Miracle-dz/UniFAR-FTUs huggingface dataset:
SM2C: Boost the Semi-supervised Segmentation for Medical Image by using Meta Pseudo Labels and Mixed Images

Recently, machine learning-based semantic segmentation algorithms have demonstrated their potential to accurately segment regions and contours in medical images, allowing the precise location of anatomical structures and abnormalities. Although medical images are difficult to acquire and annotate, semi-supervised learning methods are efficient in dealing with the scarcity of labeled data. However, overfitting is almost inevitable due to the limited images for training. Furthermore, the intricate shapes of organs and lesions in medical images introduce additional complexity in different cases, preventing networks from acquiring a strong ability to generalize. To this end, we introduce a novel method called Scaling-up Mix with Multi-Class (SM2C). This method uses three strategies - scaling-up image size, multi-class mixing, and object shape jittering - to improve the ability to learn semantic features within medical images. By diversifying the shape of the segmentation objects and enriching the semantic information within each sample, the SM2C demonstrates its potential, especially in the training of unlabelled data. Extensive experiments demonstrate the effectiveness of the SM2C on three benchmark medical image segmentation datasets. The proposed framework shows significant improvements over state-of-the-art counterparts.
A Mixed Multiscale Spectral Generalized Finite Element Method

We present a multiscale mixed finite element method for solving second order elliptic equations with general $L^{\infty}$-coefficients arising from flow in highly heterogeneous porous media. Our approach is based on a multiscale spectral generalized finite element method (MS-GFEM) and exploits the superior local mass conservation properties of mixed finite elements. Following the MS-GFEM framework, optimal local approximation spaces are built for the velocity field by solving local eigenvalue problems over generalized harmonic spaces. The resulting global velocity space is then enriched suitably to ensure inf-sup stability. We develop the mixed MS-GFEM for both continuous and discrete formulations, with Raviart-Thomas based mixed finite elements underlying the discrete method. Exponential convergence with respect to local degrees of freedom is proven at both the continuous and discrete levels. Numerical results are presented to support the theory and to validate the proposed method.
Resonant Beam Communications: A New Design Paradigm and Challenges

Resonant beam communications (RBCom), which adopt oscillating photons between two separate retroreflectors for information transmission, exhibit potential advantages over other types of wireless optical communications (WOC). However, echo interference generated by the modulated beam reflected from the receiver affects the transmission of the desired information. To tackle this challenge, a synchronization-based point-to-point RBCom system is proposed to eliminate the echo interference, and the design for the transmitter and receiver is discussed. Subsequently, the performance of the proposed RBCom is evaluated and compared with that of visible light communications (VLC) and free space optical communications (FOC). Finally, future research directions are outlined and several implementation challenges of RBCom systems are highlighted.
HPL-ESS: Hybrid Pseudo-Labeling for Unsupervised Event-based Semantic Segmentation

Event-based semantic segmentation has gained popularity due to its capability to deal with scenarios under high-speed motion and extreme lighting conditions, which cannot be addressed by conventional RGB cameras. Since it is hard to annotate event data, previous approaches rely on event-to-image reconstruction to obtain pseudo labels for training. However, this will inevitably introduce noise, and learning from noisy pseudo labels, especially when generated from a single source, may reinforce the errors. This drawback is also called confirmation bias in pseudo-labeling. In this paper, we propose a novel hybrid pseudo-labeling framework for unsupervised event-based semantic segmentation, HPL-ESS, to alleviate the influence of noisy pseudo labels. In particular, we first employ a plain unsupervised domain adaptation framework as our baseline, which can generate a set of pseudo labels through self-training. Then, we incorporate offline event-to-image reconstruction into the framework, and obtain another set of pseudo labels by predicting segmentation maps on the reconstructed images. A noisy label learning strategy is designed to mix the two sets of pseudo labels and enhance the quality. Moreover, we propose a soft prototypical alignment module to further improve the consistency of target domain features. Extensive experiments show that our proposed method outperforms existing state-of-the-art methods by a large margin on the DSEC-Semantic dataset (+5.88% accuracy, +10.32% mIoU), which even surpasses several supervised methods.
A hybrid LLM workflow can help identify user privilege related variables in programs of any size

Many programs involves operations and logic manipulating user privileges, which is essential for the security of an organization. Therefore, one common malicious goal of attackers is to obtain or escalate the privileges, causing privilege leakage. To protect the program and the organization against privilege leakage attacks, it is important to eliminate the vulnerabilities which can be exploited to achieve such attacks. Unfortunately, while memory vulnerabilities are less challenging to find, logic vulnerabilities are much more imminent, harmful and difficult to identify. Accordingly, many analysts choose to find user privilege related (UPR) variables first as start points to investigate the code where the UPR variables may be used to see if there exists any vulnerabilities, especially the logic ones. In this paper, we introduce a large language model (LLM) workflow that can assist analysts in identifying such UPR variables, which is considered to be a very time-consuming task. Specifically, our tool will audit all the variables in a program and output a UPR score, which is the degree of relationship (closeness) between the variable and user privileges, for each variable. The proposed approach avoids the drawbacks introduced by directly prompting a LLM to find UPR variables by focusing on leverage the LLM at statement level instead of supplying LLM with very long code snippets. Those variables with high UPR scores are essentially potential UPR variables, which should be manually investigated. Our experiments show that using a typical UPR score threshold (i.e., UPR score>0.8), the false positive rate (FPR) is only 13.49%, while UPR variable found is significantly more than that of the heuristic based method.
RCBEVDet: Radar-camera Fusion in Bird's Eye View for 3D Object Detection

Three-dimensional object detection is one of the key tasks in autonomous driving. To reduce costs in practice, low-cost multi-view cameras for 3D object detection are proposed to replace the expansive LiDAR sensors. However, relying solely on cameras is difficult to achieve highly accurate and robust 3D object detection. An effective solution to this issue is combining multi-view cameras with the economical millimeter-wave radar sensor to achieve more reliable multi-modal 3D object detection. In this paper, we introduce RCBEVDet, a radar-camera fusion 3D object detection method in the bird's eye view (BEV). Specifically, we first design RadarBEVNet for radar BEV feature extraction. RadarBEVNet consists of a dual-stream radar backbone and a Radar Cross-Section (RCS) aware BEV encoder. In the dual-stream radar backbone, a point-based encoder and a transformer-based encoder are proposed to extract radar features, with an injection and extraction module to facilitate communication between the two encoders. The RCS-aware BEV encoder takes RCS as the object size prior to scattering the point feature in BEV. Besides, we present the Cross-Attention Multi-layer Fusion module to automatically align the multi-modal BEV feature from radar and camera with the deformable attention mechanism, and then fuse the feature with channel and spatial fusion layers. Experimental results show that RCBEVDet achieves new state-of-the-art radar-camera fusion results on nuScenes and view-of-delft (VoD) 3D object detection benchmarks. Furthermore, RCBEVDet achieves better 3D detection results than all real-time camera-only and radar-camera 3D object detectors with a faster inference speed at 21~28 FPS. The source code will be released at https://github.com/VDIGPKU/RCBEVDet.
A Comprehensive Analysis of the Cupin Gene Family in Soybean (Glycine max)

Cupin superfamily of proteins, including germin and germin-like proteins (GLPs) from higher plants, is known to play crucial roles in plant development and defense. To date, no systematic analysis has been conducted in soybean (Glycine max) incorporating genome organization, gene structure, expression compendium. In this study, 69 putative Cupin genes were identified from the whole-genome of soybean, which were non-randomly distributed on 17 of the 20 chromosomes. These Gmcupin proteins were phylogenetically clustered into ten distinct subgroups among which the gene structures were highly conserved. Eighteen pairs (52.2%) of duplicate paralogous genes were preferentially retained in duplicated regions of the soybean genome. The distributions of GmCupin genes implied that long segmental duplications contributed significantly to the expansion of the GmCupin gene family. According to the RNA-seq data analysis, most of the Gmcupins were differentially expressed in tissue-specific expression pattern and the expression of some duplicate genes were partially redundant while others showed functional diversity, suggesting the Gmcupins have been retained by substantial subfunctionalization during soybean evolutionary processes. Selective analysis based on single nucleotide polymorphisms (SNPs) in cultivated and wild soybeans revealed sixteen Gmcupins had selected site(s), with all SNPs in Gmcupin10.3 and Gmcupin07.2 genes were selected sites, which implied these genes may have undergone strong selection effects during soybean domestication. Taken together, our results contribute to the functional characterization of Gmcupin genes in soybean.
Fine-Grained Assertion-Based Test Selection

For large software applications, running the whole test suite after each code change is time- and resource-intensive. Regression test selection techniques aim at reducing test execution time by selecting only the tests that are affected by code changes. However, existing techniques select test entities at coarse granularity levels such as test class, which causes imprecise test selection and executing unaffected tests. We propose a novel approach that increases the selection precision by analyzing test code at statement level and treating test assertions as the unit for selection. We implement our fine-grained test selection approach in a tool called SELERTION and evaluate it by comparing against two state-of-the-art test selection techniques using 11 open-source subjects. Our results show that SELERTION increases selection precision for all the subjects. Our test selection reduces, on average, 63% of the overall test time, making regression testing up to 23% faster than the other techniques. Our results also indicate that subjects with longer test execution time benefit more by our fine-grained selection technique.
Plaintext-Free Deep Learning for Privacy-Preserving Medical Image Analysis via Frequency Information Embedding

In the fast-evolving field of medical image analysis, Deep Learning (DL)-based methods have achieved tremendous success. However, these methods require plaintext data for training and inference stages, raising privacy concerns, especially in the sensitive area of medical data. To tackle these concerns, this paper proposes a novel framework that uses surrogate images for analysis, eliminating the need for plaintext images. This approach is called Frequency-domain Exchange Style Fusion (FESF). The framework includes two main components: Image Hidden Module (IHM) and Image Quality Enhancement Module~(IQEM). The~IHM performs in the frequency domain, blending the features of plaintext medical images into host medical images, and then combines this with IQEM to improve and create surrogate images effectively. During the diagnostic model training process, only surrogate images are used, enabling anonymous analysis without any plaintext data during both training and inference stages. Extensive evaluations demonstrate that our framework effectively preserves the privacy of medical images and maintains diagnostic accuracy of DL models at a relatively high level, proving its effectiveness across various datasets and DL-based models.
Characterisation of the Intel RealSense D415 Stereo Depth Camera for Motion-Corrected CT Perfusion Imaging

Even for short protocols (<1 min), head movement can compromise accurate haemodynamic modelling of cerebral CT perfusion (CTP) imaging in acute stroke. Frame-to-frame registration is the most common form of retrospective correction but neglects the fact that motion is continuous, not discrete. By contrast, external tracking devices provide continuous motion monitoring and thereby the opportunity to fully correct the acquired data for motion. The aim of this study was to characterise the Intel D415 stereo depth camera, a compact low-cost markerless tracking device, in terms of its suitability for retrospective CTP motion correction. The results showed that jitter was stable, and thermally-induced pose drift was {\le} 1.5 mm and {\le} 0.5{\deg} during the first 10-20 min, after which it also became stable. For static poses, the mean difference between the Intel D415 motion estimates and ground-truth poses for a head phantom was {\le} 1.24 \pm 0.01 mm and {\le} 0.68 \pm 0.01{\deg} for position and orientation, respectively. For dynamic poses measured while a head phantom travelled smooth continuous trajectories with median speed 0.031 ms^(-1) (speed range 0-0.500 ms^(-1)), the root-mean-square-error (RMSE) was {\le} 1.40 \pm 0.12 mm and {\le} 0.24 \pm 0.02{\deg}. When tracking a simulated patient head trajectory derived from a clinical CTP scan, the average RMSE was {\le} 0.86 \pm 0.03 mm and {\le} 0.16 \pm 0.03{\deg}. Tracking the head motion of a human volunteer inside a clinical CT scanner, the average RMSE was {\le} 2.72 \pm 0.24 mm and {\le} 0.55 \pm 0.07{\deg}. Overall, our results suggest that a single D415 tracking system can achieve promising pose estimation accuracy, though still worse than typical brain CT resolution, including CTP. The error is likely to be reduced to a practical level by combining multiple devices and this should be investigated in future work.
TAIL: A Terrain-Aware Multi-Modal SLAM Dataset for Robot Locomotion in Deformable Granular Environments

Terrain-aware perception holds the potential to improve the robustness and accuracy of autonomous robot navigation in the wilds, thereby facilitating effective off-road traversals. However, the lack of multi-modal perception across various motion patterns hinders the solutions of Simultaneous Localization And Mapping (SLAM), especially when confronting non-geometric hazards in demanding landscapes. In this paper, we first propose a Terrain-Aware multI-modaL (TAIL) dataset tailored to deformable and sandy terrains. It incorporates various types of robotic proprioception and distinct ground interactions for the unique challenges and benchmark of multi-sensor fusion SLAM. The versatile sensor suite comprises stereo frame cameras, multiple ground-pointing RGB-D cameras, a rotating 3D LiDAR, an IMU, and an RTK device. This ensemble is hardware-synchronized, well-calibrated, and self-contained. Utilizing both wheeled and quadrupedal locomotion, we efficiently collect comprehensive sequences to capture rich unstructured scenarios. It spans the spectrum of scope, terrain interactions, scene changes, ground-level properties, and dynamic robot characteristics. We benchmark several state-of-the-art SLAM methods against ground truth and provide performance validations. Corresponding challenges and limitations are also reported. All associated resources are accessible upon request at \url{https://tailrobot.github. io/}.
Magnetic Order in Honeycomb Layered U$_2$Pt$_6$Ga$_{15}$ Studied by Resonant X-ray and Neutron Scatterings

Antiferromagnetic (AF) order of U$_{2}$Pt$_{6}$Ga$_{15}$ with the ordering temperature $T_{\rm N}$ = 26 K was investigated by resonant X-ray scattering and neutron diffraction on single crystals. This compound possesses a unique crystal structure in which uranium ions form honeycomb layers and then stacks along the $c$-axis with slight offset, which gives rise to a stacking disorder. The AF order can be described with the propagation vector of $q = (1/6, 1/6, 0)$ in the hexagonal notation. The ordered magnetic moments orient perpendicular to the honeycomb layers, indicating a collinear spin structure consistent with Ising-like anisotropy. The magnetic reflections are found to be broadened along $c^*$ indicating that the stacking disorder results in anisotropic correlation lengths. The semi-quantitative analysis of neutron diffraction intensity, combined with group theory considerations based on the crystallographic symmetry, suggests a zig-zag type magnetic structure for the AF ground state, in which the AF coupling runs perpendicular to the stacking offset, characterized as $q = (1, 0, 0)_{\rm orth}$. The realization of the zig-zag magnetic structure implies the presence of frustrating intralayer exchange interactions involving both ferromagnetic (FM) first-neighbor and AF second and third-neighbor interactions in this compound.
Decoupling Path Following and Velocity Profile in Vision-Guided Laser Steering

Laser surgery requires accurate following of a path defined by the surgeon, while the velocity on this path is dependent on the laser-tissue interaction. Therefore, path following and velocity profile control must be decoupled. In this paper, nonholonomic control of the unicycle model is used to implement velocity-independent visual path following for laser surgery. The proposed controller was tested in simulation, as well as experimentally in several conditions of use: different initial velocities (step input, successive step inputs, sinusoidal inputs), optimized/nonoptimized gains, time-varying path (simulating a patient breathing), and complex curves with curvatures. Thereby, experiments at 587 Hz (frames/s) show an average accuracy lower than 0.22 pixels (~10 μm) with a standard deviation of 0.55 pixels (~25 μm) path following, and a relative velocity distortion of less than 10-6 %.
A2DMN: Anatomy-Aware Dilated Multiscale Network for Breast Ultrasound Semantic Segmentation

In recent years, convolutional neural networks for semantic segmentation of breast ultrasound (BUS) images have shown great success; however, two major challenges still exist. 1) Most current approaches inherently lack the ability to utilize tissue anatomy, resulting in misclassified image regions. 2) They struggle to produce accurate boundaries due to the repeated down-sampling operations. To address these issues, we propose a novel breast anatomy-aware network for capturing fine image details and a new smoothness term that encodes breast anatomy. It incorporates context information across multiple spatial scales to generate more accurate semantic boundaries. Extensive experiments are conducted to compare the proposed method and eight state-of-the-art approaches using a BUS dataset with 325 images. The results demonstrate the proposed method significantly improves the segmentation of the muscle, mammary, and tumor classes and produces more accurate fine details of tissue boundaries.
A Federated Parameter Aggregation Method for Node Classification Tasks with Different Graph Network Structures

Over the past few years, federated learning has become widely used in various classical machine learning fields because of its collaborative ability to train data from multiple sources without compromising privacy. However, in the area of graph neural networks, the nodes and network structures of graphs held by clients are different in many practical applications, and the aggregation method that directly shares model gradients cannot be directly applied to this scenario. Therefore, this work proposes a federated aggregation method FLGNN applied to various graph federation scenarios and investigates the aggregation effect of parameter sharing at each layer of the graph neural network model. The effectiveness of the federated aggregation method FLGNN is verified by experiments on real datasets. Additionally, for the privacy security of FLGNN, this paper designs membership inference attack experiments and differential privacy defense experiments. The results show that FLGNN performs good robustness, and the success rate of privacy theft is further reduced by adding differential privacy defense methods.
SA2RAGE: A new sequence for fast B1+‐mapping

At high magnetic field strengths (≥3T), the radiofrequency wavelength used in MRI is of the same order of magnitude of (or smaller than) the typical sample size, making transmit magnetic field (B 1+ ) inhomogeneities more prominent. Methods such as radiofrequency‐shimming and transmit SENSE have been proposed to mitigate these undesirable effects. A prerequisite for such approaches is an accurate and rapid characterization of the B 1+ field in the organ of interest. In this work, a new phase‐sensitive three‐dimensional B 1+ ‐mapping technique is introduced that allows the acquisition of a 64 × 64 × 8 B 1+ ‐map in ∼20 s, yielding an accurate mapping of the relative B 1+ with a 10‐fold dynamic range (0.2–2 times the nominal B 1+ ). Moreover, the predominant use of low flip angle excitations in the presented sequence minimizes specific absorption rate, which is an important asset for in vivo B 1+ ‐shimming procedures at high magnetic fields. The proposed methodology was validated in phantom experiments and demonstrated good results in phantom and human B 1+ ‐shimming using an 8‐channel transmit‐receive array. Magn Reson Med, 2011. © 2011 Wiley‐Liss, Inc.
Learning Spatial Adaptation and Temporal Coherence in Diffusion Models for Video Super-Resolution

Diffusion models are just at a tipping point for image super-resolution task. Nevertheless, it is not trivial to capitalize on diffusion models for video super-resolution which necessitates not only the preservation of visual appearance from low-resolution to high-resolution videos, but also the temporal consistency across video frames. In this paper, we propose a novel approach, pursuing Spatial Adaptation and Temporal Coherence (SATeCo), for video super-resolution. SATeCo pivots on learning spatial-temporal guidance from low-resolution videos to calibrate both latent-space high-resolution video denoising and pixel-space video reconstruction. Technically, SATeCo freezes all the parameters of the pre-trained UNet and VAE, and only optimizes two deliberately-designed spatial feature adaptation (SFA) and temporal feature alignment (TFA) modules, in the decoder of UNet and VAE. SFA modulates frame features via adaptively estimating affine parameters for each pixel, guaranteeing pixel-wise guidance for high-resolution frame synthesis. TFA delves into feature interaction within a 3D local window (tubelet) through self-attention, and executes cross-attention between tubelet and its low-resolution counterpart to guide temporal feature alignment. Extensive experiments conducted on the REDS4 and Vid4 datasets demonstrate the effectiveness of our approach.