1st Place in ICCV 2023 Workshop Challenge Track 1 on Resource Efficient Deep Learning for Computer Vision: Budgeted Model Training Challenge

The budgeted model training challenge aims to train an efficient classification model under resource limitations. To tackle this task in ImageNet-100, we describe a simple yet effective resource-aware backbone search framework composed of profile and instantiation phases. In addition, we employ multi-resolution ensembles to boost inference accuracy on limited resources. The profile phase obeys time and memory constraints to determine the models' optimal batch-size, max epochs, and automatic mixed precision (AMP). And the instantiation phase trains models with the determined parameters from the profile phase. For improving intra-domain generalizations, the multi-resolution ensembles are formed by two-resolution images with randomly applied flips. We present a comprehensive analysis with expensive experiments. Based on our approach, we win first place in International Conference on Computer Vision (ICCV) 2023 Workshop Challenge Track 1 on Resource Efficient Deep Learning for Computer Vision (RCV).Comment: ICCV 2023 Workshop Challenge Track 1 on RC

Efficient and Moisture-Stable Inverted Perovskite Solar Cells via n-Type Small-Molecule-Assisted Surface Treatment

Defect states at the surface and grain boundaries of perovskite films have been known to be major determinants impairing the optoelectrical properties of perovskite films and the stability of perovskite solar cells (PeSCs). Herein, an n-type conjugated small-molecule additive based on fused-unit dithienothiophen[3,2-b]-pyrrolobenzothiadiazole-core (JY16) is developed for efficient and stable PeSCs, where JY16 possesses the same backbone as the widely used Y6 but with long-linear n-hexadecyl side chains rather than branched side chains. Upon introducing JY16 into the perovskite films, the electron-donating functional groups of JY16 passivate defect states in perovskite films and increase the grain size of perovskite films through Lewis acid-base interactions. Compared to Y6, JY16 exhibits superior charge mobility owing to its molecular packing ability and prevents decomposition of perovskite films under moisture conditions owing to their hydrophobic characteristics, improving the charge extraction ability and moisture stability of PeSCs. Consequently, the PeSC with JY16 shows a high power conversion efficiency of 21.35%, which is higher than those of the PeSC with Y6 (20.12%) and without any additive (18.12%), and outstanding moisture stability under 25% relative humidity, without encapsulation. The proposed organic semiconducting additive will prove to be crucial for achieving highly efficient and moisture stable PeSCs

The Contribution of Coniferous Canopy to the Molecular Diversity of Dissolved Organic Matter in Rainfall

Rainwater interacts with tree canopies in forest ecosystems, which greatly influence its quality. However, little information is available regarding how tree canopies influence dissolved organic matter (DOM) in rainwater. To examine this, we collected bulk deposition (rainfall) and throughfall in a conifer (Chamaecyparis obtusa) plantation, western Japan, during a rain event, and analyzed their DOM molecular compositions using ultrahigh-resolution Fourier transform ion cyclotron resonance mass spectrometry. The dissolved organic carbon flux and the number of DOM molecular species detected were approximately seven times and three times higher in throughfall than in rainfall, respectively. We found that the average proportion of molecular species shared between five sample replicates was larger in throughfall (69%) than in rainfall (50%). Nonmetric multidimensional scaling revealed that the molecular species were significantly differentiated between throughfall and rainfall, and the dissimilarity among the replicates was much smaller in throughfall. This indicates that the quality of DOM in rainwater became spatially homogeneous due to contact with tree canopies. The number of lignin-like molecules was larger than those of any other biomolecular compounds in throughfall and seven times larger than in rainfall, suggesting that many of plant-derived DOM molecules were dissolved into rainwater

Application of the Reformulated Gash Analytical Model for Rainfall Interception Loss to Unmanaged High-Density Coniferous Plantations Laden with Dead Branches

Interception loss (IL) by the forest canopy removes a substantial quantity of rainwater within forested ecosystems. The large-scale unmanaged Japanese coniferous plantations with high stand density (SD) in Japan raise concerns about an additional increasing IL as a result of a new influential factor of dead branches under canopies. Thus, evaluating the usage of IL estimation models is vital to regulating the water and environment in such coniferous plantations. This study aimed to examine the applicability of the reformulated Gash analytical model (RGAM) to unmanaged coniferous plantations with high SD laden with dead branches. We established two plots (P1 and P2) laden with dead branches under the same SD of 2250 stems ha−1 but with different numbers of dead branches (56 vs. 47 branches per tree) in an unmanaged Japanese coniferous plantation. Results demonstrated that a large difference was found in canopy storage capacity (S) in P1 and P2 (3.94 vs. 3.25 mm), which was influenced by the different number of dead branches; therefore, the IL ratio to gross rainfall differed considerably (32.7% in P1 and 26.7% in P2) regardless of the SD being the same. The difference in S enables the RGAM to reflect the influence of dead branch structures on IL, leading to an acceptable RGAM performance for both P1 and P2 (“fair” IL relative errors: −20.2% vs. −16.1%) in the present study of unmanaged coniferous plantations with high SD laden with dead branches

CNN-Based Adaptive Source Node Identifier for Controller Area Network (CAN)

The controller area network (CAN) is the de facto standard for in-vehicle networks. Numerous vehicles are equipped with a CAN for the networking of electronic control units. Since the CAN has no intrinsic secured identification protocol, however, it is vulnerable to security attacks. In this paper, we propose a convolution neural network (CNN)-based message source identifier. Since the proposed scheme uses the physical characteristics of the CAN bus channel, it can be implemented without modification of the CAN protocol.1

Highly Efficient Organic Photovoltaics Enhanced Using Organic Passivation Layer Vacuum Deposition

Despite the tremendous development of various high-performing photoactive layers in organic photovoltaic (OPVs) cells, improving their performance remains the most important challenge in the field. Here, an effective and compatible strategy (i.e., the concept of vacuum deposition of an organic passivation layer (OPL) on the photoactive layer) is presented to enhance the efficiency of the state-of-the-art photoactive systems, where easy-deposition processable T2-ORH and T2-CNORH OPLs are used. After the deposition process, T2-ORH forms 2D-like edge-on crystalline structure, and the 3D-like face-on crystalline growth is induced in T2-CNORH. Resulting from its relatively higher crystalline features and increased wettability with the cathode interfacial material, the performance of T2-CNORH-deposited OPVs with both small and the scaled-up areas surpass devices without OPL and with T2-ORH. Experimental studies are conducted linking conductivity, electroluminescence quantum efficiency, carrier transport, and recombination dynamics to find the reasons for the performance difference. Furthermore, by applying the T2-CNORH to other photoactive platforms, the efficiencies are enhanced by 4.4-9.0% relative to those of the corresponding control devices; an optimal 16.4% efficiency is achieved, which validates its great applicability for photoactive layers that will be developed in the near future