Hopf bifurcation control for a class of delay differential systems with discrete-time delayed feedback controller

This paper is concerned with asymptotical stabilization for a class of delay differential equations, which undergo Hopf bifurcation at equilibrium as delay increasing. Two types of controllers, continuous-time and discrete-time delay feedback controllers, are presented. Although discrete-time control problems have been discussed by several authors, to the best of our knowledge, so few controllers relate to both delay and sampling period, and the method of Hopf bifurcation has not been seen. Here, we first give a range of control parameter which ensures the asymptotical stability of equilibrium for the continuous time controlled system. And then, for the discrete-time controller we also obtain an efficient control interval provided that the sampling period is sufficiently small. Meanwhile, we try our best to estimate a well bound on sampling period and get a more complete conclusion. Finally, the theoretical results are applied to a physiological system to illustrate the effectiveness of the two controllers

Optimizing production scheduling of steel plate hot rolling for economic load dispatch under time-of-use electricity pricing

Time-of-Use (TOU) electricity pricing provides an opportunity for industrial users to cut electricity costs. Although many methods for Economic Load Dispatch (ELD) under TOU pricing in continuous industrial processing have been proposed, there are still difficulties in batch-type processing since power load units are not directly adjustable and nonlinearly depend on production planning and scheduling. In this paper, for hot rolling, a typical batch-type and energy intensive process in steel industry, a production scheduling optimization model for ELD is proposed under TOU pricing, in which the objective is to minimize electricity costs while considering penalties caused by jumps between adjacent slabs. A NSGA-II based multi-objective production scheduling algorithm is developed to obtain Pareto-optimal solutions, and then TOPSIS based multi-criteria decision-making is performed to recommend an optimal solution to facilitate filed operation. Experimental results and analyses show that the proposed method cuts electricity costs in production, especially in case of allowance for penalty score increase in a certain range. Further analyses show that the proposed method has effect on peak load regulation of power grid.Comment: 13 pages, 6 figures, 4 table

1,4-Bis(imidazol-1-yl)benzene–terephthalic acid (1/1)

In the title compound, C12H10N4·C8H6O4, 1,4-bis­(imidazol-1-yl)benzene and terephthalic acid mol­ecules are joined via strong O—H⋯N hydrogen bonds to form infinite zigzag chains. Both mol­ecules are located on crystallographic inversion centers. The O—H⋯N hydrogen-bonded chains are assembled into two-dimensional layers through weak C—H⋯O and strong π–π stacking inter­actions [centroid–centroid distance = 3.818 (2) Å], leading to the formation of a three-dimensional supra­molecular structure

FinalMLP: An Enhanced Two-Stream MLP Model for CTR Prediction

Click-through rate (CTR) prediction is one of the fundamental tasks for online advertising and recommendation. While multi-layer perceptron (MLP) serves as a core component in many deep CTR prediction models, it has been widely recognized that applying a vanilla MLP network alone is inefficient in learning multiplicative feature interactions. As such, many two-stream interaction models (e.g., DeepFM and DCN) have been proposed by integrating an MLP network with another dedicated network for enhanced CTR prediction. As the MLP stream learns feature interactions implicitly, existing research focuses mainly on enhancing explicit feature interactions in the complementary stream. In contrast, our empirical study shows that a well-tuned two-stream MLP model that simply combines two MLPs can even achieve surprisingly good performance, which has never been reported before by existing work. Based on this observation, we further propose feature gating and interaction aggregation layers that can be easily plugged to make an enhanced two-stream MLP model, FinalMLP. In this way, it not only enables differentiated feature inputs but also effectively fuses stream-level interactions across two streams. Our evaluation results on four open benchmark datasets as well as an online A/B test in our industrial system show that FinalMLP achieves better performance than many sophisticated two-stream CTR models. Our source code will be available at MindSpore/models.Comment: Accepted by AAAI 2023. Code available at https://xpai.github.io/FinalML

Photonic Memristor for Future Computing: A Perspective

Photonic computing and neuromorphic computing could address the inherent limitations of traditional von Neumann architecture and gradually invalidate Moore’s law. As photonics applications are capable of storing and processing data in an optical manner with unprecedented bandwidth and high speed, twoâ terminal photonic memristors with a remote optical control of resistive switching behaviors at defined wavelengths ensure the benefit of onâ chip integration, low power consumption, multilevel data storage, and a large variation margin, suggesting promising advantages for both photonic and neuromorphic computing. Herein, the development of photonic memristors is reviewed, as well as their application in photonic computing and emulation on optogeneticsâ modulated artificial synapses. Different photoactive materials acting as both photosensing and storage media are discussed in terms of their opticalâ tunable memory behaviors and underlying resistive switching mechanism with consideration of photogating and photovoltaic effects. Moreover, lightâ involved logic operations, systemâ level integration, and lightâ controlled artificial synaptic memristors along with improved learning tasks performance are presented. Furthermore, the challenges in the field are discussed, such as the lack of a comprehensive understanding of microscopic mechanisms under light illumination and a general constraint of inferior nearâ infrared (NIR) sensitivity.The development of photonic memristors and their application in photonic computing and emulation on optogeneticsâ modulated artificial synapses are reviewed. Photoactive materials as photosensing and storage media are discussed, considering their opticalâ tunable memory behavior and resistive switching mechanism including photogating and photovoltaic effect. Lightâ involved logic operations, system level integration, and artificial synaptic memristors along with improved learning tasks performance are presented.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/153103/1/adom201900766.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/153103/2/adom201900766_am.pd

FAN: Fatigue-Aware Network for Click-Through Rate Prediction in E-commerce Recommendation

Since clicks usually contain heavy noise, increasing research efforts have been devoted to modeling implicit negative user behaviors (i.e., non-clicks). However, they either rely on explicit negative user behaviors (e.g., dislikes) or simply treat non-clicks as negative feedback, failing to learn negative user interests comprehensively. In such situations, users may experience fatigue because of seeing too many similar recommendations. In this paper, we propose Fatigue-Aware Network (FAN), a novel CTR model that directly perceives user fatigue from non-clicks. Specifically, we first apply Fourier Transformation to the time series generated from non-clicks, obtaining its frequency spectrum which contains comprehensive information about user fatigue. Then the frequency spectrum is modulated by category information of the target item to model the bias that both the upper bound of fatigue and users' patience is different for different categories. Moreover, a gating network is adopted to model the confidence of user fatigue and an auxiliary task is designed to guide the learning of user fatigue, so we can obtain a well-learned fatigue representation and combine it with user interests for the final CTR prediction. Experimental results on real-world datasets validate the superiority of FAN and online A/B tests also show FAN outperforms representative CTR models significantly

KIT Bus: A Shuttle Model for CARLA Simulator

With the continuous development of science and technology, self-driving vehicles will surely change the nature of transportation and realize the automotive industry\u27s transformation in the future. Compared with self-driving cars, self-driving buses are more efficient in carrying passengers and more environmentally friendly in terms of energy consumption. Therefore, it is speculated that in the future, self-driving buses will become more and more important. As a simulator for autonomous driving research, the CARLA simulator can help people accumulate experience in autonomous driving technology faster and safer. However, a shortcoming is that there is no modern bus model in the CARLA simulator. Consequently, people cannot simulate autonomous driving on buses or the scenarios interacting with buses. Therefore, we built a bus model in 3ds Max software and imported it into the CARLA to fill this gap. Our model, namely KIT bus, is proven to work in the CARLA by testing it with the autopilot simulation. The video demo is shown on our Youtube