arXiv subjects

Bo Song

Publications and source records attributed to Bo Song.

At least 19 recordsLinked to original sources

arXiv2026

Quantum Quenches across the Bose-glass Transition

Due to their intrinsic interplay between long-range order and quasi-disorder, quasicrystalline systems provide a rich platform for investigating novel quantum phenomena. Here we study the non-equilibrium dynamics following quantum quenches across the superfluid to Bose-glass transition using ultracold bosons in an optical quasicrystal. Fast quenches into the Bose glass regime induce a quantum walk in momentum space that spreads over increasingly higher momentum orders. Conversely, quenches into the superfluid regime initiate a real-space quantum walk of initially localized atoms, forming a light-cone-like structure bounded by Lieb-Robinson limits. Characteristic timescales reveal strong links to the underlying Hamiltonian and are governed primarily by quasi-disorder strength in the Bose glass phase, and by tunneling strength in the superfluid phase. Finally, we analyze slower quenches into deeper lattices and observe the decay of coherence across the phase transition.

cond-mat.quant-gas↗

arXiv2026

Toward Quantum Advantage in Learning Parities with Structured Noise via Lower Bound Optimization of the Condition Number

Learning Parities with Structured Noise (LPSN) can be reduced to solving nonlinear Boolean systems. In quantum computing, such systems are typically transformed into Macaulay linear systems and solved via quantum linear system algorithms, a process severely limited by the condition number. To address this, we propose a novel reduction method for Macaulay linear systems. Under the assumptions of Ding et al., we derive a condition number lower bound incorporating a scaling factor. This reduction not only guarantees efficient quantum state preparation but also exhibits a distinct advantage regarding the condition number interval relative to the reduced right-hand side vector, thereby reducing the lower bound of the condition number and ultimately optimizing the upper bound on the time complexity of the quantum algorithm for solving Boolean systems. Furthermore, applying this improved quantum algorithm to LPSN significantly reduces sample complexity by exploiting the Macaulay system's solution structure. We further provide a concrete logical-level quantum resource estimate, demonstrating that the optimized condition number translates directly into a reduction in circuit width, depth, and gate count. Finally, we establish an algorithm selection strategy by systematically comparing quantum and classical approaches across noise pattern adaptability, sample complexity, and time complexity. Results demonstrate that our quantum algorithm exhibits the potential to outperform classical counterparts under specific parameter regimes.

cs.CR↗

arXiv2026

Collective Coherent Perfect Absorption in a Synthetic Photon-Phonon Lattice

Coherent perfect absorption (CPA) has emerged as a powerful paradigm for controlling classical and quantum light, and has been demonstrated across a broad range of physical platforms. CPA realized in optomechanics relies on interference between the input field and the mechanically scattered field, but is intrinsically confined to the weak-cooperativity regime, resulting in a narrow absorption bandwidth and the mechanical mode remains thermally occupied. Here, we experimentally demonstrate collective interference-induced CPA in a synthetic photon--phonon lattice. By harnessing cavity-reservoir-mediated interactions among Floquet lattice sites, collective interference shifts the CPA condition deep into the high-cooperativity regime. This enables CPA to coexist with ground-state cooling of the mechanical oscillator, together with a broadened non-Lorentzian absorption lineshape and a singular group-delay response. Our results identify collective interference as a route to quantum-compatible perfect absorption and long-lived quantum storage.

quant-ph↗

arXiv2026

BrainFusionNet: a deep learning and XAI model to understand local, global, and sequential features of MRI images for improved brain tumour detection

The noise of Magnetic Resonance Imaging MRI poses challenges for Deep Learning DL when tumor boundaries are obscured tumor location and appearance are complex Therefore we develop BrainFusionNet that combines Convolutional Neural Networks CNNs Vision Transformers ViT and Gated Recurrent Units GRUs to extract spatial contextual and sequential features from MRI images for improved brain tumor classification Furthermore explainable AI such as SHAP LIME and GradCAM are integrated to visualise and highlight image regions that contribute to BrainFusionNets decisionmaking process The proposed BrainFusionNet model is evaluated on two publicly available MRI datasets Kfold validation suggests 98 accuracy on both datasets The model was compared with the six stateoftheart SOTA CNNs and transfer learning Among the SOTA CNNs DenseNet121 and VGG16 achieved the highest accuracy of 96 The novelty of BrainFusionNet is that the hybrid model effectively extracts local and global features from MRI images even in smallscale tumor regions and small tumor sizes The model has a balanced sequential CNN architecture to capture lowlevel and deeperlayer features a customized ViT that captures local features stabilizes gradient flow and reduces the risk of vanishing gradients during MRI image training The CNN and ViT outputs are fed into a GRU for final classification Furthermore we analyze pixel intensities to determine whether MRI image quality affects image classification Our findings are very novel in image interpretation as we found that the distribution of pixel intensities in MRI images affects DL performance

cs.CV↗

arXiv2026

Thouless Pumping of Large Chern Numbers in Optical Floquet Quasicrystals

Chern numbers are central to correlated and topological phenomena, yet most topological systems are associated with Chern numbers of order unity. Here we propose a scheme to achieve large Chern numbers in an optical Floquet quasicrystal with cold atoms, which can be directly measured via Thouless pumping. We study the quasienergy spectrum of Floquet quasicrystals and characterize the emergent Chern numbers using gap labeling theorem. We further investigate the Thouless pumping in the Floquet quasicrystal at different driving frequencies and amplitudes, revealing the connection between transport features and the quasienergy spectrum. Our findings open new avenues for exploring rich topological dynamics in Floquet quasicrystals and realizing fractional Chern insulating states.

cond-mat.quant-gas↗

arXiv2026

TopoHR: Hierarchical Centerline Representation for Cyclic Topology Reasoning in Driving Scenes with Point-to-Instance Relations

Topology reasoning is crucial for autonomous driving. Current methods primarily focus on instance-level learning for centerline detection, followed by a sequential module for topology reasoning that relies on simplified MLP layers. Moreover, they often neglect the importance of \textit{point-to-instance} (P2I) relationships in topology reasoning. To address these limitations, we present TopoHR (Topological Hierarchical Representation), a novel end-to-end framework that establishes cyclic interaction between centerline detection and topology reasoning, allowing them to iteratively enhance each other. Specifically, we introduce a hierarchical centerline representation including point queries, instance queries, and semantic representations. These multi-level features are seamlessly integrated and fused within a hierarchical centerline decoder. Furthermore, we design a hierarchical topology reasoning module that captures both fine-grained P2I relationships and global instance-to-instance (I2I) connections within a unified architecture. With these novel components, TopoHR ensures accurate and robust topology reasoning. On the OpenLane-V2 benchmark, TopoHR refreshes state-of-the-art performance with significant improvements. Notably, compared with previous best results, TopoHR achieves +3.8 in $\mathrm{DET}_{\text{l}}$, +5.4 in $\mathrm{TOP}_{\text{ll}}$ on $\text{subset_A}$ and +11.0 in $\mathrm{DET}_{\text{l}}$, +7.9 in $\mathrm{TOP}_{\text{ll}}$ on $\text{subset_B}$, validating the effectiveness of the proposed components. The code will be shared publicly at https://github.com/Yifeng-Bai/TopoHR.git.

cs.CV↗

arXiv2026

Optical transport of cold atoms to quantum degeneracy

Efficient transport of cold atoms is essential for continuous operation, enabling applications ranging from atomic lasers to continuously operated qubits. However, deep potentials required to overcome vibrations, axial trap nonuniformity and insufficient cooling have limited transport of cold atoms near quantum degeneracy. Here we demonstrate rapid optical transport of cold atoms to Bose-Einstein condensation using a moving optical lattice formed by two Bessel beams. A gas of $3 \times 10^5$ ytterbium atoms at a temperature of $340\,$nK is transported over $34\,$cm in $350\,$ms with efficiency over $60\%$. Furthermore, a degenerate gas of $1 \times 10^5$ atoms with a $40\%$ condensate fraction emerges from the phase synchronization process driven by atomic interactions. This demonstration enables the fast preparation of ultracold atomic beams and large-scale atom arrays for quantum sensing, simulation and computing.

cond-mat.quant-gas↗

arXiv2025

Enhancing 3D Semantic Scene Completion with a Refinement Module

We propose ESSC-RM, a plug-and-play Enhancing framework for Semantic Scene Completion with a Refinement Module, which can be seamlessly integrated into existing SSC models. ESSC-RM operates in two phases: a baseline SSC network first produces a coarse voxel prediction, which is subsequently refined by a 3D U-Net-based Prediction Noise-Aware Module (PNAM) and Voxel-level Local Geometry Module (VLGM) under multiscale supervision. Experiments on SemanticKITTI show that ESSC-RM consistently improves semantic prediction performance. When integrated into CGFormer and MonoScene, the mean IoU increases from 16.87% to 17.27% and from 11.08% to 11.51%, respectively. These results demonstrate that ESSC-RM serves as a general refinement framework applicable to a wide range of SSC models.

cs.CV↗

arXiv2025

R-Net: A Reliable and Resource-Efficient CNN for Colorectal Cancer Detection with XAI Integration

State-of-the-art (SOTA) Convolutional Neural Networks (CNNs) are criticized for their extensive computational power, long training times, and large datasets. To overcome this limitation, we propose a reasonable network (R-Net), a lightweight CNN only to detect and classify colorectal cancer (CRC) using the Enteroscope Biopsy Histopathological Hematoxylin and Eosin Image Dataset (EBHI). Furthermore, six SOTA CNNs, including Multipath-based CNNs (DenseNet121, ResNet50), Depth-based CNNs (InceptionV3), width-based multi-connection CNNs (Xception), depth-wise separable convolutions (MobileNetV2), spatial exploitation-based CNNs (VGG16), Transfer learning, and two ensemble models are also tested on the same dataset. The ensemble models are a multipath-depth-width combination (DenseNet121-InceptionV3-Xception) and a multipath-depth-spatial combination (ResNet18-InceptionV3-VGG16). However, the proposed R-Net lightweight achieved 99.37% accuracy, outperforming MobileNet (95.83%) and ResNet50 (96.94%). Most importantly, to understand the decision-making of R-Net, Explainable AI such as SHAP, LIME, and Grad-CAM are integrated to visualize which parts of the EBHI image contribute to the detection and classification process of R-Net. The main novelty of this research lies in building a reliable, lightweight CNN R-Net that requires fewer computing resources yet maintains strong prediction results. SOTA CNNs, transfer learning, and ensemble models also extend our knowledge on CRC classification and detection. XAI functionality and the impact of pixel intensity on correct and incorrect classification images are also some novelties in CRC detection and classification.

q-bio.TO↗

arXiv2025

Data-driven balanced truncation for second-order systems via the approximate Gramians

This paper studies the data-driven balanced truncation (BT) method for second-order systems based on the measurements in the frequency domain. The basic idea is to approximate Gramians used the numerical quadrature rules, and establish the relationship between the main quantities in the procedure of BT with the sample data, which paves the way for the execution of BT in a nonintrusive manner. We construct the structure-preserving reduced models approximately based on the samples of second-order systems with proportional damping, and provide the detailed execution of the data-driven counterpart of BT in real-value arithmetic. The low-rank approximation to the solution of Sylvester equations is also introduced to speed up the process of the proposed approach when a large amount of samples involved in the modeling. The performance of our approach is illustrated in detail via two numerical examples.

math.NA↗

arXiv2025

Two-color magneto-optical trapping of ytterbium atoms

We report laser cooling and trapping of ytterbium atoms in a two-color magneto-optical trap (MOT). Benefited from both the broad singlet transition ($^1\text{S}_0\rightarrow {}^1\text{P}_1$) and the narrow intercombination transition ($^1\text{S}_0\rightarrow {}^3\text{P}_1$) of ytterbium atoms, the two-color MOT enables rapid loading and efficient cooling. We systematically investigate the shielding effect of the intercombination transition by examining the atom loading and loss rates of single-color and two-color MOTs. Our findings are general and can be extended to other alkaline earth(-like) atoms.

physics.atom-ph↗

arXiv2024

DVS: Blood cancer detection using novel CNN-based ensemble approach

Blood cancer can only be diagnosed properly if it is detected early. Each year, more than 1.24 million new cases of blood cancer are reported worldwide. There are about 6,000 cancers worldwide due to this disease. The importance of cancer detection and classification has prompted researchers to evaluate Deep Convolutional Neural Networks for the purpose of classifying blood cancers. The objective of this research is to conduct an in-depth investigation of the efficacy and suitability of modern Convolutional Neural Network (CNN) architectures for the detection and classification of blood malignancies. The study focuses on investigating the potential of Deep Convolutional Neural Networks (D-CNNs), comprising not only the foundational CNN models but also those improved through transfer learning methods and incorporated into ensemble strategies, to detect diverse forms of blood cancer with a high degree of accuracy. This paper provides a comprehensive investigation into five deep learning architectures derived from CNNs. These models, namely VGG19, ResNet152v2, SEresNet152, ResNet101, and DenseNet201, integrate ensemble learning techniques with transfer learning strategies. A comparison of DenseNet201 (98.08%), VGG19 (96.94%), and SEresNet152 (90.93%) shows that DVS outperforms CNN. With transfer learning, DenseNet201 had 95.00% accuracy, VGG19 had 72.29%, and SEresNet152 had 94.16%. In the study, the ensemble DVS model achieved 98.76% accuracy. Based on our study, the ensemble DVS model is the best for detecting and classifying blood cancers.

eess.IV↗

arXiv2024

A comprehensive study on Blood Cancer detection and classification using Convolutional Neural Network

Over the years in object detection several efficient Convolutional Neural Networks (CNN) networks, such as DenseNet201, InceptionV3, ResNet152v2, SEresNet152, VGG19, Xception gained significant attention due to their performance. Moreover, CNN paradigms have expanded to transfer learning and ensemble models from original CNN architectures. Research studies suggest that transfer learning and ensemble models are capable of increasing the accuracy of deep learning (DL) models. However, very few studies have conducted comprehensive experiments utilizing these techniques in detecting and localizing blood malignancies. Realizing the gap, this study conducted three experiments; in the first experiment -- six original CNNs were used, in the second experiment -- transfer learning and, in the third experiment a novel ensemble model DIX (DenseNet201, InceptionV3, and Xception) was developed to detect and classify blood cancer. The statistical result suggests that DIX outperformed the original and transfer learning performance, providing an accuracy of 99.12%. However, this study also provides a negative result in the case of transfer learning, as the transfer learning did not increase the accuracy of the original CNNs. Like many other cancers, blood cancer diseases require timely identification for effective treatment plans and increased survival possibilities. The high accuracy in detecting and categorization blood cancer detection using CNN suggests that the CNN model is promising in blood cancer disease detection. This research is significant in the fields of biomedical engineering, computer-aided disease diagnosis, and ML-based disease detection.

eess.IV↗

arXiv2024

Realizing a spatially correlated lattice interferometer

Atom interferometers provide a powerful tool for measuring physical constants and testifying fundamental physics with unprecedented precision. Conventional atom interferometry focuses on the phase difference between two paths and utilizes matter waves with fixed coherence. Here, we report on realizing a Ramsey-Bord\'e interferometer of coherent matter waves dressed by a moving optical lattice in the gravity direction, and explore the resulting interference along multiple paths with tunable coherence. We investigate spatial correlations of atoms both within the lattice and between two arms by interferometry, and observe the emerging multiple interference peaks owing to the long-range coherence nature of the Bose-Einstein condensate. Our findings agree well with theoretical simulations, paving the way for high-precision interferometry with ultracold atoms.

cond-mat.quant-gas↗

arXiv2024

Automating the Training and Deployment of Models in MLOps by Integrating Systems with Machine Learning

This article introduces the importance of machine learning in real-world applications and explores the rise of MLOps (Machine Learning Operations) and its importance for solving challenges such as model deployment and performance monitoring. By reviewing the evolution of MLOps and its relationship to traditional software development methods, the paper proposes ways to integrate the system into machine learning to solve the problems faced by existing MLOps and improve productivity. This paper focuses on the importance of automated model training, and the method to ensure the transparency and repeatability of the training process through version control system. In addition, the challenges of integrating machine learning components into traditional CI/CD pipelines are discussed, and solutions such as versioning environments and containerization are proposed. Finally, the paper emphasizes the importance of continuous monitoring and feedback loops after model deployment to maintain model performance and reliability. Using case studies and best practices from Netflix, the article presents key strategies and lessons learned for successful implementation of MLOps practices, providing valuable references for other organizations to build and optimize their own MLOps practices.

cs.SE↗

arXiv2024

RNA Secondary Structure Prediction Using Transformer-Based Deep Learning Models

The Human Genome Project has led to an exponential increase in data related to the sequence, structure, and function of biomolecules. Bioinformatics is an interdisciplinary research field that primarily uses computational methods to analyze large amounts of biological macromolecule data. Its goal is to discover hidden biological patterns and related information. Furthermore, analysing additional relevant information can enhance the study of biological operating mechanisms. This paper discusses the fundamental concepts of RNA, RNA secondary structure, and its prediction.Subsequently, the application of machine learning technologies in predicting the structure of biological macromolecules is explored. This chapter describes the relevant knowledge of algorithms and computational complexity and presents a RNA tertiary structure prediction algorithm based on ResNet. To address the issue of the current scoring function's unsuitability for long RNA, a scoring model based on ResNet is proposed, and a structure prediction algorithm is designed. The chapter concludes by presenting some open and interesting challenges in the field of RNA tertiary structure prediction.

q-bio.BM↗

arXiv2024

ViTCN: Vision Transformer Contrastive Network For Reasoning

Machine learning models have achieved significant milestones in various domains, for example, computer vision models have an exceptional result in object recognition, and in natural language processing, where Large Language Models (LLM) like GPT can start a conversation with human-like proficiency. However, abstract reasoning remains a challenge for these models, Can AI really thinking like a human? still be a question yet to be answered. Raven Progressive Matrices (RPM) is a metric designed to assess human reasoning capabilities. It presents a series of eight images as a problem set, where the participant should try to discover the underlying rules among these images and select the most appropriate image from eight possible options that best completes the sequence. This task always be used to test human reasoning abilities and IQ. Zhang et al proposed a dataset called RAVEN which can be used to test Machine Learning model abstract reasoning ability. In this paper, we purposed Vision Transformer Contrastive Network which build on previous work with the Contrastive Perceptual Inference network (CoPiNet), which set a new benchmark for permutationinvariant models Raven Progressive Matrices by incorporating contrast effects from psychology, cognition, and education, and extends this foundation by leveraging the cutting-edge Vision Transformer architecture. This integration aims to further refine the machine ability to process and reason about spatial-temporal information from pixel-level inputs and global wise features on RAVEN dataset.

cs.CV↗

arXiv2024

CurveFormer++: 3D Lane Detection by Curve Propagation with Temporal Curve Queries and Attention

In autonomous driving, accurate 3D lane detection using monocular cameras is important for downstream tasks. Recent CNN and Transformer approaches usually apply a two-stage model design. The first stage transforms the image feature from a front image into a bird's-eye-view (BEV) representation. Subsequently, a sub-network processes the BEV feature to generate the 3D detection results. However, these approaches heavily rely on a challenging image feature transformation module from a perspective view to a BEV representation. In our work, we present CurveFormer++, a single-stage Transformer-based method that does not require the view transform module and directly infers 3D lane results from the perspective image features. Specifically, our approach models the 3D lane detection task as a curve propagation problem, where each lane is represented by a curve query with a dynamic and ordered anchor point set. By employing a Transformer decoder, the model can iteratively refine the 3D lane results. A curve cross-attention module is introduced to calculate similarities between image features and curve queries. To handle varying lane lengths, we employ context sampling and anchor point restriction techniques to compute more relevant image features. Furthermore, we apply a temporal fusion module that incorporates selected informative sparse curve queries and their corresponding anchor point sets to leverage historical information. In the experiments, we evaluate our approach on two publicly real-world datasets. The results demonstrate that our method provides outstanding performance compared with both CNN and Transformer based methods. We also conduct ablation studies to analyze the impact of each component.

cs.CV↗