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In this paper, we investigated the thermocapillary migration of a self-rewetting droplet on an inclined surface using a phase field based lattice Boltzmann method. Unlike the normal fluid whose surface tension decreases linearly with temperature, the self-rewetting fluid consider in the current work has a quadratic temperature dependence of surface tension with a well-defined minimum. we first explored the influence of the Marangoni number on droplet migration, and found that the droplet hardly deforms and migrates slowly when the Marangoni number is small. However, as the Marangoni number increases, the droplet begins to deform and elongate, and its migration speed increases. Subsequently, we studied the effect of surface wettability on droplet migration. The results show that the droplet migrate towards regions of higher surface energy on hydrophilic surfaces and in the opposite direction on hydrophobic surfaces. Furthermore, by varying the viscosity ratio and the inclination angle of the plate, we found that the droplet's migration speed decreases with an increase in the viscosity ratio. In particular, two vortices appear inside the droplet at a high viscosity ratio, whereas only one vortex is present at a low viscosity ratio.

Despite the significant advancements in natural language processing capabilities demonstrated by large language models such as ChatGPT, their proficiency in comprehending and processing spatial information, especially within the domains of 2D and 3D route planning, remains notably underdeveloped. This paper investigates the inherent limitations of ChatGPT and similar models in spatial reasoning and navigation-related tasks, an area critical for applications ranging from autonomous vehicle guidance to assistive technologies for the visually impaired. In this paper, we introduce a novel evaluation framework complemented by a baseline dataset, meticulously crafted for this study. This dataset is structured around three key tasks: plotting spatial points, planning routes in two-dimensional (2D) spaces, and devising pathways in three-dimensional (3D) environments. We specifically developed this dataset to assess the spatial reasoning abilities of ChatGPT. Our evaluation reveals key insights into the model's capabilities and limitations in spatial understanding.

State-of-the-art Y6-type molecular acceptors exhibit nanosecond excited-state lifetimes despite their low optical gaps (~1.4 eV), thus allowing organic solar cells (OSCs) to achieve highly efficient charge generation with extended near-infrared (NIR) absorption range (up to ~1000 nm). However, the precise molecular-level mechanism that enables low-energy excited states in Y6-type acceptors to achieve nanosecond lifetimes has remained elusive. Here, we demonstrate that the distinct packing of Y6 molecules in film leads to a strong intermolecular charge-transfer (iCT) character of the lowest excited state in Y6 aggregates, which is absent in other low-gap acceptors such as ITIC. Due to strong electronic couplings between the adjacent Y6 molecules, the iCT-exciton energies are greatly reduced by up to ~0.25 eV with respect to excitons formed in separated molecules. Importantly, despite their low energies, the iCT excitons have reduced non-adiabatic electron-vibration couplings with the electronic ground state, thus suppressing non-radiative recombination and allowing Y6 to overcome the well-known energy gap law. Our results reveal the fundamental relationship between molecular packing and nanosecond excited-state lifetimes in NIR-absorbing Y6-type acceptors underlying the outstanding performance of Y6-based OSCs.

The task of video virtual try-on aims to fit the target clothes to a person in the video with spatio-temporal consistency. Despite tremendous progress of image virtual try-on, they lead to inconsistency between frames when applied to videos. Limited work also explored the task of video-based virtual try-on but failed to produce visually pleasing and temporally coherent results. Moreover, there are two other key challenges: 1) how to generate accurate warping when occlusions appear in the clothing region; 2) how to generate clothes and non-target body parts (e.g. arms, neck) in harmony with the complicated background; To address them, we propose a novel video virtual try-on framework, ClothFormer, which successfully synthesizes realistic, harmonious, and spatio-temporal consistent results in complicated environment. In particular, ClothFormer involves three major modules. First, a two-stage anti-occlusion warping module that predicts an accurate dense flow mapping between the body regions and the clothing regions. Second, an appearance-flow tracking module utilizes ridge regression and optical flow correction to smooth the dense flow sequence and generate a temporally smooth warped clothing sequence. Third, a dual-stream transformer extracts and fuses clothing textures, person features, and environment information to generate realistic try-on videos. Through rigorous experiments, we demonstrate that our method highly surpasses the baselines in terms of synthesized video quality both qualitatively and quantitatively., Comment: CVPR2022 Oral, project page https://cloth-former.github.io

Organic solar cells (OSCs) based on non-fullerene acceptors can show high charge generation yields despite near-zero donor-acceptor energy offsets to drive charge separation and overcome the mutual Coulomb attraction between electron and hole. Here we use time-resolved optical spectroscopy to show that free charges in these systems are generated by thermally activated dissociation of interfacial charge-transfer excitons (CTEs) that occurs over hundreds of picoseconds at room temperature, three orders of magnitude slower than comparable fullerene-based systems. Upon free electron-hole encounters at later times, CTEs and emissive excitons are regenerated, thus setting up an equilibrium between excitons, CTEs and free charges. This endothermic charge separation process enables these systems to operate close to quasi-thermodynamic equilibrium conditions with no requirement for energy offsets to drive charge separation and achieve greatly suppressed non-radiative recombination., Comment: 30 pages, 4 figures

Face recognition has advanced considerably with the availability of large-scale labeled datasets. However, how to further improve the performance with the easily accessible unlabeled dataset remains a challenge. In this paper, we propose the novel Unknown Identity Rejection (UIR) loss to utilize the unlabeled data. We categorize identities in unconstrained environment into the known set and the unknown set. The former corresponds to the identities that appear in the labeled training dataset while the latter is its complementary set. Besides training the model to accurately classify the known identities, we also force the model to reject unknown identities provided by the unlabeled dataset via our proposed UIR loss. In order to 'reject' faces of unknown identities, centers of the known identities are forced to keep enough margin from centers of unknown identities which are assumed to be approximated by the features of their samples. By this means, the discriminativeness of the face representations can be enhanced. Experimental results demonstrate that our approach can provide obvious performance improvement by utilizing the unlabeled data., Comment: 8 pages, 2 figures, Workshop paper accepted by Lightweight Face Recognition Challenge & Workshop (ICCV 2019)

Recent years have witnessed increasing attention in cartoon media, powered by the strong demands of industrial applications. As the first step to understand this media, cartoon face recognition is a crucial but less-explored task with few datasets proposed. In this work, we first present a new challenging benchmark dataset, consisting of 389,678 images of 5,013 cartoon characters annotated with identity, bounding box, pose, and other auxiliary attributes. The dataset, named iCartoonFace, is currently the largest-scale, high-quality, richannotated, and spanning multiple occurrences in the field of image recognition, including near-duplications, occlusions, and appearance changes. In addition, we provide two types of annotations for cartoon media, i.e., face recognition, and face detection, with the help of a semi-automatic labeling algorithm. To further investigate this challenging dataset, we propose a multi-task domain adaptation approach that jointly utilizes the human and cartoon domain knowledge with three discriminative regularizations. We hence perform a benchmark analysis of the proposed dataset and verify the superiority of the proposed approach in the cartoon face recognition task. We believe this public availability will attract more research attention in broad practical application scenarios., Comment: 9 papers, 6 figures

Person identification in the wild is very challenging due to great variation in poses, face quality, clothes, makeup and so on. Traditional research, such as face recognition, person re-identification, and speaker recognition, often focuses on a single modal of information, which is inadequate to handle all the situations in practice. Multi-modal person identification is a more promising way that we can jointly utilize face, head, body, audio features, and so on. In this paper, we introduce iQIYI-VID, the largest video dataset for multi-modal person identification. It is composed of 600K video clips of 5,000 celebrities. These video clips are extracted from 400K hours of online videos of various types, ranging from movies, variety shows, TV series, to news broadcasting. All video clips pass through a careful human annotation process, and the error rate of labels is lower than 0.2\%. We evaluated the state-of-art models of face recognition, person re-identification, and speaker recognition on the iQIYI-VID dataset. Experimental results show that these models are still far from being perfect for the task of person identification in the wild. We proposed a Multi-modal Attention module to fuse multi-modal features that can improve person identification considerably. We have released the dataset online to promote multi-modal person identification research.

We consider the problem of separating error messages generated in large distributed data center networks into error events. In such networks, each error event leads to a stream of messages generated by hardware and software components affected by the event. These messages are stored in a giant message log. We consider the unsupervised learning problem of identifying the signatures of events that generated these messages; here, the signature of an error event refers to the mixture of messages generated by the event. One of the main contributions of the paper is a novel mapping of our problem which transforms it into a problem of topic discovery in documents. Events in our problem correspond to topics and messages in our problem correspond to words in the topic discovery problem. However, there is no direct analog of documents. Therefore, we use a non-parametric change-point detection algorithm, which has linear computational complexity in the number of messages, to divide the message log into smaller subsets called episodes, which serve as the equivalents of documents. After this mapping has been done, we use a well-known algorithm for topic discovery, called LDA, to solve our problem. We theoretically analyze the change-point detection algorithm, and show that it is consistent and has low sample complexity. We also demonstrate the scalability of our algorithm on a real data set consisting of $97$ million messages collected over a period of $15$ days, from a distributed data center network which supports the operations of a large wireless service provider., Comment: To Appear in IEEE Transactions on Networking, Appeared in Workshop on MiLeTS, SIGKDD 2018