169 research outputs found
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
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
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
dose.The range of spatial resolutions of 6.5
8.2 and 8.8 12.3 are
achieved by the AC-LGAD sensors with 100 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
The performance of large-pitch AC-LGAD with different N+ dose
AC-Coupled LGAD (AC-LGAD) is a new 4D detector developed based on the Low
Gain Avalanche Diode (LGAD) technology, which can accurately measure the time
and spatial information of particles. Institute of High Energy Physics (IHEP)
designed a large-size AC-LGAD with a pitch of 2000 {\mu}m and AC pad of 1000
{\mu}m, and explored the effect of N+ layer dose on the spatial resolution and
time resolution. The spatial resolution varied from 32.7 {\mu}m to 15.1 {\mu}m
depending on N+ dose. The time resolution does not change significantly at
different N+ doses, which is about 15-17 ps. AC-LGAD with a low N+ dose has a
large attenuation factor and better spatial resolution. Large signal
attenuation factor and low noise level are beneficial to improve the spatial
resolution of the AC-LGAD sensor
The role of smoking and alcohol in mediating the effect of gastroesophageal reflux disease on lung cancer: A Mendelian randomization study
Observational studies have suggested a positive association between gastroesophageal reflux disease and lung cancer, but due to the existence of confounders, it remains undetermined whether gastroesophageal reflux disease (GERD) has a causal association with lung cancer. Therefore, Mendelian randomization (MR) analyses were applied to investigate the relationship between the two conditions. Two-sample Mendelian randomization analysis was utilized with summary genetic data from the European Bioinformatics Institute (602,604 individuals) and International Lung Cancer Consortium, which provides information on lung cancer and its histological subgroups. Furthermore, we used two-step Mendelian randomization and multivariable Mendelian randomization to estimate whether smoking initiation (311,629 cases and 321,173 controls) and alcohol intake frequency (n = 462,346) mediate any effect of gastroesophageal reflux disease on lung cancer risk. The Mendelian randomization analyses indicated that gastroesophageal reflux disease was associated with and significantly increased the risk of lung cancer (ORIVW = 1.35, 95% CI = 1.18–1.54; p = 1.36 × 10–5). Smoking initiation and alcohol intake frequency mediated 35% and 3% of the total effect of gastroesophageal reflux disease on lung cancer, respectively. The combined effect of these two factors accounted for 60% of the total effect. In conclusion, gastroesophageal reflux disease is associated with an increased risk of lung cancer, and interventions to reduce smoking and alcohol intake may reduce the incidence of lung cancer
Characterization of the response of IHEP-IME LGAD with shallow carbon to Gamma Irradiation
Low Gain Avalanche Detectors (LGAD), as part of High-Granularity Timing
Detector (HGTD), is crucial to reducing pileup in the upgrading to HL-LHC. Many
studies have been done on the bulk damages of the LGAD. However, there's no
study about the surface radiation hardness of the LGAD sensors with carbon
implanted. The IHEP-IME LGAD version 3 with the shallow carbon and different
interpad separations were irradiated up to 2 MGy by gamma irradiation. The
performance of the IHEP-IME LGAD version 3 before and after irradiation had
been tested, such as the leakage current, break-down voltage, capacitance,
V, and inter-pad resistance. The results showed that apart from minor
fluctuations in some samples, no significant changes concerning inter-pad
separation were observed before and after irradiation. Leakage current and
break-down voltage increase after irradiation, which is considered due to
surface passivation; the overall inter-pad resistance are larger than $10^9\
\Omega_{gl}$ after irradiation. All parameters meet the
requirements of HGTD, and the results indicated that IHEP-IME LGAD v3 has
excellent anti-irradiation performance
Characterisation of Spatial and Timing Resolution of IHEP AC-LGAD Strip
AC-coupled LGAD(AC-LGAD) Strip is a new design of LGAD that allows
high-precision detection of particle spatiotemporal information whereas
reducing the density of readout electronics. For AC-LGAD Strips, there is
limited research on the impact of different strip pitches on the spatiotemporal
detection performance at the small amount of injected charge. The Institute of
High Energy Physics has designed an AC-LGAD Strip prototype with pitches of 150
, 200 , and 250 . The spatial and timing resolutions of
the prototype are studied through the laser Transient Current Technique (TCT)
scan with different amounts of injected charge. The results show that both the
spatial and timing resolution improves as the strip pitch decreases. Increases
in both temporal and spatial resolutions as the amount of charge injected
increases are observed. The spatial and timing resolution is better than 60 ps
and 40 at 1 Minimum Ionizing Particle (MIP), and better than 10 ps and
5 at 40 MIPs. Increasing Signal-to-Noise Ratio (SNR) is the key to
improving spatial and temporal resolution, whereas increasing the signal
attenuation rate by reducing the gap between adjacent electrodes also helps to
improve spatial resolution. The enhancements of spatial and timing resolutions
by both SNR and signal attenuation rate decrease with increasing amount of MIP.
This study can help design and optimize the AC-LGAD Strip detectors and readout
electronics
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