Efficient Spiking Transformer Enabled By Partial Information

Spiking neural networks (SNNs) have received substantial attention in recent years due to their sparse and asynchronous communication nature, and thus can be deployed in neuromorphic hardware and achieve extremely high energy efficiency. However, SNNs currently can hardly realize a comparable performance to that of artificial neural networks (ANNs) because their limited scalability does not allow for large-scale networks. Especially for Transformer, as a model of ANNs that has accomplished remarkable performance in various machine learning tasks, its implementation in SNNs by conventional methods requires a large number of neurons, notably in the self-attention module. Inspired by the mechanisms in the nervous system, we propose an efficient spiking Transformer (EST) framework enabled by partial information to address the above problem. In this model, we not only implemented the self-attention module with a reasonable number of neurons, but also introduced partial-information self-attention (PSA), which utilizes only partial input signals, further reducing computational resources compared to conventional methods. The experimental results show that our EST can outperform the state-of-the-art SNN model in terms of accuracy and the number of time steps on both Cifar-10/100 and ImageNet datasets. In particular, the proposed EST model achieves 78.48% top-1 accuracy on the ImageNet dataset with only 16 time steps. In addition, our proposed PSA reduces flops by 49.8% with negligible performance loss compared to a self-attention module with full information

Isobavachalcone Sensitizes Cells to E2-Induced Paclitaxel Resistance by Down-Regulating CD44 Expression in ER+ Breast Cancer Cells

Oestrogen receptor (ER) is expressed in approximately 60%‐70% of human breast cancer. Clinical trials and retrospective analyses have shown that ER‐positive (ER+) tumours are more tolerant to chemotherapeutic drug resistance than ER‐negative (ER−) tumours. In addition, isobavachalcone (IBC) is known as a kind of phytoestrogen with antitumour effect. However, the underlying mechanism of IBC in ER+ breast cancer needs to be elucidated further. Our in vitro experiments showed that IBC could attenuate 17β‐estradiol (E2)‐induced paclitaxel resistance and that E2 could stimulate CD44 expression in ER+ breast cancer cells but not in ER− cells. Meanwhile, E2 could promote ERα expression to render ER+ breast cancer cells resistant to paclitaxel. Furthermore, we established paclitaxel‐resistant breast cancer cell lines and determined the function of ERα in the enhancement of paclitaxel resistance via the regulation of CD44 transcription. IBC down‐regulated ERα and CD44 expression and thus inhibited tumour growth in paclitaxel‐resistant xenograft models. Overall, our data demonstrated for the first time that IBC could decrease CD44 expression level via the ERα pathway and make ER+ breast cancer cells sensitive to paclitaxel treatment

A novel hybrid machine learning model for auxiliary diagnosing myocardial ischemia

IntroductionAccurate identification of the myocardial texture features of fat around the coronary artery on coronary computed tomography angiography (CCTA) images are crucial to improve clinical diagnostic efficiency of myocardial ischemia (MI). However, current coronary CT examination is difficult to recognize and segment the MI characteristics accurately during earlier period of inflammation.Materials and methodsWe proposed a random forest model to automatically segment myocardium and extract peripheral fat features. This hybrid machine learning (HML) model is integrated by CCTA images and clinical data. A total of 1,316 radiomics features were extracted from CCTA images. To further obtain the features that contribute the most to the diagnostic model, dimensionality reduction was applied to filter features to three: LNS, GFE, and WLGM. Moreover, statistical hypothesis tests were applied to improve the ability of discriminating and screening clinical features between the ischemic and non-ischemic groups.ResultsBy comparing the accuracy, recall, specificity and AUC of the three models, it can be found that HML had the best performance, with the value of 0.848, 0.762, 0.704 and 0.729.ConclusionIn sum, this study demonstrates that ML-based radiomics model showed good predictive value in MI, and offer an enhanced tool for predicting prognosis with greater accuracy

Ultrasound-Mediated DNA Transformation in Thermophilic Gram-Positive Anaerobes

Thermophilic, Gram-positive, anaerobic bacteria (TGPAs) are generally recalcitrant to chemical and electrotransformation due to their special cell-wall structure and the low intrinsic permeability of plasma membranes. transformants/µg of methylated DNA. Delivery into X514 cells was confirmed via detecting the kanamycin-resistance gene for pIKM2, while confirmation of pHL015 was detected by visualization of fluorescence signals of secondary host-cells following a plasmid-rescue experiment. Furthermore, the foreign β-1,4-glucanase gene was functionally expressed in X514, converting the host into a prototypic thermophilic consolidated bioprocessing organism that is not only ethanologenic but cellulolytic.In this study, we developed an ultrasound-based sonoporation method in TGPAs. This new DNA-delivery method could significantly improve the throughput in developing genetic systems for TGPAs, many of which are of industrial interest yet remain difficult to manipulate genetically

Age-specific reference values for low psoas muscle index at the L3 vertebra level in healthy populations: A multicenter study

Background and aimsThe progressive and generalized loss of skeletal muscle mass, strength and physical function is defined as sarcopenia. Sarcopenia is closely related to the prognosis of patients. Accurate diagnosis and adequate management of sarcopenia are crucial. The psoas muscle mass index taken at the third lumbar vertebra (L3-PMI, cm2/m2) is one of the established methods for evaluating skeletal muscle mass. However, the cutoff values of L3-PMI for diagnosis of sarcopenia are not yet to be clarified in Asian populations. We attempted to establish reference values for low L3-PMI that would be suitable for defining sarcopenia in the Northern Chinese population.MethodsThis was a retrospective, multicenter cross-sectional study. A search of abdominal CT imaging reports was conducted in four representative cities in northern China. Transverse CT images were measured using the analysis software Slice-O-Matic. Low psoas muscle index was defined as the 5th percentile or mean-2SD of the study group.Results1,787 healthy individuals in the study were grouped by age. The sex and number of people in each group were similar. L3-PMI had a negative linear correlation with age, and a strong correlation with the skeletal muscle index taken at the third lumbar vertebrae (L3-SMI, cm2/m2). The L3-PMI reference values in males were 5.41 cm2/m2 for 20–29 years, 4.71 cm2/m2 for 30–39 years, 4.65 cm2/m2 for 40–49 years, 4.10 cm2/m2 for 50–59 years and 3.68 cm2/m2 for over 60 years by using 5th percentile threshold. Similarly, the reference values in females were 3.32, 3.40, 3.18, 2.91, and 2.62 cm2/m2. When using mean-2SD as the reference, the values for each age group were 4.57, 4.16, 4.03, 3.37, and 2.87 cm2/m2 for males and 2.79, 2.70, 2.50, 2.30, and 2.26 cm2/m2 for females, respectively.ConclusionWe defined the reference values of age-specific low skeletal muscle mass when simply evaluated by L3-PMI. Further studies about the association of sarcopenia using these reference values with certain clinical outcomes or diseases are needed

Geospatially Constrained Workflow Modeling and Implementation

With rapid development and application of mobile internet, geographic information in the field of business process is now more widely used. There are more and more researches in the field of the relationships between geographic information and workflow modeling. According to the workflow with geospatial constraints, this paper first discusses the geospatial constraints theory deeply, proposes a new concept of geospatial constraints unit, and then designs a geospatial constraint net model (GCNet). Secondly, this paper designs a new workflow model with geospatial constraints (GCWF-net) based on GCNet and workflow net (WF-net), and then analyzes some properties of the model. Finally, this paper discusses how to put GCWF-net into application practice from three aspects: extending PNML (Petri Net Markup Language) labels for GCWF-net, converting PNML to BPEL (Business Process Execution Language) and implementing BPEL

Ordering for Non-Replacement SGD

One approach for reducing run time and improving efficiency of machine learning is to reduce the convergence rate of the optimization algorithm used. Shuffling is an algorithm technique that is widely used in machine learning, but it only started to gain attention theoretically in recent years. With different convergence rates developed for random shuffling and incremental gradient descent, we seek to find an ordering that can improve the convergence rates for the non-replacement form of the algorithm. Based on existing bounds of the distance between the optimal and current iterate, we derive an upper bound that is dependent on the gradients at the beginning of the epoch. Through analysis of the bound, we are able to develop optimal orderings for constant and decreasing step sizes for strongly convex and convex functions. We further test and verify our results through experiments on synthesis and real data sets. In addition, we are able to combine the ordering with mini-batch and further apply it to more complex neural networks, which show promising results

A Novel Petri Nets-Based Modeling Method for the Interaction between the Sensor and the Geographic Environment in Emerging Sensor Networks

The service of sensor device in Emerging Sensor Networks (ESNs) is the extension of traditional Web services. Through the sensor network, the service of sensor device can communicate directly with the entity in the geographic environment, and even impact the geographic entity directly. The interaction between the sensor device in ESNs and geographic environment is very complex, and the interaction modeling is a challenging problem. This paper proposed a novel Petri Nets-based modeling method for the interaction between the sensor device and the geographic environment. The feature of the sensor device service in ESNs is more easily affected by the geographic environment than the traditional Web service. Therefore, the response time, the fault-tolerant ability and the resource consumption become important factors in the performance of the whole sensor application system. Thus, this paper classified IoT services as Sensing services and Controlling services according to the interaction between IoT service and geographic entity, and classified GIS services as data services and processing services. Then, this paper designed and analyzed service algebra and Colored Petri Nets model to modeling the geo-feature, IoT service, GIS service and the interaction process between the sensor and the geographic enviroment. At last, the modeling process is discussed by examples

Synthetic Error Modeling for NC Machine Tools based on Intelligent Technology

AbstractThe precision of machine tools is greatly constrained by errors either built into the machine tools or occurring on a periodic basis on account of temperature changes or variation in cutting forces, so it is essential to obtain these errors, and then eliminate or compensate for them. However, the interaction between many factors inducing errors, such as the heat source, thermal expansion coefficient, the machine system configuration and the running environment, creates complex behavior of a machine tool, and also makes synthetic error prediction difficult with traditional mathematics. Therefore, several modeling methods based on non-classical mathematics have been presented in recent years. The intelligent technology methods of neural network, support vector machines, Bayesian networks are the effective modeling and forecasting methods for machine errors. All these three methods were introduced in briefly in the paper, and the characteristics of them were discussed. A series of experiments were carried out to evaluate their merits and defects. Finally, some important conclusions about how to use these methods in different situations were provided. The works in this paper make a special summary of the error modeling with intelligent technology, and provide a useful guidance to further research on error compensation of NC machine tools