Efficient Hardware RNS Decomposition for Post-Quantum Signature Scheme FALCON

The recently announced National Institute of Standards and Technology (NIST) Post-quantum cryptography (PQC) third-round standardization process has released its candidates to be standardized and Falcon is one of them. On the other hand, however, very few hardware implementation works for Falcon have been released due to its very complicated computation procedure and intensive complexity. With this background, in this paper, we propose an efficient hardware structure to implement residue numeral system (RNS) decomposition within NTRUSolve (a key arithmetic component for key generation of Falcon). In total, we have proposed three stages of coherent interdependent efforts to finish the proposed work. First, we have identified the necessary algorithmic operation related to RNS decomposition. Then, we have innovatively designed a hardware structure to realize these algorithms. Finally, field-programmable gate array (FPGA)-based implementation has been carried out to verify the superior performance of the proposed hardware structure. For instance, the proposed hardware design involves at least 3.91x faster operational time than the software implementation. To the authors\u27 best knowledge, this is the first paper about the hardware acceleration of RNS decomposition for Falcon, and we hope the outcome of this work will facilitate the research in this area

Quantifying the retention of emotions across story retellings

Story retelling is a fundamental medium for the transmission of information between individuals and among social groups. Besides conveying factual information, stories also contain affective information. Though natural language processing techniques have advanced considerably in recent years, the extent to which machines can be trained to identify and track emotions across retellings is unknown. This study leverages the powerful RoBERTa model, based on a transformer architecture, to derive emotion-rich story embeddings from a unique dataset of 25,728 story retellings. The initial stories were centered around five emotional events (joy, sadness, embarrassment, risk, and disgust—though the stories did not contain these emotion words) and three intensities (high, medium, and low). Our results indicate (1) that RoBERTa can identify emotions in stories it was not trained on, (2) that the five emotions and their intensities are preserved when they are transmitted in the form of retellings, (3) that the emotions in stories are increasingly well-preserved as they experience additional retellings, and (4) that among the five emotions, risk and disgust are least well-preserved, compared with joy, sadness, and embarrassment. This work is a first step toward quantifying situation-driven emotions with machines

Work-in-progress: High-performance systolic hardware accelerator for RBLWE-based post-quantum cryptography

Ring-Binary-Learning-with-Errors (RBLWE)-based post-quantum cryptography (PQC) is a promising scheme suitable for lightweight applications. This paper presents an efficient hardware systolic accelerator for RBLWE-based PQC, targeting highperformance applications. We have briefly given the algorithmic background for the proposed design. Then, we have transferred the proposed algorithmic operation into a new systolic accelerator. Lastly, field-programmable gate array (FPGA) implementation results have confirmed the efficiency of the proposed accelerator

Efficient hardware arithmetic for inverted binary ring-LWE based post-quantum cryptography

Ring learning-with-errors (RLWE)-based encryption scheme is a lattice-based cryptographic algorithm that constitutes one of the most promising candidates for Post-Quantum Cryptography (PQC) standardization due to its efficient implementation and low computational complexity. Binary Ring-LWE (BRLWE) is a new optimized variant of RLWE, which achieves smaller computational complexity and higher efficient hardware implementations. In this paper, two efficient architectures based on Linear-Feedback Shift Register (LFSR) for the arithmetic used in Inverted Binary Ring-LWE (InvBRLWE)-based encryption scheme are presented, namely the operation of A center dot B+C over the polynomial ring ${Z}_q/(xn+1)$ . The first architecture optimizes the resource usage for major computation and has a novel input processing setup to speed up the overall processing latency with minimized input loading cycles. The second architecture deploys an innovative serial-in serial-out processing format to reduce the involved area usage further yet maintains a regular input loading time-complexity. Experimental results show that the architectures presented here improve the complexities obtained by competing schemes found in the literature, e.g., involving 71.23% less area-delay product than recent designs. Both architectures are highly efficient in terms of area-time complexities and can be extended for deploying in different lightweight application environments

CASA: A Compact and Scalable Accelerator for Approximate Homomorphic Encryption

Approximate arithmetic-based homomorphic encryption (HE) scheme CKKS [CKKS17] is arguably the most suitable one for real-world data-privacy applications due to its wider computation range than other HE schemes such as BGV [BGV14], FV and BFV [Bra12, FV12]. However, the most crucial homomorphic operation of CKKS called key-switching induces a great amount of computational burden in actual deployment situations, and creates scalability challenges for hardware acceleration. In this paper, we present a novel Compact And Scalable Accelerator (CASA) for CKKS on the field-programmable gate array (FPGA) platform. The proposed CASA addresses the aforementioned computational and scalability challenges in homomorphic operations, including key-exchange, homomorphic multiplication, homomorphic addition, and rescaling. On the architecture layer, we propose a new design methodology for efficient acceleration of CKKS. We design this novel hardware architecture by carefully studying the homomorphic operation patterns and data dependency amongst the primitive oracles. The homomorphic operations are efficiently mapped into an accelerator with simple control and smooth operation, which brings benefits for scalable implementation and enhanced pipeline and parallel processing (even with the potential for further improvement). On the component layer, we carry out a detailed and extensive study and present novel micro-architectures for primitive function modules, including memory bank, number theoretic transform (NTT) module, modulus switching bank, and dyadic multiplication and accumulation. On the arithmetic layer, we develop a new partially reduction-free modular arithmetic technique to eliminate part of the reduction cost over different prime moduli within the moduli chain of the Residue Number System (RNS). The proposed structure can support arbitrary numbers of security primes of CKKS during key exchange, which offers better security options for adopting the scalable design methodology. As a proof-of-concept, we implement CASA on the FPGA platform and compare it with state-of-the-art designs. The implementation results showcase the superior performance of the proposed CASA in many aspects such as compact area, scalable architecture, and overall better area-time complexities. In particular, we successfully implement CASA on a mainstream resource-constrained Artix-7 FPGA. To the authors’ best knowledge, this is the first compact CKKS accelerator implemented on an Artix-7 device, e.g., CASA achieves a 10.8x speedup compared with the state-of-the-art CPU implementations (with power consumption of only 5.8%). Considering the power-delay product metric, CASA also achieves 138x and 105x improvement compared with the recent GPU implementation

Response of the root morphological structure of Fokienia hodginsii seedlings to competition from neighboring plants in a heterogeneous nutrient environment

IntroductionCritical changes often occur in Fokienia hodginsii seedlings during the process of growth owing to differences in the surrounding environment. The most common differences are heterogeneous nutrient environments and competition from neighboring plants.MethodsIn this study, we selected one-year-old, high-quality Fokienia hodginsii seedlings as experimental materials. Three planting patterns were established to simulate different competitive treatments, and seedlings were also exposed to three heterogeneous nutrient environments and a homogeneous nutrient environment (control) to determine their effect on the root morphology and structure of F. hodginsii seedlings.ResultsHeterogeneous nutrient environments, compared with a homogeneous environment, significantly increased the dry matter accumulation and root morphology indexes of the root system of F. hodginsii, which proliferated in nutrient-rich patches, and the P heterogeneous environment had the most significant enhancement effect, with dry matter accumulation 70.2%, 7.0%, and 27.0% higher than that in homogeneous and N and K heterogeneous environments, respectively. Homogeneous environments significantly increased the specific root length and root area of the root system; the dry matter mass and morphological structure of the root system of F. hodginsii with a heterospecific neighbor were higher than those under conspecific neighbor and single-plant treatments, and the root area of the root system under the conspecific neighbor treatment was higher than that under the heterospecific neighbor treatment, by 20% and 23%, respectively. Moreover, the root system under heterospecific neighbor treatment had high sensitivity; the heterogeneous nutrient environment increased the mean diameter of the fine roots of the seedlings of F. hodginsii and the diameter of the vascular bundle, and the effect was most significant in the P heterogeneous environment, exceeding that in the N and K heterogeneous environments. The effect was most significant in the P heterogeneous environment, which increased fine root diameter by 20.5% and 10.3%, respectively, compared with the homogeneous environment; in contrast, the fine root vascular ratio was highest in the homogeneous environment, and most of the indicators of the fine root anatomical structure in the nutrient-rich patches were of greater values than those in the nutrient-poor patches in the different heterogeneous environments; competition promoted most of the indicators of the fine root anatomical structure of F. hodginsii seedlings. According a principal component analysis (PCA), the N, Pm and K heterogeneous environments with heterospecific neighbors and the P heterogeneous environment with a conspecific neighbor had higher evaluation in the calculation of eigenvalues of the PCA.DiscussionThe root dry matter accumulation, root morphology, and anatomical structure of F. hodginsii seedlings in the heterogeneous nutrient environment were more developed than those in the homogeneous nutrient environment. The effect of the P heterogeneous environment was the most significant. The heterospecific neighbor treatment was more conducive to the expansion and development of root morphology of F. hodginsii seedlings than were the conspecific neighbor and single-plant treatments

Clinical Analysis of Pediatric Systemic Juvenile Xanthogranulomas: A Retrospective Single-Center Study

Objective: To investigate the clinical characteristics, treatment, and prognosis of children with systemic juvenile xanthogranuloma (JXG).Methods: Clinical data of children with JXG who were hospitalized in Beijing Children's Hospital, Capital Medical University, from January 2012 to December 2019 were retrospectively analyzed, including clinical manifestations, laboratory determinations, treatment, and prognosis of the children. Patients were treated with vindesine + prednisone as the first-line treatment and cytarabine + vindesine + dexamethasone ± cladribine as the second-line treatment.Results: Ten patients, including 8 males and 2 females, with a median of onset age of 1.95 (0.80–7.30) years, exhibited multi-system dysfunction. The median age of diagnosis was 2.45 (1.30–12.10) years. The most common location of extracutaneous lesions was the central nervous system (6 cases), followed by the lung (5 cases) and bone (4 cases). Nine patients underwent first-line chemotherapy, and 6 patients underwent second-line chemotherapy, including 5 patients with poorly controlled disease after first-line treatment. The median observation time was 29 (3–115) months. Nine patients survived, whereas one patient died of respiratory failure caused by pulmonary infection. At the end of follow-up, 7 patients were in active disease (AD)/regression state (AD-better), and 2 patients were in an AD/stable state (AD-stable). Three patients had permanent sequelae, mainly central diabetes insipidus. The rates of response to the first-line treatment and the second-line treatment were 40.0 and 66.7% respectively.Conclusion: The chemotherapy protocol for Langerhans cell histiocytosis (LCH) may be effective for patients with systemic JXG. Central nervous system involvement may not impact overall survival, but serious permanent sequelae may occur

A Comprehensive Review of One-Dimensional Metal-Oxide Nanostructure Photodetectors

One-dimensional (1D) metal-oxide nanostructures are ideal systems for exploring a large number of novel phenomena at the nanoscale and investigating size and dimensionality dependence of nanostructure properties for potential applications. The construction and integration of photodetectors or optical switches based on such nanostructures with tailored geometries have rapidly advanced in recent years. Active 1D nanostructure photodetector elements can be configured either as resistors whose conductions are altered by a charge-transfer process or as field-effect transistors (FET) whose properties can be controlled by applying appropriate potentials onto the gates. Functionalizing the structure surfaces offers another avenue for expanding the sensor capabilities. This article provides a comprehensive review on the state-of-the-art research activities in the photodetector field. It mainly focuses on the metal oxide 1D nanostructures such as ZnO, SnO2, Cu2O, Ga2O3, Fe2O3, In2O3, CdO, CeO2, and their photoresponses. The review begins with a survey of quasi 1D metal-oxide semiconductor nanostructures and the photodetector principle, then shows the recent progresses on several kinds of important metal-oxide nanostructures and their photoresponses and briefly presents some additional prospective metal-oxide 1D nanomaterials. Finally, the review is concluded with some perspectives and outlook on the future developments in this area

Using histogram analysis of the intrinsic brain activity mapping to identify essential tremor

BackgroundEssential tremor (ET) is one of the most common movement disorders. Histogram analysis based on brain intrinsic activity imaging is a promising way to identify ET patients from healthy controls (HCs) and further explore the spontaneous brain activity change mechanisms and build the potential diagnostic biomarker in ET patients.MethodsThe histogram features based on the Resting-state functional magnetic resonance imaging (Rs-fMRI) data were extracted from 133 ET patients and 135 well-matched HCs as the input features. Then, a two-sample t-test, the mutual information, and the least absolute shrinkage and selection operator methods were applied to reduce the feature dimensionality. Support vector machine (SVM), logistic regression (LR), random forest (RF), and k-nearest neighbor (KNN) were used to differentiate ET and HCs, and classification performance of the established models was evaluated by the mean area under the curve (AUC). Moreover, correlation analysis was carried out between the selected histogram features and clinical tremor characteristics.ResultsEach classifier achieved a good classification performance in training and testing sets. The mean accuracy and area under the curve (AUC) of SVM, LR, RF, and KNN in the testing set were 92.62%, 0.948; 92.01%, 0.942; 93.88%, 0.941; and 92.27%, 0.939, respectively. The most power-discriminative features were mainly located in the cerebello-thalamo-motor and non-motor cortical pathways. Correlation analysis showed that there were two histogram features negatively and one positively correlated with tremor severity.ConclusionOur findings demonstrated that the histogram analysis of the amplitude of low-frequency fluctuation (ALFF) images with multiple machine learning algorithms could identify ET patients from HCs and help to understand the spontaneous brain activity pathogenesis mechanisms in ET patients