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Four-wave mixing Bragg scattering for small frequency shift from silicon coupled microrings

Chinese Physics B 2025年第1期34卷 351-355页

作者： Chang Zhao Chao Wu Pingyu Zhu Yuxing Du Yan Wang Miaomiao Yu Kaikai Zhang Ping Xu Institute for Quantum Information&State Key Laboratory of High Performance Computing College of Computer Science and TechnologyNational University of Defense TechnologyChangsha 410073China

Frequency conversion is pivotal in nonlinear optics and quantum optics for manipulating and translating light signals across different wavelength *** frequency conversion between two light beams with a small frequency interval is a central *** this work,we design a pair of coupled silicon microrings wherein coupled-induced modesplitting exists to achieve a small frequency shift by the process of four-wave mixing Bragg *** an example,the signal can be up or down converted to the idler which is 15.5 GHz spaced when two pumps align with another pair of split *** results unveil the potential of coupled microring resonators for small interval frequency conversion in a high-fidelity,all-optical,and signal processing quantum frequency interface.

关键词： four-wave mixing Bragg scattering silicon coupled microrings

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Effective Scene Graph Generation by Statistical Relation Distillation

Effective Scene Graph Generation by Statistical Relation Dis...

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2025 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2025

作者： Nguyen, Thanh-Son Yang, Hong Fernando, Basura Institute of High Performance Computing Agency for Science Technology and Research Singapore Agency for Science Technology and Research Singapore

ISBN: (纸本)9798331510831

Annotating scene graphs for images is a time-consuming task, resulting in many instances of missing relations within existing datasets. In this paper, we introduce the Statistical Relation Distillation (SRD) method to enhance scene graph datasets. SRD leverages human-annotated relations alongside object-to-object and predicate-to-predicate similarities to reinforce the existence likelihood of scene graph relations. Moreover, SRD can augment relational frequency using relations of non-selected object and predicate categories that are usually omitted by scene graph generation (SGG) task. The output from SRD derives the prior probability which is combined with model-predicted probabilities to annotate missing relations in training images and subsequently re-train SGG models on the augmented dataset. We evaluate our proposed method on Visual Genome and GQA-200 datasets. Experimental results show that training on the augmented dataset enhances the performance of prominent scene-graph generation models. The implementation code is at https://***/LUNAProject22/SRD. © 2025 IEEE.

关键词： Visualization

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An Efficient Fixture Removal Embedded Modeling Method Based on TDR and CNN Technique

IEEE Transactions on Signal and Power Integrity

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IEEE Transactions on Signal and Power Integrity 2025年 4卷 132-135页

作者： Tang, Si-Yao Wei, Xing-Chang Gao, Richard Xian-Ke Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China Agency for Science Technology and Research Institute of High Performance Computing 138632 Singapore

S-parameters are typically employed in equivalent circuit (EC) modeling of electronic devices. However, for existing modeling procedures, the impact of fixtures on S-parameter measurement cannot be neglected and needs to be eliminated through de-embedding before modeling. This letter proposes a new approach that integrates fixture removal into the modeling procedure by combining time-domain reflection and convolutional neural network techniques. The proposed approach bypasses the need for separate de-embedding, allowing for direct derivation of the EC model. Different to the traditional modeling procedure, its advantages in simplifying the modeling procedure and avoiding the errors introduced by de-embedding have been validated by the physical measurement. © 2022 IEEE.

关键词： Scattering parameters

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Situational Scene Graph for Structured Human-Centric Situation Understanding

Situational Scene Graph for Structured Human-Centric Situati...

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2025 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2025

作者： Sugandhika, Chinthani Li, Chen Rajan, Deepu Fernando, Basura College of Computing and Data Science Nanyang Technological University Singapore Centre for Frontier Ai Research Agency for Science Technology and Research Singapore Institute of High-Performance Computing Agency for Science Technology and Research Singapore

ISBN: (纸本)9798331510831

Graph based representation has been widely used in modelling spatio-temporal relationships in video understanding. Although effective, existing graph-based approaches focus on capturing the human-object relationships while ignoring fine-grained semantic properties of the action components. These semantic properties are crucial for understanding the current situation, such as where does the action takes place, what tools are used and functional properties of the objects. In this work, we propose a graph-based representation called Situational Scene Graph (SSG) to encode both human-object relationships and the corresponding semantic properties. The semantic details are represented as predefined roles and values inspired by situation frame, which is originally designed to represent a single action. Based on our proposed representation, we introduce the task of situational scene graph generation and propose a multi-stage pipeline Interactive and Complementary Network (InComNet) to address the task. Given that the existing datasets are not applicable to the task, we further introduce a SSG dataset whose annotations consist of semantic role-value frames for human, objects and verb predicates of human-object relations. Finally, we demonstrate the effectiveness of our proposed SSG representation by testing on different downstream tasks. Experimental results show that the unified representation can not only benefit predicate classification and semantic role-value classification, but also benefit reasoning tasks on human-centric situation understanding. Code and data are available at https://***/LUNAProject22/SSG. © 2025 IEEE.

关键词： Human engineering

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Can LLMs Perform Structured Graph Reasoning Tasks? 27th

Can LLMs Perform Structured Graph Reasoning Tasks?

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Agrawal, Palaash Vasania, Shavak Tan, Cheston Center for Frontier AI Research Agency for Science Technology and Research Singapore Singapore Institute of High Performance Computing Agency for Science Technology and Research Singapore Singapore UWCSEA Dover High School Singapore Singapore

ISBN: (纸本)9783031781711

Pretrained Large Language Models (LLMs) have demonstrated various reasoning capabilities through language-based prompts alone, particularly in unstructured task settings (i.e. tasks purely based on language semantics). However, LLMs often struggle with structured tasks, because of the inherent incompatibility of input representation. Reducing structured tasks to uni-dimensional language semantics often renders the problem trivial. Keeping the trade-off between LLM compatibility and structure complexity in mind, we design various graph reasoning tasks as a proxy to semi-structured tasks in this paper, in order to test LLMs’ ability to use representations beyond plain text. In particularly, we design 10 distinct problems of graph traversal, each representing increasing levels of complexity, and benchmark 5 different instruct-finetuned LLMs (GPT-4, GPT-3.5, Claude-2, Llama-2 and Palm-2) on the aforementioned tasks. Further, we analyse the performance of models across various settings such as varying sizes of graphs, as well as different forms of k-shot prompting. We highlight various limitations, biases and properties of LLMs through this benchmarking process, such as an inverse relation to the average degrees of freedom of traversal per node in graphs, the overall negative impact of k-shot prompting on graph reasoning tasks, and a positive response bias which prevents LLMs from identifying the absence of a valid solution. Finally, we introduce a new prompting technique specially designed for graph traversal tasks (PathCompare), which demonstrates a notable increase in the performance of LLMs in comparison to standard prompting techniques such as Chain-of-Thought (CoT) (The code for reproducing the results of this paper can be found at https://***/PalaashAgrawal/LLMGraphTraversal). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semantics

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A fully connected polarization-entangled network via integrated spontaneous four-wave mixing engineering

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Science China(Physics,Mechanics & Astronomy) 2025年第06期Mechanics & Astronomy) .卷 76-83页

作者： Pingyu Zhu Yan Wang Yuxing Du Miaomiao Yu Kaikai Zhang Kun Wang Ping Xu Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer Science and TechnologyNational University of Defense Technology Hefei National Laboratory

Quantum communication is rapidly developing and is gradually being commercialized due to its technological maturity. Establishing dense communication links among multiple users in a scalable and efficient way is of great significance for realizing a large-scale quantum communication network. Here, we propose a novel scheme to construct a fully connected polarizationentangled network, utilizing the engineering of spontaneous four-wave mixings(SFWMs) and a path-polarization converter. It does not require active optical switches which limit the communication speed, or trusted nodes which lead to potential security risks. The required frequency channels in the network grow linearly with the number of users. We experimentally demonstrate a six-user fully connected network with on-chip SFWM processes motivated by four pumps. Each user in the network receives a frequency channel, and all fifteen connections between the users are implemented simultaneously. Our work opens up a promising scheme to efficiently construct fully connected large-scale networks.

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Predicting the Next Action by Modeling the Abstract Goal 27th

Predicting the Next Action by Modeling the Abstract Goal

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Roy, Debaditya Fernando, Basura Institute of High-Performance Computing Agency for Science Technology and Research Singapore Singapore Centre for Frontier AI Research Agency for Science Technology and Research Singapore Singapore

ISBN: (纸本)9783031783531

The problem of predicting human actions from observed videos is an inherently uncertain one. We present an action anticipation model that leverages latent goal information to reduce the uncertainty in future predictions. We develop a latent variable representing goal information called abstract goal which is conditioned on observed sequences of visual features for action anticipation. We design the abstract goal as a distribution whose parameters are estimated using a variational recurrent model. We sample multiple candidates for the next action and use goal consistency criterion to determine the best candidate that follows from the abstract goal. Our method obtains impressive results on the very challenging Epic-Kitchens55 (EK55) and good results in Epic-Kitchens100 (EK100) datasets. Code is available at https://***/LAHAproject/Abstract_Goal. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Prediction models

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Ultra-low power MoS2 optoelectronic synapse with wavelength sensitivity for color target recognition

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Science China(Information Sciences) 2025年第4期68卷 187-196页

作者： Bo WEI Yabo CHEN Xiaotong HAN Yan KANG Bujia LIANG Cheng LI Xiaokuo YANG Liang FANG Yuanxi PENG Institute for Quantum Information & State Key Laboratory of High Performance Computing College of ComputerNational University of Defense Technology Fundamentals Department Air Force Engineering University College of Advanced Interdisciplinary Studies National University of Defense Technology Institute of Quantum Information Science and Technology College of ScienceNational University of Defense Technology College of Computer National University of Defense Technology

Optoelectronic synapses that integrate visual perception and pre-processing hold significant potential for neuromorphic vision systems（NVSs）. However, due to a lack of wavelength sensitivity, existing NVS mainly focuses on gray-scale image processing, making it challenging to recognize color images. Additionally, the high power consumption of optoelectronic synapses, compared to the 10 fJ energy consumption of biological synapses, limits their broader application. To address these challenges, an energy-efficient NVS capable of color target recognition in a noisy environment was developed,utilizing a MoS2optoelectronic synapse with wavelength sensitivity. Benefiting from the distinct photon capture capabilities of 450, 535, and 650 nm light, the optoelectronic synapse exhibits wavelength-dependent synaptic plasticity, including excitatory postsynaptic current（EPSC）, paired-pulse facilitation（PPF）, and long-term plasticity（LTP）. These properties can effectively mimic the visual memory and color discrimination functions of the human vision system. Results demonstrate that the NVS, based on MoS2optoelectronic synapses, can eliminate the color noise at the sensor level, increasing color image recognition accuracy from 50% to 90%. Importantly, the optoelectronic synapse operates at a low voltage spike of0.0005 V, consuming only 0.075 fJ per spike, surpassing the energy efficiency of both existing optoelectronic and biological synapses. This ultra-low power, color-sensitive device eliminates the need for color filters and offers great promise for future deployment in filter-free NVS.

关键词： optoelectronic synapse neuromorphic vision system color recognition 2D materials image pre-processing

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Learnable Prompting SAM-Induced Knowledge Distillation for Semi-Supervised Medical Image Segmentation

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IEEE Transactions on Medical Imaging 2025年第5期44卷 2295-2306页

作者： Huang, Kaiwen Zhou, Tao Fu, Huazhu Zhang, Yizhe Zhou, Yi Gong, Chen Liang, Dong Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China Institute of High-Performance Computing Agency for Science Technology and Research Fusionopolis Way 138632 Singapore Southeast University School of Computer Science and Engineering Nanjing211189 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Lauterbur Research Center for Biomedical Imaging Research Center for Medical AI Shenzhen518055 China

The limited availability of labeled data has driven advancements in semi-supervised learning for medical image segmentation. Modern large-scale models tailored for general segmentation, such as the Segment Anything Model (SAM), have revealed robust generalization capabilities. However, applying these models directly to medical image segmentation still exposes performance degradation. In this paper, we propose a learnable prompting SAM-induced Knowledge distillation framework (KnowSAM) for semi-supervised medical image segmentation. Firstly, we propose a Multi-view Co-training (MC) strategy that employs two distinct sub-networks to employ a co-teaching paradigm, resulting in more robust outcomes. Secondly, we present a Learnable Prompt Strategy (LPS) to dynamically produce dense prompts and integrate an adapter to fine-tune SAM specifically for medical image segmentation tasks. Moreover, we propose SAM-induced Knowledge Distillation (SKD) to transfer useful knowledge from SAM to two sub-networks, enabling them to learn from SAM’s predictions and alleviate the effects of incorrect pseudo-labels during training. Notably, the predictions generated by our subnets are used to produce mask prompts for SAM, facilitating effective inter-module information exchange. Extensive experimental results on various medical segmentation tasks demonstrate that our model outperforms the state-of-the-art semi-supervised segmentation approaches. Crucially, our SAM distillation framework can be seamlessly integrated into other semi-supervised segmentation methods to enhance performance. © 1982-2012 IEEE.

关键词： Self-supervised learning

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Transforming desalination brine into highly reactive magnesium oxide and life cycle analysis

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Watershed Ecology and the Environment 2025年 7卷 36-46页

作者： Senevirathna, Hasanthi L. Lee, W.P. Cathie Wu, Shunnian Bai, Kewu Wu, Ping Entropic Interface Group Engineering Product Development Singapore University of Technology and Design 8 Somapah Road 487372 Singapore Institute of High-Performance Computing Agency for Science Technology and Research Fusionopolis Way #16-16 Connexis 138632 Singapore

The sustainable production of magnesium oxide (MgO) from reject brine at a desalination facility in Singapore presents a promising alternative to traditional building materials. This study investigates the properties and extraction methods of MgO derived from reject brine, focusing on environmental sustainability and resource efficiency. By utilizing a combination of thermodynamic modelling of concentrated salt aqueous solutions and experimental validation, we optimized the recovery process for Mg²⁺ from reject brine, achieving an impressive 99 % recovery rate to produce MgO with ∼ 98 % purity. The MgO produced, calcined at 700 °C, exhibits high reactivity and a large surface area of 58.01 m²/g, making it a highly viable option for various industrial applications. This approach addresses reject brine disposal challenges and emphasizes brine valorization, aligning with strict environmental regulations by treating both freshwater and concentrated brine as valuable products. The technology enables cost-effective reject brine treatment by recycling Mg²⁺ to produce sustainable Mg-based materials. A comprehensive Life Cycle Assessment (LCA) was conducted to evaluate the environmental impact of MgO production from reject brine. This analysis provides a thorough examination of the transformation process, assessing the sustainability of employing MgO in commercial applications. The findings of this study offer valuable insights for enhancing sustainability in various sectors, underscoring the promise of MgO as an environmentally friendly and cost-effective material. © 2025 The Authors

关键词： CO2 emission MgO Reactivity Reject brine Solution model

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