Federated Learning with Intermediate Representation Regularization

In contrast to centralized model training that involves data collection, federated learning (FL) enables remote clients to collaboratively train a model without exposing their private data. However, model performance usually degrades in FL due to the heterogeneous data generated by clients of diverse characteristics. One promising strategy to maintain good performance is by limiting the local training from drifting far away from the global model. Previous studies accomplish this by regularizing the distance between the representations learned by the local and global models. However, they only consider representations from the early layers of a model or the layer preceding the output layer. In this study, we introduce FedIntR, which provides a more fine-grained regularization by integrating the representations of intermediate layers into the local training process. Specifically, FedIntR computes a regularization term that encourages the closeness between the intermediate layer representations of the local and global models. Additionally, FedIntR automatically determines the contribution of each layer's representation to the regularization term based on the similarity between local and global representations. We conduct extensive experiments on various datasets to show that FedIntR can achieve equivalent or higher performance compared to the state-of-the-art approaches. Our code is available at https://github.com/YLTun/FedIntR.Comment: IEEE BigComp 202

Functional enhancement of neuronal cell behaviors and differentiation by elastin-mimetic recombinant protein presenting Arg-Gly-Asp peptides

Background: Integrin-mediated interaction of neuronal cells with extracellular matrix (ECM) is important for the control of cell adhesion, morphology, motility, and differentiation in both in vitro and in vivo systems. Arg-Gly-Asp (RGD) sequence is one of the most potent integrin-binding ligand found in many native ECM proteins. An elastin-mimetic recombinant protein, TGPG[VGRGD(VGVPG)6]20WPC, referred to as [RGD-V6]20, contains multiple RGD motifs to bind cell-surface integrins. This study aimed to investigate how surface-adsorbed recombinant protein can be used to modulate the behaviors and differentiation of neuronal cells in vitro. For this purpose, biomimetic ECM surfaces were prepared by isothermal adsorption of [RGD-V6]20 onto the tissue culture polystyrene (TCPS), and the effects of protein-coated surfaces on neuronal cell adhesion, spreading, migration, and differentiation were quantitatively measured using N2a neuroblastoma cells.Results: The [RGD-V6]20 was expressed in E. coli and purified by thermally-induced phase transition. N2a cell attachment to either [RGD-V6]20 or fibronectin followed hyperbolic binding kinetics saturating around 2 μM protein concentration. The apparent maximum cell binding to [RGD-V6]20 was approximately 96% of fibronectin, with half-maximal adhesion on [RGD-V6]20 and fibronectin occurring at a coating concentration of 2.4 × 10-7 and 1.4 × 10-7 M, respectively. The percentage of spreading cells was in the following order of proteins: fibronectin (84.3% ± 6.9%) > [RGD-V6]20 (42.9% ± 6.5%) > [V7]20 (15.5% ± 3.2%) > TCPS (less than 10%). The migration speed of N2a cells on [RGD-V6]20 was similar to that of cells on fibronectin. The expression of neuronal marker proteins Tuj1, MAP2, and GFAP was approximately 1.5-fold up-regulated by [RGD-V6]20 relative to TCPS. Moreover, by the presence of both [RGD-V6]20 and RA, the expression levels of NSE, TuJ1, NF68, MAP2, and GFAP were significantly elevated.Conclusion: We have shown that an elastin-mimetic protein consisting of alternating tropoelastin structural domains and cell-binding RGD motifs is able to stimulate neuronal cell behaviors and differentiation. In particular, adhesion-induced neural differentiation is highly desirable for neural development and nerve repair. In this context, our data emphasize that the combination of biomimetically engineered recombinant protein and isothermal adsorption approach allows for the facile preparation of bioactive matrix or coating for neural tissue regeneration. © 2012 Jeon et al.; licensee BioMed Central Ltd.1

Prevalent Multidrug-resistant Nonvaccine Serotypes in Pneumococcal Carriage of Healthy Korean Children Associated with the Low Coverage of the Seven-valent Pneumococcal Conjugate Vaccine

AbstractObjectivesOur previous longitudinal multicenter-based carriage study showed that the average carriage rate of Streptococcus pneumoniae was 16.8% in 582 healthy children attending kindergarten or elementary school in Seoul, Korea. We assessed serotype-specific prevalence and antimicrobial resistance among colonizing pneumococcal isolates from young children in the era of low use of the seven-valent pneumococcal conjugate vaccine (PCV7).MethodsSerotypes were determined by an agglutination test with specific antisera or by a multiplex polymerase chain reaction (PCR) assay. An antimicrobial susceptibility test was performed with broth microdilution in Korean 96-well panels from Dade-MicroScan (Sacramento, CA, USA).ResultsPneumococcal colonization patterns were dynamic and longterm persistent carriage was rare, which indicated a sequential turnover of pneumococcal strains. Of the 369 pneumococci (except for 23 killed isolates), 129 (34.9%) isolates were PCV7 vaccine serotypes (VTs); 213 (57.8%) isolates were nonvaccine serotypes (NVTs); and the remaining 27 (7.2%) isolates were nontypable (NT). The highest rates of multidrug resistance (MDR) were observed in VTs (86.0%; 111/129 isolates) and NVTs (70.0%; 149/213 isolates).ConclusionThis study overall showed the frequent carriage of VTs and NVTs with MDR in healthy children attending kindergarten or elementary school. Efforts should be directed toward reducing the extensive prescription of antibiotics and using new broader vaccines to reduce the expansion of MDR strains of NVTs in our community

Unusual transport characteristics of nitrogen-doped single-walled carbon nanotubes

Electrical transport characteristics of nitrogen-doped single-walled carbon nanotubes (N-SWCNTs), in which the nitrogen dopant is believed to form a pyridinelike bonding configuration, are studied with the field effect transistor operations. Contrary to the expectation that the nitrogen atoms may induce a n -type doping, the electrical transports through our N-SWCNTs are either ambipolar in vacuum or p -type in air. Through the first-principles electronic structure calculations, we show that the nitrogen dopant indeed favors the pyridinelike configuration and the Fermi level of the pyridinelike N-SWCNT is almost at the intrinsic level.open01

GLAD: Global-Local View Alignment and Background Debiasing for Unsupervised Video Domain Adaptation with Large Domain Gap

In this work, we tackle the challenging problem of unsupervised video domain adaptation (UVDA) for action recognition. We specifically focus on scenarios with a substantial domain gap, in contrast to existing works primarily deal with small domain gaps between labeled source domains and unlabeled target domains. To establish a more realistic setting, we introduce a novel UVDA scenario, denoted as Kinetics->BABEL, with a more considerable domain gap in terms of both temporal dynamics and background shifts. To tackle the temporal shift, i.e., action duration difference between the source and target domains, we propose a global-local view alignment approach. To mitigate the background shift, we propose to learn temporal order sensitive representations by temporal order learning and background invariant representations by background augmentation. We empirically validate that the proposed method shows significant improvement over the existing methods on the Kinetics->BABEL dataset with a large domain gap. The code is available at https://github.com/KHUVLL/GLAD.Comment: This is an accepted WACV 2024 paper. Our code is available at https://github.com/KHUVLL/GLA

Clinical Approach to the Standardization of Oriental Medical Diagnostic Pattern Identification in Stroke Patients

In Korea, many stroke patients receive oriental medical care, in which pattern-identification plays a major role. Pattern-identification is Oriental Medicine's unique diagnostic system. This study attempted to standardize oriental medical pattern-identification for stroke patients. This was a community-based multicenter study that enrolled stroke patients within 30 days after their ictus. We assessed the patients' general characteristics and symptoms related to pattern-identification. Each patient's pattern was determined when two doctors had the same opinion. To determine which variables affect the pattern-identification, binary logistic regression analysis was used with the backward method. A total of 806 stroke patients were enrolled. Among 480 patients who were identified as having a certain pattern, 100 patients exhibited the Fire Heat Pattern, 210 patients the Phlegm Dampness Pattern, nine patients the Blood Stasis Pattern, 110 patients the Qi Deficiency Pattern, and 51 patients the Yin Deficiency Pattern. After the regression analysis, the predictive logistic equations for the Fire Heat, Phlegm Dampness, Qi Deficiency, and Yin Deficiency patterns were determined. The Blood Stasis Pattern was omitted because the sample size was too small. Predictive logistic equations were suggested for four of the patterns. These criteria would be useful in determining each stroke patient's pattern in clinics. However, further studies with large samples are necessary to validate and confirm these criteria

Kalman Filter Sensor Fusion for Mecanum Wheeled Automated Guided Vehicle Localization

The Mecanum automated guided vehicle (AGV), which can move in any direction by using a special wheel structure with a LIMwheel and a diagonally positioned roller, holds considerable promise for the field of industrial electronics. A conventional method for Mecanum AGV localization has certain limitations, such as slip phenomena, because there are variations in the surface of the road and ground friction. Therefore, precise localization is a very important issue for the inevitable slip phenomenon situation. So a sensor fusion technique is developed to cope with this drawback by using the Kalman filter. ENCODER and StarGazer were used for sensor fusion. StarGazer is a position sensor for an image recognition device and always generates some errors due to the limitations of the image recognition device. ENCODER has also errors accumulating over time. On the other hand, there are no moving errors. In this study, we developed a Mecanum AGV prototype system and showed by simulation that we can eliminate the disadvantages of each sensor. We obtained the precise localization of the Mecanum AGV in a slip phenomenon situation via sensor fusion using a Kalman filter

Kalman Filter Sensor Fusion for Mecanum Wheeled Automated Guided Vehicle Localization

The Mecanum automated guided vehicle (AGV), which can move in any direction by using a special wheel structure with a LIM-wheel and a diagonally positioned roller, holds considerable promise for the field of industrial electronics. A conventional method for Mecanum AGV localization has certain limitations, such as slip phenomena, because there are variations in the surface of the road and ground friction. Therefore, precise localization is a very important issue for the inevitable slip phenomenon situation. So a sensor fusion technique is developed to cope with this drawback by using the Kalman filter. ENCODER and StarGazer were used for sensor fusion. StarGazer is a position sensor for an image recognition device and always generates some errors due to the limitations of the image recognition device. ENCODER has also errors accumulating over time. On the other hand, there are no moving errors. In this study, we developed a Mecanum AGV prototype system and showed by simulation that we can eliminate the disadvantages of each sensor. We obtained the precise localization of the Mecanum AGV in a slip phenomenon situation via sensor fusion using a Kalman filter