95 research outputs found
Phase-change metasurfaces for dynamic image display and information encryption
Optical metasurfaces enable to engineer the electromagnetic space and control
light propagation at an unprecedented level, offering a powerful tool to
achieve modulation of light over multiple physical dimensions. Here, we
demonstrate a SbS phase-change metasurface platform that allows
active manipulation of both amplitude and phase. In particular, we implement
dynamic nanoprinting and holographic image display through tuning
crystallization levels of this phase-change material. The SbS
nanobricks tailored to function the amplitude, geometric and propagation phase
modulation constitute the dynamic meta-atoms in the multiplexed metasurfaces.
Using the incident polarizations as decoding keys, the encoded information can
be reproduced into a naonprinting grayscale image in the near field and two
holographic images in the far field. These images can be switched on and off by
taking advantages of the reversible tunability of SbS nanostructure
between amorphous and crystalline states. The proposed phase-change
metasurfaces featuring manifold information and multifold encryption promise
ultracompact data storage with high capacity and high security, which suggests
an exciting direction for modern cryptography and security applications
Online Learning in Vocational Education of China during COVID-19: Achievements, Challenges, and Future Developments
COVID-19 has challenged education systems globally. Traditional teaching and learning activities of more than 1,300 vocational colleges and nearly 11,000 vocational high schools in China have had to be paused and transformed into an online mode. A study had been conducted to trace the unprecedented change which would provide reflections on policies and practical experience worthy of reference for the follow-up development of online vocational education in China and other countries in the world. The study used two methods to collect data: (1) delivering questionnaires to 767 schools, 17009 teachers, 270,732 students, and (2) gathering 110 institute cases from 21 provinces and 170 curriculum cases from 14 provinces. The result showed that vocational institutions coped with the pandemic’s outbreak through online learning and achieved the overall goal of “Not Going to School but Classes still Ongoing.” Further, vocational institutions have faced problems and challenges of online learning in practice training and internship, organization, and technical environment. The development of vocational education in the information era requires thinking about the system-driven reform path and online learning strategy and putting it into action
MotionEditor: Editing Video Motion via Content-Aware Diffusion
Existing diffusion-based video editing models have made gorgeous advances for
editing attributes of a source video over time but struggle to manipulate the
motion information while preserving the original protagonist's appearance and
background. To address this, we propose MotionEditor, a diffusion model for
video motion editing. MotionEditor incorporates a novel content-aware motion
adapter into ControlNet to capture temporal motion correspondence. While
ControlNet enables direct generation based on skeleton poses, it encounters
challenges when modifying the source motion in the inverted noise due to
contradictory signals between the noise (source) and the condition (reference).
Our adapter complements ControlNet by involving source content to transfer
adapted control signals seamlessly. Further, we build up a two-branch
architecture (a reconstruction branch and an editing branch) with a
high-fidelity attention injection mechanism facilitating branch interaction.
This mechanism enables the editing branch to query the key and value from the
reconstruction branch in a decoupled manner, making the editing branch retain
the original background and protagonist appearance. We also propose a skeleton
alignment algorithm to address the discrepancies in pose size and position.
Experiments demonstrate the promising motion editing ability of MotionEditor,
both qualitatively and quantitatively.Comment: 18 pages, 15 figures. Project page at
https://francis-rings.github.io/MotionEditor
Nonlinear magnetotransport shaped by Fermi surface topology and convexity in WTe2
The nature of Fermi surface defines the physical properties of conductors and
many physical phenomena can be traced to its shape. Although the recent
discovery of a current-dependent nonlinear magnetoresistance in spin-polarized
non-magnetic materials has attracted considerable attention in spintronics,
correlations between this phenomenon and the underlying fermiology remain
unexplored. Here, we report the observation of nonlinear magnetoresistance at
room temperature in a semimetal WTe2, with an interesting temperature-driven
inversion. Theoretical calculations reproduce the nonlinear transport
measurements and allow us to attribute the inversion to temperature-induced
changes in Fermi surface convexity. We also report a large anisotropy of
nonlinear magnetoresistance in WTe2, due to its low symmetry of Fermi surfaces.
The good agreement between experiments and theoretical modeling reveals the
critical role of Fermi surface topology and convexity on the nonlinear
magneto-response. These results lay a new path to explore ramifications of
distinct fermiology for nonlinear transport in condensed-matter
Smart Agent-Based Modeling: On the Use of Large Language Models in Computer Simulations
Computer simulations offer a robust toolset for exploring complex systems
across various disciplines. A particularly impactful approach within this realm
is Agent-Based Modeling (ABM), which harnesses the interactions of individual
agents to emulate intricate system dynamics. ABM's strength lies in its
bottom-up methodology, illuminating emergent phenomena by modeling the
behaviors of individual components of a system. Yet, ABM has its own set of
challenges, notably its struggle with modeling natural language instructions
and common sense in mathematical equations or rules. This paper seeks to
transcend these boundaries by integrating Large Language Models (LLMs) like GPT
into ABM. This amalgamation gives birth to a novel framework, Smart Agent-Based
Modeling (SABM). Building upon the concept of smart agents -- entities
characterized by their intelligence, adaptability, and computation ability --
we explore in the direction of utilizing LLM-powered agents to simulate
real-world scenarios with increased nuance and realism. In this comprehensive
exploration, we elucidate the state of the art of ABM, introduce SABM's
potential and methodology, and present three case studies (source codes
available at https://github.com/Roihn/SABM), demonstrating the SABM methodology
and validating its effectiveness in modeling real-world systems. Furthermore,
we cast a vision towards several aspects of the future of SABM, anticipating a
broader horizon for its applications. Through this endeavor, we aspire to
redefine the boundaries of computer simulations, enabling a more profound
understanding of complex systems.Comment: Source codes are available at https://github.com/Roihn/SAB
Implicit Temporal Modeling with Learnable Alignment for Video Recognition
Contrastive language-image pretraining (CLIP) has demonstrated remarkable
success in various image tasks. However, how to extend CLIP with effective
temporal modeling is still an open and crucial problem. Existing factorized or
joint spatial-temporal modeling trades off between the efficiency and
performance. While modeling temporal information within straight through tube
is widely adopted in literature, we find that simple frame alignment already
provides enough essence without temporal attention. To this end, in this paper,
we proposed a novel Implicit Learnable Alignment (ILA) method, which minimizes
the temporal modeling effort while achieving incredibly high performance.
Specifically, for a frame pair, an interactive point is predicted in each
frame, serving as a mutual information rich region. By enhancing the features
around the interactive point, two frames are implicitly aligned. The aligned
features are then pooled into a single token, which is leveraged in the
subsequent spatial self-attention. Our method allows eliminating the costly or
insufficient temporal self-attention in video. Extensive experiments on
benchmarks demonstrate the superiority and generality of our module.
Particularly, the proposed ILA achieves a top-1 accuracy of 88.7% on
Kinetics-400 with much fewer FLOPs compared with Swin-L and ViViT-H. Code is
released at https://github.com/Francis-Rings/ILA .Comment: ICCV 2023 oral. 14 pages, 7 figures. Code released at
https://github.com/Francis-Rings/IL
Enhancing the conversational agent with an emotional support system for mental health digital therapeutics
As psychological diseases become more prevalent and are identified as the leading cause of acquired disability, it is essential to assist people in improving their mental health. Digital therapeutics (DTx) has been widely studied to treat psychological diseases with the advantage of cost savings. Among the techniques of DTx, a conversational agent can interact with patients through natural language dialog and has become the most promising one. However, conversational agents' ability to accurately show emotional support (ES) limits their role in DTx solutions, especially in mental health support. One of the main reasons is that the prediction of emotional support systems does not extract effective information from historical dialog data and only depends on the data derived from one single-turn interaction with users. To address this issue, we propose a novel emotional support conversation agent called the STEF agent that generates more supportive responses based on a thorough view of past emotions. The proposed STEF agent consists of the emotional fusion mechanism and strategy tendency encoder. The emotional fusion mechanism focuses on capturing the subtle emotional changes throughout a conversation. The strategy tendency encoder aims at foreseeing strategy evolution through multi-source interactions and extracting latent strategy semantic embedding. Experimental results on the benchmark dataset ESConv demonstrate the effectiveness of the STEF agent compared with competitive baselines
- …