Photoresponsive supramolecular soft materials in aqueous media

Nature has provided the most elegant examples of self-assembled systems derived from amphiphiles. Natural phospholipids self-assemble into biological membranes in living organisms, which demonstrate automatic smart response in the presence of functional proteins. Inspired by nature, supramolecular self-assembly of photoresponsive molecular amphiphiles in aqueous media, an emerging area of materials science, is a promising synthetic strategy towards creating biomimetic functions. It is a bottom-up approach towards the development of smart soft materials with well-defined structures, ranging from one-dimensional nanostructures to isotropic entangled three-dimensional networks and anisotropic three-dimensional structures. In this thesis, we focus on designing self-assembled soft materials consisting of azobenzene-based or molecular-motor-based amphiphiles in aqueous media, allowing for energy conversion and amplification from molecular motions to macroscopic delicate functions. In addition to identifying the key processes for the amplification from nanoscale motions into macroscopic response, the smart soft materials also show interesting applications in a wide range of areas. The smart soft materials are employed in industrial processes to solve practical problems, e.g., minimizing pollutants discharge in textile coloring process, as well as in biological systems to creating biomimetic materials, e.g., muscle-like strings which exhibit photoactuation. In this thesis, we focus on structures and functions of photoresponsive molecular amphiphiles and aim at proving insight into the fascinating supramolecular self-assembly of photoresponsive amphiphiles in aqueous media

CPMLHO:Hyperparameter Tuning via Cutting Plane and Mixed-Level Optimization

The hyperparameter optimization of neural network can be expressed as a bilevel optimization problem. The bilevel optimization is used to automatically update the hyperparameter, and the gradient of the hyperparameter is the approximate gradient based on the best response function. Finding the best response function is very time consuming. In this paper we propose CPMLHO, a new hyperparameter optimization method using cutting plane method and mixed-level objective function.The cutting plane is added to the inner layer to constrain the space of the response function. To obtain more accurate hypergradient,the mixed-level can flexibly adjust the loss function by using the loss of the training set and the verification set. Compared to existing methods, the experimental results show that our method can automatically update the hyperparameters in the training process, and can find more superior hyperparameters with higher accuracy and faster convergence

Surface Charge Effects for the Hydrogen Evolution Reaction on Pt(111) Using a Modified Grand-Canonical Potential Kinetics Method

Surface charges of catalysts have important influences on the thermodynamics and kinetics of electrochemical reactions. Herein, we develop a modified version of the grand-canonical potential kinetics (GCP-K) method based on density functional theory (DFT) calculations to explore the effect of surface charges on reaction thermodynamics and kinetics. Using the hydrogen evolution reaction (HER) on the Pt(111) surface as an example, we show how to track the change of surface charge in a reaction and how to analyze its influence on the kinetics. Grand-canonical calculations demonstrate that the optimum hydrogen adsorption energy on Pt under the standard hydrogen electrode condition (SHE) is around -0.2 eV, rather than 0 eV established under the canonical ensemble, due to the high density of surface negative charges. By separating the surface charges that can freely exchange with the external electron reservoir, we obtain a Tafel barrier that is in good agreement with the experimental result. During the Tafel reaction, the net electron inflow into the catalyst leads to a stabilization of canonical energy and a destabilization of the charge-dependent grand-canonical component. This study provides a practical method for obtaining accurate grand-canonical reaction energetics and analyzing the surface charge induced changes

Self-Assembly of Photoresponsive Molecular Amphiphiles in Aqueous Media

Amphiphilic molecules, comprising hydrophobic and hydrophilic moieties and the intrinsic propensity to self-assemble in aqueous environment, sustain a fascinating spectrum of structures and functions ranging from biological membranes to ordinary soap. Facing the challenge to design responsive, adaptive, and out-of-equilibrium systems in water, the incorporation of photoresponsive motifs in amphiphilic molecular structures offers ample opportunity to design supramolecular systems that enables functional responses in water in a non-invasive way using light. Here, we discuss the design of photoresponsive molecular amphiphiles, their self-assembled structures in aqueous media and at air–water interfaces, and various approaches to arrive at adaptive and dynamic functions in isotropic and anisotropic systems, including motion at the air–water interface, foam formation, reversible nanoscale assembly, and artificial muscle function. Controlling the delicate interplay of structural design, self-assembling conditions and external stimuli, these responsive amphiphiles open several avenues towards application such as soft adaptive materials, controlled delivery or soft actuators, bridging a gap between artificial and natural dynamic systems

Long non-coding RNA MYU promotes ovarian cancer cell proliferation by sponging miR-6827-5p and upregulating HMGA1

Background: Long non-coding RNAs (lncRNAs) have been confirmed to play vital roles in tumorigenesis. LncRNA MYU has recently been reported as an oncogene in several kinds of tumors. However, MYU’s expression status and potential involvement in ovarian cancer (OC) remain unclear. In this study, we explored the underlying role of MYU in OC.Methods and results: The expression of MYU was upregulated in OC tissues, and MYU’s overexpression was significantly correlated with the FIGO stage and lymphatic metastasis. Knockdown of MYU inhibited cell proliferation in SKOV3 and A2780 cells. Mechanistically, MYU directly interacted with miR-6827-5p in OC cells; HMGA1 is a downstream target gene of miR-6827-5p. Furthermore, MYU knockdown increased the expression of miR-6827-5p and decreased the expression of HMGA1. Restoration of HMGA1 expression reversed the influence on cell proliferation caused by MYU knockdown.Conclusion: MYU functions as a ceRNA that positively regulates HMGA1 expression by sponging miR-6827-5p in OC cells, which may provide a potential target and biomarker for the diagnosis or prognosis of OC

APICom: Automatic API Completion via Prompt Learning and Adversarial Training-based Data Augmentation

Based on developer needs and usage scenarios, API (Application Programming Interface) recommendation is the process of assisting developers in finding the required API among numerous candidate APIs. Previous studies mainly modeled API recommendation as the recommendation task, which can recommend multiple candidate APIs for the given query, and developers may not yet be able to find what they need. Motivated by the neural machine translation research domain, we can model this problem as the generation task, which aims to directly generate the required API for the developer query. After our preliminary investigation, we find the performance of this intuitive approach is not promising. The reason is that there exists an error when generating the prefixes of the API. However, developers may know certain API prefix information during actual development in most cases. Therefore, we model this problem as the automatic completion task and propose a novel approach APICom based on prompt learning, which can generate API related to the query according to the prompts (i.e., API prefix information). Moreover, the effectiveness of APICom highly depends on the quality of the training dataset. In this study, we further design a novel gradient-based adversarial training method {\atpart} for data augmentation, which can improve the normalized stability when generating adversarial examples. To evaluate the effectiveness of APICom, we consider a corpus of 33k developer queries and corresponding APIs. Compared with the state-of-the-art baselines, our experimental results show that APICom can outperform all baselines by at least 40.02\%, 13.20\%, and 16.31\% in terms of the performance measures EM@1, MRR, and MAP. Finally, our ablation studies confirm the effectiveness of our component setting (such as our designed adversarial training method, our used pre-trained model, and prompt learning) in APICom.Comment: accepted in Internetware 202

Observation of fast sound in two-dimensional dusty plasma liquids

Equilibrium molecular dynamics simulations are performed to study two-dimensional (2D) dusty plasma liquids. Based on the stochastic thermal motion of simulated particles, the longitudinal and transverse phonon spectra are calculated, and used to determine the corresponding dispersion relations. From there, the longitudinal and transverse sound speeds of 2D dusty plasma liquids are obtained. It is discovered that, for wavenumbers beyond the hydrodynamic regime, the longitudinal sound speed of a 2D dusty plasma liquid exceeds its adiabatic value, i.e., the so-called fast sound. This phenomenon appears at roughly the same length scale of the cutoff wavenumber for transverse waves, confirming its relation to the emergent solidity of liquids in the non-hydrodynamic regime. Using the thermodynamic and transport coefficients extracted from the previous studies, and relying on the Frenkel theory, the ratio of the longitudinal to the adiabatic sound speeds is derived analytically, providing the optimal conditions for fast sound, which are in quantitative agreement with the current simulation results.Comment: v1: 7 pages, 6 figure

Lane Graph as Path: Continuity-preserving Path-wise Modeling for Online Lane Graph Construction

Online lane graph construction is a promising but challenging task in autonomous driving. Previous methods usually model the lane graph at the pixel or piece level, and recover the lane graph by pixel-wise or piece-wise connection, which breaks down the continuity of the lane. Human drivers focus on and drive along the continuous and complete paths instead of considering lane pieces. Autonomous vehicles also require path-specific guidance from lane graph for trajectory planning. We argue that the path, which indicates the traffic flow, is the primitive of the lane graph. Motivated by this, we propose to model the lane graph in a novel path-wise manner, which well preserves the continuity of the lane and encodes traffic information for planning. We present a path-based online lane graph construction method, termed LaneGAP, which end-to-end learns the path and recovers the lane graph via a Path2Graph algorithm. We qualitatively and quantitatively demonstrate the superiority of LaneGAP over conventional pixel-based and piece-based methods on challenging nuScenes and Argoverse2 datasets. Abundant visualizations show LaneGAP can cope with diverse traffic conditions. Code and models will be released at \url{https://github.com/hustvl/LaneGAP} for facilitating future research

VAD: Vectorized Scene Representation for Efficient Autonomous Driving

Autonomous driving requires a comprehensive understanding of the surrounding environment for reliable trajectory planning. Previous works rely on dense rasterized scene representation (e.g., agent occupancy and semantic map) to perform planning, which is computationally intensive and misses the instance-level structure information. In this paper, we propose VAD, an end-to-end vectorized paradigm for autonomous driving, which models the driving scene as a fully vectorized representation. The proposed vectorized paradigm has two significant advantages. On one hand, VAD exploits the vectorized agent motion and map elements as explicit instance-level planning constraints which effectively improves planning safety. On the other hand, VAD runs much faster than previous end-to-end planning methods by getting rid of computation-intensive rasterized representation and hand-designed post-processing steps. VAD achieves state-of-the-art end-to-end planning performance on the nuScenes dataset, outperforming the previous best method by a large margin. Our base model, VAD-Base, greatly reduces the average collision rate by 29.0% and runs 2.5x faster. Besides, a lightweight variant, VAD-Tiny, greatly improves the inference speed (up to 9.3x) while achieving comparable planning performance. We believe the excellent performance and the high efficiency of VAD are critical for the real-world deployment of an autonomous driving system. Code and models will be released for facilitating future research.Comment: Code&Demos: https://github.com/hustvl/VA