Artificial intelligence (AI) techniques are transforming analog circuit design by automating device-level tuning and enabling system-level co-optimization. This paper integrates two approaches: (1) AI-assisted transistor sizing using Multi-Objective Bayesian Optimization (MOBO) for direct circuit parameter optimization, demonstrated on a linearly tunable transconductor; and (2) AI-integrated circuit transfer function modeling for system-level optimization in a keyword spotting (KWS) application, demonstrated by optimizing an analog bandpass filter within a machine learning training loop. The combined insights highlight how AI can improve analog performance, reduce design iteration effort, and jointly optimize analog components and application-level metrics.

Unsupervised Domain Adaptation (UDA) has emerged as a powerful solution for the domain shift problem via transferring the knowledge from a labeled source domain to a shifted unlabeled target domain. Despite the prevalence of UDA for visual applications, it remains relatively less explored for time-series applications. In this work, we propose a novel lightweight contrastive domain adaptation framework called CoTMix for time-series data. Unlike existing approaches that either use statistical distances or adversarial techniques, we leverage contrastive learning solely to mitigate the distribution shift across the different domains. Specifically, we propose a novel temporal mixup strategy to generate two intermediate augmented views for the source and target domains. Subsequently, we leverage contrastive learning to maximize the similarity between each domain and its corresponding augmented view. The generated views consider the temporal dynamics of time-series data during the adaptation process while inheriting the semantics among the two domains. Hence, we gradually push both domains towards a common intermediate space, mitigating the distribution shift across them. Extensive experiments conducted on five real-world time-series datasets show that our approach can significantly outperform all state-of-the-art UDA methods. The implementation code of CoTMix is available at \href{https://github.com/emadeldeen24/CoTMix}{github.com/emadeldeen24/CoTMix}.

Using finely tuned hardware, a specialized network, and large data storage, supercomputers have long been used for computationally intense projects that require large amounts of data processing. With the rise of artificial intelligence and machine learning, there is an increasing demand for these powerful computers and, as a result, processing power is rapidly increasing. As such, the growth of AI is inextricably linked to the growth in processing power of these high-performing devices. The term appeared in the late 1920s and the CDC 6600 (released in 1964) is generally considered to be the first true supercomputer. Early supercomputers used only a few extremely powerful processors but, in the late 1990s, computer experts realized that stringing together thousands of off-the-shelf processors would yield the greatest processing power.

We are looking for capable and responsible scientists to work on and make contributions to data analytics and machine learning in the advanced manufacturing and engineering (AME) domain, in particular for battery state of health (SOH) and remaining useful life (RUL) prediction. Candidates who additionally have experiences in processing battery data or other types of sensor data are preferred.

We are looking for strong candidates in the field of numerical simulations that involve solving partial differential equations (e.g. Candidates who additionally possess a background in machine learning and deep learning are preferred.

Self-driving vehicles, dating apps which give out relationship advice, humanoid robots that crack jokes and get upset... With a global market that is expected to reach US$35,870 million by 2025 from its direct revenue sources, artificial intelligence (AI) is no longer just the subject of science fiction books. According to a study carried out by IDC, in the ASEAN region, AI adoption rates are currently on the rise and growth has almost doubled in comparison to last year. When it comes to adopting this emerging technology, Indonesia is leading the way, with 24.6% of companies already embracing AI in some capacity. Thailand comes in second and the bronze medal goes to Singapore. This is somewhat surprising, considering the city state is normally something of a trailblazer in the region when it comes to embracing new technologies.

These are just some of the headline-grabbing technologies that could be part of our lives once fifth-generation, or 5G, mobile networks roll out, telcos say. A pilot network in the one-north district is in the pipeline, announced jointly by Singtel and Ericsson in July. M1 will start South-east Asia field trials, and StarHub plans to switch on 5G base stations, both by year-end. But experts and industry players acknowledge that there may be a long road to 5G coverage in Singapore, not least because the dots that are to be connected aren't all in place yet. While the propounded benefits of 5G include higher data volumes and speeds, some raise eyebrows at holding trials and building infrastructure for a system not yet in place.

Self-driving vehicles, dating apps able to give relationship advice, humanoid robots that crack jokes and get upset... With a market expected to reach $35,870 million by 2025 globally from its direct revenue sources, artificial intelligence (AI) is not just the subject of science fiction books but a very real possibility for everyday life. In the ASEAN region, AI adoption rates are currently on the rise and growth has almost doubled in comparison to last year, according to a study carried out by IDC. Top AI uses in Southeast Asia include algorithmic market forecasting and automated asset and infrastructure management. Indonesia and Thailand are the undisputed regional leaders at the time of adopting AI.

Science fiction author Isaac Asimov came up with the three'laws' of robotics in a story he published in 1942. The first of these laws says a robot may not injure a human being or, through inaction, allow a human being to come to harm. Now artist and roboticist Alexander Reben has developed the first robot that breaks this rule, by hurting humans in an unpredictable way. Artist and roboticist Alexander Reben claims he has developed the first robot (pictured) that hurts humans autonomously and in an unpredictable way. It is a'near certainty' that a major technological disaster will threaten humanity in the next 1,000 to 10,000 years.

An autonomous robot developed by researchers in Singapore scans for missing or misplaced library books. You know you've done it. You grab a library book off the shelf, flip through the pages, then go to put it back and realize you forgot where it came from. So you just glance over your shoulder and take a wild guess. Misplaced library books frustrate patrons and give librarians migraines.