Analysis of the thermo-elastic vibration for axially moving Euler beam

The thermo-elastic vibration response of simple supported axially moving Euler beam is investigated. The differential equation of moving beam is established by recourse to Hamilton principle and the thermal effects is considered by introducing the equivalent thermal bending moment. A 2-D transient temperature field is calculated by the alternating-directional implicit (ADI) method and the equivalent thermal moment is calculated numerically. The dimensionless equation is discretized by Galerkin method and the modal analysis of gyroscopic system is used to calculate the forced vibration response. The time-history curve of the beam’s upper middle point is obtained for thermal or non-thermal situations

Effective AER Object Classification Using Segmented Probability-Maximization Learning in Spiking Neural Networks

Address event representation (AER) cameras have recently attracted more attention due to the advantages of high temporal resolution and low power consumption, compared with traditional frame-based cameras. Since AER cameras record the visual input as asynchronous discrete events, they are inherently suitable to coordinate with the spiking neural network (SNN), which is biologically plausible and energy-efficient on neuromorphic hardware. However, using SNN to perform the AER object classification is still challenging, due to the lack of effective learning algorithms for this new representation. To tackle this issue, we propose an AER object classification model using a novel segmented probability-maximization (SPA) learning algorithm. Technically, 1) the SPA learning algorithm iteratively maximizes the probability of the classes that samples belong to, in order to improve the reliability of neuron responses and effectiveness of learning; 2) a peak detection (PD) mechanism is introduced in SPA to locate informative time points segment by segment, based on which information within the whole event stream can be fully utilized by the learning. Extensive experimental results show that, compared to state-of-the-art methods, not only our model is more effective, but also it requires less information to reach a certain level of accuracy.Comment: AAAI 2020 (Oral

Moving beyond Deletions: Program Simplification via Diverse Program Transformations

To reduce the complexity of software, Developers manually simplify program (known as developer-induced program simplification in this paper) to reduce its code size yet preserving its functionality but manual simplification is time-consuming and error-prone. To reduce manual effort, rule-based approaches (e.g., refactoring) and deletion-based approaches (e.g., delta debugging) can be potentially applied to automate developer-induced program simplification. However, as there is little study on how developers simplify programs in Open-source Software (OSS) projects, it is unclear whether these approaches can be effectively used for developer-induced program simplification. Hence, we present the first study of developer-induced program simplification in OSS projects, focusing on the types of program transformations used, the motivations behind simplifications, and the set of program transformations covered by existing refactoring types. Our study of 382 pull requests from 296 projects reveals that there exist gaps in applying existing approaches for automating developer-induced program simplification. and outlines the criteria for designing automatic program simplification techniques. Inspired by our study and to reduce the manual effort in developer-induced program simplification, we propose SimpT5, a tool that can automatically produce simplified programs (semantically-equivalent programs with reduced source lines of code). SimpT5 is trained based on our collected dataset of 92,485 simplified programs with two heuristics: (1) simplified line localization that encodes lines changed in simplified programs, and (2)checkers that measure the quality of generated programs. Our evaluation shows that SimpT5 are more effective than prior approaches in automating developer-induced program simplification

Development Of Structural-functional Integrated Energy Storage Concrete With Innovative Macro-encapsulated PCM By Hollow Steel Ball

Phase change materials (PCMs) have great potential for applications in energy efficient buildings. In this study, an innovative method of macro-encapsulation of PCM using hollow steel balls (HSB) was developed and the thermal and mechanical performance of PCM-HSB concrete was examined. The macro-encapsulation system (PCM-HSB) was attached with a metal clamp (c) for better mechanical interlocking with the mortar matrix. The latent heat of PCM-HSB-c that can be acquired is approximately 153.1 J/g, which can be considered to rank highly among PCM composites. According to the self-designed thermal performance evaluation, the PCM–HSB-c concrete panel is capable of reducing and deferring the peak indoor temperature. The indoor temperature of the room model using PCM-HSB-c panels was significantly lower than the ones with normal concrete panels by a range of 3–6%. Furthermore, the test room using a higher PCM-HSB-c content demonstrated a greater ability to maintain a lower indoor room temperature for a longer period of time during heating cycles. In consideration of the mechanical properties, thermal performance and other aspects of cost factors, 50% and 75% PCM-HSB-c replacement levels are recommended in producing concrete

MicroAST: Towards Super-Fast Ultra-Resolution Arbitrary Style Transfer

Arbitrary style transfer (AST) transfers arbitrary artistic styles onto content images. Despite the recent rapid progress, existing AST methods are either incapable or too slow to run at ultra-resolutions (e.g., 4K) with limited resources, which heavily hinders their further applications. In this paper, we tackle this dilemma by learning a straightforward and lightweight model, dubbed MicroAST. The key insight is to completely abandon the use of cumbersome pre-trained Deep Convolutional Neural Networks (e.g., VGG) at inference. Instead, we design two micro encoders (content and style encoders) and one micro decoder for style transfer. The content encoder aims at extracting the main structure of the content image. The style encoder, coupled with a modulator, encodes the style image into learnable dual-modulation signals that modulate both intermediate features and convolutional filters of the decoder, thus injecting more sophisticated and flexible style signals to guide the stylizations. In addition, to boost the ability of the style encoder to extract more distinct and representative style signals, we also introduce a new style signal contrastive loss in our model. Compared to the state of the art, our MicroAST not only produces visually superior results but also is 5-73 times smaller and 6-18 times faster, for the first time enabling super-fast (about 0.5 seconds) AST at 4K ultra-resolutions. Code is available at https://github.com/EndyWon/MicroAST.Comment: Accepted by AAAI 202

Attack Deterministic Conditional Image Generative Models for Diverse and Controllable Generation

Existing generative adversarial network (GAN) based conditional image generative models typically produce fixed output for the same conditional input, which is unreasonable for highly subjective tasks, such as large-mask image inpainting or style transfer. On the other hand, GAN-based diverse image generative methods require retraining/fine-tuning the network or designing complex noise injection functions, which is computationally expensive, task-specific, or struggle to generate high-quality results. Given that many deterministic conditional image generative models have been able to produce high-quality yet fixed results, we raise an intriguing question: is it possible for pre-trained deterministic conditional image generative models to generate diverse results without changing network structures or parameters? To answer this question, we re-examine the conditional image generation tasks from the perspective of adversarial attack and propose a simple and efficient plug-in projected gradient descent (PGD) like method for diverse and controllable image generation. The key idea is attacking the pre-trained deterministic generative models by adding a micro perturbation to the input condition. In this way, diverse results can be generated without any adjustment of network structures or fine-tuning of the pre-trained models. In addition, we can also control the diverse results to be generated by specifying the attack direction according to a reference text or image. Our work opens the door to applying adversarial attack to low-level vision tasks, and experiments on various conditional image generation tasks demonstrate the effectiveness and superiority of the proposed method.Comment: 9 pages, 7 figures, accepted by AAAI2

A new, potential and safe neoadjuvant therapy strategy in epidermal growth factor receptor mutation-positive resectable non-small-cell lung cancer-targeted therapy: a retrospective study

BackgroundStudies of epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) in resectable non-small-cell lung cancer (NSCLC) have been conducted. The purpose of our study was to evaluate the benefits of osimertinib as neoadjuvant therapy for resectable EGFR-mutated NSCLC.MethodThis retrospective study evaluated patients with EGFR mutations in exon 19 or 21 who received targeted therapy with osimertinib (80 mg per day) before surgery between January 2019 and October 2023 in Henan Cancer Hospital.ResultsTwenty patients were evaluated, all of whom underwent surgery. The rate of R0 resection was 100% (20/20). The objective response rate was 80% (16/20), and the disease control rate was 95% (19/20). Postoperative pathological analysis showed a 25% (5/20) major pathological response rate and 15% (3/20) pathological complete response rate. In total, 25% (5/20) developed adverse events (AEs), and the rate of grades 3–4 AEs was 10% (2/20). One patient experienced a grade 3 skin rash, and 1 patient experienced grade 3 diarrhea.ConclusionOsimertinib as neoadjuvant therapy for resectable EGFR-mutated NSCLC is safe and well tolerated. Osimertinib has the potential to improve the radical resection rate and prognosis

Analytically optimal parameters of fractional-order dynamic vibration absorber

In this paper the optimal parameters of the fractional-order Voigt type dynamic vibration absorber (DVA) are analytically studied for two cases, named as H∞ and H2 optimization criteria. At first the approximately analytical solution is obtained by the averaging method when the primary system is subjected to harmonic excitation. Then the optimal fractional coefficient and order are obtained based on H∞ optimization criterion, which is designed to minimize the maximum amplitude magnification factor of the primary system. Based on H2 optimization criterion, the optimal fractional parameters are obtained to reduce the total vibration energy of the primary system over the whole-frequency range. The comparisons of the approximate solutions with the numerical ones in the two cases are fulfilled, and the results verify that the approximately analytical solutions are correct and satisfactorily precise. At last the control performance of the fractional-order Voigt type DVA is compared with the classical integer-order counterpart, and it could be concluded that the fractional-order DVA has superiority in vibration engineering, and fractional-order element could replace the traditional damper and spring simultaneously in some cases