A Case for Reversible Coherence Protocol

We propose the first Reversible Coherence Protocol (RCP), a new protocol designed from ground up that enables invisible speculative load. RCP takes a bold approach by including the speculative loads and merge/purge operation in the interface between processor and cache coherence, and allowing them to participate in the coherence protocol. It means, speculative load, ordinary load/store, and merge/purge can all affect the state of a given cache line. RCP is the first coherence protocol that enables the commit and squash of the speculative load among distributed cache components in a general memory hierarchy. RCP incurs an average slowdown of (3.0%,8.3%,7.4%) on (SPEC2006,SPEC2017,PARSEC), which is lower compared to (26.5%,12%,18.3%) in InvisiSpec and (3.2%,9.4%,24.2%) in CleanupSpec. The coherence traffic overhead is on average 46%, compared to 40% and 27% of InvisiSpec and CleanupSpec, respectively. Even with higher traffic overhead (~46%), the performance overhead of RCP is lower than InvisiSpec and comparable to CleanupSpec. It reveals a key advantage of RCP: the coherence actions triggered by the merge and purge operations are not in the critical path of the execution and can be performed in the cache hierarchy concurrently with processor executio

RCPS: Rectified Contrastive Pseudo Supervision for Semi-Supervised Medical Image Segmentation

Medical image segmentation methods are generally designed as fully-supervised to guarantee model performance, which require a significant amount of expert annotated samples that are high-cost and laborious. Semi-supervised image segmentation can alleviate the problem by utilizing a large number of unlabeled images along with limited labeled images. However, learning a robust representation from numerous unlabeled images remains challenging due to potential noise in pseudo labels and insufficient class separability in feature space, which undermines the performance of current semi-supervised segmentation approaches. To address the issues above, we propose a novel semi-supervised segmentation method named as Rectified Contrastive Pseudo Supervision (RCPS), which combines a rectified pseudo supervision and voxel-level contrastive learning to improve the effectiveness of semi-supervised segmentation. Particularly, we design a novel rectification strategy for the pseudo supervision method based on uncertainty estimation and consistency regularization to reduce the noise influence in pseudo labels. Furthermore, we introduce a bidirectional voxel contrastive loss to the network to ensure intra-class consistency and inter-class contrast in feature space, which increases class separability in the segmentation. The proposed RCPS segmentation method has been validated on two public datasets and an in-house clinical dataset. Experimental results reveal that the proposed method yields better segmentation performance compared with the state-of-the-art methods in semi-supervised medical image segmentation. The source code is available at https://github.com/hsiangyuzhao/RCPS

TSE-GAN: strain elastography using generative adversarial network for thyroid disease diagnosis

Over the past 35 years, studies conducted worldwide have revealed a threefold increase in the incidence of thyroid cancer. Strain elastography is a new imaging technique to identify benign and malignant thyroid nodules due to its sensitivity to tissue stiffness. However, there are certain limitations of this technique, particularly in terms of standardization of the compression process, evaluation of results and several assumptions used in commercial strain elastography modes for the purpose of simplifying imaging analysis. In this work, we propose a novel conditional generative adversarial network (TSE-GAN) for automatically generating thyroid strain elastograms, which adopts a global-to-local architecture to improve the ability of extracting multi-scale features and develops an adaptive deformable U-net structure in the sub-generator to apply effective deformation. Furthermore, we introduce a Lab-based loss function to induce the networks to generate realistic thyroid elastograms that conform to the probability distribution of the target domain. Qualitative and quantitative assessments are conducted on a clinical dataset provided by Shanghai Sixth People’s Hospital. Experimental results demonstrate that thyroid elastograms generated by the proposed TSE-GAN outperform state-of-the-art image translation methods in meeting the needs of clinical diagnostic applications and providing practical value

Facile and Scalable Preparation of Graphene Oxide-Based Magnetic Hybrids for Fast and Highly Efficient Removal of Organic Dyes

This study reports the facile preparation and the dye removal efficiency of nanohybrids composed of graphene oxide (GO) and Fe[subscript 3]O[subscript 4] nanoparticles with various geometrical structures. In comparison to previously reported GO/Fe[subscript 3]O[subscript 4] composites prepared through the one-pot, in situ deposition of Fe[subscript 3]O[subscript 4] nanoparticles, the GO/Fe[subscript 3]O[subscript 4] nanohybrids reported here were obtained by taking advantage of the physical affinities between sulfonated GO and Fe[subscript 3]O[subscript 4] nanoparticles, which allows tuning the dimensions and geometries of Fe3O4 nanoparticles in order to decrease their contact area with GO, while still maintaining the magnetic properties of the nanohybrids for easy separation and adsorbent recycling. Both the as-prepared and regenerated nanohybrids demonstrate a nearly 100% removal rate for methylene blue and an impressively high removal rate for Rhodamine B. This study provides new insights into the facile and controllable industrial scale fabrication of safe and highly efficient GO-based adsorbents for dye or other organic pollutants in a wide range of environmental-related applications

Experimental study on permeability and mechanical properties of coal under different pore pressure and confining pressure

With the continuous increase of coal mining depth, the response of coal mechanics and the mechanism of gas migration have become extremely complicated. In order to explore the coal damage evolution and gas seepage mechanism under the integrated operation of first extraction and subsequent mining in engineering, the K2 coal seam briquette sample of Chongqing Songzao Coal Mine was used as the research object. Using the triaxial servo seepage device of thermal-fluid-solid coupling of gas-bearing coal, the reduced pore pressure seepage test and the triaxial compression-seepage test were successively carried out on the same specimen. According to the elasto-plasticity theory, a statistical damage constitutive model that characterized the whole stress-strain relationship of coal was derived, and the permeability model of coal under consideration of damage was further constructed. The results of the research shown that, in the reduced pore pressure seepage test, the permeability of coal under constant external stress shown a trend of first rising gently and then rising sharply with the decrease of pore pressure. In this process, the change of coal permeability was affected by the competition between effective stress and gas desorption. In the process of the triaxial compression-seepage test, the characteristics of coal deformation stages under different confining stresses were basically similar. As the confining stress increased, the coal mechanics properties were strengthened. The coal permeability curve changed as a negative exponential function with the increasing axial strain . The damage variable curves and plastic strain curves shown a trend of first rising slowly and then rising sharply with the increase of axial strain, the damage evolution process was corresponded to the whole stress-strain curve of each stage of coal deformation and failure. The rationality of the constructed damage constitutive model and permeability model were verified by comparison with test data, which shown that the model can more accurately reflect the characteristics of coal deformation stages and the law of gas seepage

SmartMal: A Service-Oriented Behavioral Malware Detection Framework for Mobile Devices

This paper presents SmartMal—a novel service-oriented behavioral malware detection framework for vehicular and mobile devices. The highlight of SmartMal is to introduce service-oriented architecture (SOA) concepts and behavior analysis into the malware detection paradigms. The proposed framework relies on client-server architecture, the client continuously extracts various features and transfers them to the server, and the server’s main task is to detect anomalies using state-of-art detection algorithms. Multiple distributed servers simultaneously analyze the feature vector using various detectors and information fusion is used to concatenate the results of detectors. We also propose a cycle-based statistical approach for mobile device anomaly detection. We accomplish this by analyzing the users’ regular usage patterns. Empirical results suggest that the proposed framework and novel anomaly detection algorithm are highly effective in detecting malware on Android devices

Uni-COAL: A Unified Framework for Cross-Modality Synthesis and Super-Resolution of MR Images

Cross-modality synthesis (CMS), super-resolution (SR), and their combination (CMSR) have been extensively studied for magnetic resonance imaging (MRI). Their primary goals are to enhance the imaging quality by synthesizing the desired modality and reducing the slice thickness. Despite the promising synthetic results, these techniques are often tailored to specific tasks, thereby limiting their adaptability to complex clinical scenarios. Therefore, it is crucial to build a unified network that can handle various image synthesis tasks with arbitrary requirements of modality and resolution settings, so that the resources for training and deploying the models can be greatly reduced. However, none of the previous works is capable of performing CMS, SR, and CMSR using a unified network. Moreover, these MRI reconstruction methods often treat alias frequencies improperly, resulting in suboptimal detail restoration. In this paper, we propose a Unified Co-Modulated Alias-free framework (Uni-COAL) to accomplish the aforementioned tasks with a single network. The co-modulation design of the image-conditioned and stochastic attribute representations ensures the consistency between CMS and SR, while simultaneously accommodating arbitrary combinations of input/output modalities and thickness. The generator of Uni-COAL is also designed to be alias-free based on the Shannon-Nyquist signal processing framework, ensuring effective suppression of alias frequencies. Additionally, we leverage the semantic prior of Segment Anything Model (SAM) to guide Uni-COAL, ensuring a more authentic preservation of anatomical structures during synthesis. Experiments on three datasets demonstrate that Uni-COAL outperforms the alternatives in CMS, SR, and CMSR tasks for MR images, which highlights its generalizability to wide-range applications

Are intrinsic neural timescales related to sensory processing? Evidence from abnormal behavioral states

The brain exhibits a complex temporal structure which translates into a hierarchy of distinct neural timescales. An open question is how these intrinsic timescales are related to sensory or motor information processing and whether these dynamics have common patterns in different behavioral states. We address these questions by investigating the brain\u27s intrinsic timescales in healthy controls, motor (amyotrophic lateral sclerosis, locked-in syndrome), sensory (anesthesia, unresponsive wakefulness syndrome), and progressive reduction of sensory processing (from awake states over N1, N2, N3). We employed a combination of measures from EEG resting-state data: auto-correlation window (ACW), power spectral density (PSD), and power-law exponent (PLE). Prolonged neural timescales accompanied by a shift towards slower frequencies were observed in the conditions with sensory deficits, but not in conditions with motor deficits. Our results establish that the spontaneous activity\u27s intrinsic neural timescale is related to the neural capacity that specifically supports sensory rather than motor information processing in the healthy brain