Tongtong Li

Adam Schmidt, Mert Asim Karaoglu, Soham Sinha et al.

Understanding tissue motion in surgery is crucial to enable applications in downstream tasks such as segmentation, 3D reconstruction, virtual tissue landmarking, autonomous probe-based scanning, and subtask autonomy. Labeled data are essential to enabling algorithms in these downstream tasks since they allow us to quantify and train algorithms. This paper introduces a point tracking challenge to address this, wherein participants can submit their algorithms for quantification. The submitted algorithms are evaluated using a dataset named surgical tattoos in infrared (STIR), with the challenge aptly named the STIR Challenge 2024. The STIR Challenge 2024 comprises two quantitative components: accuracy and efficiency. The accuracy component tests the accuracy of algorithms on in vivo and ex vivo sequences. The efficiency component tests the latency of algorithm inference. The challenge was conducted as a part of MICCAI EndoVis 2024. In this challenge, we had 8 total teams, with 4 teams submitting before and 4 submitting after challenge day. This paper details the STIR Challenge 2024, which serves to move the field towards more accurate and efficient algorithms for spatial understanding in surgery. In this paper we summarize the design, submissions, and results from the challenge. The challenge dataset is available here: https://zenodo.org/records/14803158 , and the code for baseline models and metric calculation is available here: https://github.com/athaddius/STIRMetrics

Lizuo Liu, Tongtong Li, Anne Gelb et al.

We propose an entropy-stable conservative flux form neural network (CFN) that integrates classical numerical conservation laws into a data-driven framework using the entropy-stable, second-order, and non-oscillatory Kurganov-Tadmor (KT) scheme. The proposed entropy-stable CFN uses slope limiting as a denoising mechanism, ensuring accurate predictions in both noisy and sparse observation environments, as well as in both smooth and discontinuous regions. Numerical experiments demonstrate that the entropy-stable CFN achieves both stability and conservation while maintaining accuracy over extended time domains. Furthermore, it successfully predicts shock propagation speeds in long-term simulations, {\it without} oracle knowledge of later-time profiles in the training data.

Ehab Zaghloul, Tongtong Li, Matt Mutka et al.

A cryptocurrency is a decentralized digital currency that is designed for secure and private asset transfer and storage. As a currency, it should be difficult to counterfeit and double-spend. In this paper, we review and analyze the major security and privacy issues of Bitcoin. In particular, we focus on its underlying foundation, blockchain technology. First, we present a comprehensive background of Bitcoin and the preliminary on security. Second, the major security threats and countermeasures of Bitcoin are investigated. We analyze the risk of double-spending attacks, evaluate the probability of success in performing the attacks and derive the profitability for the attacker to perform such attacks. Third, we analyze the underlying Bitcoin peer-to-peer network security risks and Bitcoin storage security. We compare three types of Bitcoin wallets in terms of security, type of services and their trade-offs. Finally, we discuss the security and privacy features of alternative cryptocurrencies and present an overview of emerging technologies today. Our results can help Bitcoin users to determine a trade-off between the risk of double-spending attempts and the transaction time delay or confidence before accepting transactions. These results can also assist miners to develop suitable strategies to get involved in the mining process and maximize their profits.

Ehab Zaghloul, Tongtong Li, Matt Mutka et al.

Current systems used by medical institutions for the management and transfer of Electronic Medical Records (EMR) can be vulnerable to security and privacy threats. In addition, these centralized systems often lack interoperability and give patients limited or no access to their own EMRs. In this paper, we propose a novel distributed data sharing scheme that applies the security benefits of blockchain to handle these concerns. With blockchain, we incorporate smart contracts and a distributed storage system to alleviate the dependence on the record-generating institutions to manage and share patient records. To preserve privacy of patient records, we implement our smart contracts as a method to allow patients to verify attributes prior to granting access rights. Our proposed scheme also facilitates selective sharing of medical records among staff members that belong to different levels of a hierarchical institution. We provide extensive security, privacy, and evaluation analyses to show that our proposed scheme is both efficient and practical.

Jian Li, Tongtong Li, Jian Ren

Regenerating code is a class of code very suitable for distributed storage systems, which can maintain optimal bandwidth and storage space. Two types of important regenerating code have been constructed: the minimum storage regeneration (MSR) code and the minimum bandwidth regeneration (MBR) code. However, in hostile networks where adversaries can compromise storage nodes, the storage capacity of the network can be significantly affected. In this paper, we propose two optimal constructions of regenerating codes through rate-matching that can combat against this kind of adversaries in hostile networks: 2-layer rate-matched regenerating code and $m$-layer rate-matched regenerating code. For the 2-layer code, we can achieve the optimal storage efficiency for given system requirements. Our comprehensive analysis shows that our code can detect and correct malicious nodes with higher storage efficiency compared to the universally resilient regenerating code which is a straightforward extension of regenerating code with error detection and correction capability. Then we propose the $m$-layer code by extending the 2-layer code and achieve the optimal error correction efficiency by matching the code rate of each layer's regenerating code. We also demonstrate that the optimized parameter can achieve the maximum storage capacity under the same constraint. Compared to the universally resilient regenerating code, our code can achieve much higher error correction efficiency.

Jian Li, Tongtong Li, Jian Ren

Distributed storage plays a crucial role in the current cloud computing framework. After the theoretical bound for distributed storage was derived by the pioneer work of the regenerating code, Reed-Solomon code based regenerating codes were developed. The RS code based minimum storage regeneration code (RS-MSR) and the minimum bandwidth regeneration code (RS-MBR) can achieve theoretical bounds on the MSR point and the MBR point respectively in code regeneration. They can also maintain the MDS property in code reconstruction. However, in the hostile network where the storage nodes can be compromised and the packets can be tampered with, the storage capacity of the network can be significantly affected. In this paper, we propose a Hermitian code based minimum storage regenerating (H-MSR) code and a minimum bandwidth regenerating (H-MBR) code. We first prove that our proposed Hermitian code based regenerating codes can achieve the theoretical bounds for MSR point and MBR point respectively. We then propose data regeneration and reconstruction algorithms for the H-MSR code and the H-MBR code in both error-free network and hostile network. Theoretical evaluation shows that our proposed schemes can detect the erroneous decodings and correct more errors in hostile network than the RS-MSR code and the RS-MBR code with the same code rate. Our analysis also demonstrates that the proposed H-MSR and H-MBR codes have lower computational complexity than the RS-MSR/RS-MBR codes in both code regeneration and code reconstruction.