Mezoporozni katalizator PtSnO2/C s povećanom katalitičkom aktivnošću za elektrooksidaciju etanola

In this paper, we report the synthesis, characterization, and electrochemical evaluation of a mesoporous PtSnO2/C catalyst, called PtSnO2(M)/C, with a nominal Pt : Sn ratio of 3 : 1. Brunauer–Emmett–Teller and transmission electron microscopy characterizations showed the obvious mesoporous structure of SnO2 in PtSnO2(M)/C catalyst. X-ray photoelectron spectroscopy analysis exhibited the interaction between Pt and mesoporous SnO2. Compared with Pt/C and commercial PtSnO2/C catalysts, PtSnO2(M)/C catalyst has a lower active site, but higher catalytic activity for ethanol electro-oxidation reaction (EOR). The enhanced activity could be attributed to Pt nanoparticles deposited on mesoporous SnO2 that could decrease the amount of poisonous intermediates produced during EOR by the interaction between Pt and mesoporous SnO2. This work is licensed under a Creative Commons Attribution 4.0 International License.U ovom radu prikazana je sinteza, karakterizacija i elektrokemijska ocjena mezoporoznog katalizatora PtSnO2/C (PtSnO2(M)/C), s nominalnim omjerom Pt : Sn od 3 : 1. Karakterizacije Brunauer-Emmett-Tellerovom metodom i transmisijskom elektronskom mikroskopijom pokazale su očitu mezoporoznu strukturu SnO2 u katalizatoru PtSnO2(M)/C. Analiza rendgenskom fotoelektronskom spektroskopijom pokazala je interakciju između Pt i mezoporoznog SnO2. U usporedbi s Pt/C i komercijalnim katalizatorima PtSnO2/C, katalizator PtSnO2(M)/C ima slabije aktivno mjesto, ali veću katalitičku aktivnost za reakciju elektrooksidacije etanola (EOR). Poboljšana aktivnost mogla bi se pripisati nanočesticama Pt pohranjenim na mezoporoznom SnO2, što bi moglo smanjiti količinu otrovnih međuprodukata proizvedenih tijekom elektrooksidacije etanola interakcijom između Pt i mezoporoznog SnO2. Ovo djelo je dano na korištenje pod licencom Creative Commons Imenovanje 4.0 međunarodna

TOAST: Transfer Learning via Attention Steering

Transfer learning involves adapting a pre-trained model to novel downstream tasks. However, we observe that current transfer learning methods often fail to focus on task-relevant features. In this work, we explore refocusing model attention for transfer learning. We introduce Top-Down Attention Steering (TOAST), a novel transfer learning algorithm that keeps the pre-trained backbone frozen, selects task-relevant features in the output, and feeds those features back to the model to steer the attention to the task-specific features. By refocusing the attention only, TOAST achieves state-of-the-art results on a number of transfer learning benchmarks, while having a small number of tunable parameters. Compared to fully fine-tuning, LoRA, and prompt tuning, TOAST substantially improves performance across a range of fine-grained visual classification datasets (e.g., 81.1% -> 86.2% on FGVC). TOAST also outperforms the fully fine-tuned Alpaca and Vicuna models on instruction-following language generation. Code is available at https://github.com/bfshi/TOAST.Comment: Code is available at https://github.com/bfshi/TOAS

Draining the Water Hole: Mitigating Social Engineering Attacks with CyberTWEAK

Cyber adversaries have increasingly leveraged social engineering attacks to breach large organizations and threaten the well-being of today's online users. One clever technique, the "watering hole" attack, compromises a legitimate website to execute drive-by download attacks by redirecting users to another malicious domain. We introduce a game-theoretic model that captures the salient aspects for an organization protecting itself from a watering hole attack by altering the environment information in web traffic so as to deceive the attackers. Our main contributions are (1) a novel Social Engineering Deception (SED) game model that features a continuous action set for the attacker, (2) an in-depth analysis of the SED model to identify computationally feasible real-world cases, and (3) the CyberTWEAK algorithm which solves for the optimal protection policy. To illustrate the potential use of our framework, we built a browser extension based on our algorithms which is now publicly available online. The CyberTWEAK extension will be vital to the continued development and deployment of countermeasures for social engineering.Comment: IAAI-20, AICS-2020 Worksho

Point Normal Orientation and Surface Reconstruction by Incorporating Isovalue Constraints to Poisson Equation

Oriented normals are common pre-requisites for many geometric algorithms based on point clouds, such as Poisson surface reconstruction. However, it is not trivial to obtain a consistent orientation. In this work, we bridge orientation and reconstruction in implicit space and propose a novel approach to orient point clouds by incorporating isovalue constraints to the Poisson equation. Feeding a well-oriented point cloud into a reconstruction approach, the indicator function values of the sample points should be close to the isovalue. Based on this observation and the Poisson equation, we propose an optimization formulation that combines isovalue constraints with local consistency requirements for normals. We optimize normals and implicit functions simultaneously and solve for a globally consistent orientation. Owing to the sparsity of the linear system, an average laptop can be used to run our method within reasonable time. Experiments show that our method can achieve high performance in non-uniform and noisy data and manage varying sampling densities, artifacts, multiple connected components, and nested surfaces

A critical review of improved deep learning methods for the remaining useful life prediction of lithium-ion batteries.

As widely used for secondary energy storage, lithium-ion batteries have become the core component of the power supply system and accurate remaining useful life prediction is the key to ensure its reliability. Because of the complex working characteristics of lithium-ion batteries as well as the model parameter changing along with the aging process, the accuracy of the online remaining useful life prediction is difficult but urgent to be improved for the reliable power supply application. The deep learning algorithm improves the accuracy of the remaining useful life prediction, which also reduces the characteristic testing time requirement, providing the possibility to improve the power profitability of predictive energy management. This article analyzes, reviews, classifies, and compares different adaptive mathematical models on deep learning algorithms for the remaining useful life prediction. The features are identified for the modeling ability, according to which the adaptive prediction methods are classified. The specific criteria are defined to evaluate different modeling accuracy in the deep learning calculation procedure. The key features of effective life prediction are used to draw relevant conclusions and suggestions are provided, in which the high-accuracy deep convolutional neural network — extreme learning machine algorithm is chosen to be utilized for the stable remaining useful life prediction of lithium-ion batteries

Bi-Mapper: Holistic BEV Semantic Mapping for Autonomous Driving

A semantic map of the road scene, covering fundamental road elements, is an essential ingredient in autonomous driving systems. It provides important perception foundations for positioning and planning when rendered in the Bird's-Eye-View (BEV). Currently, the prior knowledge of hypothetical depth can guide the learning of translating front perspective views into BEV directly with the help of calibration parameters. However, it suffers from geometric distortions in the representation of distant objects. In addition, another stream of methods without prior knowledge can learn the transformation between front perspective views and BEV implicitly with a global view. Considering that the fusion of different learning methods may bring surprising beneficial effects, we propose a Bi-Mapper framework for top-down road-scene semantic understanding, which incorporates a global view and local prior knowledge. To enhance reliable interaction between them, an asynchronous mutual learning strategy is proposed. At the same time, an Across-Space Loss (ASL) is designed to mitigate the negative impact of geometric distortions. Extensive results on nuScenes and Cam2BEV datasets verify the consistent effectiveness of each module in the proposed Bi-Mapper framework. Compared with exiting road mapping networks, the proposed Bi-Mapper achieves 2.1% higher IoU on the nuScenes dataset. Moreover, we verify the generalization performance of Bi-Mapper in a real-world driving scenario. The source code is publicly available at https://github.com/lynn-yu/Bi-Mapper.Comment: Accepted to IEEE Robotics and Automation Letters (RA-L). The source code is publicly available at https://github.com/lynn-yu/Bi-Mappe

Self-assembling multidomain peptides tailor biological responses through biphasic release

Delivery of small molecules and drugs to tissues is a mainstay of several tissue engineering strategies. Next generation treatments focused on localized drug delivery offer a more effective means in dealing with refractory healing when compared to systemic approaches. Here we describe a novel multidomain peptide hydrogel that capitalizes on synthetic peptide chemistry, supramolecular self-assembly and cytokine delivery to tailor biological responses. This material is biomimetic, shows shear stress recovery and offers a nanofibrous matrix that sequesters cytokines. The biphasic pattern of cytokine release results in the spatio-temporal activation of THP-1 monocytes and macrophages. Furthermore, macrophage–material interactions are promoted without generation of a proinflammatory environment. Subcutaneous implantation of injectable scaffolds showed a marked increase in macrophage infiltration and polarization dictated by cytokine loading as early as 3 days, with complete scaffold resorption by day 14. Macrophage interaction and response to the peptide composite facilitated the (i) recruitment of monocytes/macrophages, (ii) sustained residence of immune cells until degradation, and (iii) promotion of a pro-resolution M2 environment. Our results suggest the potential use of this injectable cytokine loaded hydrogel scaffold in a variety of tissue engineering applications