Modeling of Silicon Photonic Devices for Optical Interconnect Transceiver Circuit Design

Optical interconnect system efficiency is dependent on the ability to optimize the transceiver circuitry for low-power and high-bandwidth operation, motivating co-simulation environments with compact optical device simulation models. This chapter presents compact Verilog-A silicon carrier-injection and carrier-depletion ring modulator models which accurately capture both nonlinear electrical and optical dynamics. Experimental verification of the carrier-injection ring modulator model is performed both at 8 Gb/s with symmetric drive signals to study the impact of pre-emphasis pulse duration, pulse depth, and dc bias, and at 9 Gb/s with a 65-nm CMOS driver capable of asymmetric pre-emphasis pulse duration. Experimental verification of the carrier-depletion ring modulator model is performed at 25 Gb/s with a 65-nm CMOS driver capable of asymmetric equalization

Modeling of Photonic Devices and Photonic Integrated Circuits for Optical Interconnect and RF Photonic Front-End Applications

Photonic integrated circuits (PICs) offer compelling solutions for applications in many areas due to the sufficient functionality and excellent performance. Optical interconnects and radio frequency (RF) photonics are two areas in which PICs have potential to be widely used. Optical interconnect system efficiency is dependent on the ability to optimize the transceiver circuitry for low-power and high-bandwidth operation, motivating co-simulation environments with compact optical device simulation models. Compact models for vertical-cavity surface-emitting lasers (VCSELs) and silicon carrier-injection/depletion ring modulators which include both non-linear electrical and optical dynamics are presented, and excellent matching between co-simulated and measured optical eye diagrams is achieved. Advanced modulation schemes, such as four-level pulse-amplitude modulation (PAM4), are currently under consideration in both high-speed electrical and optical interconnect systems. How NRZ and PAM4 modulation impacts the energy efficiency of an optical link architecture based on silicon photonic microring resonator modulators and drop filters is analyzed. Two ring modulator device structures are proposed for PAM4 modulation, including a single-segment device driven with a multi-level PAM4 transmitter and a two-segment device driven by two simple NRZ (MSB/LSB) transmitters. Modeling results show that the PAM4 architectures achieve superior energy efficiency at higher data rates due to the relaxed circuit bandwidth. While RF photonics offer the promise of chip-scale opto-electrical systems with high levels of functionality, in order to avoid long and unsuccessful design cycles, efficient models that allow for co-simulation are necessary. In order to address this, an optical element modeling framework is proposed based on Verilog-A which allows for the co-simulation of optical elements with transistor-level circuits in a Cadence design environment. Three components in the RF photonic system, Mach Zehnder (MZ) modulators, 4th order all pass filter (APF)-based optical filters, and jammer-suppression notch filters are presented to demonstrate the capability of efficient system design in co-simulation environments

Poison Dart Frog: A Clean-Label Attack with Low Poisoning Rate and High Attack Success Rate in the Absence of Training Data

To successfully launch backdoor attacks, injected data needs to be correctly labeled; otherwise, they can be easily detected by even basic data filters. Hence, the concept of clean-label attacks was introduced, which is more dangerous as it doesn't require changing the labels of injected data. To the best of our knowledge, the existing clean-label backdoor attacks largely relies on an understanding of the entire training set or a portion of it. However, in practice, it is very difficult for attackers to have it because of training datasets often collected from multiple independent sources. Unlike all current clean-label attacks, we propose a novel clean label method called 'Poison Dart Frog'. Poison Dart Frog does not require access to any training data; it only necessitates knowledge of the target class for the attack, such as 'frog'. On CIFAR10, Tiny-ImageNet, and TSRD, with a mere 0.1\%, 0.025\%, and 0.4\% poisoning rate of the training set size, respectively, Poison Dart Frog achieves a high Attack Success Rate compared to LC, HTBA, BadNets, and Blend. Furthermore, compared to the state-of-the-art attack, NARCISSUS, Poison Dart Frog achieves similar attack success rates without any training data. Finally, we demonstrate that four typical backdoor defense algorithms struggle to counter Poison Dart Frog

The impact of empirical Marshall vein ethanol infusion as a first-choice intraoperative strategy on the long-term outcomes in patients with persistent atrial fibrillation undergoing mitral isthmus ablation

BackgroundMarshall vein ethanol infusion (MVEI) as an additional therapy to conventional catheter ablation (CA) has been proved to be efficacious in patients with persistent atrial fibrillation (PeAF). However, whether empirical MVEI could be the first-line strategy in mitral isthmus (MI) ablation has seldom been investigated. Here, we aim to compare the efficacy, safety, and long-term outcomes between provisional and empirical MVEI in PeAF patients undergoing the index MI ablation procedure.MethodsWe enrolled 133 patients with PeAF either in the provisional group (n = 38, MVEI was performed when conventional endocardial and/or epicardial ablation procedures were inadequate to achieve bidirectional MI block) or in the empirical group (n = 95, MVEI was performed empirically before MI CA).ResultsAll of the baseline characteristics were comparable. Less spontaneous or inducible atrial tachycardias (ATs) were encountered in the empirical group of patients (P < 0.001). More epicardial ablations were applied (26.3% vs. 9.5%, P = 0.016) and a higher incidence of CA-facilitated restoration of sinus rhythm was recorded (86.8% vs. 11.7%, P < 0.001) in the provisional group of patients. Although more fluoroscopy time (6.4[4.2, 9.3] vs. 9.5[5.9, 11.6] min, P = 0.019) and radiation exposure (69.0[25.3, 160.2] vs. 122.0[62.5, 234.1] mGy, P = 0.010) were documented in the empirical group with comparable procedure time, less time (455.9 ± 192.2 vs. 366.5 ± 161.3 s, P = 0.038) was consumed to achieve bidirectional MI block during endocardial ablation in the provisional group. Incidences of procedure-related complications were similar between the two groups. During a 16.5 ± 4.4-month follow-up, the empirical group of patients showed a significantly higher rate of freedom from AT recurrence (95.8% vs. 81.6%, log-rank P = 0.003), while the rate of freedom from AF or atrial tachyarrhythmias (combining AF and AT) was similar. Both univariate (HR 0.19, 95% CI 0.05–0.64, P = 0.008) and multivariate (HR 0.25, 95% CI 0.07–0.92, P = 0.037) Cox regression analyses indicated that empirical MVEI was independently associated with lower long-term AT recurrence.ConclusionAmong patients with PeAF who underwent the index MI ablation procedure, empirical MVEI could reduce endocardial MI ablation time and provide greater long-term freedom from AT recurrence

A High-Precision Machine Learning Algorithm to Classify Left and Right Outflow Tract Ventricular Tachycardia

Introduction: Multiple algorithms based on 12-lead ECG measurements have been proposed to identify the right ventricular outflow tract (RVOT) and left ventricular outflow tract (LVOT) locations from which ventricular tachycardia (VT) and frequent premature ventricular complex (PVC) originate. However, a clinical-grade machine learning algorithm that automatically analyzes characteristics of 12-lead ECGs and predicts RVOT or LVOT origins of VT and PVC is not currently available. The effective ablation sites of RVOT and LVOT, confirmed by a successful ablation procedure, provide evidence to create RVOT and LVOT labels for the machine learning model. Methods: We randomly sampled training, validation, and testing data sets from 420 patients who underwent successful catheter ablation (CA) to treat VT or PVC, containing 340 (81%), 38 (9%), and 42 (10%) patients, respectively. We iteratively trained a machine learning algorithm supplied with 1,600,800 features extracted via our proprietary algorithm from 12-lead ECGs of the patients in the training cohort. The area under the curve (AUC) of the receiver operating characteristic curve was calculated from the internal validation data set to choose an optimal discretization cutoff threshold. Results: The proposed approach attained the following performance: accuracy (ACC) of 97.62 (87.44–99.99), weighted F1-score of 98.46 (90–100), AUC of 98.99 (96.89–100), sensitivity (SE) of 96.97 (82.54–99.89), and specificity (SP) of 100 (62.97–100). Conclusions: The proposed multistage diagnostic scheme attained clinical-grade precision of prediction for LVOT and RVOT locations of VT origin with fewer applicability restrictions than prior studies

Data Interpreter: An LLM Agent For Data Science

Large Language Model (LLM)-based agents have demonstrated remarkable effectiveness. However, their performance can be compromised in data science scenarios that require real-time data adjustment, expertise in optimization due to complex dependencies among various tasks, and the ability to identify logical errors for precise reasoning. In this study, we introduce the Data Interpreter, a solution designed to solve with code that emphasizes three pivotal techniques to augment problem-solving in data science: 1) dynamic planning with hierarchical graph structures for real-time data adaptability;2) tool integration dynamically to enhance code proficiency during execution, enriching the requisite expertise;3) logical inconsistency identification in feedback, and efficiency enhancement through experience recording. We evaluate the Data Interpreter on various data science and real-world tasks. Compared to open-source baselines, it demonstrated superior performance, exhibiting significant improvements in machine learning tasks, increasing from 0.86 to 0.95. Additionally, it showed a 26% increase in the MATH dataset and a remarkable 112% improvement in open-ended tasks. The solution will be released at https://github.com/geekan/MetaGPT

Safety and Efficacy of Left Atrial Catheter Ablation in Patients with Left Atrial Appendage Occlusion Devices

Background: Left atrial appendage occlusion (LAAO) is an alternative to oral anticoagulation for thromboembolic prevention in patients with atrial fibrillation (AF). Left atrial (LA) catheter ablation (CA) in patients with LAAO devices has not been well investigated. Here, we report on the safety and efficacy of LA CA in patients with nitinol cage or plug LAAO devices. Methods: A total of 18 patients (aged 67 ± 11 years; 14 males; 5 paroxysmal AF) with LAAO devices (nitinol cage, n = 10; nitinol plug, n = 8) and symptomatic LA tachyarrhythmias were included. Periprocedural and follow-up data were assessed. Results: A total of 20 LA CA procedures were performed at a median of 130 (63, 338) days after LAAO. The strategy of CA consisted of circumferential pulmonary vein isolation (n = 16), linear lesions (n = 14) and complex fractionated atrial electrogram ablation (n = 6). No major adverse events occurred periprocedurally. Repeated transesophageal echocardiography showed no device-related thrombus, newly developed peridevice leakage or device dislodgement. After a median follow-up period of 793 (376, 1090) days, four patients (22%) experienced LA tachyarrhythmias recurrence and two received redo LA CA. No patients suffered stroke or major bleeding events during follow-up. Conclusions: LA CA in patients with LAAO devices (either nitinol cages or nitinol plugs) seems to be safe and efficient in our single-center experience

ISTVP: Independent single transaction verification protocol for light node using fraud proofs without collaborator

Abstract Most blockchain users run light nodes on mobile devices. Due to limited storage and computation, light nodes cannot perform transaction validation. This shortage makes opportunities for malicious nodes to produce blocks containing invalid transactions, which results in the loss of funds for light nodes. Fraud proofs play a significant role in ensuring transaction security for light nodes. However, existing fraud proof schemes require honest collaborators and the processing of entire blocks. To address these limitations, Independent Single Transaction Verification Protocol for Light node Using Fraud Proofs without Collaborator called ISTVP is proposed that enables light nodes to independently verify transactions and generate fraud proofs without relying on collaborators or processing the entire block. To support ISTVP, SVST is introduced, an efficient block structure for single‐transaction verification. SVST not only efficiently indexes historical transaction outputs to improve verification efficiency, but also significantly reduces the storage requirements for verifying transactions to O(h+logn). Furthermore, the authors analyze the security of ISTVP and demonstrate that it satisfies both persistence and liveness, while maintaining the level of security of full node