SoK: Fully Homomorphic Encryption Accelerators

Fully Homomorphic Encryption~(FHE) is a key technology enabling privacy-preserving computing. However, the fundamental challenge of FHE is its inefficiency, due primarily to the underlying polynomial computations with high computation complexity and extremely time-consuming ciphertext maintenance operations. To tackle this challenge, various FHE accelerators have recently been proposed by both research and industrial communities. This paper takes the first initiative to conduct a systematic study on the 14 FHE accelerators -- cuHE/cuFHE, nuFHE, HEAT, HEAX, HEXL, HEXL-FPGA, 100$\times$, F1, CraterLake, BTS, ARK, Poseidon, FAB and TensorFHE. We first make our observations on the evolution trajectory of these existing FHE accelerators to establish a qualitative connection between them. Then, we perform testbed evaluations of representative open-source FHE accelerators to provide a quantitative comparison on them. Finally, with the insights learned from both qualitative and quantitative studies, we discuss potential directions to inform the future design and implementation for FHE accelerators

Detecting Sequential Genre Change in Eighteenth-Century Texts

Machine classification of historical books into genres is a common task for NLP-based classifiers and has a number of applications, from literary analysis to information retrieval. However it is not a straightforward task, as genre labels can be ambiguous and subject to temporal change, and moreoever many books consist of mixed or miscellaneous genres. In this paper we describe a work-in-progress method by which genre predictions can be used to determine longer sequences of genre change within books, which we test out with visualisations of some hand-picked texts. We apply state-of-the-art methods to the task, including a BERT-based transformer and character-level Perceiver model, both pre-trained on a large collection of eighteenth century works (ECCO), using a new set of hand-annotated documents created to reflect historical divisions. Results show that both models perform significantly better than a linear baseline, particularly when ECCO-BERT is combined with tfidf features, though for this task the character-level model provides no obvious advantage. Initial evaluation of the genre sequence method shows it may in the future be useful in determining and dividing the multiple genres of miscellaneous and hybrid historical texts.</p

Detecting Sequential Genre Change in Eighteenth-Century Texts

Machine classification of historical books into genres is a common task for NLP-based classifiers and has a number of applications, from literary analysis to information retrieval. However it is not a straightforward task, as genre labels can be ambiguous and subject to temporal change, and moreoever many books consist of mixed or miscellaneous genres. In this paper we describe a work-in-progress method by which genre predictions can be used to determine longer sequences of genre change within books, which we test out with visualisations of some hand-picked texts. We apply state-of-the-art methods to the task, including a BERT-based transformer and character-level Perceiver model, both pre-trained on a large collection of eighteenth century works (ECCO), using a new set of hand-annotated documents created to reflect historical divisions. Results show that both models perform significantly better than a linear baseline, particularly when ECCO-BERT is combined with tfidf features, though for this task the character-level model provides no obvious advantage. Initial evaluation of the genre sequence method shows it may in the future be useful in determining and dividing the multiple genres of miscellaneous and hybrid historical texts.Peer reviewe

Integrating View Conditions for Image Synthesis

In the field of image processing, applying intricate semantic modifications within existing images remains an enduring challenge. This paper introduces a pioneering framework that integrates viewpoint information to enhance the control of image editing tasks. By surveying existing object editing methodologies, we distill three essential criteria, consistency, controllability, and harmony, that should be met for an image editing method. In contrast to previous approaches, our method takes the lead in satisfying all three requirements for addressing the challenge of image synthesis. Through comprehensive experiments, encompassing both quantitative assessments and qualitative comparisons with contemporary state-of-the-art methods, we present compelling evidence of our framework's superior performance across multiple dimensions. This work establishes a promising avenue for advancing image synthesis techniques and empowering precise object modifications while preserving the visual coherence of the entire composition

TiO2 Nanotube Array Sensor for Detecting the SF6 Decomposition Product SO2

The detection of partial discharge through analysis of SF6 gas components in gas-insulated switchgear, is significant for the diagnosis and assessment of the operating state of power equipment. The present study proposes the use of a TiO2 nanotube array sensor for detecting the SF6 decomposition product SO2, and the application of the anodic oxidation method for the directional growth of highly ordered TiO2 nanotube arrays. The sensor response of 10–50 ppm SO2 gas is tested, and the sensitive response mechanism is discussed. The test results show that the TiO2 nanotube sensor array has good response to SO2 gas, and by ultraviolet radiation, the sensor can remove attached components very efficiently, shorten recovery time, reduce chemical poisoning, and prolong the life of the components

Site selection and capacity determination of charging stations considering the uncertainty of users’ dynamic charging demands

Aiming at the problems of high investment and low efficiency in the planning and construction of electric vehicle (EV) charging stations in cities, an optimization model for site selection and capacity determination of charging stations considering the uncertainty of users’ dynamic charging demands is proposed. Firstly, based on the travel chain theory and the Origin-Destination (OD) matrix, the travel characteristics of EVs are studied, and the spatial and temporal distribution prediction model of EV charging load is established through the dynamic Dijkstra algorithm combined with the Monte Carlo method. Secondly, a site selection model for the charging station is established which takes the minimum annualized cost of the charging station operator and the annualized economic loss of the EV users as the goal. At the same time, the weighted Voronoi diagram and Adaptive Simulated Annealing Particle Swarm Optimization algorithm (ASPSO) are adopted to determine the optimal number/site selection and service scope of charging stations. Finally, an uncertain scenario set is introduced into the capacity determination model to describe the uncertainty of the users’ dynamic charging demands, and the robust optimization theory is utilized to solve the capacity of the charging station. A case study is carried out for the EV charging station planning problem in some urban areas of a northern city, and the validity of the model is verified

The Impact of Fertilizer Amendments on Soil Autotrophic Bacteria and Carbon Emissions in Maize Field on the Semiarid Loess Plateau

Soil autotrophic bacteria play a crucial role in regulating CO2 fixation and crop productivity. However, the information is limited to how fertilization amendments alter soil autotrophic bacterial community, crop yield, and carbon emission efficiency (CEE). Here, we estimated the impact of the structure and co-occurrence network of soil autotrophic bacterial community on maize yield and CEE. A long-term field experiment was conducted with five fertilization treatments in semiarid Loess Plateau, including no amendment (NA), chemical fertilizer (CF), chemical fertilizer plus commercial organic fertilizer (SC), commercial organic fertilizer (SM), and maize straw (MS). The results showed that fertilization amendments impacted the structure and network of soil Calvin–Benson–Bassham (CBB) (cbbL) gene-carrying bacterial community via changing soil pH and NO3–N. Compared with no amendment, the cbbL-carrying bacterial diversity was increased under the SC, SM, and MS treatments but decreased under the CF treatment. Soil autotrophic bacterial network contained distinct microbial modules that consisted of closely associated microbial species. We detected the higher abundances of soil cbbL-carrying bacterial genus Xanthobacter, Bradyrhizobium, and Nitrosospira. Structural equation modeling further suggested that the diversity, composition, and network of autotrophic bacterial community had strongly positive relationships with CEE and maize yield. Taken together, our results suggest that soil autotrophic bacterial community may drive crop productivity and CEE, and mitigate the atmospheric greenhouse effect

Characterization of domain distributions by second harmonic generation in ferroelectrics

Domain orientations and their volume ratios in ferroelectrics are recognized as a compelling topic recently for domain switching dynamics and domain stability in devices application. Here, an optimized second harmonic generation method has been explored for ferroelectric domain characterization. Combing a unique theoretical model with azimuth-polarization-dependent second harmonic generation response, the complex domain components and their distributions can be rigidly determined in ferroelectric thin films. Using the proposed model, the domain structures of rhombohedral BiFeO3 films with 71° and 109° domain wall, and, tetragonal BiFeO3, Pb(Zr0.2Ti0.8)O3, and BaTiO3 ferroelectric thin films are analyzed and the corresponding polarization variants are determined. This work could provide a powerful and all-optical method to track and evaluate the evolution of ferroelectric domains in the ferroelectric-based devices