Last-Level Cache (LLC) is increasingly required to be energy-efficient and area-saving. Emerging Non-volatile memory (NVM), such as Magnetic-resistive Random Access Memory (MRAM), present potential solutions for LLC as its ultra-low leakage and area. However, the high replacement-latency caused by its high write-latency and power consumption hinders MRAM in LLC applications. Thus, this paper proposes a novel Sense-While-Replace (SWR) strategy for dual-port SOT-MRAM, which liberates the conflict between reading and writing to conceal the impact of high write-latency on system performance. Furthermore, Self-aware access circuits are proposed, which accelerate reading and obtain utmost writing-energy saving. Under 40-nm CMOS technology, the 4Kb Macro achieves <3ns@32bits read and <75% energy-saving. Most crucially, SWR supports CPU continuously read whereas preserved from replacement latency, which improves performance by up to 8% even compared to SRAM.

The increasing sophistication of image manipulation techniques demands robust forensic solutions that can both reliably detect alterations and precisely localize tampered regions. Recent Multimodal Large Language Models (MLLMs) show promise by leveraging world knowledge and semantic understanding for context-aware detection, yet they struggle with perceiving subtle, low-level forensic artifacts crucial for accurate manipulation localization. This paper presents a novel Propose-Rectify framework that effectively bridges semantic reasoning with forensic-specific analysis. In the proposal stage, our approach utilizes a forensic-adapted LLaVA model to generate initial manipulation analysis and preliminary localization of suspicious regions based on semantic understanding and contextual reasoning. In the rectification stage, we introduce a Forensics Rectification Module that systematically validates and refines these initial proposals through multi-scale forensic feature analysis, integrating technical evidence from several specialized filters. Additionally, we present an Enhanced Segmentation Module that incorporates critical forensic cues into SAM’s encoded image embeddings, thereby overcoming inherent semantic biases to achieve precise delineation of manipulated regions. By synergistically combining advanced multimodal reasoning with established forensic methodologies, our framework ensures that initial semantic proposals are systematically validated and enhanced through concrete technical evidence, resulting in comprehensive detection accuracy and localization precision. Extensive experimental validation demonstrates state-of-the-art performance across diverse datasets with exceptional robustness and generalization capabilities.

Recently, one-time-programmable (OTP) memory has been widely used in micro controller unit (MCU) due to its storage reliability and tamper-proof. Based on the analysis of the breakdown mechanism of magnetic tunnel junction and the measurement results used for modeling, this article demonstrates a 96-Kb versatile MRAM-OTP macro integrated with a 6-Kb OTP-based physical unclonable function (PUF) in 55-nm fully depleted silicon on insulator process. The MRAM-OTP macro realize minimum 3.0 V program voltage and 100 ns for 32-bit program speed. The on-chip power supply method trims with PVT variations and completes voltage switching between two modes. The multi-bit programming design saves about 97% time and 5% energy using self-termination. The discernible dual-mode sense amplifier saves 57.4% power in OTP reading with no lacks of sensing yield. We also presented the application scheme of the proposed MRAM-OTP macro within the trimming information storage and security MCU design, with the PUF helped to complete the authentication process together with OTP and the error correcting code assisted encrypting and correcting process. The trimming information can help to enhance the reliability of STT-MRAM main area. With the reconfigurable bit-cell design, the Inter-HD and Intra-HD of the OTP-based PUF can reach 49.77% and 0.08%, respectively.

Graph collaborative filtering has made great progress in the recommender systems, but these methods often struggle with the data sparsity issue in real-world recommendation scenarios. To mitigate the effect of data sparsity, graph collaborative filtering incorporates contrastive learning as an auxiliary task to improve model performance. However, existing contrastive learning-based methods generally use a single data augmentation graph to construct the auxiliary contrastive learning task, which has problems such as loss of key information and low robustness. To address these problems, this paper proposes a Perturbation-driven Dual Auxiliary Contrastive Learning for Collaborative Filtering Recommendation (PDACL). PDACL designs structure perturbation and weight perturbation to construct two data augmentation graphs. The Structure Perturbation Augmentation (SPA) graph perturbs the topology of the user-item interaction graph, while the Weight Perturbation Augmentation (WPA) graph reconstructs the implicit feedback unweighted graph into a weighted graph similar to the explicit feedback. These two data augmentation graphs are combined with the user-item interaction graph to construct the dual auxiliary contrastive learning task to extract the self-supervised signals without losing key information and jointly optimize it together with the supervised recommendation task, to alleviate the data sparsity problem and improve the performance. Experimental results on multiple public datasets show that PDACL outperforms numerous benchmark models, demonstrating that the dual-perturbation data augmentation graph in PDACL can overcome the shortcomings of a single data augmentation graph, leading to superior recommendation results. The implementation of our work will be found at https://github.com/zky77/PDACL.

Recent advances in AI-powered image editing tools have significantly lowered the barrier to image modification, raising pressing security concerns those related to spreading misinformation and disinformation on social platforms. Image provenance analysis is crucial in this context, as it identifies relevant images within a database and constructs a relationship graph by mining hidden manipulation and transformation cues, thereby providing concrete evidence chains. This paper introduces a novel end-to-end deep learning framework designed to explore the structural information of provenance graphs. Our proposed method distinguishes from previous approaches in two main ways. First, unlike earlier methods that rely on prior knowledge and have limited generalizability, our framework relies upon a patch attention mechanism to capture image provenance clues for local manipulations and global transformations, thereby enhancing graph construction performance. Second, while previous methods primarily focus on identifying tampering traces only between image pairs, they often overlook the hidden information embedded in the topology of the provenance graph. Our approach aligns the model training objectives with the final graph construction task, incorporating the overall structural information of the graph into the training process. We integrate graph structure information with the attention mechanism, enabling precise determination of the direction of transformation. Experimental results show the superiority of the proposed method over previous approaches, underscoring its effectiveness in addressing the challenges of image provenance analysis.

Electric Vehicle (EV) is regarded as the optimal alternative to traditional fuel-powered vehicles. However, the exponential surge in EV charging demand poses challenges in charging infrastructure planning and charging behavior management. The efficacy of traditional Grid-to-Vehicle (G2V) charging mode, which obtains power from the grid, is curtailed by the limited number and uneven distribution of charging facilities, inevitably leading to charging congestion. Instead, the concept of Vehicle-to-Vehicle (V2V) charging has emerged as a spatio-temporally flexible charging mode, which expands EV's role from consumer to provider, forming V2V pairs and utilizing urban Parking Lots (PLs) as charging locations. In this paper, we propose a Hybrid Heterogeneous Modes (HHM)-based EV charging optimization scheme in urban settings. Building upon the G2V mode, we introduce synchronous and asynchronous V2V charging modes as optimization strategies, integrating and exploiting the unique advantages of each mode. We also utilize global EV charging scheduling and comprehensively take four dimensions into consideration (energy trading cost, travel cost, waiting time cost and loss cost), thus achieving flexible mode selection, V2V pairing, and designated charging locations. Simulations confirm the effectiveness of the proposed scheme in reducing total charging costs, optimizing user service experiences, and improving charging facility utilization rates.

With the urbanization process worldwide, modeling the dynamics of people's activities in urban environments has become a crucial socioeconomic task. We present Urbanity, a novel system that leverages geo-tagged social media streams for modeling urban dynamics. Urbanity automatically discovers the spatial and temporal hotspots where people's activities concentrate; and captures the cross-modal correlations among location, time, and text by jointly mapping different units into the same latent space. With Urbanity, the end users are able to use flexible query schemes to retrieve different resources (e.g., POIs, hotspots, hours, activities) that meet their needs. Furthermore, Urbanity can handle continuous streams to update the learned model, thus revealing up-to-date patterns of urban activities.

Spatiotemporalactivity modeling is an important task for applications like tour recommendation and place search. The recently developed geographical topic models have demonstrated compelling results in using geo-tagged social media (GTSM) for spatiotemporal activity modeling. Nevertheless, they all operate in batch and cannot dynamically accommodate the latest information in the GTSM stream to reveal up-to-date spatiotemporal activities. We propose ReAct, a method that processes continuous GTSM streams and obtains recency-aware spatiotemporal activity models on the fly. Distinguished from existing topic-based methods, ReAct embeds all the regions, hours, and keywords into the same latent space to capture their correlations. To generate high-quality embeddings, it adopts a novel semi-supervised multimodal embedding paradigm that leverages the activity category information to guide the embedding process. Furthermore, as new records arrive continuously, it employs strategies to effectively incorporate the new information while preserving the knowledge encoded in previous embeddings. Our experiments on the geo-tagged tweet streams in two major cities have shown that ReAct significantly outperforms existing methods for location and activity retrieval tasks.

With the ever-increasing urbanization process, systematically modeling people's activities in the urban space is being recognized as a crucial socioeconomic task. This task was nearly impossible years ago due to the lack of reliable data sources, yet the emergence of geo-tagged social media (GTSM) data sheds new light on it. Recently, there have been fruitful studies on discovering geographical topics from GTSM data. However, their high computational costs and strong distributional assumptions about the latent topics hinder them from fully unleashing the power of GTSM.

Understanding human mobility is of great importance to various applications, such as urban planning, traffic scheduling, and location prediction. While there has been fruitful research on modeling human mobility using tracking data (e.g., GPS traces), the recent growth of geo-tagged social media (GeoSM) brings new opportunities to this task because of its sheer size and multi-dimensional nature. Nevertheless, how to obtain quality mobility models from the highly sparse and complex GeoSM data remains a challenge that cannot be readily addressed by existing techniques. We propose GMove, a group-level mobility modeling method using GeoSM data. Our insight is that the GeoSM data usually contains multiple user groups, where the users within the same group share significant movement regularity. Meanwhile, user grouping and mobility modeling are two intertwined tasks: (1) better user grouping offers better within-group data consistency and thus leads to more reliable mobility models; and (2) better mobility models serve as useful guidance that helps infer the group a user belongs to. GMove thus alternates between user grouping and mobility modeling, and generates an ensemble of Hidden Markov Models (HMMs) to characterize group-level movement regularity. Furthermore, to reduce text sparsity of GeoSM data, GMove also features a text augmenter. The augmenter computes keyword correlations by examining their spatiotemporal distributions. With such correlations as auxiliary knowledge, it performs sampling-based augmentation to alleviate text sparsity and produce high-quality HMMs.

To judge how much a pair of words (or texts) are semantically related is acognitive process. However, previous algorithms for computing semanticrelatedness are largely based on co-occurrences within textualwindows, and do not actively leverage cognitive human perceptions ofrelatedness. To bridge this perceptional gap, we propose to utilizefree association as signals to capture such human perceptions.However, free association, being manually evaluated,has limited lexical coverage and is inherently sparse. We propose to expand lexical coverage and overcome sparseness by constructing an association network of terms and concepts that combines signals from free association norms and five types of co-occurrences extracted from therich structures of Wikipedia. Our evaluation results validate thatsimple algorithms on this network give competitive results incomputing semantic relatedness between words and between shorttexts.