RAIN: RegulArization on Input and Network for Black-Box Domain Adaptation

Source-Free domain adaptation transits the source-trained model towards target domain without exposing the source data, trying to dispel these concerns about data privacy and security. However, this paradigm is still at risk of data leakage due to adversarial attacks on the source model. Hence, the Black-Box setting only allows to use the outputs of source model, but still suffers from overfitting on the source domain more severely due to source model's unseen weights. In this paper, we propose a novel approach named RAIN (RegulArization on Input and Network) for Black-Box domain adaptation from both input-level and network-level regularization. For the input-level, we design a new data augmentation technique as Phase MixUp, which highlights task-relevant objects in the interpolations, thus enhancing input-level regularization and class consistency for target models. For network-level, we develop a Subnetwork Distillation mechanism to transfer knowledge from the target subnetwork to the full target network via knowledge distillation, which thus alleviates overfitting on the source domain by learning diverse target representations. Extensive experiments show that our method achieves state-of-the-art performance on several cross-domain benchmarks under both single- and multi-source black-box domain adaptation.Comment: Accepted by IJCAI 202

Create Your World: Lifelong Text-to-Image Diffusion

Text-to-image generative models can produce diverse high-quality images of concepts with a text prompt, which have demonstrated excellent ability in image generation, image translation, etc. We in this work study the problem of synthesizing instantiations of a use's own concepts in a never-ending manner, i.e., create your world, where the new concepts from user are quickly learned with a few examples. To achieve this goal, we propose a Lifelong text-to-image Diffusion Model (L2DM), which intends to overcome knowledge "catastrophic forgetting" for the past encountered concepts, and semantic "catastrophic neglecting" for one or more concepts in the text prompt. In respect of knowledge "catastrophic forgetting", our L2DM framework devises a task-aware memory enhancement module and a elastic-concept distillation module, which could respectively safeguard the knowledge of both prior concepts and each past personalized concept. When generating images with a user text prompt, the solution to semantic "catastrophic neglecting" is that a concept attention artist module can alleviate the semantic neglecting from concept aspect, and an orthogonal attention module can reduce the semantic binding from attribute aspect. To the end, our model can generate more faithful image across a range of continual text prompts in terms of both qualitative and quantitative metrics, when comparing with the related state-of-the-art models. The code will be released at https://wenqiliang.github.io/.Comment: 15 pages,10 figure

AIR-DA: Adversarial Image Reconstruction for Unsupervised Domain Adaptive Object Detection

Unsupervised domain adaptive object detection is a challenging vision task where object detectors are adapted from a label-rich source domain to an unlabeled target domain. Recent advances prove the efficacy of the adversarial based domain alignment where the adversarial training between the feature extractor and domain discriminator results in domain-invariance in the feature space. However, due to the domain shift, domain discrimination, especially on low-level features, is an easy task. This results in an imbalance of the adversarial training between the domain discriminator and the feature extractor. In this work, we achieve a better domain alignment by introducing an auxiliary regularization task to improve the training balance. Specifically, we propose Adversarial Image Reconstruction (AIR) as the regularizer to facilitate the adversarial training of the feature extractor. We further design a multi-level feature alignment module to enhance the adaptation performance. Our evaluations across several datasets of challenging domain shifts demonstrate that the proposed method outperforms all previous methods, of both one- and two-stage, in most settings.Comment: Accepted at IEEE Robotics and Automation Letters 202

Flexible Energy Storage Systems Based on Electrically Conductive Hydrogels

The final publication is available at Elsevier via https://doi.org/10.1016/j.progpolymsci.2018.09.001 © 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/To power wearable electronic devices, various flexible energy storage systems have been designed to work in consecutive bending, stretching and even twisting conditions. Supercapacitors and batteries have been considered to be the most promising energy/power sources for wearable electronics; however, they need to be electrochemically sustainable and mechanically robust. Electrically conductive hydrogels (ECHs), combining the electrical properties of conductive materials with the unique features of hydrogels, are ideal frameworks to design and construct flexible supercapacitors and batteries. ECHs are intrinsically flexible to sustain large mechanical deformation; they can hold a large amount of electrolyte solution in a 3D nanostructured conducting network, providing an extremely high surface area for the required electrochemical reactions. To date, nanostructured three-dimensional ECHs have exhibited high performance when applied as active electrode materials for supercapacitors and lithium-ion batteries. Future research may attempt to develop functional ECHs with controllable size, composition, morphology, and interface. This review summarizes the material design and synthetic approach of ECHs, demonstrating the advances of percolation theory in ECH materials, and subsequently presents their effective application in flexible energy storage systems and discusses the challenges and opportunities in this field.NNSFC [grants 11472080, 51731004, 51708108]NSF of Jiangsu Province [grant BK20141336]Fundamental Research Funds for the Central UniversitiesNatural Sciences and Engineering Research Council [grant RGPIN-2014-04663

Microwave-induced phase escape in a Josephson tunnel junction

This is the published version, also available here: http://dx.doi.org/10.1103/PhysRevB.77.104531.We perform both theoretical and experimental investigations on the phase escape of a current-biased Josephson tunnel junction under microwave irradiation. The switching current distributions exhibit abundant nonlinear behaviors depending on the power and frequency of the applied microwave. We present a model to describe the behavior of the primary peak in the switching current distribution, which is confirmed by our experimental results. The obtained features can be used to characterize the damping parameter of Josephson junctions

Fretting wear behavior of graphite-like carbon films with bias-graded deposition

Although graphite-like carbon (GLC) films have been used to protect the engineering components due to their high mechanical properties and low friction coefficients, the poor interfacial bonding strength and high internal stress can lead their rapid failure. In this study, the bias-gradient (30–120 V) as well as the usual constant bias protocols (30, 60 and 120 V) has been adopted to deposit the GLC films on 316 L stainless steel and silicon using unbalanced magnetron sputtering technology. Based upon the microstructure and composition analysis by SEM, AFM, XRD, Raman and XPS, the sp3 content and compactness of the films are increased with the increase of the deposition bias. Compared to the film at the constant bias of 120 V, the bias-graded film has a comparable hardness but superior adhesive strength. Detailed fretting wear testing under ambient air and dry N2 atmospheres against 25 mm diameter Si3N4 ball has been carried out. The friction curves disclosed a three-stage evolution feature: the surface working area, the interlayer transition area and the coating failure area. The bias-graded film displayed the lowest friction coefficient and the longest fatigue life. Further the fretting mechanisms at different stages have been elaborated in terms of the chemical composition, microstructure and mechanical properties

Trans-lymphatic Contrast-Enhanced Ultrasound in Combination with Blue Dye Injection is Feasible for Detection and Biopsy of Sentinel Lymph Nodes in Breast Cancer

Objective: The best method for sentinel lymph node biopsy (SLNB) in early-staged breast cancer (EBC) remains controversial. This study aimed to evaluate a novel method by combining trans-lymphatic contrast-enhanced ultrasound (TLCEUS) with blue dye injection as a guidance of SLNB. Methods: TLCEUS was performed in 88 patients with newly diagnosed EBC. Methylene blue dye was percutaneously injected into enhanced sentinel lymph nodes (SLNs) under ultrasound guidance, followed by standard SLNB and axillary lymph node dissection. Enhancement patterns and the arriving time (AT) of contrast agent within SLNs were evaluated. Histopathological examination of dissected nodes was performed to confirm metastasis. Results: A total of 95 enhanced SLNs were identified and biopsied in 86 of 88 patients with identification rate of 97.7%. The specificity was 75.0%, sensitivity was 83.3%, and false-negative rate was 16.7%. Contrast-enhanced SLNs with type I, type II, and type III patterns had a metastatic positive rate of 11.4% (5/44), 57.1% (12/21) and 80.0% (24/30), respectively. Metastatic positive SLNs showed a mean AT of 61.6 ± 58.7 s while metastatic negative SLNs showed a mean AT of 41.3 ± 19.9 s, which was statistically significantly different. Conclusion: The TLCEUS/blue dye method can be used as an alternative to the radioisotope/blue dye method for its feasibility and accuracy

Sleep behavior and depression: Findings from the China Kadoorie Biobank of 0.5 million Chinese adults.

BACKGROUND: Mixed results have shown the association between sleep behavior and depression, but evidence relating the joint effect of sleep duration and sleep disturbances is limited, especially in Chinese population. METHODS: A total of 512,891 adults aged 30-79 years from China Kadoorie Biobank (CKB) were included. Depression was defined by Composite International Diagnostic Inventory-short form (CIDI-SF). Sleep duration and sleep disturbances, including difficulty initiating and maintaining sleep (DIMS), early morning awakening (EMA), daytime dysfunction (DDF) and any sleep disturbances (ASD), were obtained by a self-reported questionnaire. Logistic regression was applied to examine the association between sleep behavior and depression. RESULTS: About 23.1% of participants reported short sleep duration (≤ 6h), and 5.1% reported long sleep duration (> 9h). Compared with normal sleep duration (7-9h), both groups were associated greater likelihood of having depression (short sleep: OR = 2.32, 95%CI: 2.14-2.51; long sleep: OR = 1.56, 96%CI: 1.34-1.81). Participants reported sleep disturbances were significantly associated with depression (odds ratios ranged from 3.31 to 4.17). Moreover, the associations tended to be stronger for those who reported both abnormal sleep duration and sleep disturbances (p for interactions < 0.05), especially for those who slept long. LIMITATIONS: The cross-sectional nature of the study design limits the interpretation of the results. CONCLUSIONS: Abnormal sleep duration and sleep disturbances were associated with depression. The associations were stronger for abnormal sleep duration accompanied with sleep disturbances, especially for a long duration. More attention should be paid on these persons in clinical practice