Highly efficient non-degenerate four-wave mixing under dual-mode injection in InP/InAs quantum-dash and quantum-dot lasers at 1.55 μm

This article may be downloaded for personal use only. Any other use requires prior permission of the author and AIP Publishing. This article appeared in Appl. Phys. Lett. 107, 191111 (2015) and may be found at https://doi.org/10.1063/1.4935796.This work reports on non-degenerate four-wave mixing under dual-mode injection in metalorganic vapor phase epitaxy grown InP/InAs quantum-dash and quantum dot Fabry-Perot laser operating at 1550 nm. High values of normalized conversion efficiency of −18.6 dB, optical signal-to-noise ratio of 37 dB, and third order optical susceptibility normalized to material gain χ(3)/g0 of ∼4 × 10−19 m3/V3 are measured for 1490 μm long quantum-dash lasers. These values are similar to those obtained with distributed-feedback lasers and semiconductor optical amplifiers, which are much more complicated to fabricate. On the other hand, due to the faster gain saturation and enhanced modulation of carrier populations, quantum-dot lasers demonstrate 12 dB lower conversion efficiency and 4 times lower χ(3)/g0 compared to quantum dash lasers.DFG, 43659573, SFB 787: Halbleiter - Nanophotonik: Materialien, Modelle, BauelementeEC/FP7/EU/264687/Postgraduate Research on Photonics as an Enabling Technology/PROPHE

CodeCoT and Beyond: Learning to Program and Test like a Developer

In natural language processing, transformer-based large language models (LLMs) like GPT-x models developed by OpenAI have revolutionized the landscape. Despite their impressive capabilities, these models often encounter challenges when handling tasks that differ from their training data, resulting in compromised performance. To address this, few-shot learning has emerged as a valuable technique, allowing LLMs to adapt with minimal task-specific data. One innovative strategy, known as Chain-of-Thought Prompting (CoT), has been introduced to guide LLMs in revealing cognitive processes during multi-step reasoning. In this paper, we propose Code Chain-of-Thought~(CodeCoT), which consists of two components: the Vanilla CodeCoT and the Self-exam CodeCoT. The latter incorporates self-examination, empowering the model to iteratively generate code, formulate test cases, and refine its outputs. Specifically, the process entails the generation of test examples by the model corresponding to the code it is tasked to implement. If it fails on the test examples, then it regenerates the code based on the erroneous code and associated error types. Through comprehensive experiments, we observed that both techniques significantly enhance code generation accuracy across various LLM variants. Our evaluation results reveal that CodeCoT improves the code generation effectiveness, including an unprecedented pass@1 accuracy of 79.27\% using the Self-exam CodeCoT approach on the gpt-3.5-turbo-0613 model in the HumanEval dataset

Multimode optical feedback dynamics in InAs/GaAs quantum dot lasers emitting exclusively on ground or excited states: transition from short- to long-delay regimes

© 2018 Optical Society of America. Users may use, reuse, and build upon the article, or use the article for text or data mining, so long as such uses are for non-commercial purposes and appropriate attribution is maintained. All other rights are reserved.The optical feedback dynamics of two multimode InAs/GaAs quantum dot lasers emitting exclusively on sole ground or excited lasing states is investigated. The transition from long- to short-delay regimes is analyzed, while the boundaries associated to the birth of periodic and chaotic oscillations are unveiled to be a function of the external cavity length. The results show that depending on the initial lasing state, different routes to chaos are observed. These results are of importance for the development of isolator-free transmitters in short-reach networks

The thermal and electrical properties of the promising semiconductor MXene Hf2CO2

In this work, we investigate the thermal and electrical properties of oxygen-functionalized M2CO2 (M = Ti, Zr, Hf) MXenes using first-principles calculations. Hf2CO2 is found to exhibit a thermal conductivity better than MoS2 and phosphorene. The room temperature thermal conductivity along the armchair direction is determined to be 86.25-131.2 Wm-1K-1 with a flake length of 5-100 um, and the corresponding value in the zigzag direction is approximately 42% of that in the armchair direction. Other important thermal properties of M2CO2 are also considered, including their specific heat and thermal expansion coefficients. The theoretical room temperature thermal expansion coefficient of Hf2CO2 is 6.094x10-6 K-1, which is lower than that of most metals. Moreover, Hf2CO2 is determined to be a semiconductor with a band gap of 1.657 eV and to have high and anisotropic carrier mobility. At room temperature, the Hf2CO2 hole mobility in the armchair direction (in the zigzag direction) is determined to be as high as 13.5x103 cm2V-1s-1 (17.6x103 cm2V-1s-1), which is comparable to that of phosphorene. Broader utilization of Hf2CO2 as a material for nanoelectronics is likely because of its moderate band gap, satisfactory thermal conductivity, low thermal expansion coefficient, and excellent carrier mobility. The corresponding thermal and electrical properties of Ti2CO2 and Zr2CO2 are also provided here for comparison. Notably, Ti2CO2 presents relatively low thermal conductivity and much higher carrier mobility than Hf2CO2, which is an indication that Ti2CO2 may be used as an efficient thermoelectric material.Comment: 26 pages, 5 figures, 2 table

FMT: Removing Backdoor Feature Maps via Feature Map Testing in Deep Neural Networks

Deep neural networks have been widely used in many critical applications, such as autonomous vehicles and medical diagnosis. However, their security is threatened by backdoor attack, which is achieved by adding artificial patterns to specific training data. Existing defense strategies primarily focus on using reverse engineering to reproduce the backdoor trigger generated by attackers and subsequently repair the DNN model by adding the trigger into inputs and fine-tuning the model with ground-truth labels. However, once the trigger generated by the attackers is complex and invisible, the defender can not successfully reproduce the trigger. Consequently, the DNN model will not be repaired since the trigger is not effectively removed. In this work, we propose Feature Map Testing~(FMT). Different from existing defense strategies, which focus on reproducing backdoor triggers, FMT tries to detect the backdoor feature maps, which are trained to extract backdoor information from the inputs. After detecting these backdoor feature maps, FMT will erase them and then fine-tune the model with a secure subset of training data. Our experiments demonstrate that, compared to existing defense strategies, FMT can effectively reduce the Attack Success Rate (ASR) even against the most complex and invisible attack triggers. Second, unlike conventional defense methods that tend to exhibit low Robust Accuracy (i.e., the model's accuracy on the poisoned data), FMT achieves higher RA, indicating its superiority in maintaining model performance while mitigating the effects of backdoor attacks~(e.g., FMT obtains 87.40\% RA in CIFAR10). Third, compared to existing feature map pruning techniques, FMT can cover more backdoor feature maps~(e.g., FMT removes 83.33\% of backdoor feature maps from the model in the CIFAR10 \& BadNet scenario).Comment: 12 pages, 4 figure

Feature Map Testing for Deep Neural Networks

Due to the widespread application of deep neural networks~(DNNs) in safety-critical tasks, deep learning testing has drawn increasing attention. During the testing process, test cases that have been fuzzed or selected using test metrics are fed into the model to find fault-inducing test units (e.g., neurons and feature maps, activating which will almost certainly result in a model error) and report them to the DNN developer, who subsequently repair them~(e.g., retraining the model with test cases). Current test metrics, however, are primarily concerned with the neurons, which means that test cases that are discovered either by guided fuzzing or selection with these metrics focus on detecting fault-inducing neurons while failing to detect fault-inducing feature maps. In this work, we propose DeepFeature, which tests DNNs from the feature map level. When testing is conducted, DeepFeature will scrutinize every internal feature map in the model and identify vulnerabilities that can be enhanced through repairing to increase the model's overall performance. Exhaustive experiments are conducted to demonstrate that (1) DeepFeature is a strong tool for detecting the model's vulnerable feature maps; (2) DeepFeature's test case selection has a high fault detection rate and can detect more types of faults~(comparing DeepFeature to coverage-guided selection techniques, the fault detection rate is increased by 49.32\%). (3) DeepFeature's fuzzer also outperforms current fuzzing techniques and generates valuable test cases more efficiently.Comment: 12 pages, 5 figures. arXiv admin note: text overlap with arXiv:2307.1101

Delay-Dependent State Estimation of Static Neural Networks with Time-Varying and Distributed Delays

This paper focuses on studying the state estimation problem of static neural networks with time-varying and distributed delays. By constructing a suitable Lyapunov functional and employing two integral inequalities, a sufficient condition is obtained under which the estimation error system is globally asymptotically stable. It can be seen that this condition is dependent on the two kinds of time delays. To reduce the conservatism of the derived result, Wirtinger inequality is employed to handle a cross term in the time-derivative of Lyapunov functional. It is further shown that the design of the gain matrix of state estimator is transformed to finding a feasible solution of a linear matrix inequality, which is efficiently facilitated by available algorithms. A numerical example is explored to demonstrate the effectiveness of the developed result

Bias Assessment and Mitigation in LLM-based Code Generation

Utilizing state-of-the-art Large Language Models (LLMs), automatic code generation models play a pivotal role in enhancing the productivity and efficiency of software development coding procedures. As the adoption of LLMs becomes more widespread in software coding ecosystems, a pressing issue has emerged: does the generated code contain social biases, such as those related to age, gender, and race? This issue concerns the integrity, fairness, and ethical foundation of software applications that depend on the code generated by these models, yet is under-explored in the literature. This paper presents a novel bias assessment framework that is specifically designed for code generation tasks. Based on this framework, we conduct an extensive evaluation on the bias of nine state-of-the-art LLM-based code generation models. Our findings reveal that first, 31.45\% to 79.93\% code functions generated by our evaluated code generation models are biased, and 9.68\% to 37.37\% code functions' functionality are affected by the bias, which means biases not only exist in code generation models but in some cases, directly affect the functionality of the generated code, posing risks of unintended and possibly harmful software behaviors. To mitigate bias from code generation models, we propose three mitigation strategies, which can decrease the biased code ratio to a very low level of 0.4\% to 4.57\%

Neuron Sensitivity Guided Test Case Selection for Deep Learning Testing

Deep Neural Networks~(DNNs) have been widely deployed in software to address various tasks~(e.g., autonomous driving, medical diagnosis). However, they could also produce incorrect behaviors that result in financial losses and even threaten human safety. To reveal the incorrect behaviors in DNN and repair them, DNN developers often collect rich unlabeled datasets from the natural world and label them to test the DNN models. However, properly labeling a large number of unlabeled datasets is a highly expensive and time-consuming task. To address the above-mentioned problem, we propose NSS, Neuron Sensitivity guided test case Selection, which can reduce the labeling time by selecting valuable test cases from unlabeled datasets. NSS leverages the internal neuron's information induced by test cases to select valuable test cases, which have high confidence in causing the model to behave incorrectly. We evaluate NSS with four widely used datasets and four well-designed DNN models compared to SOTA baseline methods. The results show that NSS performs well in assessing the test cases' probability of fault triggering and model improvement capabilities. Specifically, compared with baseline approaches, NSS obtains a higher fault detection rate~(e.g., when selecting 5\% test case from the unlabeled dataset in MNIST \& LeNet1 experiment, NSS can obtain 81.8\% fault detection rate, 20\% higher than baselines)

Unveiling the dynamical diversity of quantum dot lasers subject to optoelectronic feedback

This paper investigates experimentally and numerically the nonlinear dynamics of an epitaxial quantum dot laser on silicon subjected to optoelectronic feedback. Experimental results showcase a diverse range of dynamics, encompassing square wave patterns, quasi-chaotic states, and mixed waveforms exhibiting fast and slow oscillations. These measurements unequivocally demonstrate that quantum dot lasers on silicon readily and stably generate a more extensive repertoire of nonlinear dynamics compared to quantum well lasers. This pronounced sensitivity of quantum dot lasers to optoelectronic feedback represents a notable departure from their inherent insensitivity to optical feedback arising from reflections. Moreover, based on the Ikeda-like model, our simulations illustrate that the inherent characteristics of quantum dot lasers on silicon enable rapid and diverse dynamic transformations in response to optoelectronic feedback. The emergence of these exotic dynamics paves the way for further applications like integrated optical clocks, optical logic, and optical computing