Achieve Significant Throughput Gains in Wireless Networks with Large Delay-Bandwidth Product

AbstractTraditionally, Bandwidth-Delay Product can be used to measure the capacity of network “pipe” between two nodes. However, in multi-hop wireless networks, Bandwidth-Delay Product cannot reveal the network condition accurately. In this paper, we define a new metric called Delay-Bandwidth Product (DBP) for wireless networks, which measures the capacity of a one-hop pipe in wireless networks. Wireless networks with a large DBP can have a throughput larger than the one based on traditional understanding. We propose a scheduling algorithm aims for making use of the large DBP in wireless networks. We then design simulations to figure out how much throughput gains can be achieved in wireless networks, with small DBPs and large DBPs respectively. The simulation result demonstrates that we can achieve significant throughput gains in wireless networks with large DBP

Iterative Soft Shrinkage Learning for Efficient Image Super-Resolution

Image super-resolution (SR) has witnessed extensive neural network designs from CNN to transformer architectures. However, prevailing SR models suffer from prohibitive memory footprint and intensive computations, which limits further deployment on edge devices. This work investigates the potential of network pruning for super-resolution to take advantage of off-the-shelf network designs and reduce the underlying computational overhead. Two main challenges remain in applying pruning methods for SR. First, the widely-used filter pruning technique reflects limited granularity and restricted adaptability to diverse network structures. Second, existing pruning methods generally operate upon a pre-trained network for the sparse structure determination, hard to get rid of dense model training in the traditional SR paradigm. To address these challenges, we adopt unstructured pruning with sparse models directly trained from scratch. Specifically, we propose a novel Iterative Soft Shrinkage-Percentage (ISS-P) method by optimizing the sparse structure of a randomly initialized network at each iteration and tweaking unimportant weights with a small amount proportional to the magnitude scale on-the-fly. We observe that the proposed ISS-P can dynamically learn sparse structures adapting to the optimization process and preserve the sparse model's trainability by yielding a more regularized gradient throughput. Experiments on benchmark datasets demonstrate the effectiveness of the proposed ISS-P over diverse network architectures. Code is available at https://github.com/Jiamian-Wang/Iterative-Soft-Shrinkage-SRComment: Accepted by ICCV 2023, code released at https://github.com/Jiamian-Wang/Iterative-Soft-Shrinkage-S

S^2-Transformer for Mask-Aware Hyperspectral Image Reconstruction

The technology of hyperspectral imaging (HSI) records the visual information upon long-range-distributed spectral wavelengths. A representative hyperspectral image acquisition procedure conducts a 3D-to-2D encoding by the coded aperture snapshot spectral imager (CASSI) and requires a software decoder for the 3D signal reconstruction. By observing this physical encoding procedure, two major challenges stand in the way of a high-fidelity reconstruction. (i) To obtain 2D measurements, CASSI dislocates multiple channels by disperser-titling and squeezes them onto the same spatial region, yielding an entangled data loss. (ii) The physical coded aperture leads to a masked data loss by selectively blocking the pixel-wise light exposure. To tackle these challenges, we propose a spatial-spectral (S^2-) Transformer network with a mask-aware learning strategy. First, we simultaneously leverage spatial and spectral attention modeling to disentangle the blended information in the 2D measurement along both two dimensions. A series of Transformer structures are systematically designed to fully investigate the spatial and spectral informative properties of the hyperspectral data. Second, the masked pixels will induce higher prediction difficulty and should be treated differently from unmasked ones. Thereby, we adaptively prioritize the loss penalty attributing to the mask structure by inferring the pixel-wise reconstruction difficulty upon the mask-encoded prediction. We theoretically discusses the distinct convergence tendencies between masked/unmasked regions of the proposed learning strategy. Extensive experiments demonstrates that the proposed method achieves superior reconstruction performance. Additionally, we empirically elaborate the behaviour of spatial and spectral attentions under the proposed architecture, and comprehensively examine the impact of the mask-aware learning.Comment: 11 pages, 16 figures, 6 tables, Code: https://github.com/Jiamian-Wang/S2-transformer-HS

Cooperative Hardware-Prompt Learning for Snapshot Compressive Imaging

Snapshot compressive imaging emerges as a promising technology for acquiring real-world hyperspectral signals. It uses an optical encoder and compressively produces the 2D measurement, followed by which the 3D hyperspectral data can be retrieved via training a deep reconstruction network. Existing reconstruction models are trained with a single hardware instance, whose performance is vulnerable to hardware perturbation or replacement, demonstrating an overfitting issue to the physical configuration. This defect limits the deployment of pre-trained models since they would suffer from large performance degradation when are assembled to unseen hardware. To better facilitate the reconstruction model with new hardware, previous efforts resort to centralized training by collecting multi-hardware and data, which is impractical when dealing with proprietary assets among institutions. In light of this, federated learning (FL) has become a feasible solution to enable cross-hardware cooperation without breaking privacy. However, the naive FedAvg is subject to client drift upon data heterogeneity owning to the hardware inconsistency. In this work, we tackle this challenge by marrying prompt tuning with FL to snapshot compressive imaging for the first time and propose an federated hardware-prompt learning (FedHP) method. Rather than mitigating the client drift by rectifying the gradients, which only takes effect on the learning manifold but fails to touch the heterogeneity rooted in the input data space, the proposed FedHP globally learns a hardware-conditioned prompter to align the data distribution, which serves as an indicator of the data inconsistency stemming from different pre-defined coded apertures. Extensive experiments demonstrate that the proposed method well coordinates the pre-trained model to indeterminate hardware configurations.Comment: 11 figures, 4 table

Electrospraying: Possibilities and Challenges of Engineering Carriers for Biomedical Applications—A Mini Review

Electrospraying, a liquid atomization-based technique, has been used to produce and formulate micro/nanoparticular cargo carriers for various biomedical applications, including drug delivery, biomedical imaging, implant coatings, and tissue engineering. In this mini review, we begin with the main features of electrospraying methods to engineer carriers with various bioactive cargos, including genes, growth factors, and enzymes. In particular, this review focuses on the improvement of traditional electrospraying technology for the fabrication of carriers for living cells and providing a suitable condition for gene transformation. Subsequently, the major applications of the electrosprayed carriers in the biomedical field are highlighted. Finally, we finish with conclusions and future perspectives of electrospraying for high efficiency and safe production

The ectonucleotidases CD39 and CD73 on T cells: The new pillar of hematological malignancy

Hematological malignancy develops and applies various mechanisms to induce immune escape, in part through an immunosuppressive microenvironment. Adenosine is an immunosuppressive metabolite produced at high levels within the tumor microenvironment (TME). Adenosine signaling through the A2A receptor expressed on immune cells, such as T cells, potently dampens immune responses. Extracellular adenosine generated by ectonucleoside triphosphate diphosphohydrolase-1 (CD39) and ecto-5’-nucleotidase (CD73) molecules is a newly recognized ‘immune checkpoint mediator’ and leads to the identification of immunosuppressive adenosine as an essential regulator in hematological malignancies. In this Review, we provide an overview of the detailed distribution and function of CD39 and CD73 ectoenzymes in the TME and the effects of CD39 and CD73 inhibition on preclinical hematological malignancy data, which provides insights into the potential clinical applications for immunotherapy

The clinical predictive value of geriatric nutritional risk index in elderly rectal cancer patients received surgical treatment after neoadjuvant therapy

ObjectiveThe assessment of nutritional status has been recognized as crucial in the treatment of geriatric cancer patients. The objective of this study is to determine the clinical predictive value of the geriatric nutritional risk index (GNRI) in predicting the short-term and long-term prognosis of elderly rectal cancer (RC) patients who undergo surgical treatment after neoadjuvant therapy.MethodsBetween January 2014 and December 2020, the clinical materials of 639 RC patients aged ≥70 years who underwent surgical treatment after neoadjuvant therapy were retrospectively analysed. Propensity score matching was performed to adjust for baseline potential confounders. Logistic regression analysis and competing risk analysis were conducted to evaluate the correlation between the GNRI and the risk of postoperative major complications and cumulative incidence of cancer-specific survival (CSS). Nomograms were then constructed for postoperative major complications and CSS. Additionally, 203 elderly RC patients were enrolled between January 2021 and December 2022 as an external validation cohort.ResultsMultivariate logistic regression analysis showed that GNRI [odds ratio = 1.903, 95% confidence intervals (CI): 1.120–3.233, p = 0.017] was an independent risk factor for postoperative major complications. In competing risk analysis, the GNRI was also identified as an independent prognostic factor for CSS (subdistribution hazard ratio = 3.90, 95% CI: 2.46–6.19, p < 0.001). The postoperative major complication nomogram showed excellent performance internally and externally in the area under the receiver operating characteristic curve (AUC), calibration plots and decision curve analysis (DCA). When compared with other models, the competing risk prognosis nomogram incorporating the GNRI achieved the highest outcomes in terms of the C-index, AUC, calibration plots, and DCA.ConclusionThe GNRI is a simple and effective tool for predicting the risk of postoperative major complications and the long-term prognosis of elderly RC patients who undergo surgical treatment after neoadjuvant therapy

Fluorescent trimethyl-substituted naphthyridine as a label-free signal reporter for one-step and highly sensitive fluorescent detection of DNA in serum samples

A facile label-free sensing method is developed for the one-step and highly sensitive fluorescent detection of DNA, which couples the specific C-C mismatch bonding and fluorescent quenching property of a trimethyl-substituted naphthyridine dye (ATMND) with the exonuclease III (Exo III) assisted cascade target recycling amplification strategy. In the absence of target DNA, the DNA hairpin probe with a C-C mismatch in the stem and more than 4 bases overhung at the 3' terminus could entrap and quench the fluorescence of ATMND and resist the digestion of Exo III, thus showing a low fluorescence background. In the presence of the target, however, the hybridization event between the two protruding segments and the target triggers the digestion reaction of Exo III, recycles the initial target, and simultaneously releases both the secondary target analogue and the ATMND caged in the stem. The released initial and secondary targets take part in another cycle of digestion, thus leading to the release of a huge amount of free ATMND for signal transducing. Based on the fluorescence recovery, the as-proposed label-free fluorescent sensing strategy shows very good analytical performances towards DNA detection, such as a wide linear range from 10 pM to 1 mu M, a low limit of detection of 6 pM, good selectivity, and a facile one-step operation at room temperature. Practical sample analysis in serum samples indicates the method has good precision and accuracy, which may thus have application potentials for point-of-care screening of DNA in complex clinical and environmental samples

Evaluating the efficiency of primary health care institutions in China: an improved three-stage data envelopment analysis approach

Abstract Background Primary health care (PHC) institutions are key to realizing the main functions of the health care system. Since the new health care reform in 2009, the Chinese government has invested heavily in PHC institutions and launched favorable initiatives to improve the efficiency of such institutions. This study is designed to gauge the efficiency of PHC institutions by using 2012–2020 panel data covering 31 provinces in China. Methods This study applied an improved three-stage data envelopment analysis (DEA) model to evaluate the efficiency of PHC institutions in China. Unlike the traditional three-stage DEA model, the input-oriented global super-efficiency slack-based measurement (SBM) DEA model is used to calculate the efficiency in the first and third stages of the improved three-stage DEA model, which not only allows the effects of environmental factors and random noise to be taken into account but also deal with the problem of slack, super-efficiency and the comparability of interperiod efficiency values throughout the efficiency measurement. Results The results show that the efficiency of PHC institutions has been overestimated due to the impact of external environmental factors and random noise. From 2012 to 2020, the efficiency of PHC institutions displayed a downward trend. Moreover, there are significant differences in the efficiency of PHC institutions between regions, with the lowest efficiency being found in the northeast region. The efficiency of PHC institutions is significantly affected by residents’ annual average income, per capita GDP, population density, the percentage of the population aged 0–14, the percentage of the population aged 65 and older, the number of people with a college education and above per 100,000 residents, and the proportion of the urban population. Conclusions Substantial investment in PHC institutions has not led to the expected efficiency gains. Therefore, more effective measures should be taken to improve the efficiency of PHC institutions in China based on local conditions. This study provides a new analytical approach to calculating the efficiency of PHC institutions, and this approach can be applied to efficiency evaluation either in other fields or in other countries