Bridging Machine Learning and Cryptanalysis via EDLCT

Machine learning aided cryptanalysis is an interesting but challenging research topic. At CRYPTO\u2719, Gohr proposed a Neural Distinguisher (ND) based on a plaintext difference. The ND takes a ciphertext pair as input and outputs its class (a real or random ciphertext pair). At EUROCRYPTO\u2720, Benamira et al proposed a deeper analysis of how two specific NDs against Speck32/64 work. However, there are still three research gaps that researchers are eager to fill in. (1) what features related to a ciphertext pair are learned by the ND? (2) how to explain various phenomena related to NDs? (3) what else can machine learning do in conventional cryptanalysis? In this paper, we filled in the three research gaps: (1) we first propose the Extended Differential-Linear Connectivity Table (EDLCT) which is a generic tool describing a cipher. Features corresponding to the EDLCT are designed to describe a ciphertext pair. Based on these features, various machine learning-based distinguishers including the ND are built. To explore various NDs from the EDLCT view, we propose a Feature Set Sensitivity Test (FSST) to identify which features may have a significant influence on NDs. Features identified by FSST share the same characteristic related to the cipher\u27s round function. Surrogate models of NDs are also built based on identified features. Experiments on Speck32/64 and DES confirm that features corresponding to the EDLCT are learned by NDs. (2) We explain phenomena related to NDs via EDLCT. (3) We show how to use machine learning to search differential-linear propagations ∆ → λ with a high correlation, which is a tough task in the differential-linear attack. Applications in Chaskey and DES demonstrate the advantages of machine learning. Furthermore, we provide some optional inputs to improve N

Improved Neural Aided Statistical Attack for Cryptanalysis

At CRYPTO 2019, Gohr improved attacks on Speck32/64 using deep learning. In 2020, Chen et al. proposed a neural aided statistical attack that is more generic. Chen et’s attack is based on a statistical distinguisher that covers a prepended differential transition and a neural distinguisher. When the probability of the differential transition is pq, its impact on the data complexity is O(p^{-2}q^{-2}. In this paper, we propose an improved neural aided statistical attack based on a new concept named Homogeneous Set. Since partial random ciphertext pairs are filtered with the help of homogeneous sets, the differential transition’s impact on the data complexity is reduced to O(p^{−1}q^{−2}). As a demonstration, the improved neural aided statistical attack is applied to round-reduced Speck. And several better attacks are obtained

Research Progress in the Molecular Mechanisms, Therapeutic Targets, and Drug Development of Idiopathic Pulmonary Fibrosis

Idiopathic pulmonary fibrosis (IPF) is a fatal interstitial lung disease. Recent studies have identified the key role of crosstalk between dysregulated epithelial cells, mesenchymal, immune, and endothelial cells in IPF. In addition, genetic mutations and environmental factors (e.g., smoking) have also been associated with the development of IPF. With the recent development of sequencing technology, epigenetics, as an intermediate link between gene expression and environmental impacts, has also been reported to be implicated in pulmonary fibrosis. Although the etiology of IPF is unknown, many novel therapeutic targets and agents have emerged from clinical trials for IPF treatment in the past years, and the successful launch of pirfenidone and nintedanib has demonstrated the promising future of anti-IPF therapy. Therefore, we aimed to gain an in-depth understanding of the underlying molecular mechanisms and pathogenic factors of IPF, which would be helpful for the diagnosis of IPF, the development of anti-fibrotic drugs, and improving the prognosis of patients with IPF. In this study, we summarized the pathogenic mechanism, therapeutic targets and clinical trials from the perspective of multiple cell types, gene mutations, epigenetic and environmental factors

Differential-Linear Approximation Semi-Unconstrained Searching and Partition Tree: Application to LEA and Speck

The differential-linear attack is one of the most effective attacks against ARX ciphers. However, two technical problems are preventing it from being more effective and having more applications: (1) there is no efficient method to search for good differential-linear approximations. Existing methods either have many constraints or are currently inefficient. (2) partitioning technique has great potential to reduce the time complexity of the key-recovery attack, but there is no general tool to construct partitions for ARX ciphers. In this work, we step forward in solving the two problems. First, we propose a novel idea for generating new good differential-linear approximations from known ones, based on which new searching algorithms are designed. Second, we propose a general tool named partition tree, for constructing partitions for ARX ciphers. Based on these new techniques, we present better attacks for two ISO/IEC standards, i.e., LEA and Speck. For LEA, we present the first 17-round distinguisher which is 1 round longer than the previous best distinguisher. Furthermore, we present the first key recovery attacks on 17-round LEA-128, 18-round LEA-192, and 18-round LEA-256, which attack 3, 4, and 3 rounds more than the previous best attacks. For Speck, we find better differential-linear distinguishers for Speck48 and Speck64. The first differential-linear distinguishers for Speck96 and Speck128 are also presented

Supplementary Material to “Distributed Consensus-based Weight Design for Cooperative Spectrum Sensing”

Abstract—This material is a supplement to the paper “Distributed Consensus-based Weight Design for Cooperative Spectrum Sensing”. Section 1 offers related literature review on cooperative spectrum sensing and consensus algorithms. Section 2 presents related notations and models of the consensus-based graph theory. Section 3 offers further analysis of the proposed spectrum sensing scheme including detection threshold settings and convergence properties in terms of detection performance. Section 4 presents the proofs for the convergence of the proposed consensus algorithm, and discusses the convergence of the proposed algorithm under random link failure network models. Section 5 shows additional simulation results

DifferSketching: How Differently Do People Sketch 3D Objects?

Multiple sketch datasets have been proposed to understand how people draw 3D objects. However, such datasets are often of small scale and cover a small set of objects or categories. In addition, these datasets contain freehand sketches mostly from expert users, making it difficult to compare the drawings by expert and novice users, while such comparisons are critical in informing more effective sketch-based interfaces for either user groups. These observations motivate us to analyze how differently people with and without adequate drawing skills sketch 3D objects. We invited 70 novice users and 38 expert users to sketch 136 3D objects, which were presented as 362 images rendered from multiple views. This leads to a new dataset of 3,620 freehand multi-view sketches, which are registered with their corresponding 3D objects under certain views. Our dataset is an order of magnitude larger than the existing datasets. We analyze the collected data at three levels, i.e., sketch-level, stroke-level, and pixel-level, under both spatial and temporal characteristics, and within and across groups of creators. We found that the drawings by professionals and novices show significant differences at stroke-level, both intrinsically and extrinsically. We demonstrate the usefulness of our dataset in two applications: (i) freehand-style sketch synthesis, and (ii) posing it as a potential benchmark for sketch-based 3D reconstruction. Our dataset and code are available at https://chufengxiao.github.io/DifferSketching/.Comment: SIGGRAPH Asia 2022 (Journal Track

Emerging ctDNA detection strategies in clinical cancer theranostics

Circulating tumor DNA (ctDNA) is naked DNA molecules shed from the tumor cells into the peripheral blood circulation. They contain tumor‐specific gene mutations and other valuable information. ctDNA is considered to be one of the most significant analytes in liquid biopsies. Over the past decades, numerous researchers have developed various detection strategies to perform quantitative or qualitative ctDNA analysis, including PCR‐based detection and sequencing‐based detection. More and more studies have illustrated the great value of ctDNA as a biomarker in the diagnosis, prognosis and heterogeneity of tumor. In this review, we first outlined the development of digital PCR (dPCR)‐based and next generation sequencing (NGS)‐based ctDNA detection systems. Besides, we presented the introduction of the emerging ctDNA analysis strategies based on various biosensors, such as electrochemical biosensors, fluorescent biosensors, surface plasmon resonance and Raman spectroscopy, as well as their applications in the field of biomedicine. Finally, we summarized the essentials of the preceding discussions, and the existing challenges and prospects for the future are also involved

PSC 352.01: American Political Thought

In this paper, we study the problem of multi-view sketch correspondence, where we take as input multiple freehand sketches with different views of the same object and predict as output the semantic correspondence among the sketches. This problem is challenging since the visual features of corresponding points at different views can be very different. To this end, we take a deep learning approach and learn a novel local sketch descriptor from data. We contribute a training dataset by generating the pixel-level correspondence for the multi-view line drawings synthesized from 3D shapes. To handle the sparsity and ambiguity of sketches, we design a novel multi-branch neural network that integrates a patch-based representation and a multiscale strategy to learn the pixel-level correspondence among multi-view sketches. We demonstrate the effectiveness of our proposed approach with extensive experiments on hand-drawn sketches and multi-view line drawings rendered from multiple 3D shape datasets