A Novel CCA Attack for NTRU+ KEM

The KpqC competition has begun in 2022, that aims to standardize Post-Quantum Cryptography (PQC) in the Republic of Korea. Among the 16 submissions of the KpqC competition, the lattice-based schemes exhibit the most promising and balanced features in performance. In this paper, we propose an effective classical CCA attack to recover the transmitted session key for NTRU+, one of the lattice-based Key Encapsulation Mechanisms (KEM) proposed in the KpqC competition, for the first time. With the proposed attacks, we show that all the suggested parameters of NTRU+ do not satisfy the claimed security. We also suggest a way to modify the NTRU+ scheme to defend our attack

Small-molecule inhibitors of ferrochelatase are antiangiogenic agents

Activity of the heme synthesis enzyme ferrochelatase (FECH) is implicated in multiple diseases. In particular, it is a mediator of neovascularization in the eye and thus an appealing therapeutic target for preventing blindness. However, no drug-like direct FECH inhibitors are known. Here, we set out to identify small-molecule inhibitors of FECH as potential therapeutic leads using a high-throughput screening approach to identify potent inhibitors of FECH activity. A structure-activity relationship study of a class of triazolopyrimidinone hits yielded drug-like FECH inhibitors. These compounds inhibit FECH in cells, bind the active site in cocrystal structures, and are antiangiogenic in multiple in vitro assays. One of these promising compounds was antiangiogenic in vivo in a mouse model of choroidal neovascularization. This foundational work may be the basis for new therapeutic agents to combat not only ocular neovascularization but also other diseases characterized by FECH activity

Hankel operators and measures (Recent developments of operator theory by Banach space technique and related topics)

In this paper we consider Hankel operators of Schatten p-classes. If $mu$ is a (finite) positive Borel measure on mathrm{D}, it induces an infinite Hankel matrix H_{$mu$}. There are several results for conditions that H_{$mu$} belong to Schatten p-classes. We loot at these results and its generalizations

Selectively Connected Self-Attentions for Semantic Role Labeling

Semantic role labeling is an effective approach to understand underlying meanings associated with word relationships in natural language sentences. Recent studies using deep neural networks, specifically, recurrent neural networks, have significantly improved traditional shallow models. However, due to the limitation of recurrent updates, they require long training time over a large data set. Moreover, they could not capture the hierarchical structures of languages. We propose a novel deep neural model, providing selective connections among attentive representations, which remove the recurrent updates, for semantic role labeling. Experimental results show that our model performs better in accuracy compared to the state-of-the-art studies. Our model achieves 86.6 F1 scores and 83.6 F1 scores on the CoNLL 2005 and CoNLL 2012 shared tasks, respectively. The accuracy gains are improved by capturing the hierarchical information using the connection module. Moreover, we show that our model can be parallelized to avoid the repetitive updates of the model. As a result, our model reduces the training time by 62 percentages from the baseline

Toeplitz Operators whose Symbols Are Borel Measures

In this paper, we are concerned with Toeplitz operators whose symbols are complex Borel measures. When a complex Borel measure μ on the unit circle is given, we give a formal definition of a Toeplitz operator Tμ with symbol μ, as an unbounded linear operator on the Hardy space. We then study various properties of Tμ. Among them, there is a theorem that the domain of Tμ is represented by a trichotomy. Also, it was shown that if the domain of Tμ contains at least one polynomial, then Tμ is densely defined. In addition, we give evidence for the conjecture that Tμ with a singular measure μ reduces to a trivial linear operator

EFFICIENTLY PROCESSING OF TOP-K TYPICALITY QUERY FOR STRUCTURED DATA

This work presents a novel ranking scheme for structured data. We show how to apply the notion of typicality analysis from cognitive science and how to use this notion to formulate the problem of ranking data with categorical attributes. First, we formalize the typicality query model for relational databases. We adopt Pearson correlation coefficient to quantify the extent of the typicality of an object. The correlation coefficient estimates the extent of statistical relationships between two variables based on the patterns of occurrences and absences of their values. Second, we develop a top-k query processing method for efficient computation. TPFilter prunes unpromising objects based on tight upper bounds and selectively joins tuples of highest typicality score. Our methods efficiently prune unpromising objects based on upper bounds. Experimental results show our approach is promising for real data

Improving Complex Scene Generation by Enhancing Multi-Scale Representations of GAN Discriminators

While recent advances of GAN models enabled photo-realistic synthesis of various object images, challenges still remain in modeling more complex image distributions such as scenes with multiple objects. The difficulty lies in the high structural complexity of scene images, where the discriminator carries a heavy burden in discriminating complex structural differences between real and fake scene images. Therefore, enhancing the discriminative capability of the discriminator could be one of the effective strategies to improve the generation performance of GAN models. In this paper, we explore ways to boost the discriminative capability by leveraging two recent paradigms on visual representation learning: self-supervised learning and transfer learning. As the first approach, we propose a self-supervised auxiliary task tailored to enhance the multi-scale representations of the discriminator. In the second approach, we further enhance the discriminator by utilizing pretrained representations from various scene understanding models. To fully utilize knowledge from multiple expert models, we propose a multi-scale feature ensemble to mix multi-sale representations. Empirical results on challenging scene datasets demonstrate that the proposed strategies significantly advance the generation performance, enabling diverse and photo-realistic synthesis of complex scene images

Ethanol-based green-solution processing of alpha-formamidinium lead triiodide perovskite layers

Manufacturing of perovskite solar cells would benefit from the avoidance of hazardous solvents and multistep processing. Now, Yun et al. report an ethanol-based perovskite precursor solution that does not need an antisolvent step, enabling devices with 25% efficiency. The use of non-toxic or less-toxic solvents in the mass production of solution-processed perovskite solar cells is essential. However, halide perovskites are generally not completely soluble in most non-toxic solvents. Here we report the deposition of dense and uniform alpha-formamidinium lead triiodide (alpha-FAPbI(3)) films using perovskite precursor solutions dissolved in ethanol-based solvent. The process does not require an antisolvent dripping step. The combination of a Lewis base, such as dimethylacetamide (or dimethylsulfoxide), and an alkylammonium chloride (RNH3Cl) in ethanol results in the stable solvation of FAPbI(3). The RNH3Cl added to the FAPbI(3) precursor solution is removed during spin-coating and high-temperature annealing via iodoplumbate complexes, such as PbI2 center dot RNH2 and PbI2 center dot HCl, coordinated with dimethylacetamide (or dimethylsulfoxide). It is possible to form very dense and uniform alpha-FAPbI(3) perovskite films with high crystallinity by combining several types of RNH3Cl. We obtain power conversion efficiencies of 24.3% using a TiO2 electrode, and of 25.1% with a SnO2 electrode