Learning context-aware adaptive solvers to accelerate quadratic programming

Convex quadratic programming (QP) is an important sub-field of mathematical optimization. The alternating direction method of multipliers (ADMM) is a successful method to solve QP. Even though ADMM shows promising results in solving various types of QP, its convergence speed is known to be highly dependent on the step-size parameter $\rho$. Due to the absence of a general rule for setting $\rho$, it is often tuned manually or heuristically. In this paper, we propose CA-ADMM (Context-aware Adaptive ADMM)) which learns to adaptively adjust $\rho$ to accelerate ADMM. CA-ADMM extracts the spatio-temporal context, which captures the dependency of the primal and dual variables of QP and their temporal evolution during the ADMM iterations. CA-ADMM chooses $\rho$ based on the extracted context. Through extensive numerical experiments, we validated that CA-ADMM effectively generalizes to unseen QP problems with different sizes and classes (i.e., having different QP parameter structures). Furthermore, we verified that CA-ADMM could dynamically adjust $\rho$ considering the stage of the optimization process to accelerate the convergence speed further.Comment: 9 pages, 4 figure

Structural characterization of the Fddd phase in a diblock copolymer thin film by electron microtomography

A 3-dimensional Fddd network structure of a polystyrene-block-polyisoprene (PS-b-PI) diblock copolymer (M(n) = 31 500, f(PI) = 0.645) was observed for the first time in real space by transmission electron microtomography (TEMT). In a 650 nm thick film of the PS-b-PI thin film on a silicon wafer, the Fddd phase was developed after annealing at 215 degrees C for 24 h. The single network structure consists of the connected tripodal units of minor PS block domains. The {111}(Fddd) plane, the densest plane of the minor PS phase, was found to orient parallel to the film plane. The transitional structure from the wetting layer at the free surface to the internal {111}(Fddd) plane via a perforated layer structure was also observed.X111313sciescopu

High-Throughput Deep Convolutional Neural Networks on Fully Homomorphic Encryption Using Channel-By-Channel Packing

Secure Machine Learning as a Service is a viable solution where clients seek secure delegation of the ML computation while protecting their sensitive data. We propose an efficient method to securely evaluate deep standard convolutional neural networks based on CKKS fully homomorphic encryption, in the manner of batch inference. In this paper, we introduce a packing method called Channel-by-Channel Packing that maximizes the slot compactness and single-instruction-multipledata capabilities in ciphertexts. Along with further optimizations such as lazy rescaling, lazy Baby-Step Giant-Step, and ciphertext level management, we could significantly reduce the computational cost of standard ResNet inference. Simulation results show that our work has improvements in amortized time by 5.04× (from 79.46s to 15.76s) and 5.20×(from 455.56s to 87.60s) for ResNet-20 and ResNet-110, compared to the previous best results, resp. We also got a dramatic reduction in memory usage for rotation keys from several hundred GBs to 6.91GB, which is about 38× smaller than the previous result

A microfluidic chip for screening individual cancer cells via eavesdropping on autophagyinducing crosstalk in the stroma niche

Autophagy is a cellular homeostatic mechanism where proteins and organelles are digested and recycled to provide an alternative source of building blocks and energy to cells. The role of autophagy in cancer microenvironment is still poorly understood. Here, we present a microfluidic system allowing monitoring of the crosstalk between single cells. We used this system to study how tumor cells induced autophagy in the stromal niche. Firstly, we could confirm that transforming growth factor beta 1 (TGF beta 1) secreted from breast tumor cells is a paracrine mediator of tumor-stroma interaction leading to the activation of autophagy in the stroma component fibroblasts. Through proof of concept experiments using TGF beta 1 as a model factor, we could demonstrate real time monitoring of autophagy induction in fibroblasts by single tumor cells. Retrieval of individual tumor cells from the microfluidic system and their subsequent genomic analysis was possible, allowing us to determine the nature of the factor mediating tumor-stroma interactions. Therefore, our microfluidic platform might be used as a promising tool for quantitative investigation of tumor-stroma interactions, especially for and high-throughput screening of paracrine factors that are secreted from heterogeneous tumor cell populations

A Smart Checkpointing Scheme for Improving the Reliability of Clustering Routing Protocols

In wireless sensor networks, system architectures and applications are designed to consider both resource constraints and scalability, because such networks are composed of numerous sensor nodes with various sensors and actuators, small memories, low-power microprocessors, radio modules, and batteries. Clustering routing protocols based on data aggregation schemes aimed at minimizing packet numbers have been proposed to meet these requirements. In clustering routing protocols, the cluster head plays an important role. The cluster head collects data from its member nodes and aggregates the collected data. To improve reliability and reduce recovery latency, we propose a checkpointing scheme for the cluster head. In the proposed scheme, backup nodes monitor and checkpoint the current state of the cluster head periodically. We also derive the checkpointing interval that maximizes reliability while using the same amount of energy consumed by clustering routing protocols that operate without checkpointing. Experimental comparisons with existing non-checkpointing schemes show that our scheme reduces both energy consumption and recovery latency

Auxin response factor 2 (ARF2) plays a major role in regulating auxin-mediated leaf longevity

Auxin regulates a variety of physiological and developmental processes in plants. Although auxin acts as a suppressor of leaf senescence, its exact role in this respect has not been clearly defined, aside from circumstantial evidence. It was found here that ARF2 functions in the auxin-mediated control of Arabidopsis leaf longevity, as discovered by screening EMS mutant pools for a delayed leaf senescence phenotype. Two allelic mutations, ore14-1 and 14-2, caused a highly significant delay in all senescence parameters examined, including chlorophyll content, the photochemical efficiency of photosystem II, membrane ion leakage, and the expression of senescence-associated genes. A delay of senescence symptoms was also observed under various senescence-accelerating conditions, where detached leaves were treated with darkness, phytohormones, or oxidative stress. These results indicate that the gene defined by these mutations might be a key regulatory genetic component controlling functional leaf senescence. Map-based cloning of ORE14 revealed that it encodes ARF2, a member of the auxin response factor (ARF) protein family, which modulates early auxin-induced gene expression in plants. The ore14/arf2 mutation also conferred an increased sensitivity to exogenous auxin in hypocotyl growth inhibition, thereby demonstrating that ARF2 is a repressor of auxin signalling. Therefore, the ore14/arf2 lesion appears to cause reduced repression of auxin signalling with increased auxin sensitivity, leading to delayed senescence. Altogether, our data suggest that ARF2 positively regulates leaf senescence in Arabidopsis

Deep learning-based statistical noise reduction for multidimensional spectral data

In spectroscopic experiments, data acquisition in multi-dimensional phase space may require long acquisition time, owing to the large phase space volume to be covered. In such case, the limited time available for data acquisition can be a serious constraint for experiments in which multidimensional spectral data are acquired. Here, taking angle-resolved photoemission spectroscopy (ARPES) as an example, we demonstrate a denoising method that utilizes deep learning as an intelligent way to overcome the constraint. With readily available ARPES data and random generation of training data set, we successfully trained the denoising neural network without overfitting. The denoising neural network can remove the noise in the data while preserving its intrinsic information. We show that the denoising neural network allows us to perform similar level of second-derivative and line shape analysis on data taken with two orders of magnitude less acquisition time. The importance of our method lies in its applicability to any multidimensional spectral data that are susceptible to statistical noise.Comment: 8 pages, 8 figure

A cooperative biphasic MoOx–MoPx promoter enables a fast-charging lithium-ion battery

The realisation of fast-charging lithium-ion batteries with long cycle lifetimes is hindered by the uncontrollable plating of metallic Li on the graphite anode during high-rate charging. Here we report that surface engineering of graphite with a cooperative biphasic MoOx–MoPx promoter improves the charging rate and suppresses Li plating without compromising energy density. We design and synthesise MoOx–MoPx/graphite via controllable and scalable surface engineering, i.e., the deposition of a MoOx nanolayer on the graphite surface, followed by vapour-induced partial phase transformation of MoOx to MoPx. A variety of analytical studies combined with thermodynamic calculations demonstrate that MoOx effectively mitigates the formation of resistive films on the graphite surface, while MoPx hosts Li+ at relatively high potentials via a fast intercalation reaction and plays a dominant role in lowering the Li+ adsorption energy. The MoOx–MoPx/graphite anode exhibits a fast-charging capability (<10 min charging for 80% of the capacity) and stable cycling performance without any signs of Li plating over 300 cycles when coupled with a LiNi0.6Co0.2Mn0.2O2 cathode. Thus, the developed approach paves the way to the design of advanced anode materials for fast-charging Li-ion batteries. © 2021, The Author(s).1