Accurate identification and classification of white blood cells are essential for diagnosing hematological malignancies and analyzing blood disorders. Existing approaches predominantly leverage masked autoencoders (MAEs) to extract intrinsic blood cell features through image reconstruction as a pretext task. However, these methods encounter two critical challenges: (1) their generalization performance deteriorates under domain shifts caused by variations in staining techniques, illumination conditions, and microscope settings, and (2) the learned data distribution often deviates from the true distribution of blood cell features. To overcome these limitations, we propose CD-CBC, a novel framework for cross-domain blood cell image classification that integrates contrastive representation learning with a denoising mechanism. CD-CBC consists of two key components: a LoRA-based segmentation anything model (LoRA-SAM) and a contrastive masked autoencoder (CMAE). LoRA-SAM mitigates shortcut learning in contrastive learning by eliminating background noise and platelet interference, while CMAE captures fine-grained semantic features and models spatial relationships, enhancing cross-domain robustness. Additionally, we introduce a denoising mechanism in the latent space, which guides the model to focus on unmasked patches during reconstruction, allowing it to better capture the true distribution of blood cell features. Extensive experiments on two benchmark blood cell datasets demonstrate that CD-CBC achieves superior cross-domain performance, reaching an average accuracy of 62.47%, which is 3.17% higher than the current state-of-the-art, thereby confirming its strong generalization capability.

The research on identifying drug-disease associations (DDAs) is widely used in scenarios such as drug development, clinical decision-making, and drug repurposing, holding significant biological and medical significance. Existing methods for drug-disease association prediction have achieved decent performance, they primarily rely on simplistic drug-disease association graphs or similarity graphs. These methods often struggle to capture the high-order correlations of complex multimodal data, limiting their ability to handle the complexity of data associations effectively. In addition, real drug-disease associations are highly sparse, posing a significant challenge to prediction accuracy. To tackle these issues, we propose a general hypergraph neural network framework for drug-disease association prediction based on hierarchical contrastive learning and cross-attention learning. It leverages hypergraph neural networks to learn representations of drugs and diseases carrying high-order correlations and strengthens representation quality using interactive attention learning and hierarchical contrastive learning. Meanwhile, the $\lambda$-weighted loss function is utilized to adapt to the high sparsity property of real drug-disease associations during model training and improve prediction performance. Extensive experiments demonstrate that DD-HGNN$^+$ surpasses other state-of-the-art methods in predicting drug-disease associations and further validation through case studies on Leukemia and Colorectal Neoplasms underscores its reliability.

Multi-view clustering (MVC) exploits the information captured from diverse views to partition data into different groups and attracts much attention recently. Despite significant progress, most MVC methods fuse multi-view information via one-stage fusion while neglecting the merits of multi-stage fusion which causes insufficient in utilizing rich information within data and therefore degrades the clustering performance. To this end, designing a functional framework that can fully exploit multi-view information becomes a key challenge in multi-view clustering research. In this paper, we propose a novel multi-stage fusion method, which elegantly unifies the late and early fusion into one unified framework, to capture sufficient information underlying the multi-view data and to effectively reduce the effect of low-quality views. Specifically, we construct a low dimensional latent representation from multi-view data by learning proper correlation among multi-view data in the early fusion stage. The late fusion establishes a new optimal combinational data partition from base partitions constructed by spectral clustering, which suppresses the influence of low-quality basic partitions. Then we couple the low dimensional latent representation with the learned combinational data partition to share the same cluster structure by$k$-means and maximization alignment. As a result, we collaboratively learn an accurate and robust partition representation for the following clustering task. Besides, the late fusion and early fusion are jointly learned to achieve mutual collaboration for better performance. Finally, an alternating optimization algorithm is designed to solve the resultant optimization problem. Extensive experiments conducted on eight datasets show the superiority of our method in terms of effectiveness and efficiency.

Depth estimation of light field images played a significant role in various technology applications such as virtual reality, 3D modeling, and autonomous driving. However, existing deep learning methods tend to overlook the structural intricacies of the light field, leading to suboptimal performance in challenging areas like occlusion and textureless regions. Therefore, our paper proposes an attention cost volume network that combines local and global features to enhance performance in these challenging areas. We introduce a macro-pixel cost volume to effectively extract global context information, specifically targeting the challenge of objects in textureless regions. Then, our strategy combines attention cost volume from both local and global feature information to address the impact of occlusion. Finally, we introduce a new attention mechanism that generates attention weights to guide the cost volume. This mechanism is effective in eliminating redundancies and highlights crucial information, resulting in improved depth estimation quality. The experimental results on the HCI 4D light field dataset demonstrate that our proposed method exhibits smaller errors in occlusion and textureless regions compared to existing depth estimation methods.

Depth estimation of light field images is a crucial technique in various applications, including 3D reconstruction, autonomous driving, and object tracking. However, current deep-learning methods ignore the geometric information of the light field image and are limited to learning repetitive textures, which leads to inaccurate estimates of depth. The paper proposes a light field depth estimation network that fuses multi-scale semantic information with geometric information to address the problem of non-adaptation for repeated texture regions. The main focus of the network is the semantic and geometric information fusion (SGI) module, which can adaptively combine semantic and geometric information to improve the efficiency of cost aggregation. Furthermore, SGI module establishes a direct link between feature extraction and cost aggregation, providing feedback for feature extraction and guiding more efficient feature extraction. The experimental results from the optical field synthesis dataset HCI 4D demonstrate that the method has high accuracy and generalisation performance.

Object detection under low-light surveillance is a crucial problem that less efforts have been made on it. In this paper, we proposed a hybrid method that jointly use enhancement and object detection for the above challenge, namely Dedark+Detection. In this method, the low-light surveillance video is processed by the proposed de-dark method, and the video can thus be converted to appearance under normal lighting condition. This enhancement bring more benefits to the subsequent stage of object detection. After that, an object detection network is trained on the enhanced dataset for practical applications under low-light surveillance. Experiments are performed on 18 low-light surveillance video test sequences, and superior performance can be found when comparing to state-of-the-arts.

Estimation scene geometry is an important and fundamental task in light field processing. In conventional light field, there exist homogeneous texture surfaces, which brings ambiguity and heavy computation load in estimating the depth. In this paper, we propose phase-coding structured light field (PSLF), which projects sinusoidal waveform patterns and the phase is assigned to every pixel as the code. With the EPI of PSLF, we propose a depth estimation method. To be specific, the cost is convex with respect to the inclination angle of the candidate line in the EPI, and we propose to iterate rotating the candidate line until it converges to the optimal one. In addition, to cope with problem that the candidate samples cover multiple depth layers, we propose a method to reject the outlier samples. Experimental results demonstrate that, compared with conventional LF, the proposed PSLF improves the depth quality with mean absolute error being 0.007 pixels. In addition, the proposed optimization-based depth estimation method improves efficiency obviously with the processing speed being about 2.71 times of the tradition method.

Phase unwrapping is a fundamental task in phase‐based profilometry. Existing spatial and temporal approaches are facing challenges such as error propagation and low efficiency. In this study, the authors propose a learning‐based method that uses a support vector machine (SVM) to perform phase unwrapping, where the problem is solved as a classification task. To be specific, seven elements, extracted from the captured patterns and the wrapped phase, form the input feature vector and the fringe order is the output class. Besides, a radial basis function kernel SVM is adopted as the model. The proposed method is conducted independently for every pixel, and does not suffer from error propagation in the spatial unwrapping. Moreover, it needs fewer patterns than temporal unwrapping since only one phase map is required. Simulation and experimental results demonstrate that the proposed scheme produces precise depth maps, which are comparable with the complex quality‐guided methods but at a much faster speed.

In this paper, we propose a hybrid scheme to measure depth values for a stereo structured light system with only a single shot. We design a dual-frequency monochromatic pattern, based on which depth values are computed in three steps. Firstly, phases and coarse depth maps are computed by following the idea of Fourier transform profilometry, where a novel phase unwrapping method is proposed. Afterward, errors in coarse depth maps are detected according to cross-view geometry consistency. Finally, spatial stereo matching is conducted to refine the detected errors. Experiments demonstrate that the proposed scheme can generate accurate stereo depth maps with only one shot, which can be used in a range of real-time applications.

Depth videos affect the visual quality of virtual view greatly, while conventional encoders are not adapted to encode depth videos. To solve this problem, we propose, in the paper, a fault-tolerance based joint block-level bit allocation scheme for depth video coding. The scheme classifies depth blocks into two classes by the fault-tolerance, and constructs virtual view perceptual quality based rate-distortion (R-D) model for each class. Based on the model, a joint block-level bit allocation scheme is proposed to obtain the optimized quantization parameter (QP) to encode each class of blocks. Further, an adaptive RDO algorithm is designed to determine λMODE, in order to achieve better visual quality of virtual views. Experimental results demonstrate that, compared with 3D-HEVC, the proposed method improves the visual quality of virtual views, especially at preserving details and boundaries of objects.

The quality of depth is crucial in all depth-based applications. Unfortunately, the error-free ground truth is often unattainable for depth. Therefore, no-reference quality assessment is very much desired. This paper presents a novel depth quality assessment scheme that is completely different from conventional approaches. In particular, this scheme focuses on depth edge misalignment errors in texture-plus-depth (T + D) images and develops a robust method to detect them. Based on the detected misalignments, a no-reference metric is calculated to evaluate the quality of depth maps. In the proposed scheme, misalignments are detected by matching texture and depth edges through three constraints: 1) spatial similarity; 2) edge orientation similarity; and 3) segment length similarity. Furthermore, the matching is performed on edge segments instead of individual pixels, which enables robust edge matching. Experimental results demonstrate that the proposed scheme can detect misalignment errors accurately. The proposed no-reference depth quality metric is highly consistent with the full-reference metric, and is also well-correlated with the quality of synthesized virtual views. Moreover, the proposed scheme can also use the detected edge misalignments to facilitate depth enhancement in various practical texture-plus-depth-based applications.

In the applications based on depth, multiple TOF cameras are often required to capture the same scene. But if multiple cameras operate simultaneously on the same frequency, they interfere with each other. The multi-camera interference causes a lot of errors in depth measurement. The depth quality is severely reduced, which limits the application of TOF cameras and needs to be resolved. In this paper, a multi-camera interference model is presented. The interference signal for multiple frames is proved to be an ergodic and wide-sense stationary stochastic process. The least square estimation of noninterference signal is proposed to remove the multi-camera interference. The results of experiments prove the approach can recover the depth and amplitude information of multiple TOF cameras from the severe interference.

In 3D video (3DV) and free-viewpoint video (FVV), it is vitally important to detect the errors and assess depth quality. However, since ground-truth depth maps are often unattainable, assessing depth quality without reference becomes an imperative task for many applications. This research considers the texture-plus-depth format in 3DV and FVV, and focuses on the misalignment error at depth discontinuities. A matching algorithm between depth and texture edges is proposed to determine corresponding matching pairs and to identify serious mismatches. The matching procedure is based on both spatial distances and direction similarities between texture and depth edges, and the algorithm is performed between edge segments, instead of edge pixels, in order to improve the robustness of matching. Furthermore, an adaptive algorithm is designed to divide depth edges into segments with different lengths based on the curvature of the edges. After the matching is completed, misalignments between matching pairs are used to generate a no-reference assessment metric. Experimental results demonstrate that the proposed matching scheme is able to achieve accurate matches between depth and texture edges. More importantly, it has been shown that the correlations between the proposed metric and the widely accepted full-reference metric are greater than 0.9, making this no-reference depth quality assessment scheme suitable for contemporary 3DV and FVV applications.

Microsoft Kinect cameras provide a fast and convenient way to acquire depth information. However, the interference problem of multiple Kinect cameras dramatically degrades the depth quality. In this paper, we study the interference problem and propose an interference cancelation approach based on the statistical properties of depth maps. The gradient values are investigated and propagated from the interference-free region to the interfered region. The gradient values are obtained based on the statistic gradient features of the depth maps and the optimal solution of depth values is derived with a least error criterion. Experiment results demonstrate that our proposed method can eliminate interference efficiently, and lead to better qualities of depth maps and rendered virtual views.

View synthesis technology generates virtual views for display and high quality virtual view is of significant importance to free-viewpoint TV (FTV) and three dimensional video(3DV). This paper proposes a novel virtual view synthesis method in multi-view system based on multi-view plus depth. Firstly, we project two reference depth maps to the intermediate virtual view and rectify the two candidate virtual depth maps. Secondly, we project reference texture to the virtual view with the rectified depth maps and blend the candidate virtual views. Finally, we in paint the remaining holes according to the adjacent depth and color samples in background regions. Experiment results demonstrate that the proposed method works well and generates high quality intermediate virtual views.

Sora is a fully programmable, high performance software radio platform based on commodity general-purpose PC. In this demonstration, we illustrate the main features of the Sora platform that provide researchers flexible and powerful means to conduct wireless experiments at different levels with various goals. Specifically, the demonstrator will show four useful applications for wireless research that are built based on the Sora platform: 1) A capture tool that allows one to take a snapshot on a wireless channel; 2) a signal generation tool that allows one to transmit arbitrary baseband wave-form over the air, from a monophonic tone to a complex modulated frame; 3) an on-line real-time receiving application that uses the Sora User-Mode Extension; and 4) a fully featured Software radio WiFi driver (SoftWiFi) that can seamlessly inter-operate with commercial WiFi cards.

Runtime stacks are critical components of any modern software--they are used to implement powerful control structures such as function call/return, stack cutting and unwinding, coroutines, and thread context switch. Stack operations, however, are very hard to reason about: there are no known formal specifications for certifying C-style setjmp/longjmp, stack cutting and unwinding, or weak continuations (in C--). In many proof-carrying code (PCC) systems, return code pointers and exception handlers are treated as general first-class functions (as in continuation-passing style) even though both should have more limited scopes.In this paper we show that stack-based control abstractions follow a much simpler pattern than general first-class code pointers. We present a simple but flexible Hoare-style framework for modular verification of assembly code with all kinds of stackbased control abstractions, including function call/return, tail call, setjmp/longjmp, weak continuation, stack cutting, stack unwinding, multi-return function call, coroutines, and thread context switch. Instead of presenting a specific logic for each control structure, we develop all reasoning systems as instances of a generic framework. This allows program modules and their proofs developed in different PCC systems to be linked together. Our system is fully mechanized. We give the complete soundness proof and a full verification of several examples in the Coq proof assistant.