LOHEN: Layer-wise Optimizations for Neural Network Inferences over Encrypted Data with High Performance or Accuracy

Fully Homomorphic Encryption (FHE) presents unique challenges in programming due to the contrast between traditional and FHE language paradigms. A key challenge is selecting ciphertext configurations (CCs) to achieve the desired level of security, performance, and accuracy simultaneously. Finding the design point satisfying the goal is often labor-intensive (probably impossible), for which reason previous works settle down to a reasonable CC that brings acceptable performance. When FHE is applied to neural networks (NNs), we have observed that the distinct layered architecture of NN models opens the door for a performance improvement by using layer-wise CCs, because a globally chosen CC may not be the best possible CC for every layer individually. This paper introduces LOHEN, a technique crafted to attain high performance of NN inference by enabling to use layer-wise CC efficiently. Empowered with a cryptographic gadget that allows switching between arbitrary CCs, LOHEN allocates layer-wise CCs for individual layers tailored to their structural properties, while minimizing the increased overhead incurred by CC switching with its capability to replace costly FHE operations. LOHEN can also be engineered to attain higher accuracy, yet deliver higher performance compared to state-of-the-art studies, by additionally adopting the multi-scheme techniques in a layer-wise manner. Moreover, the developers using LOHEN are given the capability of customizing the selection policy to adjust the desired levels of performance and accuracy, subject to their demands. Our evaluation shows that LOHEN improves the NN inference performance in both of these cases when compared to the state-of-the-art. When used to improve the CKKS-only inference, LOHEN improves the NN inference performance of various NNs 1.08--2.88x. LOHEN also improves the performance of mixed-scheme NN inference by 1.34--1.75x without accuracy loss. These two results along with other empirical analyses, advocate that LOHEN can widely help improve the performance of NN inference over FHE

Ultrahigh strength, modulus, and conductivity of graphitic fibers by macromolecular coalescence

Theoretical considerations suggest that the strength of carbon nanotube (CNT) fibers be exceptional; however, their mechanical performance values are much lower than the theoretical values. To achieve macroscopic fibers with ultrahigh performance, we developed a method to form multidimensional nanostructures by coalescence of individual nanotubes. The highly aligned wet-spun fibers of single- or double-walled nanotube bundles were graphitized to induce nanotube collapse and multi-inner walled structures. These advanced nanostructures formed a network of interconnected, close-packed graphitic domains. Their near-perfect alignment and high longitudinal crystallinity that increased the shear strength between CNTs while retaining notable flexibility. The resulting fibers have an exceptional combination of high tensile strength (6.57 GPa), modulus (629 GPa), thermal conductivity (482 W/m·K), and electrical conductivity (2.2 MS/m), thereby overcoming the limits associated with conventional synthetic fibers

Genetic diagnosis of inborn errors of immunity using clinical exome sequencing

Inborn errors of immunity (IEI) include a variety of heterogeneous genetic disorders in which defects in the immune system lead to an increased susceptibility to infections and other complications. Accurate, prompt diagnosis of IEI is crucial for treatment plan and prognostication. In this study, clinical utility of clinical exome sequencing (CES) for diagnosis of IEI was evaluated. For 37 Korean patients with suspected symptoms, signs, or laboratory abnormalities associated with IEI, CES that covers 4,894 genes including genes related to IEI was performed. Their clinical diagnosis, clinical characteristics, family history of infection, and laboratory results, as well as detected variants, were reviewed. With CES, genetic diagnosis of IEI was made in 15 out of 37 patients (40.5%). Seventeen pathogenic variants were detected from IEI-related genes, BTK, UNC13D, STAT3, IL2RG, IL10RA, NRAS, SH2D1A, GATA2, TET2, PRF1, and UBA1, of which four variants were previously unreported. Among them, somatic causative variants were identified from GATA2, TET2, and UBA1. In addition, we identified two patients incidentally diagnosed IEI by CES, which was performed to diagnose other diseases of patients with unrecognized IEI. Taken together, these results demonstrate the utility of CES for the diagnosis of IEI, which contributes to accurate diagnosis and proper treatments

HyperCLOVA X Technical Report

We introduce HyperCLOVA X, a family of large language models (LLMs) tailored to the Korean language and culture, along with competitive capabilities in English, math, and coding. HyperCLOVA X was trained on a balanced mix of Korean, English, and code data, followed by instruction-tuning with high-quality human-annotated datasets while abiding by strict safety guidelines reflecting our commitment to responsible AI. The model is evaluated across various benchmarks, including comprehensive reasoning, knowledge, commonsense, factuality, coding, math, chatting, instruction-following, and harmlessness, in both Korean and English. HyperCLOVA X exhibits strong reasoning capabilities in Korean backed by a deep understanding of the language and cultural nuances. Further analysis of the inherent bilingual nature and its extension to multilingualism highlights the model's cross-lingual proficiency and strong generalization ability to untargeted languages, including machine translation between several language pairs and cross-lingual inference tasks. We believe that HyperCLOVA X can provide helpful guidance for regions or countries in developing their sovereign LLMs.Comment: 44 pages; updated authors list and fixed author name

Load-follow operation capability of soluble boron-free small modular reactor ATOM

This study investigates the feasibility of Daily Load-Follow Operation (DLFO) for the Autonomous Transportable On-demand Reactor Module (ATOM), a Soluble Boron-Free (SBF) small modular reactor (SMR). The ATOM core was selected as a reference model due to its adoption of key SBF-compatible design features, including Centrally-Shielded Burnable Absorbers (CSBAs)—burnable absorbers with controlled self-shielding—and a Truly-Optimized Pressurized Water Reactor (TOP) lattice, which employs enhanced moderation to ensure favorable neutron economy and temperature feedback. Together, these features provide stable excess reactivity and favorable Moderator Temperature Coefficient (MTC) characteristics across the reactor cycle. To enable effective reactivity and axial power distribution control in such an environment, the Mode-Y control logic was applied. Mode-Y is a newly developed control strategy that relies solely on Control Element Assembly (CEA) movements and allow independent insertion of gray banks by eliminating conventional overlap constraints. A challenging DLFO scenario was simulated at three representative burnup conditions—Beginning-of-Cycle (BOC), Middle-of-Cycle (MOC), and approximately 90% End-of-Cycle (EOC)—to evaluate the performance of Mode-Y control logic. The scenario involved rapid power ramps with 50%p changes within 3 h, followed by irregular hold periods, to test the control logic under highly dynamic conditions. The analysis employed a conventional two-step approach: multigroup cross-sections were generated using the SERPENT2 Monte Carlo code with ENDF/B-VII.1 library, and whole-core transient simulations were performed using KANT nodal diffusion code. Results confirm accurate power tracking, stable Axial Shape Index (ASI) control, acceptable coolant temperature management, and sufficient nodal and pin-wise power peaking margins throughout all burnup stages