Squeeze, Recover and Relabel: Dataset Condensation at ImageNet Scale From A New Perspective

We present a new dataset condensation framework termed Squeeze, Recover and Relabel (SRe$^2$L) that decouples the bilevel optimization of model and synthetic data during training, to handle varying scales of datasets, model architectures and image resolutions for effective dataset condensation. The proposed method demonstrates flexibility across diverse dataset scales and exhibits multiple advantages in terms of arbitrary resolutions of synthesized images, low training cost and memory consumption with high-resolution training, and the ability to scale up to arbitrary evaluation network architectures. Extensive experiments are conducted on Tiny-ImageNet and full ImageNet-1K datasets. Under 50 IPC, our approach achieves the highest 42.5% and 60.8% validation accuracy on Tiny-ImageNet and ImageNet-1K, outperforming all previous state-of-the-art methods by margins of 14.5% and 32.9%, respectively. Our approach also outperforms MTT by approximately 52$\times$ (ConvNet-4) and 16$\times$ (ResNet-18) faster in speed with less memory consumption of 11.6$\times$ and 6.4$\times$ during data synthesis. Our code and condensed datasets of 50, 200 IPC with 4K recovery budget are available at https://zeyuanyin.github.io/projects/SRe2L/.Comment: Technical repor

A mini-review: recent advancements in temporal interference stimulation in modulating brain function and behavior

Numerous studies have assessed the effect of Temporal Interference (TI) on human performance. However, a comprehensive literature review has not yet been conducted. Therefore, this review aimed to search PubMed and Web of Science databases for TI-related literature and analyze the findings. We analyzed studies involving preclinical, human, and computer simulations, and then discussed the mechanism and safety of TI. Finally, we identified the gaps and outlined potential future directions. We believe that TI is a promising technology for the treatment of neurological movement disorders, due to its superior focality, steerability, and tolerability compared to traditional electrical stimulation. However, human experiments have yielded fewer and inconsistent results, thus animal and simulation experiments are still required to perfect stimulation protocols for human trials

Identification of Protein Pupylation Sites Using Bi-Profile Bayes Feature Extraction and Ensemble Learning

Pupylation, one of the most important posttranslational modifications of proteins, typically takes place when prokaryotic ubiquitin-like protein (Pup) is attached to specific lysine residues on a target protein. Identification of pupylation substrates and their corresponding sites will facilitate the understanding of the molecular mechanism of pupylation. Comparing with the labor-intensive and time-consuming experiment approaches, computational prediction of pupylation sites is much desirable for their convenience and fast speed. In this study, a new bioinformatics tool named EnsemblePup was developed that used an ensemble of support vector machine classifiers to predict pupylation sites. The highlight of EnsemblePup was to utilize the Bi-profile Bayes feature extraction as the encoding scheme. The performance of EnsemblePup was measured with a sensitivity of 79.49%, a specificity of 82.35%, an accuracy of 85.43%, and a Matthews correlation coefficient of 0.617 using the 5-fold cross validation on the training dataset. When compared with other existing methods on a benchmark dataset, the EnsemblePup provided better predictive performance, with a sensitivity of 80.00%, a specificity of 83.33%, an accuracy of 82.00%, and a Matthews correlation coefficient of 0.629. The experimental results suggested that EnsemblePup presented here might be useful to identify and annotate potential pupylation sites in proteins of interest. A web server for predicting pupylation sites was developed

Recent progress in anodic oxidation of TiO2 nanotubes and enhanced photocatalytic performance: a short review

© 2021 World Scientific Publishing Company. This is the accepted version of the final published version found at https://doi.org/10.1142/S1793292021300024By adjusting the oxidation voltage, electrolyte, anodizing time and other parameters, TiO2 nanotubes with high aspect ratio can be prepared by oxidation in organic system because anodic oxidation method has the advantage of simple preparation process, low material cost and controllable morphology. Low material cost and controllable morphology by anodizing. This review focuses on the influence of anodizing parameters on the morphology of TiO2 nanotube arrays prepared by anodizing. In order to improve the photocatalytic activity of TiO2 nanotubes under visible light and prolong the life of photo-generated carriers, the research status of improving the photocatalytic activity of TiO2 nanotubes in recent years is reviewed. This review focuses on the preparation and modification of TiO2 nanotubes by anodic oxidation, which is helpful to understand the best structure of TiO2 nanotubes and the appropriate modification methods, thus guiding the application of TiO2 nanotubes in practical photocatalysis. Finally, the development of TiO2 nanotubes is prospected.Peer reviewe

HS DIC-system application for strain and displacement measurements under static-dynamic coupling loading

To study the deformation and fracture of sandstone under static-dynamic coupled load, a cylindrical specimen under pre-static axial and confining pressure was dynamically loaded using an improved split Hopkinson pressure bar (SHPB). Through the application of a special shape striker, stress equilibrium and nearly constant strain rate in specimen were achieved. During dynamic tests, the failure process of the specimen was completely monitored (7 frames at a time resolution of 25 s) by a high speed (HS) camera. Furthermore, the recorded images were matched with the loading steps through a specified trigger mode, based on which both full-field displacement values and the corresponding surface in-plane strain were obtained via digital image correlation (DIC) system. Finally, analysis on the surface deformation and failure mode of specimen shows that the sample presents an interaction of tension-shear failure and expansion failure under the axial static pressure of 72 MPa, which reflects the effect of axial static pressure on the dynamic fracture mode of the sample surface

Primary localized histoplasmosis with lesions restricted to the mouth in a Chinese HIV-negative patient

SummaryHistoplasmosis is a deep mycosis caused by Histoplasma capsulatum, which is endemic in many areas of the world but is relatively rare in China. Although the majority of cases present as a mild to moderate flu-like disease requiring only supportive therapy, approximately 1% of patients experience more serious pulmonary and extrapulmonary disease, which can be life-threatening if diagnosis is delayed or the treatment is not initiated rapidly. Definitive diagnosis is usually made by a combination of culture, detection of the organism in tissues, measurement of antibodies, and detection of antigen. We present the case of a 51-year-old patient who presented with histoplasmosis only, with several ulcerated lesions in the oral cavity and without HIV infection, who did not show any detectable signs and symptoms of systemic disease or extra-oral manifestations. Histopathological analysis indicated a chronic inflammatory process with granulomas with yeast-like organisms. Isolation of H. capsulatum and molecular identification provided the definitive diagnosis. Treatment with oral itraconazole led to remission of the oral lesions. This is the first Chinese case report of localized histoplasmosis with lesions restricted to the mouth in an HIV-negative patient

Unsupervised Video Domain Adaptation for Action Recognition: A Disentanglement Perspective

Unsupervised video domain adaptation is a practical yet challenging task. In this work, for the first time, we tackle it from a disentanglement view. Our key idea is to handle the spatial and temporal domain divergence separately through disentanglement. Specifically, we consider the generation of cross-domain videos from two sets of latent factors, one encoding the static information and another encoding the dynamic information. A Transfer Sequential VAE (TranSVAE) framework is then developed to model such generation. To better serve for adaptation, we propose several objectives to constrain the latent factors. With these constraints, the spatial divergence can be readily removed by disentangling the static domain-specific information out, and the temporal divergence is further reduced from both frame- and video-levels through adversarial learning. Extensive experiments on the UCF-HMDB, Jester, and Epic-Kitchens datasets verify the effectiveness and superiority of TranSVAE compared with several state-of-the-art methods. The code with reproducible results is publicly accessible.Comment: 18 pages, 9 figures, 7 tables. Code at https://github.com/ldkong1205/TranSVA