Flexible resource allocation for joint optimization of energy and spectral efficiency in OFDMA multi-cell networks

The radio resource allocation problem is studied, aiming to jointly optimize the energy efficiency (EE) and spectral efficiency (SE) of downlink OFDMA multi-cell networks. Different from existing works on either EE or SE optimization, a novel EE-SE tradeoff (EST) metric, which can capture both the EST relation and the individual cells’ preferences for EE or SE performance, is introduced as the utility function for each base station (BS). Then the joint EE-SE optimization problem is formulated, and an iterative subchannel allocation and power allocation algorithm is proposed. Numerical results show that the proposed algorithm can exploit the EST relation flexibly and optimize the EE and SE simultaneously to meet diverse EE and SE preferences of individual cells.<br/

Microstructures and properties of AZ31 magnesium alloys formed by multi-channel porthole extrusion

This study investigated the effects of different extrusion temperatures and extrusion ratios on the microstructures and properties of AZ31 magnesium alloys formed by multi-channel porthole extrusion. The experimental results showed that equiaxed grains were formed during the dynamic recrystallization process and that alloy grains were refined by extrusion. Increased extrusion temperatures (from 340 ℃ to 420 ℃) resulted in larger alloy grains and decreased tensile strength of the alloy. Increased extrusion ratios (from 9 to 25) resulted in refined alloy grains and increased tensile strength of the alloy. Under conditions of low extrusion temperature and high extrusion ratio, the tensile strength and elongation of magnesium alloys were effectively improved. AZ31 magnesium alloys produced by multi-channel porthole extrusion at the extrusion temperature of 340℃ and the extrusion ratio of 25 possessed the finest average grain sizes (1.6 μm in the weld zone, 6.6 μm in the non-weld zone ) and the maximum tensile strength (290 MPa) and elongation (20.8 %)

Discovering and Explaining the Non-Causality of Deep Learning in SAR ATR

In recent years, deep learning has been widely used in SAR ATR and achieved excellent performance on the MSTAR dataset. However, due to constrained imaging conditions, MSTAR has data biases such as background correlation, i.e., background clutter properties have a spurious correlation with target classes. Deep learning can overfit clutter to reduce training errors. Therefore, the degree of overfitting for clutter reflects the non-causality of deep learning in SAR ATR. Existing methods only qualitatively analyze this phenomenon. In this paper, we quantify the contributions of different regions to target recognition based on the Shapley value. The Shapley value of clutter measures the degree of overfitting. Moreover, we explain how data bias and model bias contribute to non-causality. Concisely, data bias leads to comparable signal-to-clutter ratios and clutter textures in training and test sets. And various model structures have different degrees of overfitting for these biases. The experimental results of various models under standard operating conditions on the MSTAR dataset support our conclusions. Our code is available at https://github.com/waterdisappear/Data-Bias-in-MSTAR

Hierarchical Disentanglement-Alignment Network for Robust SAR Vehicle Recognition

Vehicle recognition is a fundamental problem in SAR image interpretation. However, robustly recognizing vehicle targets is a challenging task in SAR due to the large intraclass variations and small interclass variations. Additionally, the lack of large datasets further complicates the task. Inspired by the analysis of target signature variations and deep learning explainability, this paper proposes a novel domain alignment framework named the Hierarchical Disentanglement-Alignment Network (HDANet) to achieve robustness under various operating conditions. Concisely, HDANet integrates feature disentanglement and alignment into a unified framework with three modules: domain data generation, multitask-assisted mask disentanglement, and domain alignment of target features. The first module generates diverse data for alignment, and three simple but effective data augmentation methods are designed to simulate target signature variations. The second module disentangles the target features from background clutter using the multitask-assisted mask to prevent clutter from interfering with subsequent alignment. The third module employs a contrastive loss for domain alignment to extract robust target features from generated diverse data and disentangled features. Lastly, the proposed method demonstrates impressive robustness across nine operating conditions in the MSTAR dataset, and extensive qualitative and quantitative analyses validate the effectiveness of our framework

Microstructures and properties of AZ31 magnesium alloys formed by multi-channel porthole extrusion

This study investigated the effects of different extrusion temperatures and extrusion ratios on the microstructures and properties of AZ31 magnesium alloys formed by multi-channel porthole extrusion. The experimental results showed that equiaxed grains were formed during the dynamic recrystallization process and that alloy grains were refined by extrusion. Increased extrusion temperatures (from 340 ℃ to 420 ℃) resulted in larger alloy grains and decreased tensile strength of the alloy. Increased extrusion ratios (from 9 to 25) resulted in refined alloy grains and increased tensile strength of the alloy. Under conditions of low extrusion temperature and high extrusion ratio, the tensile strength and elongation of magnesium alloys were effectively improved. AZ31 magnesium alloys produced by multi-channel porthole extrusion at the extrusion temperature of 340℃ and the extrusion ratio of 25 possessed the finest average grain sizes (1.6 μm in the weld zone, 6.6 μm in the non-weld zone ) and the maximum tensile strength (290 MPa) and elongation (20.8 %)

Converse: A Tree-Based Modular Task-Oriented Dialogue System

Creating a system that can have meaningful conversations with humans to help accomplish tasks is one of the ultimate goals of Artificial Intelligence (AI). It has defined the meaning of AI since the beginning. A lot has been accomplished in this area recently, with voice assistant products entering our daily lives and chat bot systems becoming commonplace in customer service. At first glance there seems to be no shortage of options for dialogue systems. However, the frequently deployed dialogue systems today seem to all struggle with a critical weakness - they are hard to build and harder to maintain. At the core of the struggle is the need to script every single turn of interactions between the bot and the human user. This makes the dialogue systems more difficult to maintain as the tasks become more complex and more tasks are added to the system. In this paper, we propose Converse, a flexible tree-based modular task-oriented dialogue system. Converse uses an and-or tree structure to represent tasks and offers powerful multi-task dialogue management. Converse supports task dependency and task switching, which are unique features compared to other open-source dialogue frameworks. At the same time, Converse aims to make the bot building process easy and simple, for both professional and non-professional software developers. The code is available at https://github.com/salesforce/Converse

Circulating Monocytes Act as a Common Trigger for the Calcification Paradox of Osteoporosis and Carotid Atherosclerosis via TGFB1-SP1 and TNFSF10-NFKB1 Axis

BackgroundOsteoporosis often occurs with carotid atherosclerosis and causes contradictory calcification across tissue in the same patient, which is called the “calcification paradox”. Circulating monocytes may be responsible for this unbalanced ectopic calcification. Here, we aimed to show how CD14+ monocytes contribute to the pathophysiology of coexisting postmenopausal osteoporosis and carotid atherosclerosis.MethodsWe comprehensively analyzed osteoporosis data from the mRNA array dataset GSE56814 and the scRNA-seq dataset GSM4423510. Carotid atherosclerosis data were obtained from the GSE23746 mRNA dataset and GSM4705591 scRNA-seq dataset. First, osteoblast and vascular SMC lineages were annotated based on their functional expression using gene set enrichment analysis and AUCell scoring. Next, pseudotime analysis was applied to draw their differentiated trajectory and identify the key gene expression changes in crossroads. Then, ligand–receptor interactions between CD14+ monocytes and osteoblast and vascular smooth muscle cell (SMC) lineages were annotated with iTALK. Finally, we selected calcification paradox-related expression in circulating monocytes with LASSO analysis.ResultsFirst, we found a large proportion of delayed premature osteoblasts in osteoporosis and osteogenic SMCs in atherosclerosis. Second, CD14+ monocytes interacted with the intermediate cells of the premature osteoblast and osteogenic SMC lineage by delivering TGFB1 and TNFSF10. This interaction served as a trigger activating the transcription factors (TF) SP1 and NFKB1 to upregulate the inflammatory response and cell senescence and led to a retarded premature state in the osteoblast lineage and osteogenic transition in the SMC lineage. Then, 76.49% of common monocyte markers were upregulated in the circulating monocytes between the two diseases, which were related to chemotaxis and inflammatory responses. Finally, we identified 7 calcification paradox-related genes on circulating monocytes, which were upregulated in aging cells and downregulated in DNA repair cells, indicating that the aging monocytes contributed to the development of the two diseases.ConclusionsOur work provides a perspective for understanding the triggering roles of CD14+ monocytes in the development of the calcification paradox in osteoporosis- and atherosclerosis-related cells based on combined scRNA and mRNA data. This study provided us with an elucidation of the mechanisms underlying the calcification paradox and could help in developing preventive and therapeutic strategies