Uncertainty quantification of a radiative transfer model and a machine learning technique for use as observation operators in the assimilation of microwave observations into a land surface model to improve soil moisture and terrestrial snow

Soil moisture and terrestrial snow mass are two important hydrological states needed to accurately quantify terrestrial water storage and streamflow. Soil moisture and terrestrial snow mass can be measured using ground-based instrument networks, estimated using advanced land surface models, and retrieved via satellite imagery. However, each method has its own inherent sources of error and uncertainty. This leads to the application of data assimilation to obtain optimal estimates of soil moisture and snow mass. Before conducting data assimilation (DA) experiments, this dissertation explored the use of two different observation operators within a DA framework: a L-band radiative transfer model (RTM) for soil moisture and support vector machine (SVM) regression for soil terrestrial snow mass. First, L-band brightness temperature (Tb) estimated from the RTM after being calibrated against multi-angular SMOS Tb's showed good performance in both ascending and descending overpasses across North America except in regions with sub-grid scale lakes and dense forest. Detailed analysis of RTM-derived L-band Tb in terms of soil hydraulic parameters and vegetation types suggests the need for further improvement of RTM-derived Tb in regions with relatively large porosity, large wilting point, or grassland type vegetation. Secondly, a SVM regression technique was developed with explicit consideration of the first-order physics of photon scattering as a function of different training target sets, training window lengths, and delineation of snow wetness over snow-covered terrain. The overall results revealed that prediction accuracy of the SVM was strongly linked with the first-order physics of electromagnetic responses of different snow conditions. After careful evaluation of the observation operators, C-band backscatter observations over Western Colorado collected by Sentinel-1 were merged into an advanced land surface model using a SVM and a one-dimensional ensemble Kalman filter. In general, updated snow mass estimates using the Sentinel-1 DA framework showed modest improvements in comparison to ground-based measurements of snow water equivalent (SWE) and snow depth. These results motivate further application of the outlined assimilation schemes over larger regions in order to improve the characterization of the terrestrial hydrological cycle

HyPHEN: A Hybrid Packing Method and Optimizations for Homomorphic Encryption-Based Neural Networks

Convolutional neural network (CNN) inference using fully homomorphic encryption (FHE) is a promising private inference (PI) solution due to the capability of FHE that enables offloading the whole computation process to the server while protecting the privacy of sensitive user data. Prior FHE-based CNN (HCNN) work has demonstrated the feasibility of constructing deep neural network architectures such as ResNet using FHE. Despite these advancements, HCNN still faces significant challenges in practicality due to the high computational and memory overhead. To overcome these limitations, we present HyPHEN, a deep HCNN construction that incorporates novel convolution algorithms (RAConv and CAConv), data packing methods (2D gap packing and PRCR scheme), and optimization techniques tailored to HCNN construction. Such enhancements enable HyPHEN to substantially reduce the memory footprint and the number of expensive homomorphic operations, such as ciphertext rotation and bootstrapping. As a result, HyPHEN brings the latency of HCNN CIFAR-10 inference down to a practical level at 1.4 seconds (ResNet-20) and demonstrates HCNN ImageNet inference for the first time at 14.7 seconds (ResNet-18).Comment: 15 pages, 12 figure

The trinity of ribosome-associated quality control and stress signaling for proteostasis and neuronal physiology

Translating ribosomes accompany co-translational regulation of nascent polypeptide chains, including subcellular targeting, protein folding, and covalent modifications. Ribosome-associated quality control (RQC) is a co-translational surveillance mechanism triggered by ribosomal collisions, an indication of atypical translation. The ribosome-associated E3 ligase ZNF598 ubiquitinates small subunit proteins at the stalled ribosomes. A series of RQC factors are then recruited to dissociate and triage aberrant translation intermediates. Regulatory ribosomal stalling may occur on endogenous transcripts for quality gene expression, whereas ribosomal collisions are more globally induced by ribotoxic stressors such as translation inhibitors, ribotoxins, and UV radiation. The latter are sensed by ribosome-associated kinases GCN2 and ZAKa, activating integrated stress response (ISR) and ribotoxic stress response (RSR), respectively. Hierarchical crosstalks among RQC, ISR, and RSR pathways are readily detectable since the collided ribosome is their common substrate for activation. Given the strong implications of RQC factors in neuronal physiology and neurological disorders, the interplay between RQC and ribosome-associated stress signaling may sustain proteostasis, adaptively deterrnine cell fate, and contribute to neural pathogenesis. The elucidation of underlying molecular principles in relevant human diseases should thus provide unexplored therapeutic opportunities

Reflective Filters Design for Self-Filtering Narrowband Ultraviolet Imaging Experiment Wide-Field Surveys (NUVIEWS) Project

We report the design of multilayer reflective filters for the self-filtering cameras of the NUVIEWS project. Wide angle self-filtering cameras were designed to image the C IV (154.9 nm) line emission, and H2 Lyman band fluorescence (centered at 161 nm) over a 20 deg x 30 deg field of view. A key element of the filter design includes the development of pi-multilayers optimized to provide maximum reflectance at 154.9 nm and 161 nm for the respective cameras without significant spectral sensitivity to the large cone angle of the incident radiation. We applied self-filtering concepts to design NUVIEWS telescope filters that are composed of three reflective mirrors and one folding mirror. The filters with narrowband widths of 6 and 8 rim at 154.9 and 161 nm, respectively, have net throughputs of more than 50 % with average blocking of out-of-band wavelengths better than 3 x 10(exp -4)%

NeuJeans: Private Neural Network Inference with Joint Optimization of Convolution and Bootstrapping

Fully homomorphic encryption (FHE) is a promising cryptographic primitive for realizing private neural network inference (PI) services by allowing a client to fully offload the inference task to a cloud server while keeping the client data oblivious to the server. This work proposes NeuJeans, an FHE-based solution for the PI of deep convolutional neural networks (CNNs). NeuJeans tackles the critical problem of the enormous computational cost for the FHE evaluation of convolutional layers (conv2d), mainly due to the high cost of data reordering and bootstrapping. We first propose an encoding method introducing nested structures inside encoded vectors for FHE, which enables us to develop efficient conv2d algorithms with reduced data reordering costs. However, the new encoding method also introduces additional computations for conversion between encoding methods, which could negate its advantages. We discover that fusing conv2d with bootstrapping eliminates such computations while reducing the cost of bootstrapping. Then, we devise optimized execution flows for various types of conv2d and apply them to end-to-end implementation of CNNs. NeuJeans accelerates the performance of conv2d by up to 5.68 times compared to state-of-the-art FHE-based PI work and performs the PI of a CNN at the scale of ImageNet (ResNet18) within a mere few secondsComment: 16 pages, 9 figure

Multilayer Thin Film Polarizer Design for Far Ultraviolet using Induced Transmission and Absorption Technique

Good theoretical designs of far ultraviolet polarizers have been reported using a MgF2/Al/MgF2 three layer structure on a thick Al layer as a substrate. The thicknesses were determined to induce transmission and absorption of p-polarized light. In these designs Al optical constants were used from films produced in ultrahigh vacuum (UHV: 10(exp -10) torr). Reflectance values for polarizers fabricated in a conventional high vacuum (p approx. 10(exp -6 torr)) using the UHV design parameters differed dramatically from the design predictions. Al is a highly reactive material and is oxidized even in a high vacuum chamber. In order to solve the problem other metals have been studied. It is found that a larger reflectance difference is closely related to higher amplitude and larger phase difference of Fresnel reflection coefficients between two polarizations at the boundary of MgF2/metal. It is also found that for one material a larger angle of incidence from the surface normal brings larger amplitude and phase difference. Be and Mo are found good materials to replace Al. Polarizers designed for 121.6 nm with Be at 60 deg and with Mo at 70 deg are shown as examples

Doubly open-ended TiO2 nanotube arrays decorated with a few nm-sized TiO2 nanoparticles for highly efficient dye-sensitized solar cells

Doubly open-ended TiO2 nanotube (NT) arrays decorated with a few nm-sized TiO2 nanoparticles (sNP@NT hybrid structure) were prepared and used as charge-collecting photoelectrodes in dye-sensitized solar cells (DSCs). The TiO2 nanoparticles (NPs) on the NT array surfaces increased the dye loading on the sNP@NT-based DSCs by 9% compared to the undecorated NT-based DSCs, thereby enhancing the light harvesting capabilities. The power conversion efficiency (PCE) of the sNP@NT-based DSC prepared with 11 mu m thick NT arrays was 10.0%, which constituted a 47% improvement over the corresponding NT-based DSCs (which displayed a 6.8% PCE). Despite having a dye loading level that was 22% lower than the dye loading level in the conventional TiO2 NP-based DSCs, due to the limited internal surface area, the PCE of the sNP@NT-based DSC was 28% greater than that of the conventional TiO2 NP-based DSC (8.1% PCE) prepared with a light scattering layer. The high charge collection efficiency of the NT array and the good photovoltaic performance set a new record for efficiency among NT-based DSCs.open111313sciescopu

Toward Practical Privacy-Preserving Convolutional Neural Networks Exploiting Fully Homomorphic Encryption

Incorporating fully homomorphic encryption (FHE) into the inference process of a convolutional neural network (CNN) draws enormous attention as a viable approach for achieving private inference (PI). FHE allows delegating the entire computation process to the server while ensuring the confidentiality of sensitive client-side data. However, practical FHE implementation of a CNN faces significant hurdles, primarily due to FHE's substantial computational and memory overhead. To address these challenges, we propose a set of optimizations, which includes GPU/ASIC acceleration, an efficient activation function, and an optimized packing scheme. We evaluate our method using the ResNet models on the CIFAR-10 and ImageNet datasets, achieving several orders of magnitude improvement compared to prior work and reducing the latency of the encrypted CNN inference to 1.4 seconds on an NVIDIA A100 GPU. We also show that the latency drops to a mere 0.03 seconds with a custom hardware design.Comment: 3 pages, 1 figure, appears at DISCC 2023 (2nd Workshop on Data Integrity and Secure Cloud Computing, in conjunction with the 56th International Symposium on Microarchitecture (MICRO 2023)