Runtime scheduling and updating for deep learning applications

Recent decades have witnessed the breakthrough of deep learning algorithms, which have been widely used in many areas. Typically, deployment of deep learning applications consists of compute-intensive training and latency-sensitive inference. To support deep learning applications, enterprises build large-scale computing clusters composed of expensive accelerators, such as GPUs, FPGAs or other domain-specific ASICs. However, it is challenging for deep learning applications to achieve high resource utilization and maintain high accuracy in the face of dynamic workloads. On the one hand, the workload of deep learning tasks always changes over time, which leads to a gap between the required resources and statically allocated resources. On the other hand, the distribution of input data may also change over time, and the accuracy of inference could decrease before updating the model. In this thesis, we present a new deep learning system architecture which can schedule and update deep learning applications at runtime to efficiently handle dynamic workloads. We identify and study three key components. (i) PipeSwitch: A deep learning system that allows multiple deep learning applications to time-share the same GPU with the entire GPU memory and millisecond-scale switching overhead. PipeSwitch enables unused cycles of inference applications to be dynamically filled by training or other inference applications. With PipeSwitch, GPU utilization can be significantly improved without sacrificing service level objectives. (ii) DistMind: A disaggregated deep learning system that enables efficient multiplexing of deep learning applications with near-optimal resource utilization. DistMind decouples compute from host memory, and exposes the abstractions of a GPU pool and a memory pool, each of which can be independently provisioned and dynamically allocated to deep learning tasks. (iii) RegexNet: A payload-based, automated, reactive recovery system for web services under regular expression denial of service attacks. RegexNet adopts a deep learning model, which is updated constantly in a feedback loop during runtime, to classify payloads of upcoming HTTP requests. We have built system prototypes for these components, and integrated them with existing software. Our evaluation on a variety of environments and configurations shows the benefits of our solution

Enhancing pentachlorophenol degradation by vermicomposting associated bioremediation

Vermicomposting is an effective and environmentally friendly approach for soil organic contamination clean-up. This study investigated the roles and mechanisms of earthworm (Eisenia foetida) on soil pentachlorophenol (PCP) degradation with sterile and non-sterile soil-compost treatment. Limited soil PCP degradation was observed in the control and sterile compost treatments, whereas the synergetic effects of earthworm and compost contributed to the PCP biodegradation acceleration by significantly improving microbial biomass and activities. Sequence analysis and phylogentic classification of soil bacterial and fungal community structure after 42 days treatment identified the dominancy of indigenous bacterial families Pseudomonadaceae, Sphingobacteriaceae and Xanthomonadaceae, and fungal family Trichocomaceae, which were responsible for PCP biodegradation and stimulated by vermicomposting. Further investigation revealed the dominant roles of sterile compost during PCP biodegradation as the formation of humus-PCP in soil rather than neutralizing soil pH and increasing PCP availability. The mechanisms of vermicomposting include humus-PCP complex degradation, humus consumption and soil pH neutralization. This study provides a comprehensive understanding of the synergetic effect of vermicomposting on microbial community functions and PCP degradation enhancement in soils

The impact on the soil microbial community and enzyme activity of two earthworm species during the bioremediation of pentachlorophenol-contaminated soils

The ecological effect of earthworms on the fate of soil pentachlorophenol (PCP) differs with species. This study addressed the roles and mechanisms by which two earthworm species (epigeic Eisenia fetida and endogeic Amynthas robustus E. Perrier) affect the soil microbial community and enzyme activity during the bioremediation of PCP-contaminated soils. A. robustus removed more soil PCP than did E. foetida. A. robustus improved nitrogen utilisation efficiency and soil oxidation more than did E. foetida, whereas the latter promoted the organic matter cycle in the soil. Both earthworm species significantly increased the amount of cultivable bacteria and actinomyces in soils, enhancing the utilisation rate of the carbon source (i.e. carbohydrates, carboxyl acids, and amino acids) and improving the richness and evenness of the soil microbial community. Additionally, earthworm treatment optimized the soil microbial community and increased the amount of the PCP-4-monooxygenase gene. Phylogenic classification revealed stimulation of indigenous PCP bacterial degraders, as assigned to the families Flavobacteriaceae, Pseudomonadaceae and Sphingobacteriacea, by both earthworms. A. robustus and E. foetida specifically promoted Comamonadaceae and Moraxellaceae PCP degraders, respectively

MedRG: Medical Report Grounding with Multi-modal Large Language Model

Medical Report Grounding is pivotal in identifying the most relevant regions in medical images based on a given phrase query, a critical aspect in medical image analysis and radiological diagnosis. However, prevailing visual grounding approaches necessitate the manual extraction of key phrases from medical reports, imposing substantial burdens on both system efficiency and physicians. In this paper, we introduce a novel framework, Medical Report Grounding (MedRG), an end-to-end solution for utilizing a multi-modal Large Language Model to predict key phrase by incorporating a unique token, BOX, into the vocabulary to serve as an embedding for unlocking detection capabilities. Subsequently, the vision encoder-decoder jointly decodes the hidden embedding and the input medical image, generating the corresponding grounding box. The experimental results validate the effectiveness of MedRG, surpassing the performance of the existing state-of-the-art medical phrase grounding methods. This study represents a pioneering exploration of the medical report grounding task, marking the first-ever endeavor in this domain.Comment: 12 pages, 4 figure

META analysis on the effect of taijiquan on improving negative psychological symptoms of college students and the optimal dose

BackgroundTaijiquan, as a physical and mental exercise, can improve the negative psychology of college students. However, it is still controversial, and the optimal exercise dose of taijiquan to interfere with negative psychology has not been evaluated.ObjectiveThis study is aimed at systematically evaluating the effect of taijiquan therapy on improving negative psychological symptoms of college students and its optimal intervention dose.MethodsSearch databases such as Web of Science, Embase, PubMed, CNKI, WFSD, etc. Collect high-quality relevant RCT studies. After screening, extracting, coding and counting the data, a META analysis is done through Review Manage 5.3 and Stata 15.0 software. PICOS established the eligibility criteria to select the studies as follows: (i) population - non-clinical of college students; (ii) intervention - taijiquan intervention; (iii) comparison - taijiquan intervention group and regular physical activity group; (iv) outcomes - depression, anxiety; and (v) study design - randomized controlled trial.ResultsA total of 12 articles and 1,000 samples were included. All of the participants are college students. Taijiquan therapy can significantly reduce the depression and anxiety symptoms of college students [SMD = −0.53, 95% CI (−0.82, −0.23)], [SMD = −0.49, 95% CI (−0.90, −0.09)], with statistical significance (P < 0.05). Subgroup analysis shows that: there is a precise focus on depression and anxiety symptoms. The intervention period is more than 12 weeks, and the best effect appears when people practice 3 times a week. The best single intervention time for depression symptoms is 60 min, and for anxiety symptoms 80–90 min. It is found that taijiquan combined with mindfulness intervention can significantly reduce negative psychological symptoms like depression and anxiety of college students than single taijiquan intervention. Funnel plot combined with sensitivity analysis, Begg, Egger test showed no publication bias.ConclusionTaijiquan intervention can effectively improve the negative psychological symptoms of college students, and it has great promotion value in colleges and universities.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/, identifier: CRD42022314071

Zn-Co metal organic frameworks coated with chitosand and Au nanoparticles for chemo-photothermal-targeted combination therapy of liver cancer

The toxic effects of chemotherapy drugs on normal tissues are still a major limiting factor in cancer treatment. In this paper, we report a metal-organic framework (Zn-Co ZIF) with chitosan-coated outer layer as a carrier for the drug adriamycin hydrochloride (DOX), a treatment for liver cancer, as a novel anti-cancer nanodrug-enhanced carrier. Gold nanoparticles, a good photothermal conversion agent, were combined with the target SH-RGD during surface functionalisation to prepare Zn-Co ZIF@DOX-CS-Au-RGD (ZD-CAR), a nanoplatform with good photothermal conversion properties and targeting for combined liver cancer therapy. ZD-CAR was developed after RGD accurately targeted the tumour and entered the tumour microenvironment (TME), it cleaves and releases the liver cancer therapeutic agent (DOX) in a weak acidic environment to effectively kill tumour cells. The metal skeleton cleavage releases Co2+, which catalyzes the production of oxygen from H2O2 to alleviate the tumour hypoxic environment. The dissolved oxygen could reach 14 mg/L after adding 80 mg/mL of ZD-CAR. Meanwhile, gold nanoparticles could convert light energy into heat energy under 808 NIR irradiation to induce local superheating and kill tumour cells. In summary, this study developed a nanoplatform that combines chemo-photothermal-targeted therapy. It has shown good therapeutic effeciency in cellular experiments and performance tests and has promising applications in anti-cancer therapy

Fine mapping of the major gene BhHLS1 controlling seed size in wax gourd (Benincasa hispida)

Introduction/BackgroundThe seed size of wax gourds is an important agronomic trait; however, the associated genes have not yet been reported.MethodsIn this study, we used a high-density genetic map constructed based on F8 recombinant inbred line populations derived from a cross between MY-1 (large seed) and GX-71 (small seed) strains to detect quantitative trait locis (QTLs) for seed-size-related traits in wax gourd over a two-year period.ResultsTwo stable QTLs (qSL10 and qSW10) for seed length (SL) and seed width (SW) on chromosome 10 were repeatedly detected over two years (2021–2022). qSL10 had a phenotypic variation rate of 75.30% and 80.80% in 2021 and 2022, respectively. Whereas, qSW10 had a phenotypic variation rate of 66.60% and 73.80% in 2021 and 2022, respectively. Further, a single nucleotide polymorphism mutation was found to cause early termination of Bch10G006400 (BhHLS1) translation in GX-71 through sequencing analysis of candidate genes. Based on gene functional annotation and quantitative real-time PCR analyses, BhHLS1 encoded a probable N-acetyltransferase HLS1-like protein and its expression level was significantly different between parents. Therefore, BhHLS1 is a major candidate gene associated with a one-factor polymorphism regulating the SL and SW of wax gourds. Finally, based on variation in the BhHLS1 sequence, a cleaved amplified polymorphic sequence marker was developed for the molecular marker-assisted breeding of wax gourds.DiscussionOverall, this study is of great significance for the genetic improvement of seed size, verification of gene functions, and cultivation of specific germplasm resources for wax gourds