Downscaled Representation Matters: Improving Image Rescaling with Collaborative Downscaled Images

Deep networks have achieved great success in image rescaling (IR) task that seeks to learn the optimal downscaled representations, i.e., low-resolution (LR) images, to reconstruct the original high-resolution (HR) images. Compared with super-resolution methods that consider a fixed downscaling scheme, e.g., bicubic, IR often achieves significantly better reconstruction performance thanks to the learned downscaled representations. This highlights the importance of a good downscaled representation in image reconstruction tasks. Existing IR methods mainly learn the downscaled representation by jointly optimizing the downscaling and upscaling models. Unlike them, we seek to improve the downscaled representation through a different and more direct way: optimizing the downscaled image itself instead of the down-/upscaling models. Specifically, we propose a collaborative downscaling scheme that directly generates the collaborative LR examples by descending the gradient w.r.t. the reconstruction loss on them to benefit the IR process. Furthermore, since LR images are downscaled from the corresponding HR images, one can also improve the downscaled representation if we have a better representation in the HR domain. Inspired by this, we propose a Hierarchical Collaborative Downscaling (HCD) method that performs gradient descent in both HR and LR domains to improve the downscaled representations. Extensive experiments show that our HCD significantly improves the reconstruction performance both quantitatively and qualitatively. Moreover, we also highlight the flexibility of our HCD since it can generalize well across diverse IR models.Comment: 11 pages, 8 figure

A latent profile analysis of sleep disturbance in relation to mental health among college students in China

AimsThis study aimed to examine the subtype classification characteristics of sleep disturbance (SD) in college students and their associations with sample characteristic factors and mental health outcomes.MethodsThe sample comprised 4,302 college students (Mean age = 19.92 ± 1.42 years, 58.6% females). The Youth Self-Rating Insomnia Scale, Beck Depression Inventory, 8-item Positive Subscale of the Community Assessment of Psychic Experiences, and 10-item Connor-Davidson Resilience Scale were used to assess adolescents’ sleep disturbance, depressive symptoms, psychotic-like experiences (PLEs), and resilience. Latent profile analysis, logistic regression, and liner regression analysis were used to analyze the data.ResultsThree subtypes of SD in college students were identified: the high SD profile (10.6%), the mild SD profile (37.5%), and the no SD profile (51.9%). Compared with college students in the “no SD” profile, risk factors for “high SD” include being male and poor parental marital status. Sophomores were found to predict the “high SD” profile or “mild SD” profile relative to the “no SD” profile. College students in the “mild SD” profile or “high SD” profile were more likely to have a higher level of depressive symptoms and PLEs, while a lower level of resilience.ConclusionThe findings highlighted that target intervention is urgently needed for male college students, sophomores, and those with poor parental marital status in the “mild SD” profile or “high SD” profile

Highly Efficient Synthesis of Neat Graphene Nanoscrolls from Graphene Oxide by Well-Controlled Lyophilization

Graphene nanoscroll (GNS) is an important one-dimensional tubular form of graphitic carbon with characteristic open topology. It has been predicted to possess extraordinary properties that are significantly different from the analogical multiwalled carbon nanotubes. However, comprehensive experimental investigations on its properties and applications are still hindered by the lack of its reliable synthesis in high yield. To efficiently transform the scalable graphene oxide sheets into GNSs, here, we proposed a well-controlled lyophilization that comprises four sequential steps: chemical reduction of giant GO, freezing isolation of reduced graphene sheets, freeze-drying, and thermal annealing. The combined method has an extremely high efficiency, up to the record 92%. Systemic control experiments and cryo-SEM inspections revealed that the topological transformation from 2D sheet to 1D scroll is the sublimation-induced scrolling of individually confined graphene sheets in ice, which was controlled by chemical reduction, feed concentration, and freezing rate. GNSs exhibited high structural integration and were solution-processed into macroscopic forms. We also revealed the spontaneous swelling behavior of GNS in a reversible manner for the first time, verifying the featured open topology of GNS. Through this combined protocol, GNS can be scalably synthesized from massive graphene oxide with high efficiency, which should promote comprehensive research and massive applications in the real world

Polyelectrolyte-Stabilized Graphene Oxide Liquid Crystals against Salt, pH, and Serum

Stabilization of colloids is of great significance in nanoscience for their fundamental research and practical applications. Electrostatic repulsion-stabilized anisotropic colloids, such as graphene oxide (GO), can form stable liquid crystals (LCs). However, the electrostatic field would be screened by ions. To stabilize colloidal LCs against electrolyte is an unsolved challenge. Here, an effective strategy is proposed to stabilize GO LCs under harsh conditions by association of polyelectrolytes onto GO sheets. Using sodium poly­(styrene sulfonate) (PSS) and poly­[2-(methacryloyloxy)­ethyl]­dimethyl-(3-sulfopropyl)­ammonium hydroxide (PMEDSAH), a kind of polyzwitterion, GO LCs were well-maintained in the presence of NaCl (from 0 M to saturated), extreme pH (from 1 to 13), and serum. Moreover, PSS- or PMEDSAH-coated chemically reduced GO (rGO) also showed stability against electrolyte

Oxide Film Efficiently Suppresses Dendrite Growth in Aluminum-Ion Battery

Aluminum metal foil is the optimal choice as an anode material for aluminum-ion batteries for its key advantages such as high theoretical capacity, safety, and low cost. However, the metallic nature of aluminum foil is very likely to induce severe dendrite growth with further electrode disintegration and cell failure, which is inconsistent with previous reports. Here, we discover that it is aluminum oxide film that efficiently restricts the growth of crystalline Al dendrite and thus improves the cycling stability of Al anode. The key role of surficial aluminum oxide film in protecting Al metal anode lies in decreasing the nucleation sites, controlling the metallic dendrite growth, and preventing the electrode disintegration. The defect sites in aluminum oxide film provide channels for electrolyte infiltration and further stripping/depositing. Attributed to such a protective aluminum oxide film, the Al–graphene full cells can attain up to 45 000 stable cycles

Oxide Film Efficiently Suppresses Dendrite Growth in Aluminum-Ion Battery

Aluminum metal foil is the optimal choice as an anode material for aluminum-ion batteries for its key advantages such as high theoretical capacity, safety, and low cost. However, the metallic nature of aluminum foil is very likely to induce severe dendrite growth with further electrode disintegration and cell failure, which is inconsistent with previous reports. Here, we discover that it is aluminum oxide film that efficiently restricts the growth of crystalline Al dendrite and thus improves the cycling stability of Al anode. The key role of surficial aluminum oxide film in protecting Al metal anode lies in decreasing the nucleation sites, controlling the metallic dendrite growth, and preventing the electrode disintegration. The defect sites in aluminum oxide film provide channels for electrolyte infiltration and further stripping/depositing. Attributed to such a protective aluminum oxide film, the Al–graphene full cells can attain up to 45 000 stable cycles