k-Same-Siamese-GAN: k-Same Algorithm with Generative Adversarial Network for Facial Image De-identification with Hyperparameter Tuning and Mixed Precision Training

For a data holder, such as a hospital or a government entity, who has a privately held collection of personal data, in which the revealing and/or processing of the personal identifiable data is restricted and prohibited by law. Then, "how can we ensure the data holder does conceal the identity of each individual in the imagery of personal data while still preserving certain useful aspects of the data after de-identification?" becomes a challenge issue. In this work, we propose an approach towards high-resolution facial image de-identification, called k-Same-Siamese-GAN, which leverages the k-Same-Anonymity mechanism, the Generative Adversarial Network, and the hyperparameter tuning methods. Moreover, to speed up model training and reduce memory consumption, the mixed precision training technique is also applied to make kSS-GAN provide guarantees regarding privacy protection on close-form identities and be trained much more efficiently as well. Finally, to validate its applicability, the proposed work has been applied to actual datasets - RafD and CelebA for performance testing. Besides protecting privacy of high-resolution facial images, the proposed system is also justified for its ability in automating parameter tuning and breaking through the limitation of the number of adjustable parameters

ECG Signal Super-resolution by Considering Reconstruction and Cardiac Arrhythmias Classification Loss

With recent advances in deep learning algorithms, computer-assisted healthcare services have rapidly grown, especially for those that combine with mobile devices. Such a combination enables wearable and portable services for continuous measurements and facilitates real-time disease alarm based on physiological signals, e.g., cardiac arrhythmias (CAs) from electrocardiography (ECG). However, long-term and continuous monitoring confronts challenges arising from limitations of batteries, and the transmission bandwidth of devices. Therefore, identifying an effective way to improve ECG data transmission and storage efficiency has become an emerging topic. In this study, we proposed a deep-learning-based ECG signal super-resolution framework (termed ESRNet) to recover compressed ECG signals by considering the joint effect of signal reconstruction and CA classification accuracies. In our experiments, we downsampled the ECG signals from the CPSC 2018 dataset and subsequently evaluated the super-resolution performance by both reconstruction errors and classification accuracies. Experimental results showed that the proposed ESRNet framework can well reconstruct ECG signals from the 10-times compressed ones. Moreover, approximately half of the CA recognition accuracies were maintained within the ECG signals recovered by the ESRNet. The promising results confirm that the proposed ESRNet framework can be suitably used as a front-end process to reconstruct compressed ECG signals in real-world CA recognition scenarios

Crowdsourced mapping of unexplored target space of kinase inhibitors

Despite decades of intensive search for compounds that modulate the activity of particular protein targets, a large proportion of the human kinome remains as yet undrugged. Effective approaches are therefore required to map the massive space of unexplored compound-kinase interactions for novel and potent activities. Here, we carry out a crowdsourced benchmarking of predictive algorithms for kinase inhibitor potencies across multiple kinase families tested on unpublished bioactivity data. We find the top-performing predictions are based on various models, including kernel learning, gradient boosting and deep learning, and their ensemble leads to a predictive accuracy exceeding that of single-dose kinase activity assays. We design experiments based on the model predictions and identify unexpected activities even for under-studied kinases, thereby accelerating experimental mapping efforts. The open-source prediction algorithms together with the bioactivities between 95 compounds and 295 kinases provide a resource for benchmarking prediction algorithms and for extending the druggable kinome. The IDG-DREAM Challenge carried out crowdsourced benchmarking of predictive algorithms for kinase inhibitor activities on unpublished data. This study provides a resource to compare emerging algorithms and prioritize new kinase activities to accelerate drug discovery and repurposing efforts

台灣彰化濱海離岸風機支撐結構樁基礎之力學及變形分析

本研究針對台灣彰化濱海風場場址，依據載重試算分析結果，選用E-E'土層剖面作為分析剖面。並透過三維有限元素程式Plaxis 3-D，來探討離岸風機支撐結構樁基礎之承載特性及其力學變形行為。首先，依據離岸風場之海床鑽探資料(土壤統一分類法及標準貫入試驗SPT-N值)，來推估數值模型所需之土壤材料參數。參考國際離岸風機支撐結構樁基礎之設計案例及載重規範，可決定風場場址之組合載重，並初步設計適合場址之樁基幾何尺寸。其次，進行室內兩組單樁模型試驗數值模擬，並比對模擬結果及量測成果之吻合度。由比對結果得知，單樁之水平載重~水平位移關係曲線(H~h曲線)、樁身水平位移及彎矩分布之模擬值與量測值相當吻合。另外，採用莫爾-庫倫土壤模式(MC-Model)及埋置樁(embedded beam)結構元素，來模擬土層與樁基承受水平載重之樁/土互制行為非常適宜及並可獲得滿意之結果。 隨之，建立離岸風機單樁及群樁之三維數值模型，並模擬樁基礎在承受各種組合載重作用下，土壤與樁基之互制力學行為。在數值模型中，選用不同樁徑、樁長、及樁間距作為設計參數，以測試其對樁基礎承載特性及其力學變形行為之影響。在不同設計參數條件下，包括：五種樁徑(D=1.0、1.5、2.0、2.5、3.0 m)、三種樁長(L=30、40、50 m)、三種樁間距(S=12 m、16 m、20 m)，並定義長徑比(=L/D=15、20、25)及打設間距比R(=S/D=6、8、10)，可求得樁基礎在各種組合載重作用下之載重~位移曲線、各類極限承載力變化曲線、V-H (垂直-水平組合載重)極限承載力包絡線、及p-y曲線。此外，進行群樁承受地震載重之動態模擬，以探討群樁在地震作用下之反應。最後，在垂直、水平、及彎矩載重共同作用下，可得到V-H-M三維極限承載力包絡面。此包絡面可用以評估離岸風機樁基礎承受實際載重情況下之穩定性。 由分析結果可知：(1)樁身周圍土體位移量及塑性點分布範圍，將隨著載重之增加而變大，且主要集中於海床表層土及樁頭周圍。(2)樁基礎承受水平載重時之土層反力p，將隨著土層深度、樁徑、及樁長之增加而提高。相較於單樁，群樁p-y曲線之斜率(k=p/y)明顯較高，且不易達到臨界破壞狀態。(3)增加樁徑，對於單樁及群樁之垂直、水平、及彎矩承載力之提升效果顯著。(4)增加樁長，在單樁方面，垂直承載力之提升效果顯著，而水平及彎矩承載力則影響微小；在群樁方面，垂直及彎矩承載力之提升效果顯著，而水平承載力則幾無影響。(5)群樁間距對各類承載力之影響程度由高至低依序為：彎矩>水平>垂直，尤其對於樁身彎矩影響至鉅。(6)在不同設計參數值條件下，樁基礎V-H極限承載力包絡線形狀相似，但包絡線將隨著設計參數值之增加而擴大。(7)在不同彎矩載重作用下，隨著彎矩值之增加，V-H極限承載力包絡線會縮小。又當M=Mult (彎矩載重達極限值)時，V-H極限承載力包絡線會縮小成座標原點(0,0)。(8)由V-H-M三維極限承載力包絡面可知：當樁基礎承受之組合載重落於包絡面內時，樁基礎處於穩定狀態。再者，若落於包絡面上，則樁基礎處於承載力之極限狀態。最終，若落於包絡面外，則樁基礎發生破壞。According to the numerical results of pile loading test performed on three soil profiles determined by soil boring logs obtained from the wind farm near Chan-Hua coast of western Taiwan, the E-E' soil profile which gave the lowest bearing capcity of single pile was utilized as the representive profile for the subsequent analyses. This study investigates the bearing capacities and mechanical behaviors of pile foundation installed on the seabed of wind farm near Chan-Hua coast of western Taiwan for the supporting structure of offshore wind turbine by three-dimensional (3-D) finite element program Plaxis 3-D. Firstly, using the boring logs, SPT-N values, and laboratory tests of undisturbed sampes from the wind farm, one can estimate the required material model paramters of soil strata for numerical model. In addition, consulting the commonly used interanational design criteria and recent case histories, one can preliminarily determine the combined design loading and pile geometries which is appropriate for the environments of wind farm selected for the installation of offshore turbine. Secondly, numerical analyses were performed on two lateral loading tests of single model pile in laboratory and the comparisons between the simulation and measurement of the tests were made to calibrate the required soil/pile material model parameters. The comparisons show that the simulations of H~h curves (lateral loading H vs. lateral displacement h), lateral displacement, and bending moment distribution of pile shaft are in excellent agreement with the measurements. In addition, the numerical results indicate the utilizatons of Mohr-Coulumn soil model and embedded pile structural element enable a satisfactory simulation of the soil/pile interaction behaviors when subjected to lateral loading. Subsequently, 3-D numerical models of single pile and pile group foundations for offshore turbine were constructed to simulate the soil/pile interaction behaviors subjected to various combined loadings. In numerical model, various pile diameter D, pile length L, and pile spacing S were selected as design parameters to inspect their effects on the bearing capacities and deformation behaviors of pile foundations. For different design parameters, which includes five pile diameters (D=1.0, 1.5, 2.0, 2.5, and 3.0 m), three pile lengths (L=30, 40, and 50 m), three pile spacings (S=12, 16, and 20 m), three pile length/pile diameter ratios (=L/D=15, 20, and 25), and three pile spacing ratios (R=S/D=6, 8, and 10), various loading~displacement curves, ultimate bearing capacities, ultimate bearing capcity envelopes on the V-H (Vertical-Horizontal combined loading ) plane, and the p-y curves can be determined under various combined loading conditions. In addition, a dynamic simulation was carried out on a pile group whne subjected to earthquake loading to inspect the soil/pile interaction responses. Finally, under the action of vertical, horizontal and bending moment combined loadings, a V-H-M 3-D ultimate bearing capacity envelopes can be determined and applied to evaluate the stability of pile foundation for offshore turbine when subjected to various working loads. Based on the numerical results, several conclusions can be made: (1) Large displacement and plastic points at ultimate state mostly distribute and concentrate in the topsoil of seabed and around pile head. (2) The soil resistance at the soil/pile interface for lateral loading will ascend with the increases of depth, pile diameter and pile length. The gradient of p-y curve and ultimate bearing capacity for pile group is obviously higher than that of single pile. (3) The vertical, horizontal, and bending moment bearing capacities of sigle pile and pile group will be largely promoted with the increase of pile diameter. (4) For single pile, the vertical bearing capacity will be promoted notably with the increasing pile length. On the other hand, for pile group, the vertical and bending moment bearing capacities will be greatly promoted with the increasing pile length whereas the horizontal bearing capacity is almost insensitive to the pile length. (5) The influencial extent of spacing on the various bearing capacities of pile group from high to low in sequence is: bending moment loading horiztonal loading > vertical loading. Especialy, the bending moment bearing capacity of pile group is highly influenced by the pile spacing. (6) For different design parameters, the shapes of ultimate bearing capacity envelopes of pile group on V-H plane is similar while the envelopes will expand as the magnitude of design parameter increases. (7) For different loading levels of bending moment, the ultimate bearing capacity envelopes on V-H plane will contract as the bending moment loading gradually increase. In addition, when the bending moment loading reachs ultimate value, namely, M=Mult, the ultimate bearing capacity envelopes on V-H plane will contract into the origin of V-H-M space or coordinate system (0,0). (8) For the Vult-Hult-Mult (or V-H-M) 3-D ultimate bearing capacity envelope surface (or ultimate bearing capacity space), the pile foundation situates in a stable state if the coordinate of combined loading (V, H, M) falls inside the envelope surface. Further, the pile foundation situates in a critical state if the coordinate of combined loading falls on the envelope surface. Eventually, the pile foundation fails if the coordinate of combined loading falls outside the envelope surface.摘要 i Abstract ii 第一章 前言 1 1.1研究動機 1 1.2研究目的 1 第二章 文獻回顧 2 2.1各國現有離岸風場支撐結構之基礎概況 2 2.1.1各國現有離岸風場 2 2.1.2台灣離岸風力發電政策與技術概況 5 2.1.3離岸風機支撐結構之基礎類型 7 2.2離岸風機基礎設計規範 11 2.2.1環境條件 11 2.2.2載重條件 12 2.2.3基礎設計 16 2.3離岸風機基礎之實作及模型試驗案例 25 2.3.1實作案例 25 2.3.2模型試驗案例 28 2.4離岸風機基礎之數值分析案例 37 2.5離岸風機基礎之承載特性分析案例 46 2.6有限元素程式 Plaxis 3-D (2015) 59 2.6.1理論背景 59 2.6.2元素類型 59 2.6.3土壤材料模型 61 第三章 研究方法 67 3.1研究流程 67 3.2國內離岸風場場址基本資料蒐集 68 3.2.1地質鑽探及地球物理探測資料 68 3.2.2現地及室內土壤材料試驗資料 69 3.2.3海象環境資料 69 3.2.4決定離岸風場場址土層剖面 72 3.3建立場址載重效應組合 78 3.3.1靜載重 78 3.3.2風載重 78 3.3.3波浪載重 79 3.3.4海流載重 79 3.3.5地震載重 79 3.3.6各國現有離岸風場之風機機型與設計載重資料 81 3.4建立風機支撐結構樁基礎設計驗證程序 83 3.4.1朱斌等人(2010) 83 3.4.2朱斌等人(2013) 85 3.5離岸風機支撐結構樁基礎分析與設計 87 3.5.1鋼管樁樁徑 87 3.5.2鋼管樁樁長 87 3.5.3群樁之配置方式(樁間距) 87 3.6建立支撐結構樁基礎之3-D數值模型 88 3.6.1單樁之3-D數值模型 88 3.6.2群樁之3-D數值模型 99 3.7離岸風機群樁載重案例分析 107 3.7.1選定現地組合載重 107 3.7.2群樁之尺寸及配置 107 3.7.3群樁之穩定性評估 107 3.7.4群樁之地震分析 107 第四章 結果與討論 109 4.1土層剖面分層之選定 109 4.2數值分析有效性驗證 110 4.2.1朱斌等人(2010) 110 4.2.2朱斌等人(2013) 114 4.3單樁之力學與變形行為 118 4.3.1單樁施加垂直位移v之力學與變形行為 118 4.3.2單樁施加水平位移h之力學與變形行為 121 4.3.3單樁施加彎矩載重M之力學與變形行為 125 4.4群樁之力學與變形行為 127 4.4.1群樁施加垂直位移v之力學與變形行為 127 4.4.2群樁施加水平位移h之力學與變形行為 129 4.4.3群樁施加彎矩載重M之力學與變形行為 134 4.5單樁之承載特性分析 136 4.5.1單樁承受各類載重作用下之極限承載力 136 4.5.2樁徑對單樁承載特性之影響 141 4.5.3樁長對單樁承載特性之影響 144 4.5.4長徑比對單樁承載特性之影響 147 4.5.5單樁承受組合載重作用下之承載特性 147 4.6群樁之承載特性分析 154 4.6.1群樁承受各類載重作用下之極限承載力 154 4.6.2樁徑對群樁承載特性之影響 160 4.6.3樁長對群樁承載特性之影響 163 4.6.4樁間距對群樁承載特性之影響 167 4.6.5長徑比及打設間距比對群樁承載特性之影響 170 4.6.6群樁承受組合載重作用下之承載特性 171 4.7離岸風機群樁載重案例分析結果 179 4.7.1群樁穩定性評估結果 179 4.7.2群樁地震分析結果 180 第五章 結論與建議 181 5.1結論 181 5.1.1土層剖面分層之選定 181 5.1.2數值分析有效性驗證 181 5.1.3單樁及群樁之力學與變形行為 181 5.1.4單樁及群樁之承載特性分析 182 5.1.5離岸風機群樁載重案例分析 183 5.2建議 183 參考文獻 184 附錄 A 鑽孔數據資料 189 附錄 B 鑽孔室內試驗資料 193 附錄 C 場址土層剖面資料 197 附錄 D 數值模擬之分析結果 20

Aggregation structure of Alzheimer amyloid-β (1-40) peptide with sodium dodecyl sulfate as revealed by small-angle X-ray and neutron scattering

Using small-angle X-ray scattering (SAXS) and small-angle neutron scattering (SANS) with contrast variation, we have studied the complex aggregation of Alzheimer amyloid-β(1-40) (Aβ) peptides with sodium dodecyl sulfate (SDS). With the addition of 0.11

Collective resonances near zero energy induced by a point defect in bilayer graphene

Intrinsic defects give rise to scattering processes governing the transport properties of mesoscopic systems. We investigate analytically and numerically the local density of states in Bernal stacking bilayer graphene with a point defect. With Bernal stacking structure, there are two types of lattice sites. One corresponds to connected sites, where carbon atoms from each layer stack on top of each other, and the other corresponds to disconnected sites. From our theoretical study, a picture emerges in which the pronounced zero-energy peak in the local density of states does not attribute to zero-energy impurity states associated to two different types of defects but to a collective phenomenon of the low-energy resonant states induced by the defect. To corroborate this description, we numerically show that at small system size N, where N is the number of unit cells, the zero-energy peak near the defect scales as 1/lnN for the quasi-localized zero-energy state and as 1/N for the delocalized zero-energy state. As the system size approaches to the thermodynamic limit, the former zero-energy peak becomes a power-law singularity 1/|E| in low energies, while the latter is broadened into a Lorentzian shape. A striking point is that both types of zero-energy peaks decay as 1/r2 away from the defect, manifesting the quasi-localized character. Based on our results, we propose a general formula for the local density of states in low-energy and in real space. Our study sheds light on this fundamental problem of defects