Hyperfine structure of molecular iodine measured using a light source with a laser linewidth at the megahertz level

The hyperfine structure of the absorption lines of molecular iodine at 531 nm was measured using a low-cost, coin-sized light source with a laser linewidth at the megahertz level. The measured hyperfine splittings were found to be systematically smaller than those measured using a narrow-linewidth diode laser. The theoretical fit of the measured hyperfine splittings to a four-term Hamiltonian, including the electric quadrupole, spin-rotation, tensor spin-spin, and scalar spin-spin interactions, does not clarify the observed systematic deviation in the measurement, but instead results in deviated hyperfine constants from reliable literature values beyond the uncertainties. Therefore, the theoretical fit, which is usually used to validate the measurement, does not provide the validation function in the case of megahertz level laser linewidths

Dynamic Wireless Power Transfer System With an Extensible Charging Area Suitable for Moving Objects

Extensible magnetic resonance coupling-based wireless power transfer (WPT) systems are presented in this article. A transmitter (Tx) containing an 8-shape loop and two resonators is proposed to construct a bipolar Tx array. A unipolar receiver (Rx) is placed above the Tx perpendicularly to overcome the power null phenomenon. The proposed structure ensures that magnetic flux lines are confined in the vicinity of the Rx, leading to a high power transfer efficiency (PTE) over a wide range of lateral misalignment distances. Experiments demonstrated that the proposed WPT system can achieve an efficiency of 87% under perfectly aligned operating conditions, and maintain over 70% efficiency from 0 to 30 mm lateral misalignment distances. Based on the proposed Tx module, a single-feed Tx array is constructed to further increase the charging area. The PTE of a 1 \times 2 array system is between 57.5% and 71.6% without the power null phenomenon. Meanwhile, the concern of heating due to magnetic field leakage can be significantly mitigated. These designs are proved to be very good candidates for dynamic WPT (DWPT) applications

ImDiffusion: Imputed Diffusion Models for Multivariate Time Series Anomaly Detection

Anomaly detection in multivariate time series data is of paramount importance for ensuring the efficient operation of large-scale systems across diverse domains. However, accurately detecting anomalies in such data poses significant challenges. Existing approaches, including forecasting and reconstruction-based methods, struggle to address these challenges effectively. To overcome these limitations, we propose a novel anomaly detection framework named ImDiffusion, which combines time series imputation and diffusion models to achieve accurate and robust anomaly detection. The imputation-based approach employed by ImDiffusion leverages the information from neighboring values in the time series, enabling precise modeling of temporal and inter-correlated dependencies, reducing uncertainty in the data, thereby enhancing the robustness of the anomaly detection process. ImDiffusion further leverages diffusion models as time series imputers to accurately capturing complex dependencies. We leverage the step-by-step denoised outputs generated during the inference process to serve as valuable signals for anomaly prediction, resulting in improved accuracy and robustness of the detection process. We evaluate the performance of ImDiffusion via extensive experiments on benchmark datasets. The results demonstrate that our proposed framework significantly outperforms state-of-the-art approaches in terms of detection accuracy and timeliness. ImDiffusion is further integrated into the real production system in Microsoft and observe a remarkable 11.4% increase in detection F1 score compared to the legacy approach. To the best of our knowledge, ImDiffusion represents a pioneering approach that combines imputation-based techniques with time series anomaly detection, while introducing the novel use of diffusion models to the field.Comment: To appear in VLDB 2024.Code: https://github.com/17000cyh/IMDiffusion.gi

TraceDiag: Adaptive, Interpretable, and Efficient Root Cause Analysis on Large-Scale Microservice Systems

Root Cause Analysis (RCA) is becoming increasingly crucial for ensuring the reliability of microservice systems. However, performing RCA on modern microservice systems can be challenging due to their large scale, as they usually comprise hundreds of components, leading significant human effort. This paper proposes TraceDiag, an end-to-end RCA framework that addresses the challenges for large-scale microservice systems. It leverages reinforcement learning to learn a pruning policy for the service dependency graph to automatically eliminates redundant components, thereby significantly improving the RCA efficiency. The learned pruning policy is interpretable and fully adaptive to new RCA instances. With the pruned graph, a causal-based method can be executed with high accuracy and efficiency. The proposed TraceDiag framework is evaluated on real data traces collected from the Microsoft Exchange system, and demonstrates superior performance compared to state-of-the-art RCA approaches. Notably, TraceDiag has been integrated as a critical component in the Microsoft M365 Exchange, resulting in a significant improvement in the system's reliability and a considerable reduction in the human effort required for RCA

Numerical Simulation Analysis of Mechanical Properties on Rock Brittle–Ductility Transformation Under Different Loading Rates

At present, a large number of physical tests and numerical simulations have been carried out to study the effect of confining pressure on rock deformation mechanism, and some achievements have been achieved; however, the mechanism of rock deformation in actual mine engineering needs to be further studied, for example, rock-burst is actually a unilateral unloading process of rock mass, and this process can not be completed by physical test. RFPA3D was used to simulate the brittle–ductility transformation mechanical properties of rock under different confining pressures in this paper. The damage constitutive equation of rock was derived from continuum damage mechanics; the damage coefficients of different rocks were determined based on the numerical results of stress acoustic emission, so the correctness of rock damage constitutive equation was verified. According to the derived brittle–ductility damage equation and the fitting results of ductility cumulative damage data, it was found that the development trend of rock brittleness stage was almost the same, and the extended separation occurred after entering ductility stage. The larger the Poisson’s ratio was, the longer the ductility stage was. The smaller the Poisson’s ratio was, the shorter the ductility stage was, but the larger the bearing capacity was. At the late loading stage, the ductility cumulative damage of rock showed a linear upward trend, the bearing capacity sharply decreased, the rock stability failure occurred, and the ductility damage coefficient increased gradually. The study on the brittle–ductile mechanical properties of rocks can help to deep mine’s rock-burst prediction and prevention and has significant engineering significance

Impossible meet-in-the-middle fault analysis on the LED lightweight cipher in VANETs

With the expansion of wireless technology, vehicular ad-hoc networks (VANETs) are emerging as a promising approach for realizing smart cities and addressing many serious traffic problems, such as road safety, convenience, and efficiency. To avoid any possible rancorous attacks, employing lightweight ciphers is most effective for implementing encryption/decryption, message authentication, and digital signatures for the security of the VANETs. Light encryption device (LED) is a lightweight block cipher with two basic keysize variants: LED-64 and LED-128. Since its inception, many fault analysis techniques have focused on provoking faults in the last four rounds to derive the 64-bit and 128-bit secret keys. It is vital to investigate whether injecting faults into a prior round enables breakage of the LED. This study presents a novel impossible meet-in-the-middle fault analysis on a prior round. A detailed analysis of the expected number of faults is used to uniquely determine the secret key. It is based on the propagation of truncated differentials and is surprisingly reminiscent of the computation of the complexity of a rectangle attack. It shows that the impossible meet-in-the-middle fault analysis could successfully break the LED by fault injections

Automatic Root Cause Analysis via Large Language Models for Cloud Incidents

Ensuring the reliability and availability of cloud services necessitates efficient root cause analysis (RCA) for cloud incidents. Traditional RCA methods, which rely on manual investigations of data sources such as logs and traces, are often laborious, error-prone, and challenging for on-call engineers. In this paper, we introduce RCACopilot, an innovative on-call system empowered by the large language model for automating RCA of cloud incidents. RCACopilot matches incoming incidents to corresponding incident handlers based on their alert types, aggregates the critical runtime diagnostic information, predicts the incident's root cause category, and provides an explanatory narrative. We evaluate RCACopilot using a real-world dataset consisting of a year's worth of incidents from Microsoft. Our evaluation demonstrates that RCACopilot achieves RCA accuracy up to 0.766. Furthermore, the diagnostic information collection component of RCACopilot has been successfully in use at Microsoft for over four years

Proteomic study uncovers molecular principles of single-cell-level phenotypic heterogeneity in lipid storage of Nannochloropsis oceanica

Abstract Background Nannochloropsis oceanica belongs to a large group of photoautotrophic eukaryotic organisms that play important roles in fixation and cycling of atmospheric CO2. Its capability of storing solar energy and carbon dioxide in the form of triacylglycerol (TAG) of up to 60% of total weight under nitrogen deprivation stress sparked interest in its use for biofuel production. Phenotypes varying in lipid accumulation among an N. oceanica population can be disclosed by single-cell analysis/sorting using fluorescence-activated cell sorting (FACS); yet the phenomenon of single cell heterogeneity in an algae population remains to be fully understood at the molecular level. In this study, combination of FACS and proteomics was used for identification, quantification and differentiation of these heterogeneities on the molecular level. Results For N. oceanica cultivated under nitrogen deplete (−N) and replete (+N) conditions, two groups differing in lipid content were distinguished. These differentiations could be recognized on the population as well as the single-cell levels; proteomics uncovered alterations in carbon fixation and flux, photosynthetic machinery, lipid storage and turnover in the populations. Although heterogeneity patterns have been affected by nitrogen supply and cultivation conditions of the N. oceanica populations, differentiation itself seems to be very robust against these factors: cultivation under +N, −N, in shaker bottles, and in a photo-bioreactor all split into two subpopulations. Intriguingly, population heterogeneity resumed after subpopulations were separately recultivated for a second round, refuting the possible development of genetic heterogeneity in the course of sorting and cultivation. Conclusions This work illustrates for the first time the feasibility of combining FACS and (prote)-omics for mechanistic understanding of phenotypic heterogeneity in lipid-producing microalgae. Such combinatorial method can facilitate molecular breeding and design of bioprocesses