Machine Fault Classification Based on Local Discriminant Bases and Locality Preserving Projections

Machine fault classification is an important task for intelligent identification of the health patterns for a mechanical system being monitored. Effective feature extraction of vibration data is very critical to reliable classification of machine faults with different types and severities. In this paper, a new method is proposed to acquire the sensitive features through a combination of local discriminant bases (LDB) and locality preserving projections (LPP). In the method, the LDB is employed to select the optimal wavelet packet (WP) nodes that exhibit high discrimination from a redundant WP library of wavelet packet transform (WPT). Considering that the obtained discriminatory features on these selected nodes characterize the class pattern in different sensitivity, the LPP is then applied to address mining inherent class pattern feature embedded in the raw features. The proposed feature extraction method combines the merits of LDB and LPP and extracts the inherent pattern structure embedded in the discriminatory feature values of samples in different classes. Therefore, the proposed feature not only considers the discriminatory features themselves but also considers the dynamic sensitive class pattern structure. The effectiveness of the proposed feature is verified by case studies on vibration data-based classification of bearing fault types and severities

Easy and Efficient Transformer : Scalable Inference Solution For large NLP model

Recently, large-scale transformer-based models have been proven to be effective over a variety of tasks across many domains. Nevertheless, putting them into production is very expensive, requiring comprehensive optimization techniques to reduce inference costs. This paper introduces a series of transformer inference optimization techniques that are both in algorithm level and hardware level. These techniques include a pre-padding decoding mechanism that improves token parallelism for text generation, and highly optimized kernels designed for very long input length and large hidden size. On this basis, we propose a transformer inference acceleration library -- Easy and Efficient Transformer (EET), which has a significant performance improvement over existing libraries. Compared to Faster Transformer v4.0's implementation for GPT-2 layer on A100, EET achieves a 1.5-4.5x state-of-art speedup varying with different context lengths. EET is available at https://github.com/NetEase-FuXi/EET. A demo video is available at https://youtu.be/22UPcNGcErg

Rhodapentalenes: Pincer Complexes with Internal Aromaticity

Summary(#br)Pincer complexes are a remarkably versatile family benefited from their stability, diversity, and tunability. Many of them contain aromatic organic rings at the periphery, and aromaticity plays an important role in their stability and properties, whereas their metallacyclic cores are not aromatic. Herein, we report rhodapentalenes, which can be viewed as pincer complexes in which the metallacyclic cores exhibit considerable aromatic character. Rhodapentalenes show good thermal stability, although the rhodium-carbon bonds in such compounds are fragile. Experimental and computational studies suggest that the stabilization of rigid CCC pincer architectures together with an intrinsic aromaticity is vital for these metallacyclic rhodium species. Dearomatization-aromatization reactions, corresponding to metal-ligand cooperation of classical aromatic pincer complexes, were observed in this system. These findings suggest a new concept for pincer chemistry, the internal aromaticity involving metal d -orbitals, which would be useful for exploiting the nature of construction motif and inspire further applications

Rhodapentalenes: Pincer Complexes with Internal Aromaticity.

Pincer complexes are a remarkably versatile family benefited from their stability, diversity, and tunability. Many of them contain aromatic organic rings at the periphery, and aromaticity plays an important role in their stability and properties, whereas their metallacyclic cores are not aromatic. Herein, we report rhodapentalenes, which can be viewed as pincer complexes in which the metallacyclic cores exhibit considerable aromatic character. Rhodapentalenes show good thermal stability, although the rhodium-carbon bonds in such compounds are fragile. Experimental and computational studies suggest that the stabilization of rigid CCC pincer architectures together with an intrinsic aromaticity is vital for these metallacyclic rhodium species. Dearomatization-aromatization reactions, corresponding to metal-ligand cooperation of classical aromatic pincer complexes, were observed in this system. These findings suggest a new concept for pincer chemistry, the internal aromaticity involving metal d-orbitals, which would be useful for exploiting the nature of construction motif and inspire further applications

Smart Wearables for Cardiac Monitoring-Real-World Use beyond Atrial Fibrillation

The possibilities and implementation of wearable cardiac monitoring beyond atrial fibrillation are increasing continuously. This review focuses on the real-world use and evolution of these devices for other arrhythmias, cardiovascular diseases and some of their risk factors beyond atrial fibrillation. The management of nonatrial fibrillation arrhythmias represents a broad field of wearable technologies in cardiology using Holter, event recorder, electrocardiogram (ECG) patches, wristbands and textiles. Implementation in other patient cohorts, such as ST-elevation myocardial infarction (STEMI), heart failure or sleep apnea, is feasible and expanding. In addition to appropriate accuracy, clinical studies must address the validation of clinical pathways including the appropriate device and clinical decisions resulting from the surrogate assessed

Lymphoma endothelium preferentially expresses Tim-3 and facilitates the progression of lymphoma by mediating immune evasion

Angiogenesis is increasingly recognized as an important prognosticator associated with the progression of lymphoma and as an attractive target for novel modalities. We report a previously unrecognized mechanism by which lymphoma endothelium facilitates the growth and dissemination of lymphoma by interacting with circulated T cells and suppresses the activation of CD4+ T cells. Global gene expression profiles of microdissected endothelium from lymphoma and reactive lymph nodes revealed that T cell immunoglobulin and mucin domain–containing molecule 3 (Tim-3) was preferentially expressed in lymphoma-derived endothelial cells (ECs). Clinically, the level of Tim-3 in B cell lymphoma endothelium was closely correlated to both dissemination and poor prognosis. In vitro, Tim-3+ ECs modulated T cell response to lymphoma surrogate antigens by suppressing activation of CD4+ T lymphocytes through the activation of the interleukin-6–STAT3 pathway, inhibiting Th1 polarization, and providing protective immunity. In a lymphoma mouse model, Tim-3–expressing ECs promoted the onset, growth, and dissemination of lymphoma by inhibiting activation of CD4+ T cells and Th1 polarization. Our findings strongly argue that the lymphoma endothelium is not only a vessel system but also a functional barrier facilitating the establishment of lymphoma immune tolerance. These findings highlight a novel molecular mechanism that is a potential target for enhancing the efficacy of tumor immunotherapy and controlling metastatic diseases

Contributions of Dopamine-Related Genes and Environmental Factors to Highly Sensitive Personality: A Multi-Step Neuronal System-Level Approach

Traditional behavioral genetic studies (e.g., twin, adoption studies) have shown that human personality has moderate to high heritability, but recent molecular behavioral genetic studies have failed to identify quantitative trait loci (QTL) with consistent effects. The current study adopted a multi-step approach (ANOVA followed by multiple regression and permutation) to assess the cumulative effects of multiple QTLs. Using a system-level (dopamine system) genetic approach, we investigated a personality trait deeply rooted in the nervous system (the Highly Sensitive Personality, HSP). 480 healthy Chinese college students were given the HSP scale and genotyped for 98 representative polymorphisms in all major dopamine neurotransmitter genes. In addition, two environment factors (stressful life events and parental warmth) that have been implicated for their contributions to personality development were included to investigate their relative contributions as compared to genetic factors. In Step 1, using ANOVA, we identified 10 polymorphisms that made statistically significant contributions to HSP. In Step 2, these polymorphism's main effects and interactions were assessed using multiple regression. This model accounted for 15% of the variance of HSP (p<0.001). Recent stressful life events accounted for an additional 2% of the variance. Finally, permutation analyses ascertained the probability of obtaining these findings by chance to be very low, p ranging from 0.001 to 0.006. Dividing these loci by the subsystems of dopamine synthesis, degradation/transport, receptor and modulation, we found that the modulation and receptor subsystems made the most significant contribution to HSP. The results of this study demonstrate the utility of a multi-step neuronal system-level approach in assessing genetic contributions to individual differences in human behavior. It can potentially bridge the gap between the high heritability estimates based on traditional behavioral genetics and the lack of reproducible genetic effects observed currently from molecular genetic studies