31 research outputs found
BSD-GAN: Branched Generative Adversarial Network for Scale-Disentangled Representation Learning and Image Synthesis
We introduce BSD-GAN, a novel multi-branch and scale-disentangled training
method which enables unconditional Generative Adversarial Networks (GANs) to
learn image representations at multiple scales, benefiting a wide range of
generation and editing tasks. The key feature of BSD-GAN is that it is trained
in multiple branches, progressively covering both the breadth and depth of the
network, as resolutions of the training images increase to reveal finer-scale
features. Specifically, each noise vector, as input to the generator network of
BSD-GAN, is deliberately split into several sub-vectors, each corresponding to,
and is trained to learn, image representations at a particular scale. During
training, we progressively "de-freeze" the sub-vectors, one at a time, as a new
set of higher-resolution images is employed for training and more network
layers are added. A consequence of such an explicit sub-vector designation is
that we can directly manipulate and even combine latent (sub-vector) codes
which model different feature scales.Extensive experiments demonstrate the
effectiveness of our training method in scale-disentangled learning of image
representations and synthesis of novel image contents, without any extra labels
and without compromising quality of the synthesized high-resolution images. We
further demonstrate several image generation and manipulation applications
enabled or improved by BSD-GAN. Source codes are available at
https://github.com/duxingren14/BSD-GAN.Comment: 12 pages, 20 figures, accepted to IEEE Transaction on Image
Processin
Balanced Order Batching with Task-Oriented Graph Clustering
Balanced order batching problem (BOBP) arises from the process of warehouse
picking in Cainiao, the largest logistics platform in China. Batching orders
together in the picking process to form a single picking route, reduces travel
distance. The reason for its importance is that order picking is a labor
intensive process and, by using good batching methods, substantial savings can
be obtained. The BOBP is a NP-hard combinational optimization problem and
designing a good problem-specific heuristic under the quasi-real-time system
response requirement is non-trivial. In this paper, rather than designing
heuristics, we propose an end-to-end learning and optimization framework named
Balanced Task-orientated Graph Clustering Network (BTOGCN) to solve the BOBP by
reducing it to balanced graph clustering optimization problem. In BTOGCN, a
task-oriented estimator network is introduced to guide the type-aware
heterogeneous graph clustering networks to find a better clustering result
related to the BOBP objective. Through comprehensive experiments on
single-graph and multi-graphs, we show: 1) our balanced task-oriented graph
clustering network can directly utilize the guidance of target signal and
outperforms the two-stage deep embedding and deep clustering method; 2) our
method obtains an average 4.57m and 0.13m picking distance ("m" is the
abbreviation of the meter (the SI base unit of length)) reduction than the
expert-designed algorithm on single and multi-graph set and has a good
generalization ability to apply in practical scenario.Comment: 10 pages, 6 figure
L-Leucine Templated Biomimetic Assembly of SnO2 Nanoparticles and Their Lithium Storage Properties
SnO2 nanoparticles have been synthesized by a novel route of a sol-gel method assisted with biomimetic assembly using L-leucine as a biotemplate. The microstructure of as-prepared SnO2 nanoparticles was characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), Fourier transform infrared spectra (FT-IR), and Brunner−Emmet−Teller (BET) measurements. The results demonstrated that the growth of SnO2 could be regulated by L-leucine at a high calcination temperature. The electrochemical performance of SnO2 was also measured as anodes for lithium-ion battery. It is a guidance for the growth regulation of SnO2 at high temperature to obtain SnO2/C with nanosized SnO2 coated by a graphitic carbon
Contralateral acupuncture for migraine without aura: a randomized trial protocol with multimodal MRI
IntroductionMigraine is a common clinical disorder, ranks as the second most disabling disease worldwide, and often manifests with unilateral onset. Contralateral acupuncture (CAT), as a classical acupuncture method, has been proven to be effective in the treatment of migraine without aura (MWoA). However, its neural mechanisms have not been investigated using multimodal magnetic resonance imaging (MRI).Methods and analysisIn this multimodal neuroimaging randomized trial, a total of 96 female MWoA participants and 30 female healthy controls (HCs) will be recruited. The 96 female MWoA participants will be randomized into three groups: Group A (CAT group), Group B [ipsilateral acupuncture (IAT) group], and Group C (sham CAT group) in a 1:1:1 allocation ratio. Each group will receive 30 min of treatment every other day, three times a week, for 8 weeks, followed by an 8-week follow-up period. The primary outcome is the intensity of the migraine attack. Data will be collected at baseline (week 0), at the end of the 8-week treatment period (weeks 1–8), and during the 8-week follow-up (weeks 9–16). Adverse events will be recorded. Multimodal MRI scans will be conducted at baseline and after 8-week treatment.DiscussionThis study hypothesized that CAT may treat MWoA by restoring pathological alterations in brain neural activity, particularly by restoring cross-integrated functional connectivity with periaqueductal gray (PAG) as the core pathological brain region. The findings will provide scientific evidence for CAT in the treatment of MWoA.Ethics and disseminationThe Medical Ethics Committee of the Second Affiliated Hospital of Yunnan University of Chinese Medicine has given study approval (approval no. 2022-006). This trial has been registered with the Chinese Clinical Trials Registry (registration no. ChiCTR2300069456). Peer-reviewed papers will be used to publicize the trial’s findings.Clinical trial registrationhttps://clinicaltrials.gov/, identifier ChiCTR2300069456
A thermosensitive heparin-poloxamer hydrogel bridges aFGF to treat spinal cord injury
Acidic fibroblast growth factor (aFGF) exerts a protective effect on spinal cord injury (SCI) but is limited by the lack of physicochemical stability and the ability to cross the blood spinal cord barrier (BSCB). As promising biomaterials, hydrogels contain substantial amounts of water and a three-dimensional porous structure and are commonly used to load and deliver growth factors. Heparin can not only enhance growth factor loading onto hydrogels but also can stabilize the structure and control the release behavior. Herein, a novel aFGF-loaded thermosensitive heparin-poloxamer (aFGF-HP) hydrogel was developed and applied to provide protection and regeneration after SCI. To assess the effects of the aFGF-HP hydrogel, BSCB restoration, neuron and axonal rehabilitation, glial scar inhibition, inflammatory response suppression, and motor recovery were studied both in vivo and in vitro. The aFGF-HP hydrogels exhibited sustained release of aFGF and protected the bioactivity of aFGF in vitro. Compared to groups intravenously administered either drug-free HP hydrogel or aFGF alone, the aFGF-HP hydrogel group revealed prominent and attenuated disruption of the BSCB, reduced neuronal apoptosis, reactive astrogliosis, and increased neuron and axonal rehabilitation both in vivo and in vitro. This work provides an effective approach to enhance recovery after SCI and provide a successful strategy for SCI protection