Efficient Latency-Aware CNN Depth Compression via Two-Stage Dynamic Programming

Recent works on neural network pruning advocate that reducing the depth of the network is more effective in reducing run-time memory usage and accelerating inference latency than reducing the width of the network through channel pruning. In this regard, some recent works propose depth compression algorithms that merge convolution layers. However, the existing algorithms have a constricted search space and rely on human-engineered heuristics. In this paper, we propose a novel depth compression algorithm which targets general convolution operations. We propose a subset selection problem that replaces inefficient activation layers with identity functions and optimally merges consecutive convolution operations into shallow equivalent convolution operations for efficient end-to-end inference latency. Since the proposed subset selection problem is NP-hard, we formulate a surrogate optimization problem that can be solved exactly via two-stage dynamic programming within a few seconds. We evaluate our methods and baselines by TensorRT for a fair inference latency comparison. Our method outperforms the baseline method with higher accuracy and faster inference speed in MobileNetV2 on the ImageNet dataset. Specifically, we achieve $1.41\times$ speed-up with $0.11$\%p accuracy gain in MobileNetV2-1.0 on the ImageNet.Comment: ICML 2023; Codes at https://github.com/snu-mllab/Efficient-CNN-Depth-Compressio

The Decline of Physical Activity with Age in School-Aged Children with Cerebral Palsy: A Single-Center Cross-Sectional Observational Study

Maintaining physical activity is important for children with cerebral palsy (CP). This study examined whether age predicted habitual physical activity (HPA) or cardiorespiratory fitness (CRF) in school-aged children with CP and clarified the relationship between HPA and CRF. We utilized cross-sectional data from 39 children with CP (18 girls and 21 boys; mean age 7.44 years; mean body weight 24.76 kg; mean body mass index 15.97 kg/m2; hemiplegic or diplegic CP). The participants wore an accelerometer (ActiGraph) for five days to measure HPA, physical activity energy expenditure (kcal/kg/d), sedentary physical activity (%SPA), light physical activity, moderate-to-vigorous physical activity (%MVPA), and activity counts (counts/min). Participants underwent cardiopulmonary exercise tests on a treadmill using a modified Naughton protocol. Linear regression and correlation analyses were performed. p-value (two-tailed) \u3c 0.05 was considered statistically significant. Age was positively associated with SPA. MVPA negatively correlated with resting heart rate (HR), and activity counts were negatively correlated with resting HR. In conclusion, our study found strong evidence of a negative association between HPA and age in school-aged children with CP. It highlights the importance of creating and improving recreational opportunities that promote physical activity in all children with CP, regardless of whether they are considered therapeutic

Third-order exceptional point in an ion-cavity system

We investigate a scheme for observing the third-order exceptional point (EP3) in an ion-cavity setting. In the lambda-type level configuration, the ion is driven by a pump field, and the resonator is probed with another weak laser field. We exploit the highly asymmetric branching ratio of an ion's excited state to satisfy the weak-excitation limit, which allows us to construct the non-Hermitian Hamiltonian $(H_{\textrm{nH}})$. Via fitting the cavity-transmission spectrum, the eigenvalues of $H_{\textrm{nH}}$ are obtained. The EP3 appears at a point where the Rabi frequency of the pump laser and the atom-cavity coupling constant balance the loss rates of the system. Feasible experimental parameters are provided.Comment: 9 pages, 6 figure

Interface Structure in Li-Metal/[Pyr_(14)][TFSI]-Ionic Liquid System from Ab Initio Molecular Dynamics Simulations

Ionic liquids (ILs) are promising materials for application in a new generation of Li batteries. They can be used as electrolyte or interlayer or incorporated into other materials. ILs have the ability to form a stable solid electrochemical interface (SEI), which plays an important role in protecting the Li-based electrode from oxidation and the electrolyte from extensive decomposition. Experimentally, it is hardly possible to elicit fine details of the SEI structure. To remedy this situation, we have performed a comprehensive computational study (density functional theory-based molecular dynamics) to determine the composition and structure of the SEI compact layer formed between the Li anode and [Pyr_(14)][TFSI] IL. We found that the [TFSI] anions quickly reacted with Li and decomposed, unlike the [Pyr_(14)] cations which remained stable. The obtained SEI compact layer structure is nonhomogeneous and consists of the atomized S, N, O, F, and C anions oxidized by Li atoms

Comparing the Performance of Machine Learning and Deep Learning Algorithms in Wastewater Treatment Process

This study assessed the performance of single and modified algorithms based on machine learning and deep learning for wastewater treatment process. More specifically, this study adopted support vector machine (SVM), random forest (RF), and artificial neural network (ANN) for machine learning as well as long short-term memory (LSTM) for deep learning. The performance of these (single) algorithms were compared with that of modified ones processed through hyperparameter tuning, ensemble learning (only for machine learning), and multi-layer stacking (i.e., two layers of LSTM units). The daily effluent of wastewater treatment process observed between 2017 and 2022 in the Cheong-Ju National Industrial Complex was used as input to all tested algorithms, which was evaluated with respect to mean squared error. For the model performance evaluation, discharge and biochemical oxygen demand are selected as dependent variables out of nine measured parameters. Results showed that the performance of any machine learning algorithms was superior to their competitor LSTM. This is mainly attributed to a small amount of input data provided to the LSTM algorithm and unstable effluent wastewater characteristics. Meanwhile, hyperparameter tuning improved the performance of all tested algorithms. However, ensemble learning for machine learning and two-layer stacking for LSTM generally resulted in performance degradation as compared to that of single algorithms, regardless of dependent variables. Therefore, this calls for a careful design and evaluation of modified algorithms, specifically for model architecture and performance improvement processes

Application of airborne hyperspectral imagery to retrieve spatiotemporal CDOM distribution using machine learning in a reservoir

Colored dissolved organic matter (CDOM) in inland waters is used as a proxy to estimate dissolved organic carbon (DOC) and may be a key indicator of water quality and nutrient enrichment. CDOM is optically active fraction of DOC so that remote sensing techniques can remotely monitor CDOM with wide spatial coverage. However, to effectively retrieve CDOM using optical algorithms, it may be critical to select the absorption co-efficient at an appropriate wavelength as an output variable and to optimize input reflectance wavelengths. In this study, we constructed a CDOM retrieval model using airborne hyperspectral reflectance data and a machine learning model such as random forest. We evaluated the best combination of input wavelength bands and the CDOM absorption coefficient at various wavelengths. Seven sampling events for airborne hyperspectral imagery and CDOM absorption coefficient data from 350 nm to 440 nm over two years (2016-2017) were used, and the collected data helped train and validate the random forest model in a freshwater reservoir. An absorption co-efficient of 355 nm was selected to best represent the CDOM concentration. The random forest exhibited the best performance for CDOM estimation with an R2 of 0.85, Nash-Sutcliffe efficiency of 0.77, and percent bias of 3.88, by using a combination of three reflectance bands: 475, 497, and 660 nm. The results show that our model can be utilized to construct a CDOM retrieving algorithm and evaluate its spatiotemporal variation across a reservoir

Interface Structure in Li-Metal/[Pyr_(14)][TFSI]-Ionic Liquid System from Ab Initio Molecular Dynamics Simulations

Ionic liquids (ILs) are promising materials for application in a new generation of Li batteries. They can be used as electrolyte or interlayer or incorporated into other materials. ILs have the ability to form a stable solid electrochemical interface (SEI), which plays an important role in protecting the Li-based electrode from oxidation and the electrolyte from extensive decomposition. Experimentally, it is hardly possible to elicit fine details of the SEI structure. To remedy this situation, we have performed a comprehensive computational study (density functional theory-based molecular dynamics) to determine the composition and structure of the SEI compact layer formed between the Li anode and [Pyr_(14)][TFSI] IL. We found that the [TFSI] anions quickly reacted with Li and decomposed, unlike the [Pyr_(14)] cations which remained stable. The obtained SEI compact layer structure is nonhomogeneous and consists of the atomized S, N, O, F, and C anions oxidized by Li atoms

Sclerostin inhibits Wnt signaling through tandem interaction with two LRP6 ectodomains

Low-density lipoprotein receptor-related protein 6 (LRP6) is a coreceptor of the beta -catenin-dependent Wnt signaling pathway. The LRP6 ectodomain binds Wnt proteins, as well as Wnt inhibitors such as sclerostin (SOST), which negatively regulates Wnt signaling in osteocytes. Although LRP6 ectodomain 1 (E1) is known to interact with SOST, several unresolved questions remain, such as the reason why SOST binds to LRP6 E1E2 with higher affinity than to the E1 domain alone. Here, we present the crystal structure of the LRP6 E1E2-SOST complex with two interaction sites in tandem. The unexpected additional binding site was identified between the C-terminus of SOST and the LRP6 E2 domain. This interaction was confirmed by in vitro binding and cell-based signaling assays. Its functional significance was further demonstrated in vivo using Xenopus laevis embryos. Our results provide insights into the inhibitory mechanism of SOST on Wnt signaling. The low-density lipoprotein receptor-related protein 6 (LRP6) is a co-receptor of the beta -catenin-dependent Wnt signaling pathway and interacts with the Wnt inhibitor sclerostin (SOST). Here the authors present the crystal structure of SOST in complex with the LRP6 E1E2 ectodomain construct, which reveals that the SOST C-terminus binds to the LRP6 E2 domain, and further validate this binding site with in vitro and in vivo experiments.Y

South College District Redevelopment Plan, Bryan, TX

The site, South College Corridor District, is located between the boarder of the city of Bryan and College Station where Texas A&M University is placed. South College Corridor has been served as a major throughfare to connect Texas A&M University and Downtown Bryan. In 1910, the City built a trolley system along South College Avenue.Along with the growth of Texas A&M University and its expansion toward Texas Avenue, TX6, and University Avenue, South College Avenue has lost much of its glory as a destination point. The district has been mainly developed for single family housing units, mobile homes, and few restaurants and bars. However, recent private development projects with mixed-use buildings and apartment complexes nearby will change the topography of this area. To provide a big picture and guide future development in this area, students were created redevelopment plans for several parts of the whole community.Texas A&M University, Texas Target Communities, Yunmi Par

Liberty County Strategic Plan 2016 - 2036

In the fall of 2015, the County of Liberty and Texas Target Communities partnered to create a task force to represent the community. The task force was integral to the planning process, contributing the thoughts, desires, and opinions of community members—as well as their enthusiasm about Liberty’s future. This fourteen-month planning process ended in August 2016. The result of this collaboration is the County of Liberty Strategic Plan, which is the official policy guide for the community’s growth over the next twenty years.Liberty Strategic Plan 2036 provides a guide for the future growth of the county. This document was developed by Texas Target Communities in partnership with the County of Liberty