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