44 research outputs found
LogPrompt: Prompt Engineering Towards Zero-Shot and Interpretable Log Analysis
Automated log analysis is crucial in modern software-intensive systems for
ensuring reliability and resilience throughout software maintenance and
engineering life cycles. Existing methods perform tasks such as log parsing and
log anomaly detection by providing a single prediction value without
interpretation. However, given the increasing volume of system events, the
limited interpretability of analysis results hinders analysts' trust and their
ability to take appropriate actions. Moreover, these methods require
substantial in-domain training data, and their performance declines sharply (by
up to 62.5%) in online scenarios involving unseen logs from new domains, a
common occurrence due to rapid software updates. In this paper, we propose
LogPrompt, a novel zero-shot and interpretable log analysis approach. LogPrompt
employs large language models (LLMs) to perform zero-shot log analysis tasks
via a suite of advanced prompt strategies tailored for log tasks, which
enhances LLMs' performance by up to 107.5% compared with simple prompts.
Experiments on nine publicly available evaluation datasets across two tasks
demonstrate that LogPrompt, despite using no training data, outperforms
existing approaches trained on thousands of logs by up to around 50%. We also
conduct a human evaluation of LogPrompt's interpretability, with six
practitioners possessing over 10 years of experience, who highly rated the
generated content in terms of usefulness and readability (averagely 4.42/5).
LogPrompt also exhibits remarkable compatibility with open-source and
smaller-scale LLMs, making it flexible for practical deployment
Clustering and Ranking: Diversity-preserved Instruction Selection through Expert-aligned Quality Estimation
With contributions from the open-source community, a vast amount of
instruction tuning (IT) data has emerged. Given the significant resource
allocation required by training and evaluating models, it is advantageous to
have an efficient method for selecting high-quality IT data. However, existing
methods for instruction data selection have limitations such as relying on
fragile external APIs, being affected by biases in GPT models, or reducing the
diversity of the selected instruction dataset. In this paper, we propose an
industrial-friendly, expert-aligned and diversity-preserved instruction data
selection method: Clustering and Ranking (CaR). CaR consists of two steps. The
first step involves ranking instruction pairs using a scoring model that is
well aligned with expert preferences (achieving an accuracy of 84.25%). The
second step involves preserving dataset diversity through a clustering
process.In our experiment, CaR selected a subset containing only 1.96% of
Alpaca's IT data, yet the underlying AlpaCaR model trained on this subset
outperforms Alpaca by an average of 32.1% in GPT-4 evaluations. Furthermore,
our method utilizes small models (355M parameters) and requires only 11.2% of
the monetary cost compared to existing methods, making it easily deployable in
industrial scenarios
HighâThroughput Electron Diffraction Reveals a Hidden Novel MetalâOrganic Framework for Electrocatalysis
AbstractMetalâorganic frameworks (MOFs) are known for their versatile combination of inorganic building units and organic linkers, which offers immense opportunities in a wide range of applications. However, many MOFs are typically synthesized as multiphasic polycrystalline powders, which are challenging for studies by Xâray diffraction. Therefore, developing new structural characterization techniques is highly desired in order to accelerate discoveries of new materials. Here, we report a highâthroughput approach for structural analysis of MOF nanoâ and subâmicrocrystals by threeâdimensional electron diffraction (3DED). A new zeoliticâimidazolate framework (ZIF), denoted ZIFâEC1, was first discovered in a trace amount during the study of a known ZIFâCO3â1 material by 3DED. The structures of both ZIFs were solved and refined using 3DED data. ZIFâEC1 has a dense 3D framework structure, which is built by linking monoâ and biânuclear Zn clusters and 2âmethylimidazolates (mImâ). With a composition of Zn3(mIm)5(OH), ZIFâEC1 exhibits high N and Zn densities. We show that the Nâdoped carbon material derived from ZIFâEC1 is a promising electrocatalyst for oxygen reduction reaction (ORR). The discovery of this new MOF and its conversion to an efficient electrocatalyst highlights the power of 3DED in developing new materials and their applications
GraphScope Flex: LEGO-like Graph Computing Stack
Graph computing has become increasingly crucial in processing large-scale
graph data, with numerous systems developed for this purpose. Two years ago, we
introduced GraphScope as a system addressing a wide array of graph computing
needs, including graph traversal, analytics, and learning in one system. Since
its inception, GraphScope has achieved significant technological advancements
and gained widespread adoption across various industries. However, one key
lesson from this journey has been understanding the limitations of a
"one-size-fits-all" approach, especially when dealing with the diversity of
programming interfaces, applications, and data storage formats in graph
computing. In response to these challenges, we present GraphScope Flex, the
next iteration of GraphScope. GraphScope Flex is designed to be both
resource-efficient and cost-effective, while also providing flexibility and
user-friendliness through its LEGO-like modularity. This paper explores the
architectural innovations and fundamental design principles of GraphScope Flex,
all of which are direct outcomes of the lessons learned during our ongoing
development process. We validate the adaptability and efficiency of GraphScope
Flex with extensive evaluations on synthetic and real-world datasets. The
results show that GraphScope Flex achieves 2.4X throughput and up to 55.7X
speedup over other systems on the LDBC Social Network and Graphalytics
benchmarks, respectively. Furthermore, GraphScope Flex accomplishes up to a
2,400X performance gain in real-world applications, demonstrating its
proficiency across a wide range of graph computing scenarios with increased
effectiveness
Providing HIV-related services in China for men who have sex with men.
PROBLEM: In China, human immunodeficiency virus (HIV) care provided by community-based organizations and the public sector are not well integrated. APPROACH: A community-based organization and experts from the Guangzhou Center for Disease Control and Prevention developed internet-based services for men who have sex with men, in Guangzhou, China. The internet services were linked to clinical services offering HIV testing and care. LOCAL SETTING: The expanding HIV epidemic among men who have sex with men is a public health problem in China. HIV control and prevention measures are implemented primarily through the public system. Only a limited number of community organizations are involved in providing HIV services. RELEVANT CHANGES: The programme integrated community and public sector HIV services including health education, online HIV risk assessment, on-site HIV counselling and testing, partner notification, psychosocial care and support, counting of CD4+ T-lymphocytes and treatment guidance. LESSONS LEARNT: The internet can facilitate HIV prevention among a subset of men who have sex with men by enhancing awareness, service uptake, retention in care and adherence to treatment. Collaboration between the public sector and the community group promoted acceptance by the target population. Task sharing by community groups can increase access of this high-risk group to available HIV-related services
Clay mineral transformation mechanism modelling of shale reservoir in Daâanzhai Member, Sichuan Basin, Southern China
Shale reservoirs often undergo intense clay mineral transformation, which plays a crucial role in the formation and evolution of pores. The reservoir lithofacies types of Daâanzhai Member in the Sichuan Basin are complex, the heterogeneity is strong, and the transformation mechanism of clay minerals is unclear, limiting the understanding of reservoir diagenesis and reservoir formation mechanism. In this study, we selected the typical shale reservoir in the Daâanzhai Member of the eastern Sichuan Basin and innovatively introduced the multiphase fluid-chemical-thermal multi-field coupled numerical simulation technique to focus on the dissolution, precipitation and transformation laws of diagenetic minerals in the shale reservoir. We calculated the transformation of diagenetic minerals and their physical response under different temperatures, pressure and fluid conditions and identified the main controlling factors of mineral transformation in shale reservoirs in the study area. The results show that the transformation of smectite to illite in the Daâanzhai Member is a complex physicochemical process influenced by various factors such as temperature, pressure, fluid, and lithology. The increase in temperature can promote illitization until the critical temperature of 110°Câ115°C, below which the conversion rate of smectite to illite increases as the temperature increases. However, when it is higher than the critical temperature, the degree of illitization decreases. In specific K-rich fluids, organic acids significantly affect the conversion of clay minerals in the Daâanzhai Member of the formation. The acidic fluid promotes the dissolution of minerals such as K-feldspar and releases K+, thus provides the material basis for illitization. The research results provide theoretical support for the diagenetic and formation mechanism of the shale reservoir in the Daâanzhai Member of the Sichuan Basin and even for the efficient exploration and development of shale gas
The Dynamic Analysis of Agro-ecological Economic System on the Basis of Emergy : A Case Study of Wu'an City in Hebei Province
Using the method of emergy analysis, we analyze the input and output of agro-ecological economic system, and select five indicators (net emergy yield ratio, emergy investment ratio, environmental loading ratio, emergy sustainability index, and dominance of emergy yield system) for assessment. The results show that the emergy input-output in Wu'an City is in general on the rise; the emergy investment ratio rises constantly, but the net emergy yield ratio decreases, and the comparative advantage in the prices of agricultural products is gradually lost. At the same time, with increase in the non-renewable industrial support emergy, the environmental pressures are also mounting. In the future agricultural development, it is necessary to pay more attention to the coordination between agricultural development and ecological environment, achieving sustainable development of agriculture
Storage Management Strategy in Mobile Phones for Photo Crowdsensing
In mobile crowdsensing, some users jointly finish a sensing task through the sensors equipped in their intelligent terminals. In particular, the photo crowdsensing based on Mobile Edge Computing (MEC) collects pictures for some specific targets or events and uploads them to nearby edge servers, which leads to richer data content and more efficient data storage compared with the common mobile crowdsensing; hence, it has attracted an important amount of attention recently. However, the mobile users prefer uploading the photos through Wifi APs (PoIs) rather than cellular networks. Therefore, photos stored in mobile phones are exchanged among users, in order to quickly upload them to the PoIs, which are actually the edge services. In this paper, we propose a utility-based Storage Management strategy in mobile phones for Photo Crowdsensing (SMPC), which makes a sending/deleting decision on a userâs device for either maximizing photo delivery ratio (SMPC-R) or minimizing average delay (SMPC-D). The decision is made according to the photoâs utility, which is calculated by measuring the impact of reproducing or deleting a photo on the above performance goals. We have done simulations based on the random-waypoint model and three real traces: roma/taxi, epfl, and geolife. The results show that, compared with other storage management strategies, SMPC-R gets the highest delivery ratio and SMPC-D achieves the lowest average delay