178 research outputs found
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
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
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
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
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Decadal modulation of the relationship between tropical southern Atlantic SST and subsequent ENSO by Pacific Decadal Oscillation
This study identifies the relationship between tropical southern Atlantic (TSA) sea surface temperature anomaly (SSTA) and the El Niño-Southern Oscillation (ENSO) and focuses on how the Pacific Decadal Oscillation (PDO) modulates this relationship. Results suggest a significant but non-stationary interannual TSA-ENSO relationship which undergoes a significant decadal shift. A strong TSA-ENSO relationship is observed during the positive PDO phase, while this relationship is weak during the negative PDO phase. Two processes, involving the anomalous Pacific Walker circulation (PWC) and the intensity of air-sea interactions over the Pacific, are proposed for this decadal shift. During the positive PDO phase, the weak and variable PWC and strong air-sea interaction facilitate the development of SSTA in the tropical Pacific triggered by TSA SSTA, resulting in a strong TSA-ENSO relationship and vice versa. These findings emphasize the important role of the modulation of PDO on the TSA-ENSO relationship
VMA: Divide-and-Conquer Vectorized Map Annotation System for Large-Scale Driving Scene
High-definition (HD) map serves as the essential infrastructure of autonomous
driving. In this work, we build up a systematic vectorized map annotation
framework (termed VMA) for efficiently generating HD map of large-scale driving
scene. We design a divide-and-conquer annotation scheme to solve the spatial
extensibility problem of HD map generation, and abstract map elements with a
variety of geometric patterns as unified point sequence representation, which
can be extended to most map elements in the driving scene. VMA is highly
efficient and extensible, requiring negligible human effort, and flexible in
terms of spatial scale and element type. We quantitatively and qualitatively
validate the annotation performance on real-world urban and highway scenes, as
well as NYC Planimetric Database. VMA can significantly improve map generation
efficiency and require little human effort. On average VMA takes 160min for
annotating a scene with a range of hundreds of meters, and reduces 52.3% of the
human cost, showing great application value
Improved algorithm of three-dimensional beamforming based on spatial cross-array
Three-dimensional beamforming based on microphone array signal processing is the expansion of traditional 2D beamforming. However, its identification accuracy is often badly reduced by the effect of grating lobes and side lobes. To overcome this problem, a method called the hybrid method beamforming (HMB) combining functional generalized inverse beamforming with multiplicative filter of spatial cross-array is proposed. In this method, the statistically optimal processing and iterated generalized inverse beamforming with regularized matrix function are utilized to obtain initial result. Then the high order function is applied to filter the output. Subsequently, a novel non-uniform spatial cross-array optimized by genetic algorithm is used to obtain sound pressure distribution. The array consists of three orthogonal sub-arrays. Mutual cancellation is realized by computing respectively with data of sub-arrays and multiplying together. With fewer microphones, the result of the improved method can be obtained with a higher spatial resolution. The proposed method is verified by the simulation and the source localization test in a room. Compared with the conventional frequency domain beamforming (FDBF) algorithm and statistically optimal array processing (SOAP) beamforming, the performance of the proposed method is significantly improved in terms of resolution of the acoustic source
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