Low-dimensional perovskite nanoplatelet synthesis using in situ photophysical monitoring to establish controlled growth.

Perovskite nanoparticles have attracted the attention of research groups around the world for their impressive photophysical properties, facile synthesis and versatile surface chemistry. Here, we report a synthetic route that takes advantage of a suite of soluble precursors to generate CsPbBr3 perovskite nanoplatelets with fine control over size, thickness and optical properties. We demonstrate near unit cell precision, creating well characterized materials with sharp, narrow emission lines at 430, 460 and 490 nm corresponding to nanoplatelets that are 2, 4, and 6 unit cells thick, respectively. Nanoplatelets were characterized with optical spectroscopy, atomic force microscopy, scanning electron microscopy and transmission electron microscopy to explicitly correlate growth conditions, thickness and resulting photophysical properties. Detailed in situ photoluminescence spectroscopic studies were carried out to understand and optimize particle growth by correlating light emission with nanoplatelet growth across a range of synthetic conditions. It was found that nanoplatelet thickness and emission wavelength increase as the ratio of oleic acid to oleyl amine or the reaction temperature is increased. Using this information, we control the lateral size, width and corresponding emission wavelength of the desired nanoplatelets by modulating the temperature and ratios of the ligand

Learning Pair Potentials using Differentiable Simulations

Learning pair interactions from experimental or simulation data is of great interest for molecular simulations. We propose a general stochastic method for learning pair interactions from data using differentiable simulations (DiffSim). DiffSim defines a loss function based on structural observables, such as the radial distribution function, through molecular dynamics (MD) simulations. The interaction potentials are then learned directly by stochastic gradient descent, using backpropagation to calculate the gradient of the structural loss metric with respect to the interaction potential through the MD simulation. This gradient-based method is flexible and can be configured to simulate and optimize multiple systems simultaneously. For example, it is possible to simultaneously learn potentials for different temperatures or for different compositions. We demonstrate the approach by recovering simple pair potentials, such as Lennard-Jones systems, from radial distribution functions. We find that DiffSim can be used to probe a wider functional space of pair potentials compared to traditional methods like Iterative Boltzmann Inversion. We show that our methods can be used to simultaneously fit potentials for simulations at different compositions and temperatures to improve the transferability of the learned potentials.Comment: 12 pages, 10 figure

Good regularity creates large learning rate implicit biases: edge of stability, balancing, and catapult

Large learning rates, when applied to gradient descent for nonconvex optimization, yield various implicit biases including the edge of stability (Cohen et al., 2021), balancing (Wang et al., 2022), and catapult (Lewkowycz et al., 2020). These phenomena cannot be well explained by classical optimization theory. Though significant theoretical progress has been made in understanding these implicit biases, it remains unclear for which objective functions would they be more likely. This paper provides an initial step in answering this question and also shows that these implicit biases are in fact various tips of the same iceberg. To establish these results, we develop a global convergence theory under large learning rates, for a family of nonconvex functions without globally Lipschitz continuous gradient, which was typically assumed in existing convergence analysis. Specifically, these phenomena are more likely to occur when the optimization objective function has good regularity. This regularity, together with gradient descent using a large learning rate that favors flatter regions, results in these nontrivial dynamical behaviors. Another corollary is the first non-asymptotic convergence rate bound for large-learning-rate gradient descent optimization of nonconvex functions. Although our theory only applies to specific functions so far, the possibility of extrapolating it to neural networks is also experimentally validated, for which different choices of loss, activation functions, and other techniques such as batch normalization can all affect regularity significantly and lead to very different training dynamics

Fine-Grained Prototypes Distillation for Few-Shot Object Detection

Few-shot object detection (FSOD) aims at extending a generic detector for novel object detection with only a few training examples. It attracts great concerns recently due to the practical meanings. Meta-learning has been demonstrated to be an effective paradigm for this task. In general, methods based on meta-learning employ an additional support branch to encode novel examples (a.k.a. support images) into class prototypes, which are then fused with query branch to facilitate the model prediction. However, the class-level prototypes are difficult to precisely generate, and they also lack detailed information, leading to instability in performance.New methods are required to capture the distinctive local context for more robust novel object detection. To this end, we propose to distill the most representative support features into fine-grained prototypes. These prototypes are then assigned into query feature maps based on the matching results, modeling the detailed feature relations between two branches. This process is realized by our Fine-Grained Feature Aggregation (FFA) module. Moreover, in terms of high-level feature fusion, we propose Balanced Class-Agnostic Sampling (B-CAS) strategy and Non-Linear Fusion (NLF) module from differenct perspectives. They are complementary to each other and depict the high-level feature relations more effectively. Extensive experiments on PASCAL VOC and MS COCO benchmarks show that our method sets a new state-of-the-art performance in most settings. Our code is available at https://github.com/wangchen1801/FPD.Comment: Accepted by AAAI202

EffiVED:Efficient Video Editing via Text-instruction Diffusion Models

Large-scale text-to-video models have shown remarkable abilities, but their direct application in video editing remains challenging due to limited available datasets. Current video editing methods commonly require per-video fine-tuning of diffusion models or specific inversion optimization to ensure high-fidelity edits. In this paper, we introduce EffiVED, an efficient diffusion-based model that directly supports instruction-guided video editing. To achieve this, we present two efficient workflows to gather video editing pairs, utilizing augmentation and fundamental vision-language techniques. These workflows transform vast image editing datasets and open-world videos into a high-quality dataset for training EffiVED. Experimental results reveal that EffiVED not only generates high-quality editing videos but also executes rapidly. Finally, we demonstrate that our data collection method significantly improves editing performance and can potentially tackle the scarcity of video editing data. Code can be found at https://github.com/alibaba/EffiVED

Exploration of Teaching Reform in Environmental Protection Equipment and Engineering Design Course under the Background of New Engineering

Environmental protection equipment and engineering design is an important foundational course for environmental majors in higher education institutions, which combines systematic theory with strong practicality. The article is based on research on learning situations and pain points in educational reform. Through a series of educational reform measures, a learning community is established, scientific research is strengthened to support teaching, teaching content is optimized, teaching cases are enriched, and diversified teaching practices are carried out; Expand the second classroom, build a teaching platform, establish a mentorship system for undergraduate students, and organize all students to participate in innovation and entrepreneurship competitions; Integrating ideological and political education into the curriculum, cultivating students' scientific thinking, and solving practical environmental problems. Since the implementation of this innovative model, it has broadened students' horizons and improved the quality of teaching; We have established a comprehensive and full-time education model, enhancing students' practical and innovative abilities; It cultivates students' scientific thinking and exercises their ability to solve practical environmental problems, which has certain promotion and reference significance for comprehensively promoting the reform of the environmental chemistry curriculum system. Keywords: New engineering, Environmental protection equipment and engineering design, Scientific thinking DOI: 10.7176/JEP/15-11-03 Publication date: October 30th 2024