Effective Quantization for Diffusion Models on CPUs

Diffusion models have gained popularity for generating images from textual descriptions. Nonetheless, the substantial need for computational resources continues to present a noteworthy challenge, contributing to time-consuming processes. Quantization, a technique employed to compress deep learning models for enhanced efficiency, presents challenges when applied to diffusion models. These models are notably more sensitive to quantization compared to other model types, potentially resulting in a degradation of image quality. In this paper, we introduce a novel approach to quantize the diffusion models by leveraging both quantization-aware training and distillation. Our results show the quantized models can maintain the high image quality while demonstrating the inference efficiency on CPUs. The code is publicly available at: https://github.com/intel/intel-extension-for-transformers

Metastatic patterns and prognosis of patients with primary malignant cardiac tumor

BackgroundDistant metastases are independent negative prognostic factors for patients with primary malignant cardiac tumors (PMCT). This study aims to further investigate metastatic patterns and their prognostic effects in patients with PMCT.Materials and methodsThis multicenter retrospective study included 218 patients with PMCT diagnosed between 2010 and 2017 from Surveillance, Epidemiology, and End Results (SEER) database. Logistic regression was utilized to identify metastatic risk factors. A Chi-square test was performed to assess the metastatic rate. Kaplan–Meier methods and Cox regression analysis were used to analyze the prognostic effects of metastatic patterns.ResultsSarcoma (p = 0.002) and tumor size¿4 cm (p = 0.006) were independent risk factors of distant metastases in patients with PMCT. Single lung metastasis (about 34%) was the most common of all metastatic patterns, and lung metastases occurred more frequently (17.9%) than bone, liver, and brain. Brain metastases had worst overall survival (OS) and cancer-specific survival (CSS) among other metastases, like lung, bone, liver, and brain (OS: HR = 3.20, 95% CI: 1.02–10.00, p = 0.046; CSS: HR = 3.53, 95% CI: 1.09–11.47, p = 0.036).ConclusionPatients with PMCT who had sarcoma or a tumor larger than 4 cm had a higher risk of distant metastases. Lung was the most common metastatic site, and brain metastases had worst survival among others, such as lung, bone, liver, and brain. The results of this study provide insight for early detection, diagnosis, and treatment of distant metastases associated with PMCT

Relative Nucleus Pulposus Area and Position Alters Disc Joint Mechanics.

Aging and degeneration of the intervertebral disc are noted by changes in tissue composition and geometry, including a decrease in nucleus pulposus (NP) area. The NP centroid is positioned slightly posterior of the disc's centroid, but the effect of NP size and location on disc joint mechanics is not well understood. We evaluated the effect of NP size and centroid location on disc joint mechanics under dual-loading modalities (i.e., compression in combination with axial rotation or bending). A finite element model was developed to vary the relative NP area (NP:Disc area ratio range = 0.21 - 0.60). We also evaluated the effect of NP position by shifting the NP centroid anteriorly and posteriorly. Our results showed that compressive stiffness and average first principal strains increased with NP size. Under axial compression, stresses are distributed from the NP to the annulus, and stresses were redistributed towards the NP with axial rotation. Moreover, peak stresses were greater for discs with a smaller NP area. NP centroid location had a greater impact on intradiscal pressure during flexion and extension, where peak pressures in the posterior annulus under extension was greater for discs with a more posteriorly situated NP. In conclusion, the findings from this study highlight the importance of closely mimicking NP size and location in computational models that aim to understand stress/strain distribution during complex loading and for developing repair strategies that aim to recapitulate the mechanical behavior of healthy discs

Position Distribution Matters: A Graph-Based Binary Function Similarity Analysis Method

Binary function similarity analysis evaluates the similarity of functions at the binary level to aid program analysis, which is popular in many fields, such as vulnerability detection, binary clone detection, and malware detection. Graph-based methods have relatively good performance in practice, but currently, they cannot capture similarity in the aspect of the graph position distribution and lose information in graph processing, which leads to low accuracy. This paper presents PDM, a graph-based method to increase the accuracy of binary function similarity detection, by considering position distribution information. First, an enhanced Attributed Control Flow Graph (ACFG+) of a function is constructed based on a control flow graph, assisted by the instruction embedding technique and data flow analysis. Then, ACFG+ is fed to a graph embedding model using the CapsGNN and DiffPool mechanisms, to enrich information in graph processing by considering the position distribution. The model outputs the corresponding embedding vector, and we can calculate the similarity between different function embeddings using the cosine distance. Similarity detection is completed in the Siamese network. Experiments show that compared with VulSeeker and PalmTree+VulSeeker, PDM can stably obtain three-times and two-times higher accuracy, respectively, in binary function similarity detection and can detect up to six-times more results in vulnerability detection. When comparing with some state-of-the-art tools, PDM has comparable Top-5, Top-10, and Top-20 ranking results with respect to BinDiff, Diaphora, and Kam1n0 and significant advantages in the Top-50, Top-100, and Top-200 detection results

Relative Nucleus Pulposus Area and Position Alter Disk Joint Mechanics.

Aging and degeneration of the intervertebral disk are noted by changes in tissue composition and geometry, including a decrease in nucleus pulposus (NP) area. The NP centroid is positioned slightly posterior of the disk's centroid, but the effect of NP size and location on disk joint mechanics is not well understood. We evaluated the effect of NP size and centroid location on disk joint mechanics under dual-loading modalities (i.e., compression in combination with axial rotation or bending). A finite element model (FEM) was developed to vary the relative NP area (NP:Disk area ratio range = 0.21-0.60). We also evaluated the effect of NP position by shifting the NP centroid anteriorly and posteriorly. Our results showed that compressive stiffness and average first principal strains increased with NP size. Under axial compression, stresses are distributed from the NP to the annulus, and stresses were redistributed toward the NP with axial rotation. Moreover, peak stresses were greater for disks with a smaller NP area. NP centroid location had a greater impact on intradiscal pressure during flexion and extension, where peak pressures in the posterior annulus under extension was greater for disks with a more posteriorly situated NP. In conclusion, the findings from this study highlight the importance of closely mimicking NP size and location in computational models that aim to understand stress/strain distribution during complex loading and for developing repair strategies that aim to recapitulate the mechanical behavior of healthy disks

Long-Term Cardiovascular Mortality among 80,042 Older Patients with Bladder Cancer

Background: To identify the risk of death from cardiovascular disease (CVD) in older patients with bladder cancer (BC). Methods: This population-based study included 80,042 older BC patients (≥65 years) diagnosed between 1975 and 2018, with a mean follow-up of 17.2 years. The proportion of deaths, competing risk models, standardized mortality ratio (SMR), and absolute excess risk (AER) per 10,000 person-years were applied to identify the risk of CVD-related deaths among older BC patients. Results: For older patients with BC, CVD-related death was the chief cause of death, and cumulative CVD-related mortality also exceeded primary BC as the leading cause of death mostly 5–10 years after BC diagnosis, especially in localized-stage and low-grade subgroups. The risk of short- and long-term CVD-related death in older BC patients was higher than in the general older adult population (SMR = 1.30, 95% CI 1.28–1.32; AER = 105.68). The risk of sex-specific CVD-related deaths also increased compared to the general population of older adults, including heart disease, cerebrovascular diseases, hypertension without heart disease, atherosclerosis, aortic aneurysm and dissection, and other diseases of the arteries, arterioles, and capillaries. Conclusions: CVD-related death is an important competing risk among older BC patients and has surpassed primary BC as the chief cause of death, mainly 5–10 years after BC diagnosis. The risk of CVD-related death in older patients with BC was greater than in the general population. The management of older patients with BC should focus not only on the primary cancer but also on CVD-related death

Data_Sheet_1_Metastatic patterns and prognosis of patients with primary malignant cardiac tumor.docx

BackgroundDistant metastases are independent negative prognostic factors for patients with primary malignant cardiac tumors (PMCT). This study aims to further investigate metastatic patterns and their prognostic effects in patients with PMCT.Materials and methodsThis multicenter retrospective study included 218 patients with PMCT diagnosed between 2010 and 2017 from Surveillance, Epidemiology, and End Results (SEER) database. Logistic regression was utilized to identify metastatic risk factors. A Chi-square test was performed to assess the metastatic rate. Kaplan–Meier methods and Cox regression analysis were used to analyze the prognostic effects of metastatic patterns.ResultsSarcoma (p = 0.002) and tumor size¿4 cm (p = 0.006) were independent risk factors of distant metastases in patients with PMCT. Single lung metastasis (about 34%) was the most common of all metastatic patterns, and lung metastases occurred more frequently (17.9%) than bone, liver, and brain. Brain metastases had worst overall survival (OS) and cancer-specific survival (CSS) among other metastases, like lung, bone, liver, and brain (OS: HR = 3.20, 95% CI: 1.02–10.00, p = 0.046; CSS: HR = 3.53, 95% CI: 1.09–11.47, p = 0.036).ConclusionPatients with PMCT who had sarcoma or a tumor larger than 4 cm had a higher risk of distant metastases. Lung was the most common metastatic site, and brain metastases had worst survival among others, such as lung, bone, liver, and brain. The results of this study provide insight for early detection, diagnosis, and treatment of distant metastases associated with PMCT.</p