Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction

Click-Through Rate prediction is an important task in recommender systems, which aims to estimate the probability of a user to click on a given item. Recently, many deep models have been proposed to learn low-order and high-order feature interactions from original features. However, since useful interactions are always sparse, it is difficult for DNN to learn them effectively under a large number of parameters. In real scenarios, artificial features are able to improve the performance of deep models (such as Wide & Deep Learning), but feature engineering is expensive and requires domain knowledge, making it impractical in different scenarios. Therefore, it is necessary to augment feature space automatically. In this paper, We propose a novel Feature Generation by Convolutional Neural Network (FGCNN) model with two components: Feature Generation and Deep Classifier. Feature Generation leverages the strength of CNN to generate local patterns and recombine them to generate new features. Deep Classifier adopts the structure of IPNN to learn interactions from the augmented feature space. Experimental results on three large-scale datasets show that FGCNN significantly outperforms nine state-of-the-art models. Moreover, when applying some state-of-the-art models as Deep Classifier, better performance is always achieved, showing the great compatibility of our FGCNN model. This work explores a novel direction for CTR predictions: it is quite useful to reduce the learning difficulties of DNN by automatically identifying important features

Metabolic responses of HeLa cells to silica nanoparticles by NMR-based metabolomic analyses

Silica nanoparticles are increasingly used in the biomedical fields due to their excellent solubility, high stability and favorable biocompatibility. However, despite being considered of low genotoxicity, their bio-related adverse effects have attracted particular concern from both the scientific field and the public. In this study, human cervical adenocarcinoma cells (HeLa line) were exposed to 0.01 or 1.0 mg/mL of hydrophilic silica nanoparticles. The H-1 NMR spectroscopy coupled with multivariate statistical analysis were used to characterize the metabolic variations of intracellular metabolites and the compositional changes of the corresponding culture media. At the early stage of silica nanoparticles-exposure, no obvious dose-effect of HeLa cell metabolome was observed, which implied that cellular stress-response regulated the metabolic variations of HeLa cell. Silica nanoparticles induced the increases of lipids including triglyceride, LDL, VLDL and lactate/alanine ratio and the decreases of alanine, ATP, choline, creatine, glycine, glycerol, isoleucine, leucine, phenylalanine, tyrosine, and valine, which involved in membrane modification, catabolism of carbohydrate and protein, and stress-response. Subsequently, a complicated synergistic effect of stress-response and toxicological-effect dominated the biochemical process and metabolic response, which was demonstrated in the reverse changes of some metabolites including acetate, ADP, ATP, choline, creatine, glutamine, glycine, lysine, methionine, phenylalanine and valine between 6 and 48 h post-treatment of silica nanoparticles. The toxicological-effects induced by high-dosage silica nanoparticles could be derived from the elevated levels of ATP and ADP, the utilization of glucose and amino acids and the production of metabolic end-products such as glutamate, glycine, lysine, methionine, phenylalanine, and valine. The results indicated that it is important and necessary to pursue further the physiological responses of silica nanoparticles in animal models and human before their practical use. NMR-based metabolomic analysis helps to understand the biological mechanisms of silica nanoparticles and their metabolic fate, and further, it offers an ideal platform for establishing the bio-safety of existing and new nanomaterials.Silica nanoparticles are increasingly used in the biomedical fields due to their excellent solubility, high stability and favorable biocompatibility. However, despite being considered of low genotoxicity, their bio-related adverse effects have attracted particular concern from both the scientific field and the public. In this study, human cervical adenocarcinoma cells (HeLa line) were exposed to 0.01 or 1.0 mg/mL of hydrophilic silica nanoparticles. The H-1 NMR spectroscopy coupled with multivariate statistical analysis were used to characterize the metabolic variations of intracellular metabolites and the compositional changes of the corresponding culture media. At the early stage of silica nanoparticles-exposure, no obvious dose-effect of HeLa cell metabolome was observed, which implied that cellular stress-response regulated the metabolic variations of HeLa cell. Silica nanoparticles induced the increases of lipids including triglyceride, LDL, VLDL and lactate/alanine ratio and the decreases of alanine, ATP, choline, creatine, glycine, glycerol, isoleucine, leucine, phenylalanine, tyrosine, and valine, which involved in membrane modification, catabolism of carbohydrate and protein, and stress-response. Subsequently, a complicated synergistic effect of stress-response and toxicological-effect dominated the biochemical process and metabolic response, which was demonstrated in the reverse changes of some metabolites including acetate, ADP, ATP, choline, creatine, glutamine, glycine, lysine, methionine, phenylalanine and valine between 6 and 48 h post-treatment of silica nanoparticles. The toxicological-effects induced by high-dosage silica nanoparticles could be derived from the elevated levels of ATP and ADP, the utilization of glucose and amino acids and the production of metabolic end-products such as glutamate, glycine, lysine, methionine, phenylalanine, and valine. The results indicated that it is important and necessary to pursue further the physiological responses of silica nanoparticles in animal models and human before their practical use. NMR-based metabolomic analysis helps to understand the biological mechanisms of silica nanoparticles and their metabolic fate, and further, it offers an ideal platform for establishing the bio-safety of existing and new nanomaterials

Real-time temperature estimation for power MOSFETs considering thermal ageing effects

This paper presents a novel real-time power-device temperature estimation method that monitors the power MOSFET's junction temperature shift arising from thermal aging effects and incorporates the updated electrothermal models of power modules into digital controllers. Currently, the real-time estimator is emerging as an important tool for active control of device junction temperature as well as online health monitoring for power electronic systems, but its thermal model fails to address the device's ongoing degradation. Because of a mismatch of coefficients of thermal expansion between layers of power devices, repetitive thermal cycling will cause cracks, voids, and even delamination within the device components, particularly in the solder and thermal grease layers. Consequently, the thermal resistance of power devices will increase, making it possible to use thermal resistance (and junction temperature) as key indicators for condition monitoring and control purposes. In this paper, the predicted device temperature via threshold voltage measurements is compared with the real-time estimated ones, and the difference is attributed to the aging of the device. The thermal models in digital controllers are frequently updated to correct the shift caused by thermal aging effects. Experimental results on three power MOSFETs confirm that the proposed methodologies are effective to incorporate the thermal aging effects in the power-device temperature estimator with good accuracy. The developed adaptive technologies can be applied to other power devices such as IGBTs and SiC MOSFETs, and have significant economic implications

Adaptive fuzzy Gaussian mixture models for shape approximation in Robot Grasping

Robotic grasping has always been a challenging task for both service and industrial robots. The ability of grasp planning for novel objects is necessary for a robot to autonomously perform grasps under unknown environments.In this work, we consider the task of grasp planning for a parallel gripper to grasp a novel object, given an RGB image and its corresponding depth image taken from a single view. In this paper, we show that this problem can be simplified by modeling a novel object as a set of simple shape primitives, such as ellipses. We adopt fuzzy Gaussian mixture models (GMMs) for novel objects’ shape approximation. With the obtained GMM, we decompose the object into several ellipses, while each ellipse is corresponding to a grasping rectangle. After comparing the grasp quality among these rectangles, we will obtain the most proper part for a gripper to grasp. Extensive experiments on a real robotic platform demonstrate that our algorithm assists the robot to grasp a variety of novel objects with good grasp quality and computational efficiency

AutoAssign+: Automatic Shared Embedding Assignment in Streaming Recommendation

In the domain of streaming recommender systems, conventional methods for addressing new user IDs or item IDs typically involve assigning initial ID embeddings randomly. However, this practice results in two practical challenges: (i) Items or users with limited interactive data may yield suboptimal prediction performance. (ii) Embedding new IDs or low-frequency IDs necessitates consistently expanding the embedding table, leading to unnecessary memory consumption. In light of these concerns, we introduce a reinforcement learning-driven framework, namely AutoAssign+, that facilitates Automatic Shared Embedding Assignment Plus. To be specific, AutoAssign+ utilizes an Identity Agent as an actor network, which plays a dual role: (i) Representing low-frequency IDs field-wise with a small set of shared embeddings to enhance the embedding initialization, and (ii) Dynamically determining which ID features should be retained or eliminated in the embedding table. The policy of the agent is optimized with the guidance of a critic network. To evaluate the effectiveness of our approach, we perform extensive experiments on three commonly used benchmark datasets. Our experiment results demonstrate that AutoAssign+ is capable of significantly enhancing recommendation performance by mitigating the cold-start problem. Furthermore, our framework yields a reduction in memory usage of approximately 20-30%, verifying its practical effectiveness and efficiency for streaming recommender systems

Knowledge, Attitude and Practices (KAP) towards Diet and Health among International Students in Dublin: A Cross-Sectional Study

International students may have difficulties in dietary acculturation. This study aimed to evaluate the knowledge, attitude and practices (KAP) of diet and health during the acculturation of international students. A cross-sectional survey was conducted among a convenience sample of 473 international students in Dublin. Knowledge, attitude and practices towards diet and health were evaluated by a questionnaire with open- and closed-ended questions. It was found that 45.3% of participants had a broad concept of a healthy diet, while few knew its specific contents. Furthermore, 75.3% of participants could explain the term functional food, and among them, 62.1% knew the appropriate definition of functional food. Participants who perceived their health very good and excellent were more likely to believe that their health status was determined by their own control. The consumption rate of functional food varied among regions and South and Central America students had the highest usage rate (44.5%) and Asian students had the highest daily usage rate (52.7%). Participants who were younger, single, from African and South and Central American countries, or who were in Ireland for less than one year were more likely to report dietary change after immigration. In conclusion, insufficient knowledge and self-perception towards diet and health as well as unhealthily dietary changes exist among international students living in Dublin

Automated Machine Learning for Deep Recommender Systems: A Survey

Deep recommender systems (DRS) are critical for current commercial online service providers, which address the issue of information overload by recommending items that are tailored to the user's interests and preferences. They have unprecedented feature representations effectiveness and the capacity of modeling the non-linear relationships between users and items. Despite their advancements, DRS models, like other deep learning models, employ sophisticated neural network architectures and other vital components that are typically designed and tuned by human experts. This article will give a comprehensive summary of automated machine learning (AutoML) for developing DRS models. We first provide an overview of AutoML for DRS models and the related techniques. Then we discuss the state-of-the-art AutoML approaches that automate the feature selection, feature embeddings, feature interactions, and system design in DRS. Finally, we discuss appealing research directions and summarize the survey

Fault-Tolerant Converter with a Modular Structure for HVDC Power Transmitting Applications

For the high-voltage direct-current (HVDC) power transmission system of offshore wind power, dc/dc converters are the potential solution to collect the power generated by off-shore wind farms to HVDC terminals. The converters operate with high-voltage gain, high efficiency, and fault tolerance over a wide range of operating conditions. In this paper, an isolated ultrahigh step-up dc/dc converter with a scalable modular structure is proposed for HVDC offshore wind power collection. A flyback-forward converter is employed as the power cell to form the expandable electrically isolated modular dc/dc converter. The duty ratio and phase-shift angle control are also developed for the proposed converter. Fault-tolerant characteristics of the converter are illustrated through the redundancy operation and fault-ride-through tests. Redundancy operation is designed to maintain high operation efficiency of the converters and fault-ride-through operation improves the converter reliability under harsh operating conditions. Analytical studies are carried out, and a 750-W prototype with three modular cells is built and experimentally tested to verify the performance of the proposed modular dc/dc converter