AdaEvo: Edge-Assisted Continuous and Timely DNN Model Evolution for Mobile Devices

Mobile video applications today have attracted significant attention. Deep learning model (e.g. deep neural network, DNN) compression is widely used to enable on-device inference for facilitating robust and private mobile video applications. The compressed DNN, however, is vulnerable to the agnostic data drift of the live video captured from the dynamically changing mobile scenarios. To combat the data drift, mobile ends rely on edge servers to continuously evolve and re-compress the DNN with freshly collected data. We design a framework, AdaEvo, that efficiently supports the resource-limited edge server handling mobile DNN evolution tasks from multiple mobile ends. The key goal of AdaEvo is to maximize the average quality of experience (QoE), e.g. the proportion of high-quality DNN service time to the entire life cycle, for all mobile ends. Specifically, it estimates the DNN accuracy drops at the mobile end without labels and performs a dedicated video frame sampling strategy to control the size of retraining data. In addition, it balances the limited computing and memory resources on the edge server and the competition between asynchronous tasks initiated by different mobile users. With an extensive evaluation of real-world videos from mobile scenarios and across four diverse mobile tasks, experimental results show that AdaEvo enables up to 34% accuracy improvement and 32% average QoE improvement.Comment: Accepted by IEEE Transactions on Mobile Computing 202

Ataxia-telangiectasia in China: a case report of a novel ATM variant and literature review

BackgroundAtaxia-telangiectasia (A-T) is a multisystem genetic disorder involving ataxia, oculocutaneous telangiectasia, and immunodeficiency caused by biallelic pathogenic variants in the ATM gene. To date, most ATM variants have been reported in the Caucasian population, and few studies have focused on the genotype–phenotype correlation of A-T in the Chinese population. We herein present a Chinese patient with A-T who carries compound heterozygous variants in the ATM gene and conducted a literature review for A-T in China.Case presentationA 7-year-old Chinese girl presented with growth retardation, ataxia, medium ocular telangiectasia, cerebellar atrophy, and elevated serum alpha-fetoprotein (AFP) level, which supported the suspicion of A-T. Notably, the serum levels of immunoglobulins were all normal, ruling out immunodeficiency. Exome sequencing and Sanger sequencing revealed two likely pathogenic ATM variants, namely NM_000051.4: c.4195dup (p.Thr1399Asnfs*15) and c.6006 + 1G>T (p.?), which were inherited from her father and mother, respectively. From the Chinese literature review, we found that there was a marked delay in the diagnosis of A-T, and 38.9% (7/18) of A-T patients did not suffer from immunodeficiency in China. No genotype–phenotype correlation was observed in this group of A-T patients.ConclusionThese results extend the genotype spectrum of A-T in the Chinese population and imply that the diagnosis of A-T in China should be improved

Early Detection of Disease using Electronic Health Records and Fisher\u27s Wishart Discriminant Analysis

Linear Discriminant Analysis (LDA) is a simple and effective technique for pattern classification, while it is also widely-used for early detection of diseases using Electronic Health Records (EHR) data. However, the performance of LDA for EHR data classification is frequently affected by two main factors: ill-posed estimation of LDA parameters (e.g., covariance matrix), and linear inseparability of the EHR data for classification. To handle these two issues, in this paper, we propose a novel classifier FWDA -- Fisher\u27s Wishart Discriminant Analysis, which is developed as a faster and robust nonlinear classifier. Specifically, FWDA first surrogates the distribution of potential inverse covariance matrix estimates using a Wishart distribution estimated from the training data. Then, FWDA samples a group of inverse covariance matrices from the Wishart distribution, predicts using LDA classifiers based on the sampled inverse covariance matrices, and weighted-averages the prediction results via Bayesian Voting scheme. The weights for voting are optimally updated to adapt each new input data, so as to enable the nonlinear classification

Extraction of echinacoside from Cistanche tubulosa (Schenk) R. Wight and investigation of its protective effect on liver injury in sepsis rats

Abstract In this study, echinacoside was extracted from Cistanche tubulosa (Schenk) R. Wight, and its protective effect on liver injury in sepsis rats was investigated. Forty-five rats were randomly divided into control, sepsis and echinacoside groups, 15 rats in each group. The sepsis model was established in sepsis and echinacoside groups. In echinacoside group, the rats were treated with echinacoside at 1 h before modeling. At 24 h after modeling, compared with sepsis group, in echinacoside group the serum aspartate aminotransferase and alanine aminotransferase levels were decreased, the liver injury score and hepatocyte apoptosis rate were decreased, the serum monocyte chemoattractant protein-1, tumor necrosis factor α, interleukin 6 and interleukin 1β levels were decreased, the liver tissue catalase, superoxide dismutase and glutathione peroxidase levels were increased, the liver tissue malondialdehyde level was decreased, and the liver tissue nuclear factor erythroid 2-related factor 2 (Nrf2) and heme oxygenase 1 (HO-1) protein expression levels were increased. The difference of all above comparisons was significant (P < 0.05). In conclusion, echinacoside can inhibit the inflammatory response, activate the Nrf2/HO-1 signal pathway, and reduce the oxidative stress, thus alleviating the liver injury in sepsis rats

A Simple yet Effective Framework for Active Learning to Rank

While China has become the biggest online market in the world with around 1 billion internet users, Baidu runs the world largest Chinese search engine serving more than hundreds of millions of daily active users and responding billions queries per day. To handle the diverse query requests from users at web-scale, Baidu has done tremendous efforts in understanding users' queries, retrieve relevant contents from a pool of trillions of webpages, and rank the most relevant webpages on the top of results. Among these components used in Baidu search, learning to rank (LTR) plays a critical role and we need to timely label an extremely large number of queries together with relevant webpages to train and update the online LTR models. To reduce the costs and time consumption of queries/webpages labeling, we study the problem of Activ Learning to Rank (active LTR) that selects unlabeled queries for annotation and training in this work. Specifically, we first investigate the criterion -- Ranking Entropy (RE) characterizing the entropy of relevant webpages under a query produced by a sequence of online LTR models updated by different checkpoints, using a Query-By-Committee (QBC) method. Then, we explore a new criterion namely Prediction Variances (PV) that measures the variance of prediction results for all relevant webpages under a query. Our empirical studies find that RE may favor low-frequency queries from the pool for labeling while PV prioritizing high-frequency queries more. Finally, we combine these two complementary criteria as the sample selection strategies for active learning. Extensive experiments with comparisons to baseline algorithms show that the proposed approach could train LTR models achieving higher Discounted Cumulative Gain (i.e., the relative improvement {\Delta}DCG4=1.38%) with the same budgeted labeling efforts.Comment: This paper is accepted to Machine Intelligence Research and a short version is presented in NeurIPS 2022 Workshop on Human in the Loop Learnin

PonderV2: Pave the Way for 3D Foundation Model with A Universal Pre-training Paradigm

In contrast to numerous NLP and 2D computer vision foundational models, the learning of a robust and highly generalized 3D foundational model poses considerably greater challenges. This is primarily due to the inherent data variability and the diversity of downstream tasks. In this paper, we introduce a comprehensive 3D pre-training framework designed to facilitate the acquisition of efficient 3D representations, thereby establishing a pathway to 3D foundational models. Motivated by the fact that informative 3D features should be able to encode rich geometry and appearance cues that can be utilized to render realistic images, we propose a novel universal paradigm to learn point cloud representations by differentiable neural rendering, serving as a bridge between 3D and 2D worlds. We train a point cloud encoder within a devised volumetric neural renderer by comparing the rendered images with the real images. Notably, our approach demonstrates the seamless integration of the learned 3D encoder into diverse downstream tasks. These tasks encompass not only high-level challenges such as 3D detection and segmentation but also low-level objectives like 3D reconstruction and image synthesis, spanning both indoor and outdoor scenarios. Besides, we also illustrate the capability of pre-training a 2D backbone using the proposed universal methodology, surpassing conventional pre-training methods by a large margin. For the first time, PonderV2 achieves state-of-the-art performance on 11 indoor and outdoor benchmarks. The consistent improvements in various settings imply the effectiveness of the proposed method. Code and models will be made available at https://github.com/OpenGVLab/PonderV2.Comment: arXiv admin note: text overlap with arXiv:2301.0015

From design to clinic: Engineered peptide nanomaterials for cancer immunotherapy

Immunotherapy has revolutionized the field of cancer therapy. Nanomaterials can further improve the efficacy and safety of immunotherapy because of their tunability and multifunctionality. Owing to their natural biocompatibility, diverse designs, and dynamic self-assembly, peptide-based nanomaterials hold great potential as immunotherapeutic agents for many malignant cancers, with good immune response and safety. Over the past several decades, peptides have been developed as tumor antigens, effective antigen delivery carriers, and self-assembling adjuvants for cancer immunotherapy. In this review, we give a brief introduction to the use of peptide-based nanomaterials for cancer immunotherapy as antigens, carriers, and adjuvants, and to their current clinical applications. Overall, this review can facilitate further understanding of peptide-based nanomaterials for cancer immunotherapy and may pave the way for designing safe and efficient methods for future vaccines or immunotherapies

Risk factors for brain metastases in patients with non-small-cell lung cancer.

peer reviewedBrain metastases (BM) are severe incidents in patients with non-small-cell lung cancer (NSCLC). The controversial value of prophylactic cranial irradiation (PCI) in NSCLC in terms of survival benefit prompted us to explore the possible risk factors for BM in NSCLC and identify the potential population most likely to benefit from PCI. Risk factors for brain metastases in NSCLC are reviewed in this article. Identifying patients with a higher risk of BM could possibly increase the benefit of PCI while reducing the discomfort and risks caused by unnecessary invasive procedures in the NSCLC patient population. Future studies might focus on finding a solid basis for the prediction of the occurrence of brain metastases and for the therapeutic decision on the use of PCI

The prognostic role of circulating CD8+ T cell proliferation in patients with untreated extensive stage small cell lung cancer.

peer reviewed[en] BACKGROUND: Immunosuppression caused by tumorigenesis may promote tumor progress and invasion. Here, we investigated whether the characteristics of circulating T lymphocyte subtypes in patients with extensive small cell lung cancer (ED-SCLC) can be used as an alternative marker of tumor progression. METHODS: This study included 36 newly diagnosed ED-SCLC patients before treatment and the patients were followed up. 22 age and sex-matched healthy volunteers were selected as control. The percentages and proliferation potential of T lymphocyte subpopulations from peripheral blood were measured. RESULTS: CD4+ CD25+ Foxp3+ regulatory T cells (Tregs) were elevated in ED-SCLC patients compared with healthy controls (p = 0.0083). In contrast, the percentages of CD3+ and CD3+CD4+ T cells were significantly lower in SCLC patients (p < 0.001; p = 0.0014). The proliferation (%divided) of CD8+ T cells of SCLC patients was suppressed compared with healthy controls (p = 0.0058), but not of CD4+ T cells (p = 0.1611). Multivariate analyses showed that the %divided of CD8+ T cells is an independent predictor for PFS (HR: 4.342, 95% CI 1.324-14.245; p = 0.015). The percentages of peripheral Tregs and the degree of chemotherapy or radiotherapy induced lymphopenia negatively correlated with the proliferation of CD8+ T cells (p = 0.0225, r = - 0.379; p = 0.0003, r = - 0.464). CONCLUSION: The present study indicates that SCLC patients have impaired immunity in peripheral blood, and the proliferation potential of circulating CD8+ T cells is a significant predicator for PFS