Análisis del impacto de Twitter y Weibo en la comunicación en la crisis del COVID-19 en España Y China

[ES] COVID-19 ha ejercido una enorme presión sobre los ciudadanos, los recursos y la economía de muchos países de todo el mundo. Las medidas de alienación social, las prohibiciones de viaje, el autoaislamiento y los fracasos comerciales están cambiando la estructura de la sociedad global. A medida que las personas se ven obligadas a abandonar lugares públicos, las redes sociales se convierten en los ojos de las personas que miran el mundo. Todas las tendencias relacionadas con COVID-19 se difunden en línea, como en las plataformas de redes sociales como Twitter y Weibo. En la actualidad, COVID-19 es el mayor problema al que se enfrenta el mundo. Por tanto, el contenido de mi trabajo final de master trata sobre el impacto de las redes sociales en la propagación de COVID-19.[EN] COVID-19 has put enormous pressure on citizens, resources and the economy of many countries around the world. Social alienation measures, travel bans, self-isolation, and business failures are changing the fabric of global society. As people are forced to leave public places, social media becomes the eyes of people looking at the world. All trends related to COVID-19 are being spread online, such as on social media platforms like Twitter and Weibo. Currently, COVID-19 is the biggest problem facing the world. Therefore, the content of my final master thesis deals with the impact of social networks on the spread of COVID-19.Feng, S. (2020). Análisis del impacto de Twitter y Weibo en la comunicación en la crisis del COVID-19 en España Y China. Universitat Politècnica de València. http://hdl.handle.net/10251/159396TFG

Multiresolution Feature Guidance Based Transformer for Anomaly Detection

Anomaly detection is represented as an unsupervised learning to identify deviated images from normal images. In general, there are two main challenges of anomaly detection tasks, i.e., the class imbalance and the unexpectedness of anomalies. In this paper, we propose a multiresolution feature guidance method based on Transformer named GTrans for unsupervised anomaly detection and localization. In GTrans, an Anomaly Guided Network (AGN) pre-trained on ImageNet is developed to provide surrogate labels for features and tokens. Under the tacit knowledge guidance of the AGN, the anomaly detection network named Trans utilizes Transformer to effectively establish a relationship between features with multiresolution, enhancing the ability of the Trans in fitting the normal data manifold. Due to the strong generalization ability of AGN, GTrans locates anomalies by comparing the differences in spatial distance and direction of multi-scale features extracted from the AGN and the Trans. Our experiments demonstrate that the proposed GTrans achieves state-of-the-art performance in both detection and localization on the MVTec AD dataset. GTrans achieves image-level and pixel-level anomaly detection AUROC scores of 99.0% and 97.9% on the MVTec AD dataset, respectively

Context-Aware Integration of Language and Visual References for Natural Language Tracking

Tracking by natural language specification (TNL) aims to consistently localize a target in a video sequence given a linguistic description in the initial frame. Existing methodologies perform language-based and template-based matching for target reasoning separately and merge the matching results from two sources, which suffer from tracking drift when language and visual templates miss-align with the dynamic target state and ambiguity in the later merging stage. To tackle the issues, we propose a joint multi-modal tracking framework with 1) a prompt modulation module to leverage the complementarity between temporal visual templates and language expressions, enabling precise and context-aware appearance and linguistic cues, and 2) a unified target decoding module to integrate the multi-modal reference cues and executes the integrated queries on the search image to predict the target location in an end-to-end manner directly. This design ensures spatio-temporal consistency by leveraging historical visual information and introduces an integrated solution, generating predictions in a single step. Extensive experiments conducted on TNL2K, OTB-Lang, LaSOT, and RefCOCOg validate the efficacy of our proposed approach. The results demonstrate competitive performance against state-of-the-art methods for both tracking and grounding.Comment: Accepted by CVPR202

Towards Seamless Adaptation of Pre-trained Models for Visual Place Recognition

Recent studies show that vision models pre-trained in generic visual learning tasks with large-scale data can provide useful feature representations for a wide range of visual perception problems. However, few attempts have been made to exploit pre-trained foundation models in visual place recognition (VPR). Due to the inherent difference in training objectives and data between the tasks of model pre-training and VPR, how to bridge the gap and fully unleash the capability of pre-trained models for VPR is still a key issue to address. To this end, we propose a novel method to realize seamless adaptation of pre-trained models for VPR. Specifically, to obtain both global and local features that focus on salient landmarks for discriminating places, we design a hybrid adaptation method to achieve both global and local adaptation efficiently, in which only lightweight adapters are tuned without adjusting the pre-trained model. Besides, to guide effective adaptation, we propose a mutual nearest neighbor local feature loss, which ensures proper dense local features are produced for local matching and avoids time-consuming spatial verification in re-ranking. Experimental results show that our method outperforms the state-of-the-art methods with less training data and training time, and uses about only 3% retrieval runtime of the two-stage VPR methods with RANSAC-based spatial verification. It ranks 1st on the MSLS challenge leaderboard (at the time of submission). The code is released at https://github.com/Lu-Feng/SelaVPR.Comment: ICLR202

Deep Homography Estimation for Visual Place Recognition

Visual place recognition (VPR) is a fundamental task for many applications such as robot localization and augmented reality. Recently, the hierarchical VPR methods have received considerable attention due to the trade-off between accuracy and efficiency. They usually first use global features to retrieve the candidate images, then verify the spatial consistency of matched local features for re-ranking. However, the latter typically relies on the RANSAC algorithm for fitting homography, which is time-consuming and non-differentiable. This makes existing methods compromise to train the network only in global feature extraction. Here, we propose a transformer-based deep homography estimation (DHE) network that takes the dense feature map extracted by a backbone network as input and fits homography for fast and learnable geometric verification. Moreover, we design a re-projection error of inliers loss to train the DHE network without additional homography labels, which can also be jointly trained with the backbone network to help it extract the features that are more suitable for local matching. Extensive experiments on benchmark datasets show that our method can outperform several state-of-the-art methods. And it is more than one order of magnitude faster than the mainstream hierarchical VPR methods using RANSAC. The code is released at https://github.com/Lu-Feng/DHE-VPR.Comment: Accepted by AAAI202

Non-covalent interactions in electrochemical reactions and implications in clean energy applications

Understanding and controlling non-covalent interactions associated with solvent molecules and redox-inactive ions provide new opportunities to enhance the reaction entropy changes and reaction kinetics of metal redox centers, which can increase the thermodynamic efficiency of energy conversion and storage devices. Here, we report systematic changes in the redox entropy of one-electron transfer reactions including [Fe(CN)6]3-/4-, [Fe(H2O)6]3+/2+and [Ag(H2O)4]+/0induced by the addition of redox inactive ions, where approximately twenty different known structure making/breaking ions were employed. The measured reaction entropy changes of these redox couples were found to increase linearly with higher concentration and greater structural entropy (having greater structure breaking tendency) for inactive ions with opposite charge to the redox centers. The trend could be attributed to the altered solvation shells of oxidized and reduced redox active species due to non-covalent interactions among redox centers, inactive ions and water molecules, which was supported by Raman spectroscopy. Not only were these non-covalent interactions shown to increase reaction entropy, but they were also found to systematically alter the redox kinetics, where increasing redox reaction energy changes associated with the presence of water structure breaking cations were correlated linearly with the greater exchange current density of [Fe(CN)6]3-/4-.United States. Department of Energy. Office of Basic Energy Science (Award Number DE-SC0001299/DE-FG02-09ER46577)Hong Kong (China). Innovation and Technology Commission (Project No. ITS/ 020/16FP)United States. Department of Energy (Contract No. DE-AC02-5CH11231

N-phosphorylation of amino acids by trimetaphosphate in aqueous solution-learning from prebiotic synthesis

Inspired by a reactivity study between sodium trimetaphosphate (P(3)m) and amino acids in prebiotic chemistry, a one-step reaction with efficient purification procedure in aqueous media has been developed for the synthesis of N-phosphono-amino acids (NPAA). P(3)m was used to phosphorylate amino acids to NPAA with yields of 60 similar to 91%. The by-products, inorganic polyphosphates, were recycled to regenerate the phosphorylation reagent P(3)m.Ministry of Science and Technology [2006DFA43030]; Chinese National Natural Science Foundation [20572061, 20732004

Minimising efficiency roll-off in high-brightness perovskite light-emitting diodes.

Efficiency roll-off is a major issue for most types of light-emitting diodes (LEDs), and its origins remain controversial. Here we present investigations of the efficiency roll-off in perovskite LEDs based on two-dimensional layered perovskites. By simultaneously measuring electroluminescence and photoluminescence on a working device, supported by transient photoluminescence decay measurements, we conclude that the efficiency roll-off in perovskite LEDs is mainly due to luminescence quenching which is likely caused by non-radiative Auger recombination. This detrimental effect can be suppressed by increasing the width of quantum wells, which can be easily realized in the layered perovskites by tuning the ratio of large and small organic cations in the precursor solution. This approach leads to the realization of a perovskite LED with a record external quantum efficiency of 12.7%, and the efficiency remains to be high, at approximately 10%, under a high current density of 500 mA cm-2

Platforms for Parallel Processing of Task on GPU

Import 05/08/2014Tato bakalářská práce se zabývá zpracováním úloh na grafické kartě. Konkrétním typem úloh jsou paralelní třídící algoritmy. V první části práce se vyskytuje popis technologií CUDA a OpenCL, ve kterých je později třídící algoritmus implementován. Dále je rozebrán princip daného algoritmu a jeho implementace. Následuje profilování a optimalizace třídícího algoritmu. V poslední částí je testování algoritmů na různých grafických kartách a porovnání obou technologií.This thesis deals with the processing tasks to the graphics card. Specific types of tasks are selected sorting algorithms. The first part includes description CUDA and OpenCL technology in which sorting algorithm is implemented. Next it is described the principle of the algorithm and its implementation. Next step is profiling and optimization of sorting algorithm. The last part includes testing these algorithms on different graphics cards and a comparison of both technologies.460 - Katedra informatikydobř