Evaluation of efficacy and safety of gefitinib as monotherapy in Chinese patients with advanced non-small cell lung cancer and very poor performance status

<p>Abstract</p> <p>Background</p> <p>This paper reports the outcome of gefitinib for Chinese advanced NSCLC patients with poor performance status (PS) at the Peking Union Medical College Hospital.</p> <p>Methods</p> <p>From Oct 2002 to Apr. 2006, 42 advanced NSCLC patients with PS 3/4 received gefitinib 250 mg/day treatment. Median survival (MS) were calculated using the Kaplan-Meier method and a Cox regression model was used to find main factors affecting MS.</p> <p>Results</p> <p>Adverse events (AEs) were generally mild (grade 1 and 2) and reversible. The most frequent AEs were rash 72.2% (26/42) and diarrhea 44.4% (26/42). The objective tumor response rate and stable disease rate were 40.5% and 26.2% respectively, and median survival(MS) of all patients was 10.1 months (95% confidential interval CI, 3.4 ~ 16.8), and progression-free survival(PFS) was 5.7 months (95% CI, 4.5 ~ 6.9). The MS were significantly related with objective response of gefitinib. Objective responses was significantly related with rashes induced with gefitinib.</p> <p>Conclusion</p> <p>Our study suggest that treatment with gefitinib may be well tolerated and beneficial for Chinese patients with poor PS, and the safety and efficacy were similar to patients with good PS.</p

Lie-Poisson integration for rigid body dynamics

AbstractIn this paper, the splitting midpoint rule is presented and proved to be the Lie-Poisson integrators to the rigid body systems. Further discussions are also given. Numerical experiments show that this method has well properties comparing with the Runge Kutta method and ordinary midpoint rule

FAITHSCORE: Evaluating Hallucinations in Large Vision-Language Models

We introduce FAITHSCORE (Faithfulness to Atomic Image Facts Score), a reference-free and fine-grained evaluation metric that measures the faithfulness of the generated free-form answers from large vision-language models (LVLMs). The FAITHSCORE evaluation first identifies sub-sentences containing descriptive statements that need to be verified, then extracts a comprehensive list of atomic facts from these sub-sentences, and finally conducts consistency verification between fine-grained atomic facts and the input image. Meta-evaluation demonstrates that our metric highly correlates with human judgments of faithfulness. We collect two benchmark datasets (i.e. LLaVA-1k and MSCOCO-Cap) for evaluating LVLMs instruction-following hallucinations. We measure hallucinations in state-of-the-art LVLMs with FAITHSCORE on the datasets. Results reveal that current systems are prone to generate hallucinated content unfaithful to the image, which leaves room for future improvements. Further, we find that current LVLMs despite doing well on color and counting, still struggle with long answers, relations, and multiple objects

Surgicel™ application in intracranial hemorrhage surgery contributed to giant-cell granuloma in a patient with hypertension: case report and review of the literature

Abstract Background Surgicel™ is an oxidized cellulose preparation that is widely applied in neurosurgery due to its hemostatic effect and good tissue compatibility. Tumor-like lesions induced by Surgicel® application in cerebral surgery have been rarely reported, especially for intracranial hemorrhage debridement surgery in patients with hypertension. Case presentation This case report describes a rare case in which Surgicel™ application led to a foreign body reaction, contributing to the development of an intracranial giant-cell granuloma. A 49-year-old female hypertensive patient was diagnosed with intracranial hemorrhage. She was treated with debridement surgery that employed Surgicel™ application. Although a satisfactory hemostatic effect was achieved, the patient was diagnosed with epilepsy 6 months later. Subsequent magnetic resonance imaging revealed an intracranial space-occupying lesion. After undergoing en bloc resection of the lesion, the patient was diagnosed with a Surgicel™-related intracranial giant-cell granuloma by histopathology. Conclusions Application of Surgicel™ during intracranial hemorrhage debridement surgery may be associated with a risk of granuloma development due to formation of a tumor-like space-occupying lesion in the surgery bed. Even a low risk of tumor development implies a need for caution when applying Surgicel™, especially when solely used to achieve a hemostatic effect. </jats:sec

A note for Lie-Poisson Hamilton-Jacobi equation and Lie-Poisson integrator

AbstractIn this paper, a clear Lie-Poisson Hamilton-Jacobi theory is presented. How to construct a Lie-Poisson integrator by generating function methods is also given, which is different from the Ge-Marsden methods [1]. An example on a rigid body has been given to illustrate this point

Fine-grained Data Distribution Alignment for Post-Training Quantization

While post-training quantization receives popularity mostly due to its evasion in accessing the original complete training dataset, its poor performance also stems from scarce images. To alleviate this limitation, in this paper, we leverage the synthetic data introduced by zero-shot quantization with calibration dataset and propose a fine-grained data distribution alignment (FDDA) method to boost the performance of post-training quantization. The method is based on two important properties of batch normalization statistics (BNS) we observed in deep layers of the trained network, (i.e.), inter-class separation and intra-class incohesion. To preserve this fine-grained distribution information: 1) We calculate the per-class BNS of the calibration dataset as the BNS centers of each class and propose a BNS-centralized loss to force the synthetic data distributions of different classes to be close to their own centers. 2) We add Gaussian noise into the centers to imitate the incohesion and propose a BNS-distorted loss to force the synthetic data distribution of the same class to be close to the distorted centers. By utilizing these two fine-grained losses, our method manifests the state-of-the-art performance on ImageNet, especially when both the first and last layers are quantized to the low-bit. Code is at \url{https://github.com/zysxmu/FDDA}.Comment: ECCV202

Performance of AC-LGAD strip sensor designed for the DarkSHINE experiment

AC-coupled Low Gain Avalanche Detector (AC-LGAD) is a new precise detector technology developed in recent years. Based on the standard Low Gain Avalanche Detector (LGAD) technology, AC-LGAD sensors can provide excellent timing performance and spatial resolution. This paper presents the design and performance of several prototype AC-LGAD strip sensors for the DarkSHINE tracking system, as well as the electrical characteristics and spatial resolution of the prototype sensors from two batches of wafers with different $n^+$ dose.The range of spatial resolutions of 6.5$\mathrm{\mu m}$ $\sim$ 8.2$\mathrm{\mu m}$ and 8.8$\mathrm{\mu m}$ $\sim$ 12.3$\mathrm{\mu m}$ are achieved by the AC-LGAD sensors with 100$\mu m$ pitch size.Comment: 10 pages, 12 figure

OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models

Large language models (LLMs) have revolutionized natural language processing tasks. However, their practical deployment is hindered by their immense memory and computation requirements. Although recent post-training quantization (PTQ) methods are effective in reducing memory footprint and improving the computational efficiency of LLM, they hand-craft quantization parameters, which leads to low performance and fails to deal with extremely low-bit quantization. To tackle this issue, we introduce an Omnidirectionally calibrated Quantization (OmniQuant) technique for LLMs, which achieves good performance in diverse quantization settings while maintaining the computational efficiency of PTQ by efficiently optimizing various quantization parameters. OmniQuant comprises two innovative components including Learnable Weight Clipping (LWC) and Learnable Equivalent Transformation (LET). LWC modulates the extreme values of weights by optimizing the clipping threshold. Meanwhile, LET tackles activation outliers by shifting the challenge of quantization from activations to weights through a learnable equivalent transformation. Operating within a differentiable framework using block-wise error minimization, OmniQuant can optimize the quantization process efficiently for both weight-only and weight-activation quantization. For instance, the LLaMA-2 model family with the size of 7-70B can be processed with OmniQuant on a single A100-40G GPU within 1-16 hours using 128 samples. Extensive experiments validate OmniQuant's superior performance across diverse quantization configurations such as W4A4, W6A6, W4A16, W3A16, and W2A16. Additionally, OmniQuant demonstrates effectiveness in instruction-tuned models and delivers notable improvements in inference speed and memory reduction on real devices. Codes and models are available at \url{https://github.com/OpenGVLab/OmniQuant}.Comment: Updated result with 2-bit quantization. A differentiable quantization method for LL