Forward Private Searchable Symmetric Encryption with Optimized I/O Efficiency

Recently, several practical attacks raised serious concerns over the security of searchable encryption. The attacks have brought emphasis on forward privacy, which is the key concept behind solutions to the adaptive leakage-exploiting attacks, and will very likely to become mandatory in the design of new searchable encryption schemes. For a long time, forward privacy implies inefficiency and thus most existing searchable encryption schemes do not support it. Very recently, Bost (CCS 2016) showed that forward privacy can be obtained without inducing a large communication overhead. However, Bost's scheme is constructed with a relatively inefficient public key cryptographic primitive, and has a poor I/O performance. Both of the deficiencies significantly hinder the practical efficiency of the scheme, and prevent it from scaling to large data settings. To address the problems, we first present FAST, which achieves forward privacy and the same communication efficiency as Bost's scheme, but uses only symmetric cryptographic primitives. We then present FASTIO, which retains all good properties of FAST, and further improves I/O efficiency. We implemented the two schemes and compared their performance with Bost's scheme. The experiment results show that both our schemes are highly efficient, and FASTIO achieves a much better scalability due to its optimized I/O

The dependence of the structure of planet-opened gaps in protoplanetary disks on radiative cooling

Planets can excite density waves and open annular gas gaps in protoplanetary disks. The depth of gaps is influenced by the evolving angular momentum carried by density waves. While the impact of radiative cooling on the evolution of density waves has been studied, a quantitative correlation to connect gap depth with the cooling timescale is lacking. To address this gap in knowledge, we employ the grid-based code Athena++ to simulate disk-planet interactions, treating cooling as a thermal relaxation process. We establish quantitative dependences of steady-state gap depth (Eq. 36) and width (Eq. 41) on planetary mass, Shakura-Sunyaev viscosity, disk scale height, and thermal relaxation timescale $(\beta)$. We confirm previous results that gap opening is the weakest when thermal relaxation timescale is comparable to local dynamical timescale. Significant variations in gap depth, up to an order of magnitude, are found with different $\beta$. In terms of width, a gap is at its narrowest around $\beta=1$, approximately $10\%$ to $20\%$ narrower compared to the isothermal case. When $\beta\sim100$, it can be $\sim20\%$ wider, and higher viscosity enhances this effect. We derive possible masses of the gas gap-opening planets in AS 209, HD 163296, MWC 480, and HL Tau, accounting for the uncertainties in local thermal relaxation timescale.Comment: 19 pages, 16 figures, 4 tables, accepted for publication in Ap

Direct Displacement-based Seismic Design of Glulam Frames with Buckling Restrained Braces

This paper presents a direct displacement-based design (DDBD) approach for the buckling restrained braces (BRBs) braced glue-laminated timber (glulam) frame (BRBGF) structures. First, the critical design parameters of the DDBD approach were derived for BRBGFs. Then, using experimentally verified numerical models, pushover analyses and nonlinear time-history analyses (NLTHAs) were conducted on a series of one-storey BRBGFs to calibrate the stiffness adjustment factor λ for BRB-timber connections and the spectral displacement reduction factor η. Finally, the DDBD approach was verified as a prospective approach for the seismic design of multi-storey BRBGF buildings by NLTHAs of the case study buildings

Interactive Contrastive Learning for Self-supervised Entity Alignment

Self-supervised entity alignment (EA) aims to link equivalent entities across different knowledge graphs (KGs) without seed alignments. The current SOTA self-supervised EA method draws inspiration from contrastive learning, originally designed in computer vision based on instance discrimination and contrastive loss, and suffers from two shortcomings. Firstly, it puts unidirectional emphasis on pushing sampled negative entities far away rather than pulling positively aligned pairs close, as is done in the well-established supervised EA. Secondly, KGs contain rich side information (e.g., entity description), and how to effectively leverage those information has not been adequately investigated in self-supervised EA. In this paper, we propose an interactive contrastive learning model for self-supervised EA. The model encodes not only structures and semantics of entities (including entity name, entity description, and entity neighborhood), but also conducts cross-KG contrastive learning by building pseudo-aligned entity pairs. Experimental results show that our approach outperforms previous best self-supervised results by a large margin (over 9% average improvement) and performs on par with previous SOTA supervised counterparts, demonstrating the effectiveness of the interactive contrastive learning for self-supervised EA.Comment: Accepted by CIKM 202

Semantic Segmentation to Extract Coronary Arteries in Invasive Coronary Angiograms

Accurate semantic segmentation of each coronary artery using invasive coronary angiography (ICA) is important for stenosis assessment and coronary artery disease (CAD) diagnosis. In this paper, we propose a multi-step semantic segmentation algorithm based on analyzing arterial segments extracted from ICAs. The proposed algorithm firstly extracts the entire arterial binary mask (binary vascular tree) using a deep learning-based method. Then we extract the centerline of the binary vascular tree and separate it into different arterial segments. Finally, by extracting the underlying arterial topology, position, and pixel features, we construct a powerful coronary artery segment classifier based on a support vector machine. Each arterial segment is classified into the left coronary artery (LCA), left anterior descending (LAD), and other types of arterial segments. The proposed method was tested on a dataset with 225 ICAs and achieved a mean accuracy of 70.33% for the multi-class artery classification and a mean intersection over union of 0.6868 for semantic segmentation of arteries. The experimental results show the effectiveness of the proposed algorithm, which provides impressive performance for analyzing the individual arteries in ICAs

Semantic Segmentation to Extract Coronary Arteries in Invasive Coronary Angiograms

Accurate semantic segmentation of each coronary artery using invasive coronary angiography (ICA) is important for stenosis assessment and coronary artery disease (CAD) diagnosis. In this paper, we propose a multi-step semantic segmentation algorithm based on analyzing arterial sements extraced from ICAs. The proposed algorithm firstly extracts the entire arterial binary mask (binary vascular tree) using a deep learning-based method. Then we extract the centerline of the binary vascular tree and separate it into different arterial segments. Finally, by extracting the underlyingarterial topology, position, and pixel features, we construct a powerful coronary artery segment classifier based on a support vector machine. Each arterial segment is classified into the left coronary artery (LCA), left anterior descending (LAD), and other types of arterial segments. The proposed method was tested on a dataset with 225 ICAs and achieved a mean accuracy of 70.33% for the multi-class artery classification and a mean intersection over union of 0.6868 for semantic segmentation of arteries. The experimental results show the effectiveness of the proposed algorithm, which provides impressive performance for analyzing the individual arteries in ICAs

Semantic Segmentation to Extract Coronary Arteries in Invasive Coronary Angiograms

Accurate semantic segmentation of each coronary artery using invasive coronary angiography (ICA) is important for stenosis assessment and coronary artery disease (CAD) diagnosis. In this paper, we propose a multi-step semantic segmentation algorithm based on analyzing arterial sements extraced from ICAs. The proposed algorithm firstly extracts the entire arterial binary mask (binary vascular tree) using a deep learning-based method. Then we extract the centerline of the binary vascular tree and separate it into different arterial segments. Finally, by extracting the underlyingarterial topology, position, and pixel features, we construct a powerful coronary artery segment classifier based on a support vector machine. Each arterial segment is classified into the left coronary artery (LCA), left anterior descending (LAD), and other types of arterial segments. The proposed method was tested on a dataset with 225 ICAs and achieved a mean accuracy of 70.33% for the multi-class artery classification and a mean intersection over union of 0.6868 for semantic segmentation of arteries. The experimental results show the effectiveness of the proposed algorithm, which provides impressive performance for analyzing the individual arteries in ICAs