Reducing the Overhead of Memory Space, Network Communication and Disk I/O for Analytic Frameworks in Big Data Ecosystem

To facilitate big data processing, many distributed analytic frameworks and storage systems such as Apache Hadoop, Apache Hama, Apache Spark and Hadoop Distributed File System (HDFS) have been developed. Currently, many researchers are conducting research to either make them more scalable or enabling them to support more analysis applications. In my PhD study, I conducted three main works in this topic, which are minimizing the communication delay in Apache Hama, minimizing the memory space and computational overhead in HDFS and minimizing the disk I/O overhead for approximation applications in Hadoop ecosystem. Specifically, In Apache Hama, communication delay makes up a large percentage of the overall graph processing time. While most recent research has focused on reducing the number of network messages, we add a runtime communication and computation scheduler to overlap them as much as possible. As a result, communication delay can be mitigated. In HDFS, the block location table and its corresponding maintenance could occupy more than half of the memory space and 30% of processing capacity in master node, which severely limit the scalability and performance of master node. We propose Deister that uses deterministic mathematical calculations to eliminate the huge table for storing the block locations and its corresponding maintenance. My third work proposes to enable both efficient and accurate approximations on arbitrary sub-datasets of a large dataset. Existing offline sampling based approximation systems are not adaptive to dynamic query workloads and online sampling based approximation systems suffer from low I/O efficiency and poor estimation accuracy. Therefore, we develop a distribution aware method called Sapprox. Our idea is to collect the occurrences of a sub-dataset at each logical partition of a dataset (storage distribution) in the distributed system at a very small cost, and make good use of such information to facilitate online sampling

Progress of Research on the Clinical Application of Radiation Protection Agents

With the advancement of nuclear technology, there is an increasing exposure of individuals to ionizing radiation. Incidents of radiation exposure occur occasionally, with the risk of radiation damage continuously rising. Radiation protectants are employed to prevent, alleviate, and treat ionizing radiation injuries. However, many current radiation protectants exhibit potent side effects, and there is currently no clinically established radiation protectant in routine use. Researchers have persistently endeavored to explore effective and low-toxicity radiation protectants. This article primarily focuses on internationally approved radiation protectants such as amifostine, dexrazoxane, phosphoramide mustard, polyethylene glycol-conjugated phosphoramide mustard, and sargramostim. Additionally, it discusses some natural antioxidants, resveratrol, and mesenchymal stem cells, which are currently undergoing clinical trials and promising to offer new avenues for the prevention and treatment of radiation injuries

Experimental investigation on the bamboo-concrete filled circular steel tubular stub columns

[EN] Concrete-filled steel tubes have been widely used all over the world due to their superior structural behaviour. To promote the use of ecofriendly materials and to reduce the use of concrete, this paper presents an innovative type of composite column, which can be referred as bamboo-concrete filled steel tubes. In this kind of column, concrete filled in the space between the external steel tube and the inner raw moso bamboo. Bamboo-concrete filled steel tubes inherit the merits of concrete-filled steel tubes such as high load-bearing capacity and ductility performance. Besides, global buckling behaviour of a bamboo column due to its relatively large slenderness can be significantly improved, and the bamboo column with nodes could provide confinement to the infilled concrete. This paper investigated the composite effect of bamboo-concrete filled steel tubular stub columns subjected to axial compression. In addition, concrete-filled double-skin steel tubular stub columns and hollow concrete-filled steel tubular stub columns were also tested for comparison. The main experimental parameter considered was the diameter-to-thickness ratio (D/t) of steel tube. Test results indicated that the composite columns with moso bamboo pipe as inner core elements showed better ductility than the hollow concrete-filled steel tubular stub columns. The bearing capacity and ductility visibly increased with decreasing of the D/t ratio.Gan, D.; Zhang, T.; Zhou, X.; He, Z. (2018). Experimental investigation on the bamboo-concrete filled circular steel tubular stub columns. En Proceedings of the 12th International Conference on Advances in Steel-Concrete Composite Structures. ASCCS 2018. Editorial Universitat Politècnica de València. 385-391. https://doi.org/10.4995/ASCCS2018.2018.7138OCS38539

De-noising of Power Quality Disturbance Detection Based on Ensemble Empirical Mode Decomposition Threshold Algorithm

Actual power quality signal which is often affected by noise pollution impacts the analysis results of the disturbance signal. In this paper, EEMD (Ensemble Empirical Mode Decomposition)-based threshold de-noising method is proposed for power quality signal with different SNR (Signal-to-Noise Ratio). As a comparison, we use other four thresholds, namely, the heuristic threshold, the self-adaptive threshold, the fixed threshold and the minimax threshold to filter the noises from power quality signal. Through the analysis and comparison of three characteristics of the signal pre-and-post de-noised, including waveforms, SNR and MSE (Mean Square Error), furthermore the instantaneous attribute of corresponding time by HHT (Hilbert Huang Transform). Simulation results show that EEMD threshold de-noising method can make the waveform close to the actual value. The SNR is higher and the MSE is smaller compared with other four thresholds. The instantaneous attribute can reflect the actual disturbance signal more exactly. The optimal threshold EEMD-based algorithm is proposed for power quality disturbance signal de-noising. Meanwhile, EEMD threshold de-noising method with adaptivity is suitable for composite disturbance signal de-noising

Efficient Query-Based Attack against ML-Based Android Malware Detection under Zero Knowledge Setting

The widespread adoption of the Android operating system has made malicious Android applications an appealing target for attackers. Machine learning-based (ML-based) Android malware detection (AMD) methods are crucial in addressing this problem; however, their vulnerability to adversarial examples raises concerns. Current attacks against ML-based AMD methods demonstrate remarkable performance but rely on strong assumptions that may not be realistic in real-world scenarios, e.g., the knowledge requirements about feature space, model parameters, and training dataset. To address this limitation, we introduce AdvDroidZero, an efficient query-based attack framework against ML-based AMD methods that operates under the zero knowledge setting. Our extensive evaluation shows that AdvDroidZero is effective against various mainstream ML-based AMD methods, in particular, state-of-the-art such methods and real-world antivirus solutions.Comment: To Appear in the ACM Conference on Computer and Communications Security, November, 202

3D Volumetric Super-Resolution in Radiology Using 3D RRDB-GAN

This study introduces the 3D Residual-in-Residual Dense Block GAN (3D RRDB-GAN) for 3D super-resolution for radiology imagery. A key aspect of 3D RRDB-GAN is the integration of a 2.5D perceptual loss function, which contributes to improved volumetric image quality and realism. The effectiveness of our model was evaluated through 4x super-resolution experiments across diverse datasets, including Mice Brain MRH, OASIS, HCP1200, and MSD-Task-6. These evaluations, encompassing both quantitative metrics like LPIPS and FID and qualitative assessments through sample visualizations, demonstrate the models effectiveness in detailed image analysis. The 3D RRDB-GAN offers a significant contribution to medical imaging, particularly by enriching the depth, clarity, and volumetric detail of medical images. Its application shows promise in enhancing the interpretation and analysis of complex medical imagery from a comprehensive 3D perspective

Unveiling the roles of the glutathione redox system in vivo by analyzing genetically modified mice

Redox status affects various cellular activities, such as proliferation, differentiation, and death. Recent studies suggest pivotal roles of reactive oxygen species not only in pathogenesis under oxidative insult but also in intracellular signal transduction. Glutathione is present in several millimolar concentrations in the cytoplasm and has multiple roles in the regulation of cellular homeostasis. Two enzymes, γ-glutamylcysteine synthetase and glutathione synthetase, constitute the de novo synthesis machinery, while glutathione reductase is involved in the recycling of oxidized glutathione. Multidrug resistant proteins and some other transporters are responsible for exporting oxidized glutathione, glutathione conjugates, and S-nitrosoglutathione. In addition to antioxidation, glutathione is more positively involved in cellular activity via its sulfhydryl moiety of a molecule. Animals in which genes responsible for glutathione metabolism are genetically modified can be used as beneficial and reliable models to elucidate roles of glutathione in vivo. This review article overviews recent progress in works related to genetically modified rodents and advances in the elucidation of glutathione-mediated reactions

Pattern formation in oscillatory complex networks consisting of excitable nodes

Oscillatory dynamics of complex networks has recently attracted great attention. In this paper we study pattern formation in oscillatory complex networks consisting of excitable nodes. We find that there exist a few center nodes and small skeletons for most oscillations. Complicated and seemingly random oscillatory patterns can be viewed as well-organized target waves propagating from center nodes along the shortest paths, and the shortest loops passing through both the center nodes and their driver nodes play the role of oscillation sources. Analyzing simple skeletons we are able to understand and predict various essential properties of the oscillations and effectively modulate the oscillations. These methods and results will give insights into pattern formation in complex networks, and provide suggestive ideas for studying and controlling oscillations in neural networks.Comment: 15 pages, 7 figures, to appear in Phys. Rev.

AdaCCD: Adaptive Semantic Contrasts Discovery based Cross Lingual Adaptation for Code Clone Detection

Code Clone Detection, which aims to retrieve functionally similar programs from large code bases, has been attracting increasing attention. Modern software often involves a diverse range of programming languages. However, current code clone detection methods are generally limited to only a few popular programming languages due to insufficient annotated data as well as their own model design constraints. To address these issues, we present AdaCCD, a novel cross-lingual adaptation method that can detect cloned codes in a new language without any annotations in that language. AdaCCD leverages language-agnostic code representations from pre-trained programming language models and propose an Adaptively Refined Contrastive Learning framework to transfer knowledge from resource-rich languages to resource-poor languages. We evaluate the cross-lingual adaptation results of AdaCCD by constructing a multilingual code clone detection benchmark consisting of 5 programming languages. AdaCCD achieves significant improvements over other baselines, and it is even comparable to supervised fine-tuning.Comment: 10 page

HashVFL: Defending Against Data Reconstruction Attacks in Vertical Federated Learning

Vertical Federated Learning (VFL) is a trending collaborative machine learning model training solution. Existing industrial frameworks employ secure multi-party computation techniques such as homomorphic encryption to ensure data security and privacy. Despite these efforts, studies have revealed that data leakage remains a risk in VFL due to the correlations between intermediate representations and raw data. Neural networks can accurately capture these correlations, allowing an adversary to reconstruct the data. This emphasizes the need for continued research into securing VFL systems. Our work shows that hashing is a promising solution to counter data reconstruction attacks. The one-way nature of hashing makes it difficult for an adversary to recover data from hash codes. However, implementing hashing in VFL presents new challenges, including vanishing gradients and information loss. To address these issues, we propose HashVFL, which integrates hashing and simultaneously achieves learnability, bit balance, and consistency. Experimental results indicate that HashVFL effectively maintains task performance while defending against data reconstruction attacks. It also brings additional benefits in reducing the degree of label leakage, mitigating adversarial attacks, and detecting abnormal inputs. We hope our work will inspire further research into the potential applications of HashVFL