As we delve deeper into the post-Moore era, integrated circuits (ICs) confront escalating electromagnetic reliability (EMR) challenges. Traditional electromagnetic compatibility (EMC) frameworks fall short in addressing the lifetime performance degradation of nano-scale ICs operating in multi-physics environments. This review offers a comprehensive synthesis of IC EMR advancements spanning the past decade (2013-2023), a period chosen because 28 nm to 3 nm scaling and the 2014/2020 revisions of AEC-Q100 and IEC 62132 introduced new degradation mechanisms not covered by earlier surveys. It establishes a consolidated framework of influencing factors, categorizing them into four interdependent domains: process scaling, GHz-band effects, 3D packaging, and application environments, thereby bridging critical literature gaps. The review also delves into emergent trends like multi-physics field modeling, automotive-grade AEC-Q100 compliance, and in-situ aging monitoring, proposing innovative data-driven solutions for pressing industry concerns. These solutions include standardized quantitative evaluation, chip-level electromagnetic interference (EMI) suppression, and lifetime prediction under harsh environmental conditions. The review culminates in presenting a forward-looking roadmap for next-generation EMR design, emphasizing AI-optimized shielding, self-healing circuits, and heterogeneous integration. These strategies are crucial for mitigating catastrophic system risks in safety-critical applications such as aerospace, automotive, and IoT.

Time series forecasting is critical across various domains, such as weather, finance and real estate forecasting, as accurate forecasts support informed decision-making and risk mitigation. While recent deep learning models have improved predictive capabilities, they often overlook time-lagged cross-correlations between related sequences, which are crucial for capturing complex temporal relationships. To address this, we propose the Time-Lagged Cross-Correlations-based Sequence Prediction framework (TLCCSP), which enhances forecasting accuracy by effectively integrating time-lagged cross-correlated sequences. TLCCSP employs the Sequence Shifted Dynamic Time Warping (SSDTW) algorithm to capture lagged correlations and a contrastive learning-based encoder to efficiently approximate SSDTW distances. Experimental results on weather, finance and real estate time series datasets demonstrate the effectiveness of our framework. On the weather dataset, SSDTW reduces mean squared error (MSE) by 16.01% compared with single-sequence methods, while the contrastive learning encoder (CLE) further decreases MSE by 17.88%. On the stock dataset, SSDTW achieves a 9.95% MSE reduction, and CLE reduces it by 6.13%. For the real estate dataset, SSDTW and CLE reduce MSE by 21.29% and 8.62%, respectively. Additionally, the contrastive learning approach decreases SSDTW computational time by approximately 99%, ensuring scalability and real-time applicability across multiple time series forecasting tasks.

The traditional data annotation process is often labor-intensive, time-consuming, and susceptible to human bias, which complicates the management of increasingly complex datasets. This study explores the potential of large language models (LLMs) as automated data annotators to improve efficiency and consistency in annotation tasks. By employing rationale-driven collaborative few-shot prompting techniques, we aim to improve the performance of LLMs in text annotation. We conduct a rigorous evaluation of five LLMs across four benchmark datasets, comparing seven distinct methodologies. Our results demonstrate that collaborative methods consistently outperform traditional few-shot techniques and other baseline approaches, particularly in complex annotation tasks. Our work provides valuable insights and a robust framework for leveraging collaborative learning methods to tackle challenging text annotation tasks.

Diversity in demonstration selection is critical for enhancing model generalization by enabling broader coverage of structures and concepts. Constructing appropriate demonstration sets remains a key research challenge. This paper introduces the Relevance-Diversity Enhanced Selection (RDES), an innovative approach that leverages reinforcement learning (RL) frameworks to optimize the selection of diverse reference demonstrations for tasks amenable to in-context learning (ICL), particularly text classification and reasoning, in few-shot prompting scenarios. RDES employs frameworks like Q-learning and a PPO-based variant to dynamically identify demonstrations that maximize both diversity (quantified by label distribution) and relevance to the task objective. This strategy ensures a balanced representation of reference data, leading to improved accuracy and generalization. Through extensive experiments on multiple benchmark datasets, including diverse reasoning tasks, and involving 14 closed-source and open-source LLMs, we demonstrate that RDES significantly enhances performance compared to ten established baselines. Our evaluation includes analysis of performance across varying numbers of demonstrations on selected datasets. Furthermore, we investigate incorporating Chain-of-Thought (CoT) reasoning, which further boosts predictive performance. The results highlight the potential of RL for adaptive demonstration selection and addressing challenges in ICL.

Extending serverless computing to the edge has emerged as a promising approach to support service, but startup containerized serverless functions lead to the cold-start delay. Recent research has introduced container caching methods to alleviate the cold-start delay, including cache as the entire container or the Zygote container. However, container caching incurs memory costs. The system must ensure fast function startup and low memory cost of edge servers, which has been overlooked in the literature. This paper aims to jointly optimize startup delay and memory cost. We formulate an online joint optimization problem that encompasses container scheduling decisions, including invocation distribution, container startup, and container caching. To solve the problem, we propose an online algorithm with a competitive ratio and low computational complexity. The proposed algorithm decomposes the problem into two subproblems and solves them sequentially. Each container is assigned a randomized strategy, and these container-level decisions are merged to constitute overall container caching decisions. Furthermore, a greedy-based subroutine is designed to solve the subproblem associated with invocation distribution and container startup decisions. Experiments on the real-world dataset indicate that the algorithm can reduce average startup delay by up to 23% and lower memory costs by up to 15%.

Anomaly detection has been extensively implemented in industry. The reality is that an application may have numerous scenarios where anomalies need to be monitored. However, the complete process of anomaly detection will take much time, including data acquisition, data processing, model training, and model deployment. In particular, some simple scenarios do not require building complex anomaly detection models. This results in a waste of resources. To solve these problems, we build an anomaly detection pipeline(ADOps) to modularize each step. For simple anomaly detection scenarios, no programming is required and new anomaly detection tasks can be created by simply modifying the configuration file. In addition, it can also improve the development efficiency of complex anomaly detection models. We show how users create anomaly detection tasks on the anomaly detection pipeline and how engineers use it to develop anomaly detection models.

This paper presents a fully integrated reconfigurable all-band RF transceiver for GPS/GLONASS/ Galileo/Beidou in 55-nm CMOS. The transceiver incorporates three low-IF receivers (RXs) and one direct up-conversion BPSK transmitter (TX), which can be configured to receive any two global navigation satellite system (GNSS) signals or switched to process the Chinese Beidou(I) signals. A switching module is integrated to provide the connectivity between different RF front-end and IF channel (IFC), which will effectively simplify the design complexity of the IFC and save power consumption, while the GNSS signals are received. A flexible frequency plan with two frequency synthesizers is utilized to satisfy different local oscillator requirements of the transceiver. An optimized automatic frequency calibration scheme using an error compensation logic enables fast and high-precision calibration process for optimum phase-locked loop operation. Several digitally assisted calibration modules are integrated to ensure that the chip performance only shows a weak process, voltage and temperature (PVT) dependence. While drawing about 21.5-30.2 mA per RX channel from a 1.2-V supply, the RXs achieve an image rejection ratio more than 49 dB after I/Q mismatch calibration, an automatic gain control range of 88 dB, and an input-referred 1 dB compression point of better than -25 dBm with a minimum noise figure of about 2 dB. The output power of the TX is about 5 dBm with about 6% error vector magnitude (EVM) and 30-mA current from a 1.2-V supply. The whole transceiver consumes a die area of 2.8 × 3 mm2.

An algorithm is presented for clustering sequential data in which each unit is a collection of vectors. An example of such a type of data is speaker data in a speaker clustering problem. The algorithm first constructs affinity matrices between each pair of units, using a modified version of the Point Distribution algorithm which is initially developed for mining patterns between vector and item data. The subsequent clustering procedure is based on fitting a Gaussian mixture model on multiple random projection matrices. The final class label of each unit is determined by voting from the results of the random projection matrices.

BACKGROUND: Obtaining physiological insights from microarray experiments requires computational techniques that relate gene expression data to functional information. Traditionally, this has been done in two consecutive steps. The first step identifies important genes through clustering or statistical techniques, while the second step assigns biological functions to the identified groups. Recently, techniques have been developed that identify such relationships in a single step. RESULTS: We have developed an algorithm that relates patterns of gene expression in a set of microarray experiments to functional groups in one step. Our only assumption is that patterns co-occur frequently. The effectiveness of the algorithm is demonstrated as part of a study of regulation by two-component systems in Escherichia coli. The significance of the relationships between expression data and functional annotations is evaluated based on density histograms that are constructed using product similarity among expression vectors. We present a biological analysis of three of the resulting functional groups of proteins, develop hypotheses for further biological studies, and test one of these hypotheses experimentally. A comparison with other algorithms and a different data set is presented. CONCLUSION: Our new algorithm is able to find interesting and biologically meaningful relationships, not found by other algorithms, in previously analyzed data sets. Scaling of the algorithm to large data sets can be achieved based on a theoretical model.