Smart grids rely on massive data from networked smart meters to enable fine-grained energy analytics and improve sustainability. As a representative, Non-Intrusive Load Monitoring (NILM) can infer individual appliance usage from aggregated meter readings, offering energy-saving insights without the need for per-appliance sensors. However, practical deployment at scale is hindered by three challenges: (1) limited per-device resources such as on-board computation and mobile data plans; (2) scarce labeled data due to the high cost of manual annotation; and (3) significant variability across households, including temporal changes in user behavior and appliance status.To address these challenges, we propose UNITY, a semi-supervised meta-learning NILM framework that unifies labeled and unlabeled data across diverse households to learn generalizable representations. UNITY minimizes user-side computation and communication overhead by reformulating inference as a lightweight sequence-retrieval task, accelerated by Discrete Haar Wavelet transforms. Only uncertain samples are uploaded to the server for refinement. To overcome label scarcity, UNITY employs entropy-based transductive learning, gradually enhancing model confidence on unlabeled data. Furthermore, to handle household diversity and temporal distribution shift, UNITY adopts a meta-learning approach that treats each household as a distinct task and incrementally adapts to behavioral drift over time, enabling robust long-term deployment. Experiments on public NILM datasets demonstrate that UNITY achieves 95.21% accuracy, outperforming existing methods under real- world resource constraints.

Real-time Bidding (RTB) advertisers wish to know in advance the expected cost and yield of ad campaigns to avoid trial-and-error expenses.However, Campaign Performance Forecasting (CPF), a sequence modeling task involving tens of thousands of ad auctions, poses challenges of evolving user interest, auction representation, and long context, making coarse-grained and static-modeling methods sub-optimal.We propose AdVance, a time-aware framework that integrates local auction-level and global campaign-level modeling.User preference and fatigue are disentangled using a timepositioned sequence of clicked items and a concise vector of all displayed items.Cross-attention, conditioned on the fatigue vector, captures the dynamics of user interest toward each candidate ad.Bidders compete with each other, presenting a complete graph similar to the self-attention mechanism.Hence, we employ a Transformer Encoder to compress each auction into embedding by solving auxiliary tasks.These sequential embeddings are then summarized by a conditional state space model (SSM) to comprehend long-range dependencies while maintaining global linear complexity.Considering the irregular time intervals between auctions, we

The diverse advertiser demands (brand effects or immediate outcomes) lead to distinct selling (pre-agreed volumes with an under-delivery penalty or compete per auction) and pricing (fixed prices or varying bids) patterns in Guaranteed delivery (GD) and real-time bidding (RTB) advertising. This necessitates fair impression allocation to unify the two markets for promoting ad content diversity and overall revenue. Existing approaches often deprive RTB ads of equal exposure opportunities by prioritizing GD ads, and coarse-grained methods are inferior to 1) Ambiguous reward due to varied objectives and constraints of GD fulfillment and RTB utility, hindering measurement of each allocation's contribution to the global interests; 2) Intensified competition by the coexistence of GD and RTB ads, complicating their mutual relationships; 3) Policy degradation caused by evolving user traffic and bid landscape, requiring adaptivity to distribution shifts.

User impression forecasting underpins various commercial activities, from long-term strategic decisions to short-term automated operations. As a representative that involves both kinds, the highly profitable Guaranteed Delivery (GD) advertising focuses mainly on promoting brand effect by allowing advertisers to order target impressions weeksin advance and get allocatedonline at the scheduled time. Such a business mode naturally incurs three issues making existing solutions inferior: 1) Timescale-granularity dilemma of coherently supporting the sales of day-level impressions of the distant future and the corresponding fine-grained allocation in real-time. 2) High dimensionality due to the Cartesian product of user attribute combinations. 3) Stability-plasticity dilemma of instant adaptation to emerging patterns of temporal dependency withoutcatastrophic forgetting of repeated ones facing the non-stationary traffic.

Recently, the developed techniques such as magnetic resonant coupling (MRC) and multiple-input multiple-output (MIMO) transmission have significantly improved the charging efficiency and distance for wireless power transfer (WPT) systems. However, the electromagnetic radiation (EMR) safety of wireless charging is critical in practice while mostly ignored. In this work, we take the EMR safety into account in MIMO MRC-WPT systems. We propose a safety ensured high-efficient scheduling algorithm for magnetic MIMO wireless power transfer system (called Shield). Technically, we firstly devise a simple but accurate Z-axis rotational symmetrical EMR model along with a magnetic-field-line-based meshing scheme. Further, we express the EMR safety requirement in the continuous physical space with a limited number of constraints via random sampling and rule-based filtering. Finally, we build up a system prototype for Shield and conduct extensive experiments. With the given power budget and resonant frequency, the results reveal that the EMR safety requirement only influences the charging performance of an MRC-WPT system within a certain range. Furthermore, Shield can dramatically improve the payload power transfer efficiency (PTE) by up to 66.60% compared with state-of-the-art baselines while guaranteeing the EMR safety.

Guaranteed delivery (GD) and real-time bidding (RTB) constitute two parallel profit streams for the publisher. The diverse advertiser demands (brand or instant effect) result in different selling (in bulk or via auction) and pricing (fixed unit price or various bids) patterns, which naturally raises the fusion allocation issue of breaking the two markets' barrier and selling out at the global highest price boosting the total revenue. The fusion process complicates the competition between GD and RTB, and GD contracts with overlapping targeting. The non-stationary user traffic and bid landscape further worsen the situation, making the assignment unsupervised and hard to evaluate. Thus, a static policy or coarse-grained modeling from existing work is inferior to facing the above challenges.

Fine-grained energy consumption analysis has great potential value in applications of Smart Grids, renewable energy, and Artificial Intelligence of Things. Non-Intrusive Load Monitoring (NILM) is a single-sensor alternative to the conventional one-sensor-for-one-appliance solution due to its ability to deduce individual appliances states from mixed measurements from the main power interface. Despite its advantages of low cost and easy maintenance, a few drawbacks hinders its widespread adoption. To enhance the Quality of Service (QoS) of NILM, four objectives should be achieved by careful designing: high accuracy, user transparency, low response delay, and low data redundancy.Inspired by observations of discriminative yet redundant current waveform and model sparsity, we propose LCL, a lightweight, contactless, plug-and-play solution for real-time load monitoring. The filtering module skips over unchanged input and compresses the measurements of interest using Compressed Sensing. The reconstruction-free inference module runs an attentional multi-label classification and returns all functioning appliance states directly from the compressed input. The compression module leverages model sparsity for real-time processing on edge devices. Evaluations based on our prototype deployed in real-life scenarios attest to the high QoS of LCL with a subset accuracy of 94.2% and a delay reduction of 52.2%. Our solution further filters out 96.8% of the redundant input and attains a Measurement Rate of 0.1 without noticeable impact on the performance.

The rapid proliferation of Smart Grids calls for a more in-depth understanding of user energy consumption behaviors, based on large data volumes collected by various sources of sensors such as voltmeter and ammeter. Non-Intrusive Load Monitoring (NILM) is a single sensor solution, which can effectively disaggregate individual appliance states from measurements only at the interface to the power source, albeit at the cost of requiring circuit modifications thus introducing suspension of services and potential safety hazards. To overcome the undesirable attribute of NILM and achieve a safe yet highly accurate solution, we devise a contactless sensing system based on inductive current measurements that can conduct load disaggregation without tampering with the power system. Despite using single modality, i.e., the inductive current, our scheme attains state-of-the-art accuracy in existing multi-modality datasets by leveraging modality distillation technique to handle arbitrary input structure. Our main contributions enlist: (1) devising and deploying the first, to the best of our knowledge, purely contactless non-intrusive load disaggregation system; (2) the design of an oracle-apprentice network structure to leverage multi-modality input for training, while operating with single modality; (3) a high estimation accuracy of 95.44% and 96.21%, respectively, is attested on two public datasets, which proves the efficiency of our method.