High Performance Depthwise and Pointwise Convolutions on Mobile Devices

Lightweight convolutional neural networks (e.g., MobileNets) are specifically designed to carry out inference directly on mobile devices. Among the various lightweight models, depthwise convolution (DWConv) and pointwise convolution (PWConv) are their key operations. In this paper, we observe that the existing implementations of DWConv and PWConv are not well utilizing the ARM processors in the mobile devices, and exhibit lots of cache misses under multi-core and poor data reuse at register level. We propose techniques to re-optimize the implementations of DWConv and PWConv based on ARM architecture. Experimental results show that our implementation can respectively achieve a speedup of up to 5.5x and 2.1x against TVM (Chen et al. 2018) on DWConv and PWConv.Comment: 8 pages, Thirty-Four AAAI conference on Artificial Intelligenc

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)

Exploring the Equity of Government Response in Coproduction in Chicago: Before, During, and After COVID-19

This paper aims to investigate whether government provides public services to different locations equally. I use response time as a metric to assess the performance of government in each location associated with the demographic attributes. Data from the Chicago Data Portal and the American Community Survey (ACS) are collected for the years 2019, 2020, and 2021. My analysis reveals departmental and technological heterogeneity in the changes in government performance regarding public service delivery depending on the location of the service requests during the study period. My findings indicate that areas with a higher number of educated individuals receives a quicker government response overall during the study period. Furthermore, certain departments exhibit a particularly discriminatory response towards disadvantaged groups only during the pandemic, thereby highlighting the potential for inequitable distribution of government resources under times of stress. Additionally, requests submitted through technology-based channels are prioritized for disadvantaged groups as compared to traditional channels, suggesting the potential of technology to aid the government in achieving more equitable outcomes

State Estimation for a Class of Distributed Parameter Systems with Time-Varying Delay over Mobile Sensor–Actuator Networks with Missing Measurements

This work proposes a state estimation strategy over mobile sensor–actuator networks with missing measurements for a class of distributed parameter systems (DPSs) with time-varying delay. Initially, taking advantage of the abstract development equation theory and operator semigroup method, this kind of delayed DPSs described by partial differential equations (PDEs) is derived for evolution equations. Subsequently, the distributed state estimators including consistency component and gain component are designed; the purpose is to estimate the original state distribution of the delayed DPSs with missing measurements. Then, a delay-dependent guidance approach is presented in the form of mobile control forces by constructing an appropriate Lyapunov function candidate. Furthermore, by applying Lyapunov stability theorem, operator semigroup theory, and a stochastic analysis approach, the estimation error systems have been proved asymptotically stable in the mean square sense, which indicates the estimators can approximate the original system states effectively when this kind of DPS has time-delay and the mobile sensors occur missing measurements. Finally, the correctness of control strategy is illustrated by numerical simulation results

Optimizing heat-absorption efficiency of phase change materials by mimicking leaf vein morphology

Low efficiency of heat conduction and absorption is a key problem to restrict the application of phase change materials (PCMs). Foam metals, which work as random heat transfer networks, are often used to improve the thermal conductivity of PCMs. But further improvements are still required in engineering. Interestingly, random micro-channels also widely exist in natural heat and mass transfer systems (e.g., minor veins of leaves and blood capillaries) that always appear with ordered branching networks of macro-channels. But the ordered branching networks, which perform as efficient transfer networks, are rare in metal-foam-enhanced PCMs. Therefore, this work enhances the PCMs’ heat-absorption efficiency by constructing heat transfer networks mimicking leaf veins. Given the gap that there lack trusted design criteria to design the heat transfer networks, we propose an innovative optimization criterion mimicking the generating process of leaf veins. Combine the criterion with an original flexibility-oriented optimization framework, a generating design method is established. The optimization performance is discussed in point-area PCM structures. Compared with the metal-foam-enhanced PCM plate, the heat-absorption efficiency of the generating-based PCM plate is increased to 196.67% in concentrating heat from the PCMs, and the heat-absorption efficiency is also enhanced for more than 3.79 times in dispersing heat to the PCMs. With these improvements, the proposed method is applied in cooling high-power electronic devices which solves the overheating problem and prolongs the working time to 400.00%. Further applications can be expended to PCM-cooling systems, heat pump, collection and output of solar energy and waste heat, etc

Investigation of electrostatically tunable adhesion and instability of flying head slider

Abstract The interfacial adhesion between microstructures is inevitable in a micro-electro-mechanical system (e.g., hard disk drive (HDD)), which may lead to complicated microtribodynamics problems. This research has investigated the effect of surface potential on the interfacial adhesion and microtribodynamics of the head–disk interface (HDI) in an HDD. A dynamic continuum surface force model, where the electrowetting is considered, is proposed to evaluate the interfacial interaction, and then employed into a two-degree-of-freedom (2DOF) model to theoretically analyze the potential influence mechanism on the microtribodynamics. The results confirm that the elimination of potential can effectively repress the adhesion retention, which is further proved by the measured slider response with a laser Doppler vibrometer (LDV). Moreover, the effect of the potential on the adhesion-induced instability is also analyzed through the phase portrait. It tells that the critical stable flying height can be lowered with the elimination of potential