HaTS: Hardware-Assisted Transaction Scheduler

In this paper we present HaTS, a Hardware-assisted Transaction Scheduler. HaTS improves performance of concurrent applications by classifying the executions of their atomic blocks (or in-memory transactions) into scheduling queues, according to their so called conflict indicators. The goal is to group those transactions that are conflicting while letting non-conflicting transactions proceed in parallel. Two core innovations characterize HaTS. First, HaTS does not assume the availability of precise information associated with incoming transactions in order to proceed with the classification. It relaxes this assumption by exploiting the inherent conflict resolution provided by Hardware Transactional Memory (HTM). Second, HaTS dynamically adjusts the number of the scheduling queues in order to capture the actual application contention level. Performance results using the STAMP benchmark suite show up to 2x improvement over state-of-the-art HTM-based scheduling techniques

H-RNet: hybrid rlation network for few-shot learning-based hyperspectral image classification.

Deep network models rely on sufficient training samples to perform reasonably well, which has inevitably constrained their application in classification of hyperspectral images (HSIs) due to the limited availability of labeled data. To tackle this particular challenge, we propose a hybrid relation network, H-RNet, by combining three-dimensional (3-D) convolution neural networks (CNN) and two-dimensional (2-D) CNN to extract the spectral–spatial features whilst reducing the complexity of the network. In an end-to-end relation learning module, the sample pairing approach can effectively alleviate the problem of few labeled samples and learn correlations between samples more accurately for more effective classification. Experimental results on three publicly available datasets have fully demonstrated the superior performance of the proposed model in comparison to a few state-of-the-art methods

Mechanism, prevention and treatment of cognitive impairment caused by high altitude exposure

Hypobaric hypoxia (HH) characteristics induce impaired cognitive function, reduced concentration, and memory. In recent years, an increasing number of people have migrated to high-altitude areas for work and study. Headache, sleep disturbance, and cognitive impairment from HH, severely challenges the physical and mental health and affects their quality of life and work efficiency. This review summarizes the manifestations, mechanisms, and preventive and therapeutic methods of HH environment affecting cognitive function and provides theoretical references for exploring and treating high altitude-induced cognitive impairment

StructToken : Rethinking Semantic Segmentation with Structural Prior

In this paper, we present structure token (StructToken), a new paradigm for semantic segmentation. From a perspective on semantic segmentation as per-pixel classification, the previous deep learning-based methods learn the per-pixel representation first through an encoder and a decoder head and then classify each pixel representation to a specific category to obtain the semantic masks. Differently, we propose a structure-aware algorithm that takes structural information as prior to predict semantic masks directly without per-pixel classification. Specifically, given an input image, the learnable structure token interacts with the image representations to reason the final semantic masks. Three interaction approaches are explored and the results not only outperform the state-of-the-art methods but also contain more structural information. Experiments are conducted on three widely used datasets including ADE20k, Cityscapes, and COCO-Stuff 10K. We hope that structure token could serve as an alternative for semantic segmentation and inspire future research

Refining HTN Methods via Task Insertion with Preferences

International audienceHierarchical Task Network (HTN) planning is showing its power in real-world planning. Although domain experts have partial hierarchical domain knowledge, it is time-consuming to specify all HTN methods, leaving them incomplete. On the other hand, traditional HTN learning approaches focus only on declarative goals, omitting the hierarchical domain knowledge. In this paper, we propose a novel learning framework to refine HTN methods via task insertion with completely preserving the original methods. As it is difficult to identify incomplete methods without designating declarative goals for compound tasks, we introduce the notion of prioritized preference to capture the incompleteness possibility of methods. Specifically, the framework first computes the preferred completion profile w.r.t. the prioritized preference to refine the incomplete methods. Then it finds the minimal set of refined methods via a method substitution operation. Experimental analysis demonstrates that our approach is effective, especially in solving new HTN planning instances

Nucleation of L1₂-Al₃M (M = Sc, Er, Y, Zr) Nanophases in Aluminum Alloys: A First-Principles ThermodynamicsStudy

High-performance Sc-containing aluminum alloys are limited in their industrial application due to the high cost of Sc elements. Er, Zr, and Y elements are candidates for replacing Sc elements. Combined with the first-principles thermodynamic calculation and the classical nucleation theory, the nucleation of L12-Al3M (M = Sc, Er, Y, Zr) nanophases in dilutealuminum alloys were investigated to reveal their structural stability. The calculated results showed that the critical radius and nucleation energy of the L12-Al3M phases were as follows: Al3Er > Al3Y > Al3Sc > Al3Zr. The Al3Zr phase was the easiest to nucleate in thermodynamics, while the nucleation of the Al3Y and Al3Er phases were relatively difficult in thermodynamics. Various structures of Al3(Y, Zr) phases with the radius r 3Zr(Y) phase illustrated the highest nucleation energy, while the separated structure Al3Zr/Al3Y obtained the lowest one, and had thermodynamic advantages in the nucleation process. Moreover, the core–shelled Al3Zr(Y) phase obtained a higher nucleation energy than Al3Zr(Sc) and Al3Zr(Er). Core–doubleshelled Al3Zr/Er(Y) obtained a lower nucleation energy than that of Al3Zr(Y) due to the negative ΔGchem of Al3Er and the negative Al3Er/Al3Y interfacial energy, and was preferentially precipitated in thermodynamics stability

Dietary iron modulates gut microbiota and induces SLPI secretion to promote colorectal tumorigenesis

ABSTRACTDietary iron intake is closely related to the incidence of colorectal cancer. However, the interactions among dietary iron, gut microbiota, and epithelial cells in promoting tumorigenesis have rarely been discussed. Here, we report that gut microbiota plays a crucial role in promoting colorectal tumorigenesis in multiple mice models under excessive dietary iron intake. Gut microbiota modulated by excessive dietary iron are pathogenic, irritating the permeability of the gut barrier and causing leakage of lumen bacteria. Mechanistically, epithelial cells released more secretory leukocyte protease inhibitor (SLPI) to combat the leaked bacteria and limit inflammation. The upregulated SLPI acted as a pro-tumorigenic factor and promoted colorectal tumorigenesis by activating the MAPK signaling pathway. Moreover, excessive dietary iron significantly depleted Akkermansiaceae in the gut microbiota; while supplementation with Akkermansia muciniphila could successfully attenuate the tumorigenic effect from excessive dietary iron. Overall, excessive dietary iron perturbs diet – microbiome–epithelium interactions, which contributes to intestinal tumor initiation