1,246 research outputs found
Learning Convolutional Networks for Content-weighted Image Compression
Lossy image compression is generally formulated as a joint rate-distortion
optimization to learn encoder, quantizer, and decoder. However, the quantizer
is non-differentiable, and discrete entropy estimation usually is required for
rate control. These make it very challenging to develop a convolutional network
(CNN)-based image compression system. In this paper, motivated by that the
local information content is spatially variant in an image, we suggest that the
bit rate of the different parts of the image should be adapted to local
content. And the content aware bit rate is allocated under the guidance of a
content-weighted importance map. Thus, the sum of the importance map can serve
as a continuous alternative of discrete entropy estimation to control
compression rate. And binarizer is adopted to quantize the output of encoder
due to the binarization scheme is also directly defined by the importance map.
Furthermore, a proxy function is introduced for binary operation in backward
propagation to make it differentiable. Therefore, the encoder, decoder,
binarizer and importance map can be jointly optimized in an end-to-end manner
by using a subset of the ImageNet database. In low bit rate image compression,
experiments show that our system significantly outperforms JPEG and JPEG 2000
by structural similarity (SSIM) index, and can produce the much better visual
result with sharp edges, rich textures, and fewer artifacts
Pulmonary diseases induced by ambient ultrafine and engineered nanoparticles in twenty-first century.
Air pollution is a severe threat to public health globally, affecting everyone in developed and developing countries alike. Among different air pollutants, particulate matter (PM), particularly combustion-produced fine PM (PM2.5) has been shown to play a major role in inducing various adverse health effects. Strong associations have been demonstrated by epidemiological and toxicological studies between increases in PM2.5 concentrations and premature mortality, cardiopulmonary diseases, asthma and allergic sensitization, and lung cancer. The mechanisms of PM-induced toxicological effects are related to their size, chemical composition, lung clearance and retention, cellular oxidative stress responses and pro-inflammatory effects locally and systemically. Particles in the ultrafine range (<100 nm), although they have the highest number counts, surface area and organic chemical content, are often overlooked due to insufficient monitoring and risk assessment. Yet, ample studies have demonstrated that ambient ultrafine particles have higher toxic potential compared with PM2.5. In addition, the rapid development of nanotechnology, bringing ever-increasing production of nanomaterials, has raised concerns about the potential human exposure and health impacts. All these add to the complexity of PM-induced health effects that largely remains to be determined, and mechanistic understanding on the toxicological effects of ambient ultrafine particles and nanomaterials will be the focus of studies in the near future
Non-Orthogonal Multiple Access For Near-Field Communications
The novel concept of near-field non-orthogonal multiple access (NF-NOMA)
communications is proposed. The near-filed beamfocusing enables NOMA to be
carried out in both angular and distance domains. Two novel frameworks are
proposed, namely, single-location-beamfocusing NF-NOMA (SLB-NF-NOMA) and
multiple-location-beamfocusing NF-NOMA (MLB-NF-NOMA). 1) For SLB-NF-NOMA, two
NOMA users in the same angular direction with distinct quality of service (QoS)
requirements can be grouped into one cluster. The hybrid beamformer design and
power allocation problem is formulated to maximize the sum rate of the users
with higher QoS (H-QoS) requirements. To solve this problem, the analog
beamformer is first designed to focus the energy on the H-QoS users and the
zero-forcing (ZF) digital beamformer is employed. Then, the optimal power
allocation is obtained. 2) For MLB-NF-NOMA, the two NOMA users in the same
cluster can have different angular directions. The analog beamformer is first
designed to focus the energy on both two NOMA users. Then, a singular value
decomposition (SVD) based ZF (SVD-ZF) digital beamformer is designed.
Furthermore, a novel antenna allocation algorithm is proposed. Finally, a
suboptimal power allocation algorithm is proposed. Numerical results
demonstrate that the NF-NOMA can achieve a higher spectral efficiency and
provide a higher flexibility than conventional far-field NOMA
The Mediating Effect of Psychological Quality of Innovation and Entrepreneurship Education on Graduate Students' Innovation and Entrepreneurship Ability and Willingness
This work was supported by the Degree and Graduate Education Reform Key Projects of Guangdong Province, and the title is Exploration and Practice of Postgraduate Cultivation in Incorporating Innovation and Entrepreneurship Consciousness. Also, it was supported by the project of China Higher Education Association Thirteenth Five-Year Plan for Scientific Research in Higher Education (Grant number: 2018SYSZD08). Abstract Under the background of the innovation and entrepreneurship ability improvement plan proposed by the Ministry of education of China, the research on the subject of entrepreneurship and innovation is conducive to the continuous deepening of innovation and entrepreneurship education reform in Colleges and universities, and the continuous training and delivery of high-level talents for the construction of an innovative country. This paper explores the relationship among innovation and entrepreneurship ability, innovation and entrepreneurship education psychological quality and innovation and entrepreneurship willingness through a questionnaire survey of graduate students in Colleges and universities, analyzes the current situation, further confirms the hypothesis among the three, obtains positive results, and puts forward some countermeasures and suggestions for higher education. Keywords: Higher education, Graduate students, Willingness of innovation and entrepreneurship, Innovation and entrepreneurship ability, Psychological quality of innovation and entrepreneurship education DOI: 10.7176/JEP/11-19-06 Publication date:July 31st 202
Event-based Human Pose Tracking by Spiking Spatiotemporal Transformer
Event camera, as an emerging biologically-inspired vision sensor for
capturing motion dynamics, presents new potential for 3D human pose tracking,
or video-based 3D human pose estimation. However, existing works in pose
tracking either require the presence of additional gray-scale images to
establish a solid starting pose, or ignore the temporal dependencies all
together by collapsing segments of event streams to form static event frames.
Meanwhile, although the effectiveness of Artificial Neural Networks (ANNs,
a.k.a. dense deep learning) has been showcased in many event-based tasks, the
use of ANNs tends to neglect the fact that compared to the dense frame-based
image sequences, the occurrence of events from an event camera is
spatiotemporally much sparser. Motivated by the above mentioned issues, we
present in this paper a dedicated end-to-end sparse deep learning approach for
event-based pose tracking: 1) to our knowledge this is the first time that 3D
human pose tracking is obtained from events only, thus eliminating the need of
accessing to any frame-based images as part of input; 2) our approach is based
entirely upon the framework of Spiking Neural Networks (SNNs), which consists
of Spike-Element-Wise (SEW) ResNet and a novel Spiking Spatiotemporal
Transformer; 3) a large-scale synthetic dataset is constructed that features a
broad and diverse set of annotated 3D human motions, as well as longer hours of
event stream data, named SynEventHPD. Empirical experiments demonstrate that,
with superior performance over the state-of-the-art (SOTA) ANNs counterparts,
our approach also achieves a significant computation reduction of 80% in FLOPS.
Furthermore, our proposed method also outperforms SOTA SNNs in the regression
task of human pose tracking. Our implementation is available at
https://github.com/JimmyZou/HumanPoseTracking_SNN and dataset will be released
upon paper acceptance
Towards 4D Human Video Stylization
We present a first step towards 4D (3D and time) human video stylization,
which addresses style transfer, novel view synthesis and human animation within
a unified framework. While numerous video stylization methods have been
developed, they are often restricted to rendering images in specific viewpoints
of the input video, lacking the capability to generalize to novel views and
novel poses in dynamic scenes. To overcome these limitations, we leverage
Neural Radiance Fields (NeRFs) to represent videos, conducting stylization in
the rendered feature space. Our innovative approach involves the simultaneous
representation of both the human subject and the surrounding scene using two
NeRFs. This dual representation facilitates the animation of human subjects
across various poses and novel viewpoints. Specifically, we introduce a novel
geometry-guided tri-plane representation, significantly enhancing feature
representation robustness compared to direct tri-plane optimization. Following
the video reconstruction, stylization is performed within the NeRFs' rendered
feature space. Extensive experiments demonstrate that the proposed method
strikes a superior balance between stylized textures and temporal coherence,
surpassing existing approaches. Furthermore, our framework uniquely extends its
capabilities to accommodate novel poses and viewpoints, making it a versatile
tool for creative human video stylization.Comment: Under Revie
- …