Exploring Unified Perspective For Fast Shapley Value Estimation

Shapley values have emerged as a widely accepted and trustworthy tool, grounded in theoretical axioms, for addressing challenges posed by black-box models like deep neural networks. However, computing Shapley values encounters exponential complexity in the number of features. Various approaches, including ApproSemivalue, KernelSHAP, and FastSHAP, have been explored to expedite the computation. We analyze the consistency of existing works and conclude that stochastic estimators can be unified as the linear transformation of importance sampling of feature subsets. Based on this, we investigate the possibility of designing simple amortized estimators and propose a straightforward and efficient one, SimSHAP, by eliminating redundant techniques. Extensive experiments conducted on tabular and image datasets validate the effectiveness of our SimSHAP, which significantly accelerates the computation of accurate Shapley values

Identification of Cancer Dysfunctional Subpathways by Integrating DNA Methylation, Copy Number Variation, and Gene-Expression Data

A subpathway is defined as the local region of a biological pathway with specific biological functions. With the generation of large-scale sequencing data, there are more opportunities to study the molecular mechanisms of cancer development. It is necessary to investigate the potential impact of DNA methylation, copy number variation (CNV), and gene-expression changes in the molecular states of oncogenic dysfunctional subpathways. We propose a novel method, Identification of Cancer Dysfunctional Subpathways (ICDS), by integrating multi-omics data and pathway topological information to identify dysfunctional subpathways. We first calculated gene-risk scores by integrating the three following types of data: DNA methylation, CNV, and gene expression. Second, we performed a greedy search algorithm to identify the key dysfunctional subpathways within pathways for which the discriminative scores were locally maximal. Finally, a permutation test was used to calculate the statistical significance level for these key dysfunctional subpathways. We validated the effectiveness of ICDS in identifying dysregulated subpathways using datasets from liver hepatocellular carcinoma (LIHC), head-neck squamous cell carcinoma (HNSC), cervical squamous cell carcinoma, and endocervical adenocarcinoma. We further compared ICDS with methods that performed the same subpathway identification algorithm but only considered DNA methylation, CNV, or gene expression (defined as ICDS_M, ICDS_CNV, or ICDS_G, respectively). With these analyses, we confirmed that ICDS better identified cancer-associated subpathways than the three other methods, which only considered one type of data. Our ICDS method has been implemented as a freely available R-based tool (https://cran.r-project.org/web/packages/ICDS)

A Hardware-adaptive Deep Feature Matching Pipeline for Real-time 3D Reconstruction

This paper presents a hardware-adaptive feature modeling framework to automatically generate and optimize deep neural networks to support real-time feature extraction and matching on a given hardware platform. This framework consists of a deep feature extraction and matching pipeline and a neural architecture search scheme, with which deep neural networks can be automatically generated and optimized according to given hardware to achieve reliable real-time feature matching. Built on our feature matching approach, we also developed a real-time 3D scene reconstruction pipeline that could run adaptively on hardware with different computational performance. We designed experiments to validate the proposed matching and reconstruction pipelines on hardware platforms with different performance. The results demonstrated our algorithm\u27s effectiveness on both matching and reconstruction tasks

Non-iterative structural topology optimization using deep learning

This paper presents a non-iterative topology optimizer for conductive heat transfer structures with the help of deep learning. An artificial neural network is trained to deal with the black-and-white pixel images and generate near-optimal structures. Our design is a two-stage hierarchical prediction–refinement pipeline consisting of two coupled neural networks: a generative adversarial network (GAN) for predicting a low resolution near-optimal structure and a super-resolution generative adversarial network (SRGAN) for predicting the refined structure in high resolution. Training datasets with given boundary conditions and the optimized pixel image structures are obtained after simulating a big amount of topology optimization procedures. For more effective training and inference, these datasets are generated with two different resolutions. Experiments demonstrated that our learning based optimizer can provide accurate estimation of the conductive heat transfer topology using negligible computational time. This effective incorporation of deep learning into topology optimization could enable promising applications in large-scale engineering structure design

A multi-frame graph matching algorithm for low-bandwidth RGB-D SLAM

This paper presents a novel multi-frame graph matching algorithm for reliable partial alignments among point clouds. We use this algorithm to stitch frames for 3D environment reconstruction. The idea is to utilize both descriptor similarity and mutual spatial coherency of features existed in multiple frames to match these frames. The proposed multi-frame matching algorithm can extract coarse correspondence among multiple point clouds more reliably than pairwise matching algorithms, especially when the data are noisy and the overlap is relatively small. When there are insufficient consistent features that appeared in all these frames, our algorithm reduces the number of frames to match to deal with it adaptively. Hence, it is particularly suitable for cost-efficient robotic Simultaneous Localization and Mapping (SLAM). We design a prototype system integrating our matching and reconstruction algorithm on a remotely controlled navigation iRobot, equipped with a Kinect and a Raspberry Pi. Our reconstruction experiments demonstrate the effectiveness of our algorithm and design

A New Integrated Model for Simulating Adaptive Cycle Engine Performance Considering Variations in Tip Clearance

The low-fidelity simulation method cannot meet the requirements for predicting the performance of an adaptive cycle engine (ACE), especially when considering tip clearance variations in the compression and expansion systems. The tip clearances of the components of an ACE, such as the adaptive fan and turbine, vary drastically under different operating conditions. Though the tip clearance significantly impacts the engine’s performance, including its thrust and fuel consumption, variations in tip clearance are not considered in traditional ACE simulation models. This paper developed a new integrated model for predicting ACE performance, including multi-fidelity simulation models of the components and a newly developed, simplified model for predicting tip clearance. Specifically, the integrated model consists of a zero-dimensional (0D) engine performance simulation model, a three-dimensional (3D) adaptive fan numerical simulation model, a one-dimensional (1D) low-pressure-turbine (LPT) mean line model, and a multi-dimensional (MD) tip clearance prediction model. The integrated model solved the problem of considering the impact of tip clearance on an ACE and further improved the accuracy of thrust and fuel consumption predictions. Specifically, considering variations in the tip clearances under the design conditions, the differences in the thrust and specific fuel consumption (SFC) of the ACE are 1% and 0.3%, respectively. In conclusion, the integrated model provides a useful tool for evaluating the performance of an ACE while considering tip clearance variations

Effect of Self-Recirculating Casing Treatment on the Aerodynamic Performance of Ultra-High-Pressure-Ratio Centrifugal Compressors

The motivation to design a more efficient and compact aircraft engine leads to a continuous increase in overall pressure ratio and decrease in the stage number in compressors. Compared to the traditional multi-stage compressor, a single-stage ultra-high-pressure-ratio centrifugal compressor with a pressure ratio higher than 10.0 can significantly improve the engine’s power-to-weight ratio and fuel economy with a reduced structure complexity. Thus, it has great potential to be adopted in the compression system of advanced aero engines, such as turboshaft engines, in the future. However, the highly narrow Stable Flow Range (SFR) of ultra-high-pressure-ratio centrifugal compressors is a severe restriction for engineering applications. This research focuses on the aerodynamic performance of a ultra-high-pressure-ratio centrifugal compressor, and three-dimensional simulation is employed to investigate the effect of Self-Recirculating Casing Treatment (SRCT) on the performance and stability of the centrifugal compressor. Firstly, the parametric model of SRCT is established to investigate the effect of geometry parameters (rear slot distance and rear slot width) on the aerodynamic performance of the centrifugal compressor. It is concluded that SRCT improves the compressor’s SFR but deteriorates its efficiency. Also, a non-linear and non-monotone relationship exists between the SFR and rear slot distance or width. Then, the flow mechanism behind the effect of SRCT is explored in detail. By introducing the SRCT, an additional flow path is provided across the blade along the circumferential direction, and the behavior of the shock wave and tip leakage flow is significantly changed, resulting in the obviously different loading distribution along the streamwise direction. As a result, the mixing and flow separation loss are enhanced in the impeller flow passage to deteriorate the efficiency. On the other hand, the blockage effect caused by the mixing of slot recirculation and mainstream flow near the impeller inlet increases the axial velocity and reduces the incidence angle below the 90% spanwise section, which is considered to effectively stabilize the impeller flow field and enhance the stability

Clinical Features and Risk Factors of Active Tuberculosis in Patients with Behçet’s Disease

To investigate the clinical features and potential risk factors of active tuberculosis (ATB) in Behçet’s disease (BD), we conducted a case-control study on hospitalized BD patients in our institute from 2010 to 2019. BD patients with ATB were enrolled as the case group. The control group was selected by random number sampling from the remaining BD patients, including those with latent tuberculosis infection, previous tuberculosis, or without tuberculosis. Finally, we reviewed 386 BD patients and identified 21 (5.4%) ATB cases, including four (19.0%) microbiologically confirmed and 17 (81.0%) clinically diagnosed. We found that BD patients with ATB were more prone to have systemic symptoms (fever, night sweating, and unexplained weight loss) and/or symptoms related to the infection site. Multivariate logistic regression analysis revealed that erythrocyte sedimentation rate ESR>60 mm/h (OR=13.710, 95% CI (1.101, 170.702)), increased IgG (OR=1.226, 95% CI (1.001, 1.502)), and positive T-SPOT.TB (OR=7.793, 95% CI (1.312, 48.464), for 24-200 SFC/106PBMC; OR=17.705 95% CI (2.503, 125.260), for >200 SFC/106PBMC) were potential risk factors for ATB in BD patients. Our study suggested that when BD patients have systemic symptoms with significantly elevated TB-SPOT, the diagnosis of ATB should be considered

A robust qualitative transcriptional signature for the correct pathological diagnosis of gastric cancer

Abstract Background Currently, pathological examination of gastroscopy biopsy specimens is the gold standard for gastric cancer (GC) diagnosis. However, it has a false-negative rate of 10–20% due to inaccurate sampling locations and/or insufficient sampling amount. A signature should be developed to aid the early diagnosis of GC using biopsy specimens even when they are sampled from inaccurate locations. Methods We extracted a robust qualitative transcriptional signature, based on the within-sample relative expression orderings (REOs) of gene pairs, to discriminate both GC tissues and adjacent-normal tissues from non-GC gastritis, intestinal metaplasia and normal gastric tissues. Results A signature consisting of two gene pairs for GC diagnosis was identified and validated in data of both biopsy specimens and surgical resection specimens pooled from publicly available datasets measured by different laboratories with different platforms. For gastroscopy biopsy specimens, 96.20% of 79 non-GC tissues were correctly identified as non-GC, and 96.84% of 158 GC tissues and six of seven adjacent-normal tissues were correctly identified as GC. For surgical resection specimens, 98.37% of 2560 GC tissues and 97.28% of 221 adjacent-normal tissues were correctly identified as GC. Especially, 97.67% of the 257 GC patients at stage I were exactly diagnosed as GC. We additionally measured 21 GC tissues from seven different GC patients, each with three specimens sampled from three tumor locations with different proportions of the tumor epithelial cell. All these GC tissues were correctly identified as GC, even when the proportion of the tumor epithelial cell was as low as 14%. Conclusions The qualitative transcriptional signature can distinguish both GC and adjacent-normal tissues from normal, gastritis and intestinal metaplasia tissues of non-GC patients even using inaccurately sampled biopsy specimens, which can be applied robustly at the individual level to aid the early GC diagnosis