A Novel Adaptive Elite-Based Particle Swarm Optimization Applied to VAR Optimization in Electric Power Systems

Particle swarm optimization (PSO) has been successfully applied to solve many practical engineering problems. However, more efficient strategies are needed to coordinate global and local searches in the solution space when the studied problem is extremely nonlinear and highly dimensional. This work proposes a novel adaptive elite-based PSO approach. The adaptive elite strategies involve the following two tasks: (1) appending the mean search to the original approach and (2) pruning/cloning particles. The mean search, leading to stable convergence, helps the iterative process coordinate between the global and local searches. The mean of the particles and standard deviation of the distances between pairs of particles are utilized to prune distant particles. The best particle is cloned and it replaces the pruned distant particles in the elite strategy. To evaluate the performance and generality of the proposed method, four benchmark functions were tested by traditional PSO, chaotic PSO, differential evolution, and genetic algorithm. Finally, a realistic loss minimization problem in an electric power system is studied to show the robustness of the proposed method

Shilling Black-box Review-based Recommender Systems through Fake Review Generation

Review-Based Recommender Systems (RBRS) have attracted increasing research interest due to their ability to alleviate well-known cold-start problems. RBRS utilizes reviews to construct the user and items representations. However, in this paper, we argue that such a reliance on reviews may instead expose systems to the risk of being shilled. To explore this possibility, in this paper, we propose the first generation-based model for shilling attacks against RBRSs. Specifically, we learn a fake review generator through reinforcement learning, which maliciously promotes items by forcing prediction shifts after adding generated reviews to the system. By introducing the auxiliary rewards to increase text fluency and diversity with the aid of pre-trained language models and aspect predictors, the generated reviews can be effective for shilling with high fidelity. Experimental results demonstrate that the proposed framework can successfully attack three different kinds of RBRSs on the Amazon corpus with three domains and Yelp corpus. Furthermore, human studies also show that the generated reviews are fluent and informative. Finally, equipped with Attack Review Generators (ARGs), RBRSs with adversarial training are much more robust to malicious reviews

SINC: Self-Supervised In-Context Learning for Vision-Language Tasks

Large Pre-trained Transformers exhibit an intriguing capacity for in-context learning. Without gradient updates, these models can rapidly construct new predictors from demonstrations presented in the inputs. Recent works promote this ability in the vision-language domain by incorporating visual information into large language models that can already make in-context predictions. However, these methods could inherit issues in the language domain, such as template sensitivity and hallucination. Also, the scale of these language models raises a significant demand for computations, making learning and operating these models resource-intensive. To this end, we raise a question: ``How can we enable in-context learning without relying on the intrinsic in-context ability of large language models?". To answer it, we propose a succinct and general framework, Self-supervised IN-Context learning (SINC), that introduces a meta-model to learn on self-supervised prompts consisting of tailored demonstrations. The learned models can be transferred to downstream tasks for making in-context predictions on-the-fly. Extensive experiments show that SINC outperforms gradient-based methods in various vision-language tasks under few-shot settings. Furthermore, the designs of SINC help us investigate the benefits of in-context learning across different tasks, and the analysis further reveals the essential components for the emergence of in-context learning in the vision-language domain.Comment: Accepted by ICCV 2023; Camera Ready Versio

Cascaded Local Implicit Transformer for Arbitrary-Scale Super-Resolution

Implicit neural representation has recently shown a promising ability in representing images with arbitrary resolutions. In this paper, we present a Local Implicit Transformer (LIT), which integrates the attention mechanism and frequency encoding technique into a local implicit image function. We design a cross-scale local attention block to effectively aggregate local features. To further improve representative power, we propose a Cascaded LIT (CLIT) that exploits multi-scale features, along with a cumulative training strategy that gradually increases the upsampling scales during training. We have conducted extensive experiments to validate the effectiveness of these components and analyze various training strategies. The qualitative and quantitative results demonstrate that LIT and CLIT achieve favorable results and outperform the prior works in arbitrary super-resolution tasks

Shape Effects of Iron Nanowires on Hyperthermia Treatment

This research discusses the influence of morphology of nanomagnetic materials (one-dimensional iron nanowires and zero-dimensional iron nanoparticles) on heating efficiency of the hyperthermia treatment. One-dimensional iron nanowires, synthesized by reducing method in external magnetic field, are explored in terms of their material properties, magnetic anisotropy, and cytotoxicity of EMT-6 cells. The magnetic anisotropy of an array of nanowires is examined in parallel and perpendicular magnetic fields by VSM. For the magnetic hyperthermia treatment tests, iron nanowires and nanoparticles with different concentrations are heated in alternating magnetic field to measure their actual heating efficiency and SLP heating properties. The shape effects of iron nanomaterials can be revealed from their heating properties. The cytotoxicity of nanowires with different concentrations is measured by its survival rate in EMT-6 with the cells cultivated for 6 and 24 hours

Nitrogen containing FePt Catalyst in Oxygen Reduction Reaction for Fuel Cells

為加速燃料電池之實用化，開發具高活性、高穩定性與低成本之陰極觸媒為科學界之一大課題。近年藉沉積鐵氮碳奈米粒子於零維碳材或二維結構之石墨烯，並於氨氣環境進行熱處理，開發具一定活性、低雙氧水產率與近四電子轉移數之過渡金屬觸媒，但其活性與鉑等貴金屬觸媒仍有一定差距。 因觸媒催化能力與其結構具顯著相關性，藉調控觸媒之結構，可改善其對氧氣還原反應之活性以及副反應之產生。本研究將藉添加鉑金屬於鐵氮粒子之結構，藉由鉑較鐵富電子之特性，改善鐵氮觸媒之活性，並藉此為爾後富電子金屬改質方法之研究奠定基礎。此外此合成法將觸媒乘載於將金屬離子結合之熬合劑碳化所形成之碳材。不似過往金屬觸媒合成後，需額外添加碳材再經熱處理之方法，流程精簡。相較於已發展之合金觸媒，本研究所開發之觸媒具降低成本之優勢並可有效提升此非貴重金屬觸媒之催化活性。 藉調整合成方法後，本研究以X光粉末繞射儀(X-ray powder diffraction; XRD)鑑定觸媒晶體結構與純度；以感應耦合電漿質譜分析儀(inductively coupled plasma mass spectrometry; ICP-MS)與元素分析儀(elemental analyzer; EA)分析觸媒元素組成，藉穿透式電子顯微鏡(transmission electron microscopy; TEM)進行觸媒形貌、粒徑大小與分布之分析，以循環伏安電位儀(cyclic voltammetry)量測觸媒之電化學特性、氧氣還原(ORR)活性與雙氧水產生率，以X光吸收光譜(X-ray absorption spectroscopy; XAS)之X光近吸收邊緣結構(X-ray Absorption Near Edge Structure; XANES)量測觸媒元素之電子結構。藉由上述方法，確認藉添加鉑金屬於鐵氮粒子之結構將改善鐵氮觸媒之活性。To promote the commercial of fuel cell, designing a high activity, high stability and low cost catalyst was a critical issue. Recently, the catalyst which iron nitride nanoparticle deposited on the zero dimensional and three dimensional carbon support was synthesized under the ammonia atmosphere and demonstrated a well performance in activity, electron transfer and yield of hydrogen peroxide in oxygen reduction reaction. However, its activity was still lower than commercial Platinum catalyst. The electronic structure was an important factor to enhance the activity and inhabit the side reaction of catalyst. In present study, we established method to improve the activity by importing an electron donor, platinum for example, into the iron nitride catalyst enhance the back donation of active site. In the present study, the characteristics of catalyst were identified by following technology. Crystal structure identified X-ray powder diffraction. X-ray Absorption Near Edge Structure (XANES) study by using synchrotron radiation was applied for the d-band vacancy of iron and platinum. The oxygen reduction performance was identified by cyclic voltammery. Confirm the introduction of platinum into iron nitride catalyst would enhance the catalytic activity.口試委員會審定書 i 謝誌 ii 摘要 iii Abstract iv 總目錄 v 圖目錄 vii 表目錄 x 第一章 緒論 1 1.1 燃料電池之歷史 1 1.2燃料電池介紹 3 1.2.1燃料電池特色 3 1.2.2燃料電池種類 3 1.2.3質子交換膜型燃料電池之發展 5 1.2.4質子交換膜型燃料電池之原理 5 1.2.5質子交換膜型燃料電池之構造 6 1.2.6質子交換膜型燃料電池之極化曲線 8 1-3 氧氣還原反應介紹 9 1.3.1 氧氣還原反應之重要性 9 1.3.2 氧氣還原反應之路徑 10 1.3.3 氧氣還原反應機制 10 1.4質子交換膜型燃料電池之陰極觸媒 14 1.4.1陰極觸媒之發展 14 1.4.2 鉑系陰極觸媒 15 1.4.3 非貴金屬系陰極觸媒 18 1.5研究動機與目的 22 第二章 實驗步驟與儀器分析原理 24 2.1 化學藥品 24 2.2 鐵鉑氮觸媒合成 25 2.3 觸媒電化學量測方式 27 2.4觸媒樣品之鑑定與分析 27 2.4.1 X光粉末繞射(X-ray powder diffraction; XRD) 29 2.4.2感應耦合電漿質譜分析(Inductively coupled plasma mass spectrometry; ICP-MS) 31 2.4.3元素分析(Eelemental analyzsis; EA) 34 2.4.4 穿透式電子顯微鏡(Transmission electron microscope；TEM) 35 2.4.5循環伏安電位(Cyclic voltammetry) 36 2.4.6 X光吸收光譜 (X-ray absorption spectroscopy; XAS) 37 第三章 結果與討論 41 3.1 觸媒結構分析 41 3.1.1 觸媒合成條件探討 41 3.1.2 粉末X光繞射圖譜 42 3.1.3 元素分析 46 3.1.4 高解析穿透式電子顯微鏡分析 47 3.1.5.1 X光吸收近邊緣結構 49 3.2觸媒電化學特性分析 53 3.2.1 循環伏安法分析 53 3.2.2 線性伏安法-氧氣還原反應活性測試 54 3.2.3 電子轉移數與雙氧水產率 56 第四章 結論 58 參考文獻 5

Meta-Transfer Learning for Low-Resource Abstractive Summarization

Neural abstractive summarization has been studied in many pieces of literature and achieves great success with the aid of large corpora. However, when encountering novel tasks, one may not always benefit from transfer learning due to the domain shifting problem, and overfitting could happen without adequate labeled examples. Furthermore, the annotations of abstractive summarization are costly, which often demand domain knowledge to ensure the ground-truth quality. Thus, there are growing appeals for Low-Resource Abstractive Summarization, which aims to leverage past experience to improve the performance with limited labeled examples of target corpus. In this paper, we propose to utilize two knowledge-rich sources to tackle this problem, which are large pre-trained models and diverse existing corpora. The former can provide the primary ability to tackle summarization tasks; the latter can help discover common syntactic or semantic information to improve the generalization ability. We conduct extensive experiments on various summarization corpora with different writing styles and forms. The results demonstrate that our approach achieves the state-of-the-art on 6 corpora in low-resource scenarios, with only 0.7% of trainable parameters compared to previous work.Comment: Accepted by AAAI 2021; Camera Ready Versio

Syringin Prevents 6-Hydroxydopamine Neurotoxicity by Mediating the MiR-34a/SIRT1/Beclin-1 Pathway and Activating Autophagy in SH-SY5Y Cells and the <i>Caenorhabditis elegans</i> Model

Defective autophagy is one of the cellular hallmarks of Parkinson’s disease (PD). Therefore, a therapeutic strategy could be a modest enhancement of autophagic activity in dopamine (DA) neurons to deal with the clearance of damaged mitochondria and abnormal protein aggregates. Syringin (SRG) is a phenolic glycoside derived from the root of Acanthopanax senticosus. It has antioxidant, anti-apoptotic, and anti-inflammatory properties. However, whether it has a preventive effect on PD remains unclear. The present study found that SRG reversed the increase in intracellular ROS-caused apoptosis in SH-SY5Y cells induced by neurotoxin 6-OHDA exposure. Likewise, in C. elegans, degeneration of DA neurons, DA-related food-sensitive behaviors, longevity, and accumulation of α-synuclein were also improved. Studies of neuroprotective mechanisms have shown that SRG can reverse the suppressed expression of SIRT1, Beclin-1, and other autophagy markers in 6-OHDA-exposed cells. Thus, these enhanced the formation of autophagic vacuoles and autophagy activity. This protective effect can be blocked by pretreatment with wortmannin (an autophagosome formation blocker) and bafilomycin A1 (an autophagosome–lysosome fusion blocker). In addition, 6-OHDA increases the acetylation of Beclin-1, leading to its inactivation. SRG can induce the expression of SIRT1 and promote the deacetylation of Beclin-1. Finally, we found that SRG reduced the 6-OHDA-induced expression of miR-34a targeting SIRT1. The overexpression of miR-34a mimic abolishes the neuroprotective ability of SRG. In conclusion, SRG induces autophagy via partially regulating the miR-34a/SIRT1/Beclin-1 axis to prevent 6-OHDA-induced apoptosis and α-synuclein accumulation. SRG has the opportunity to be established as a candidate agent for the prevention and cure of PD