Explore the vast range of titles available.

Domain Generalization (DG) seeks to transfer knowledge from multiple source domains to unseen target domains, even in the presence of domain shifts. Achieving effective generalization typically requires a large and diverse set of labeled source data to learn robust representations that can generalize to new, unseen domains. However, obtaining such high-quality labeled data is often costly and labor-intensive, limiting the practical applicability of DG. To address this, we investigate a more practical and challenging problem: semi-supervised domain generalization (SSDG) under a label-efficient paradigm. In this paper, we propose a novel method, CAT , which leverages semi-supervised learning with limited labeled data to achieve competitive generalization performance under domain shifts. Our method addresses key limitations of previous approaches, such as reliance on fixed thresholds and sensitivity to noisy pseudo-labels. CAT combines adaptive thresholding with noisy label refinement techniques, creating a straightforward yet highly effective solution for SSDG tasks. Specifically, our approach uses flexible thresholding to generate high-quality pseudo-labels with higher class diversity while refining noisy pseudo-labels to improve their reliability. Extensive experiments on multiple benchmark datasets demonstrate the superior performance of our method, with improvements of 3.45% on PACS , 9.47% on OfficeHome , and 10.90% on miniDomainNet datasets, highlighting its effectiveness in achieving robust generalization under domain shifts.

Microchips are widely used to separate circulating tumor cells (CTCs) from whole blood by virtues of sophisticated manipulation for microparticles. Here, we present a chip with an 8 μm high and 27.9 mm wide slit to capture cancer cells bound to 3 μm beads. Apart from a higher purity and recovery rate, the slit design allows for simplified fabrication, easy cell imaging, less clogging, lower chamber pressure and, therefore, higher throughput. The beads were conjugated with anti-epithelial cell adhesion molecules (anti-EpCAM) to selectively bind to breast cancer cells (MCF-7) used to spike the whole blood. The diameter of the cell-bead construct was in average 23.1 μm, making them separable from other cells in the blood. As a result, the cancer cells were separated from 5 mL of whole blood with a purity of 52.0% and a recovery rate of 91.1%, and also we confirmed that the device can be applicable to clinical samples of human breast cancer patients. The simple design with microslit, by eliminating any high-aspect ratio features, is expected to reduce possible defects on the chip and, therefore, more suitable for mass production without false separation outputs.

This paper presents G-DCF, a MAC protocol for wireless LANs that can improve system spectral efficiency of wireless LANs by allowing more concurrent transmissions. The 802.11 DCF creates exposed terminals which are nodes that can transmit successfully but are blocked by carrier sensing. More potential exposed terminals are created when APs are densely placed, limiting spatial reuse of channels and thus system throughput. In order to allow concurrent transmissions from exposed terminals, G-DCF establishes groups in the network. Members of a group are nodes located within the carrier sense range of each other but can transmit packets concurrently. Whenever one member of a group wins the channel and transmits its packet, other nodes in the group also start transmission, triggered by the group ID included in the preamble. Contention window is adjusted according to the group size for fair share of the channel. Performance evaluations show that G-DCF can significantly improve system throughput and fairness over 802.11 DCF, especially when the APs are densely deployed.

Industrial vehicles have no vehicle-induced wind in operation mode, so they require more than one fan to secure enough air. Thus, a separate-type HVAC system with two fans is proposed in this study. Thanks to the two fans installed on both sides, this system can provide enough air, but it causes temperature imbalance. To deal with this issue, we performed a numerical study on the mixing performance of the several separate-type HVAC systems using CFD analysis. Experiments were also carried out to validate the numerical model. The results of the temperature and mass flow rate data matched well with the CFD results. Based on this validated model, a case study was done on three different HVAC models that have different configurations in the mixing zone. The results and discussion were done on the mixing performance and the characteristics of the velocity fields and pressure losses.

In the era of modern high performance computing, GPUs have been considered an excellent accelerator for general purpose data-intensive parallel applications. To achieve application speedup from GPUs, many of performance-oriented optimization techniques have been proposed. However, in order to satisfy the recent trend of power and energy consumptions, power/energy-aware optimization of GPUs needs to be investigated with detailed analysis in addition to the performance-oriented optimization. In this work, in order to explore the impact of various optimization strategies on GPU performance, power and energy consumptions, we evaluate performance and power/energy consumption of a well-known application running on different commercial GPU devices with the different optimization strategies. In particular, in order to see the more generalized performance and power consumption patterns of GPU based accelerations, our evaluations are performed with three different Nvdia GPU generations (Fermi, Kepler and Maxwell architectures), various core clock frequencies and memory clock frequencies. We analyze how a GPU kernel execution is affected by optimization and what GPU architectural factors have much impact on its performance and power/energy consumption. This paper also categorizes which optimization technique primarily improves which metric (i.e., performance, power or energy efficiency). Furthermore, voltage frequency scaling (VFS) is also applied to examine the effect of changing a clock frequency on these metrics. In general, our work shows that effective GPU optimization strategies can improve the application performance significantly without increasing power and energy consumption.

Deep neural networks (DNNs) have proven explicit contributions in making autonomous driving cars and related tasks such as semantic segmentation, motion tracking, object detection, sensor fusion, and planning. However, in challenging situations, DNNs are not generalizable because of the inherent domain shift due to the nature of training under the i.i.d. assumption. The goal of semantic segmentation is to preserve information from a given image into multiple meaningful categories for visual understanding. Particularly for semantic segmentation, pixel-wise annotation is extremely costly and not always feasible. Domain generalization for semantic segmentation aims to learn pixel-level semantic labels from multiple source domains and generalize to predict pixel-level semantic labels on multiple unseen target domains. In this survey, for the first time, we present a comprehensive review of DG for semantic segmentation. we present a comprehensive summary of recent works related to domain generalization in semantic segmentation, which establishes the importance of generalizing to new environments of segmentation models. Although domain adaptation has gained more attention in segmentation tasks than domain generalization, it is still worth unveiling new trends that are adopted from domain generalization methods in semantic segmentation. We cover most of the recent and dominant DG methods in the context of semantic segmentation and also provide some other related applications. We conclude this survey by highlighting the future directions in this area.

Abstract Study Objectives Polysomnography (PSG) scoring is labor-intensive, subjective, and often ambiguous. Recently several deep learning (DL) models for automated sleep scoring have been developed, they are tied to a fixed amount of input channels and resolution. In this study, we constructed a standardized image-based PSG dataset in order to overcome the heterogeneity of raw signal data obtained from various PSG devices and various sleep laboratory environments. Methods All individually exported European data format files containing raw signals were converted into images with an annotation file, which contained the demographics, diagnoses, and sleep statistics. An image-based DL model for automatic sleep staging was developed, compared with a signal-based model, and validated in an external dataset Results We constructed 10253 image-based PSG datasets using a standardized format. Among these, 7745 diagnostic PSG data were used to develop our DL model. The DL model using the image dataset showed similar performance to the signal-based dataset for the same subject. The overall DL accuracy was greater than 80%, even with severe obstructive sleep apnea. Moreover, for the first time, we showed explainable DL in the field of sleep medicine as visualized key inference regions using Eigen-class activation maps. Furthermore, when a DL model for sleep scoring performs external validation, we achieved a relatively good performance. Conclusions Our main contribution demonstrates the availability of a standardized image-based dataset, and highlights that changing the data sampling rate or number of sensors may not require retraining, although performance decreases slightly as the number of sensors decreases. Graphical Abstract

As semiconductor technologies have been shrinking, the speed of circuits, integration density, and the number of I/O interfaces have been significantly increasing. As a consequence, electromagnetic emanation (EME) becomes a critical issue in digital system designs. Electronic devices must meet electromagnetic compatibility (EMC) requirements to ensure that they operate properly, and safely without interference. I/O buffers consume high currents when they operate. The bonding wires, and lead frames are long enough to play as efficient antennas to radiate electromagnetic interference (EMI). Therefore, I/O switching activities significantly contribute to the EMI. In this paper, we evaluate and analyze the impact of I/O switching activities on the EME. We will change the circuit configurations such as the supply voltage for I/O banks, their switching frequency, driving current, and slew rate. Additionally, a trade-off between the switching frequencies and the number of simultaneous switching outputs (SSOs) is also considered in terms of EME. Moreover, we evaluate the electromagnetic emissions that are associated with the different I/O switching patterns. The results show that the electromagnetic emissions associated I/O switching activities depend strongly on their operating parameters and configurations. All the circuit implementations and measurements are carried out on a Xilinx Spartan-3 FPGA.

Timing error resilience (TER) is one of the most promising approaches for eliminating design margins that are required due to process, voltage, and temperature (PVT) variations. However, traditional TER circuits have been designed typically on an application-specific integrated circuits (ASIC) where customized circuits and metastability detector designs at a transistor level are possible. On the other hand, it is difficult to implement those designs on a field-programmable gate array (FPGA) due to its predefined LUT structure and irregular wiring. In this paper, we propose an error detection and correction flip-flop (EDACFF) on an FPGA chip, where the metastability issue can be resolved by imposing proper timing constraints on the circuit structures. The proposed EDACFF exploits a transition detector for detecting a timing error along with a data correction latch for correcting the error with one-cycle performance penalty. Our proposed EDACFF is implemented in a 3-bit counter circuit employing a 5-stage pipeline on a Spartan-6 FPGA device (the XFC6SLX45) to verify the functional and timing behavior. The measurement results show that the proposed design obtains 32% less power consumption and 42% higher performance compared to a traditional worst-case design.

Shiunko ointment is composed of five ingredients including Lithospermi Radix (LR), Angelicae Gigantis Radix, sesame seed oil, beeswax, and swine oil. It is externally applied as a treatment for a wide range of skin conditions such as eczema, psoriasis, hair loss, burns, topical wounds, and atopic dermatitis. Deoxyshikonin is the major angiogenic compound extracted from LR. In this study, we investigated the efficacy of LR extract and deoxyshikonin on impaired wound healing in streptozotocin (STZ)-induced diabetic mice. Treatment with LR extract elevated tube formation in human umbilical vein endothelial cells (HUVECs) and exerted antioxidant activity. An open skin wound was produced on the backs of diabetic mice and was then topically treated with deoxyshikonin or vehicle. In addition, deoxyshikonin promoted tube formation in high glucose conditions exposed to HUVECs, and which may be regulated by increased VEGFR2 expression and phosphorylation of Akt and p38. Our results demonstrate that deoxyshikonin application promoted wound repair in STZ-induced diabetic mice. Collectively, these data suggest that deoxyshikonin is an active ingredient of LR, thereby contributing to wound healing in patients with diabetes.