When 3D Bounding-Box Meets SAM: Point Cloud Instance Segmentation with Weak-and-Noisy Supervision

Learning from bounding-boxes annotations has shown great potential in weakly-supervised 3D point cloud instance segmentation. However, we observed that existing methods would suffer severe performance degradation with perturbed bounding box annotations. To tackle this issue, we propose a complementary image prompt-induced weakly-supervised point cloud instance segmentation (CIP-WPIS) method. CIP-WPIS leverages pretrained knowledge embedded in the 2D foundation model SAM and 3D geometric prior to achieve accurate point-wise instance labels from the bounding box annotations. Specifically, CP-WPIS first selects image views in which 3D candidate points of an instance are fully visible. Then, we generate complementary background and foreground prompts from projections to obtain SAM 2D instance mask predictions. According to these, we assign the confidence values to points indicating the likelihood of points belonging to the instance. Furthermore, we utilize 3D geometric homogeneity provided by superpoints to decide the final instance label assignments. In this fashion, we achieve high-quality 3D point-wise instance labels. Extensive experiments on both Scannet-v2 and S3DIS benchmarks demonstrate that our method is robust against noisy 3D bounding-box annotations and achieves state-of-the-art performance

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

Chaos-assisted two-octave-spanning microcombs

Since its invention, optical frequency comb has revolutionized a broad range of subjects from metrology to spectroscopy. The recent development of microresonator-based frequency combs (microcombs) provides a unique pathway to create frequency comb systems on a chip. Indeed, microcomb-based spectroscopy, ranging, optical synthesizer, telecommunications and astronomical calibrations have been reported recently. Critical to many of the integrated comb systems is the broad coverage of comb spectra. Here, microcombs of more than two-octave span (450 nm to 2,008 nm) is demonstrated through χ^((2)) and χ^((3)) nonlinearities in a deformed silica microcavity. The deformation lifts the circular symmetry and creates chaotic tunneling channels that enable broadband collection of intracavity emission with a single waveguide. Our demonstration introduces a new degree of freedom, cavity deformation, to the microcomb studies, and our microcomb spectral range is useful for applications in optical clock, astronomical calibration and biological imaging

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\%

Speculative Decoding: Exploiting Speculative Execution for Accelerating Seq2seq Generation

We propose Speculative Decoding (SpecDec), for the first time ever, to formally study exploiting the idea of speculative execution to accelerate autoregressive (AR) decoding. Speculative Decoding has two innovations: Spec-Drafter -- an independent model specially optimized for efficient and accurate drafting -- and Spec-Verification -- a reliable method for verifying the drafted tokens efficiently in the decoding paradigm. Experimental results on various seq2seq tasks including machine translation and abstractive summarization show our approach can achieve around $5\times$ speedup for the popular Transformer architectures with comparable generation quality to beam search decoding, refreshing the impression that the draft-then-verify paradigm introduces only $1.4\times$$\sim$$2\times$ speedup. In addition to the remarkable speedup, we also demonstrate 3 additional advantages of SpecDec, revealing its practical value for accelerating generative models in real-world applications. Our models and codes are available at https://github.com/hemingkx/SpecDec.Comment: $\textbf{v1-v4}$ (Early 2022): Initially announced with the name "Generalized Aggressive Decoding"; $\textbf{v5}$ (September 2022): Renamed to "Speculative Decoding" as the ICLR'23 submission (https://openreview.net/pdf?id=H-VlwsYvVi), marking $\textbf{the first time}$ "Speculative Decoding" has been publicly proposed. $\textbf{v6}$: EMNLP'23 Findings camera read

Intrathoracic Endotracheal Metastasis from Nasopharyngeal Carcinoma: A First Case Report and Review of the Literature

Intrathoracic endotracheal metastasis from a very distant site is extremely rare. We report the first case of such a disease in a 68-year-old man with nasopharyngeal carcinoma who presented with a cough and hemoptysis 34 months after finishing radiotherapy. Prior to tracheal metastasis, he developed a solitary metastasis in the lung and underwent chemotherapy followed by radiotherapy. Computed tomography showed the presence of an enlarged lymph node in the para-aortic arch. Fiberoptic bronchoscopy revealed an endotracheal tumor 1 cm above the carina. Histological and immunohistochemical analyses confirmed its nasopharyngeal origin. He was treated with conventional radiotherapy and three-dimensional conformal radiotherapy; complete tumor remission was achieved. He died of nonmalignant disease with no signs of tumor recurrence 2 years after treatment completion. Radiotherapy may be an appropriate management approach to achieve long-term tumor control for this disease

An improved multi-strategy beluga whale optimization for global optimization problems

This paper presents an improved beluga whale optimization (IBWO) algorithm, which is mainly used to solve global optimization problems and engineering problems. This improvement is proposed to solve the imbalance between exploration and exploitation and to solve the problem of insufficient convergence accuracy and speed of beluga whale optimization (BWO). In IBWO, we use a new group action strategy (GAS), which replaces the exploration phase in BWO. It was inspired by the group hunting behavior of beluga whales in nature. The GAS keeps individual belugas whales together, allowing them to hide together from the threat posed by their natural enemy, the tiger shark. It also enables the exchange of location information between individual belugas whales to enhance the balance between local and global lookups. On this basis, the dynamic pinhole imaging strategy (DPIS) and quadratic interpolation strategy (QIS) are added to improve the global optimization ability and search rate of IBWO and maintain diversity. In a comparison experiment, the performance of the optimization algorithm (IBWO) was tested by using CEC2017 and CEC2020 benchmark functions of different dimensions. Performance was analyzed by observing experimental data, convergence curves, and box graphs, and the results were tested using the Wilcoxon rank sum test. The results show that IBWO has good optimization performance and robustness. Finally, the applicability of IBWO to practical engineering problems is verified by five engineering problems