model of Cross-Domain Representation Disentangler (CDRD). By observing fully annotated source-domain data and unlabeled target-domain data of interest, our model bridges the information across data domains and transfers the attribute information accordingly. Thus, cross-domain joint feature disentanglement and adaptation can be jointly performed. In the experiments, we provide qualitative results to verify our disentanglement capability. Moreover, we further confirm that our model can be applied for solving classification tasks of unsupervised domain adaptation, and performs favorably against state-of-the-art image disentanglement and translation methods.Comment: CVPR 2018 Spotligh

Single deep ultraviolet light emission from boron nitride nanotube film

Light in deep ultraviolet DUV region has a wide range of applications and the demand for finding DUV light emitting materials at nanoscale is increasingly urgent as they are vital for building miniaturized optic and optoelectronic devices. We discover that boron nitride nanotubes BNNTs with a well-crystallized cylindrical multiwall structure and diameters smaller than 10 nm can have single DUV emission at 225 nm 5.51 eV. The measured BNNTs are grown on substrate in the form of a thin film. This study suggests that BNNTs may work as nanosized DUV light sources for various applications. © 20

Towards A Holographic Model of D-Wave Superconductors

The holographic model for S-wave high T_c superconductors developed by Hartnoll, Herzog and Horowitz is generalized to describe D-wave superconductors. The 3+1 dimensional gravitational theory consists a symmetric, traceless second-rank tensor field and a U(1) gauge field in the background of the AdS black hole. Below T_c the tensor field which carries the U(1) charge undergoes the Higgs mechanism and breaks the U(1) symmetry of the boundary theory spontaneously. The phase transition characterized by the D-wave condensate is second order with the mean field critical exponent beta = 1/2. As expected, the AC conductivity is isotropic below T_c and the system becomes superconducting in the DC limit but has no hard gap.Comment: 14 pages, 2 figures, Some typos corrected, Matched with the published versio

Method-specific suicide rates and accessibility of means:a small-area analysis in Taipei City, Taiwan

Abstract. Background: Few studies have investigated whether means accessibility is related to the spatial distribution of suicide. Aims: To examine the hypothesis that indicators of the accessibility to specific suicide methods were associated with method-specific suicide rates in Taipei City, Taiwan. Method: Smoothed standardized mortality ratios for method-specific suicide rates across 432 neighborhoods and their associations with means accessibility indicators were estimated using Bayesian hierarchical models. Results: The proportion of single-person households, indicating the ease of burning charcoal in the home, was associated with charcoal-burning suicide rates (adjusted rate ratio [aRR] = 1.13, 95% credible interval [CrI] = 1.03–1.25). The proportion of households living on the sixth floor or above, indicating easy access to high places, was associated with jumping suicide rates (aRR = 1.16, 95% CrI, 1.04–1.29). Neighborhoods’ adjacency to rivers, indicating easy access to water, showed no statistical evidence of an association with drowning suicide rates (aRR = 1.27, 95% CrI = 0.92–1.69). Hanging and overall suicide rates showed no associations with any of these three accessibility indicators. Limitations: This is an ecological study; associations between means accessibility and suicide cannot be directly inferred as causal. Conclusion: The findings have implications for identifying high-risk groups for charcoal-burning suicide (e.g., vulnerable individuals living alone) and preventing jumping suicides by increasing the safety of high buildings

Navigating Text-To-Image Customization:From LyCORIS Fine-Tuning to Model Evaluation

Text-to-image generative models have garnered immense attention for their ability to produce high-fidelity images from text prompts. Among these, Stable Diffusion distinguishes itself as a leading open-source model in this fast-growing field. However, the intricacies of fine-tuning these models pose multiple challenges from new methodology integration to systematic evaluation. Addressing these issues, this paper introduces LyCORIS (Lora beYond Conventional methods, Other Rank adaptation Implementations for Stable diffusion) [https://github.com/KohakuBlueleaf/LyCORIS], an open-source library that offers a wide selection of fine-tuning methodologies for Stable Diffusion. Furthermore, we present a thorough framework for the systematic assessment of varied fine-tuning techniques. This framework employs a diverse suite of metrics and delves into multiple facets of fine-tuning, including hyperparameter adjustments and the evaluation with different prompt types across various concept categories. Through this comprehensive approach, our work provides essential insights into the nuanced effects of fine-tuning parameters, bridging the gap between state-of-the-art research and practical application.Comment: 77 pages, 54 figures, 6 table

CFEVER: A Chinese Fact Extraction and VERification Dataset

We present CFEVER, a Chinese dataset designed for Fact Extraction and VERification. CFEVER comprises 30,012 manually created claims based on content in Chinese Wikipedia. Each claim in CFEVER is labeled as "Supports", "Refutes", or "Not Enough Info" to depict its degree of factualness. Similar to the FEVER dataset, claims in the "Supports" and "Refutes" categories are also annotated with corresponding evidence sentences sourced from single or multiple pages in Chinese Wikipedia. Our labeled dataset holds a Fleiss' kappa value of 0.7934 for five-way inter-annotator agreement. In addition, through the experiments with the state-of-the-art approaches developed on the FEVER dataset and a simple baseline for CFEVER, we demonstrate that our dataset is a new rigorous benchmark for factual extraction and verification, which can be further used for developing automated systems to alleviate human fact-checking efforts. CFEVER is available at https://ikmlab.github.io/CFEVER.Comment: AAAI-2