This paper introduces a novel framework for designing efficient neural network architectures specifically tailored to tiny machine learning (TinyML) platforms. By leveraging large language models (LLMs) for neural architecture search (NAS), a vision transformer (ViT)-based knowledge distillation (KD) strategy, and an explainability module, the approach strikes an optimal balance between accuracy, computational efficiency, and memory usage. The LLM-guided search explores a hierarchical search space, refining candidate architectures through Pareto optimization based on accuracy, multiply-accumulate operations (MACs), and memory metrics. The best-performing architectures are further fine-tuned using logits-based KD with a pre-trained ViT-B/16 model, which enhances generalization without increasing model size. Evaluated on the CIFAR-100 dataset and deployed on an STM32H7 microcontroller (MCU), the three proposed models, LMaNet-Elite, LMaNet-Core, and QwNet-Core, achieve accuracy scores of 74.50%, 74.20% and 73.00%, respectively. All three models surpass current state-of-the-art (SOTA) models, such as MCUNet-in3/in4 (69.62% / 72.86%) and XiNet (72.27%), while maintaining a low computati
Our conferences face a growing crisis: an overwhelming flood of submissions, increased reviewing burdens, and diminished opportunities for meaningful engagement. With AI making paper generation easier than ever, we must ask whether the current model fosters real innovation or simply incentivizes more publications. This article advocates for a shift from passive paper presentations to interactive, participatory formats. We propose Liberating Structures, facilitation techniques that promote collaboration and deeper intellectual exchange. By restructuring conferences into two tracks, one for generating new ideas and another for discussing established work, we can prioritize quality over quantity and reinvigorate academic gatherings. Embracing this change will ensure conferences remain spaces for real insight, creativity, and impactful collaboration in the AI era.
We review solar studies using AIA, HMI, and EVE data from the SDO spacecraft that revolutionized our physical understanding of the Sun. The relevant SDO studies cover the entire 15-year lifetime of SDO, from 2010 May 1 to 2025 May 1. The discussed phenomena and their physical interpretations include (in chronological order): (1) MHD Waves and Oscillations (AIA, HMI); (2) Propagating MHD Waves (AIA); (3) Coronal Loop Cross-Sectional Temperatures (AIA); (4) Size Distributions of Solar Flare Parameters (AIA); (5) Spatio-Temporal Evolution and Diffusion (AIA); (6) The Rosner-Tucker-Vaiana (RTV) Scaling Law (AIA); (7) The Fractal-Diffusive Self-Organized Criticality Model (AIA); (8) Automated Temperature and Emission Measure Maps (AIA); (9) Automated Pattern Recognition Codes (AIA); (10) Kelvin-Helmholtz Instability in Reconnetion Outflows (AIA); (11) Hydrodstatics of Coronal Loops (AIA); (12) Magnetic Energy Dissipation (HMI); (13) Global Energetics of Solar Flares (AIA).
Mobile sensing systems have long faced a fundamental trade-off between sensing quality and efficiency due to constraints in computation, power, and other limitations. Sparse sensing, which aims to acquire and process only a subset of sensor data, has been a key strategy for maintaining performance under such constraints. However, existing sparse sensing methods often suffer from reduced accuracy, as missing information across space and time introduces uncertainty into many sensing systems. In this work, we investigate whether foundation models can change the landscape of mobile sparse sensing. Using real-world mobile AR data, our evaluations demonstrate that foundation models offer significant improvements in geometry-aware image warping, a central technique for enabling accurate reuse of cross-frame information. Furthermore, our study demonstrates the scalability of foundation model-based sparse sensing and shows its leading performance in 3D scene reconstruction. Collectively, our study reveals critical aspects of the promises and the open challenges of integrating foundation models into mobile sparse sensing systems.
Industrial projects rely heavily on lengthy, complex specification documents, making tedious manual extraction of structured information a major bottleneck. This paper introduces an innovative approach to automate this process, leveraging the capabilities of two cutting-edge AI models: Donut, a model that extracts information directly from scanned documents without OCR, and OpenAI GPT-3.5 Turbo, a robust large language model. The proposed methodology is initiated by acquiring the table of contents (ToCs) from construction specification documents and subsequently structuring the ToCs text into JSON data. Remarkable accuracy is achieved, with Donut reaching 85% and GPT-3.5 Turbo reaching 89% in effectively organizing the ToCs. This landmark achievement represents a significant leap forward in document indexing, demonstrating the immense potential of AI to automate information extraction tasks across diverse document types, boosting efficiency and liberating critical resources in various industries.
As cyber threats evolve and grow progressively more sophisticated, cyber security is becoming a more significant concern in today's digital era. Traditional security measures tend to be insufficient to defend against these persistent and dynamic threats because they are mainly intuitional. One of the most promising ways to handle this ongoing problem is utilizing the potential of data-driven intelligence, by leveraging AI and machine learning techniques. It can improve operational efficiency and saves response times by automating repetitive operations, enabling real-time threat detection, and facilitating incident response. In addition, it augments human expertise with insightful information, predictive analytics, and enhanced decision-making, enabling them to better understand and address evolving problems. Thus, data-driven intelligence could significantly improve real-world cybersecurity solutions in a wide range of application areas like critical infrastructure, smart cities, digital twin, industrial control systems and so on. In this position paper, we argue that data-driven intelligence can revolutionize the realm of cybersecurity, offering not only large-scale task automatio
Accurate hydrological understanding and water cycle prediction are crucial for addressing scientific and societal challenges associated with the management of water resources, particularly under the dynamic influence of anthropogenic climate change. Existing reviews predominantly concentrate on the development of machine learning (ML) in this field, yet there is a clear distinction between hydrology and ML as separate paradigms. Here, we introduce physics-aware ML as a transformative approach to overcome the perceived barrier and revolutionize both fields. Specifically, we present a comprehensive review of the physics-aware ML methods, building a structured community (PaML) of existing methodologies that integrate prior physical knowledge or physics-based modeling into ML. We systematically analyze these PaML methodologies with respect to four aspects: physical data-guided ML, physics-informed ML, physics-embedded ML, and physics-aware hybrid learning. PaML facilitates ML-aided hypotheses, accelerating insights from big data and fostering scientific discoveries. We first conduct a systematic review of hydrology in PaML, including rainfall-runoff hydrological processes and hydrodyna
The Sun's proximity offers us a unique opportunity to study in detail the physical processes on a star's surface; however, the highly dynamic nature of the stellar surface -- in particular, energetic eruptions such as flares and coronal mass ejections -- presents tremendous observational challenges. Spectroscopy probes the physical state of the solar atmosphere, but conventional scanning spectrographs and spectrometers are unable to capture the full evolutionary history of these dynamic events with a sufficiently wide field of view and high spatial, spectral, and temporal resolution. Resolving the physics of the dynamic sun requires gathering simultaneous spectra across a contiguous area over the full duration of these events, a goal now tantalizingly close to achievable with continued investment in developing powerful new Integral Field Spectrographs to serve as the foundation of both future ground- and space-based missions. This technology promises to revolutionize our ability to study solar flares and CMEs, addressing NASA's strategic objective to "understand the Sun, solar system, and universe." Since such events generate electromagnetic radiation and high-energy particles that
In an era of rapid technological advancements, agentification of software tools has emerged as a critical innovation, enabling systems to function autonomously and adaptively. This paper introduces MediaMind as a case study to demonstrate the agentification process, highlighting how existing software can be transformed into intelligent agents capable of independent decision-making and dynamic interaction. Developed by aiXplain, MediaMind leverages agent-based architecture to autonomously monitor, analyze, and provide insights from multilingual media content in real time. The focus of this paper is on the technical methodologies and design principles behind agentifying MediaMind, showcasing how agentification enhances adaptability, efficiency, and responsiveness. Through detailed case studies and practical examples, we illustrate how the agentification of MediaMind empowers organizations to streamline workflows, optimize decision-making, and respond to evolving trends. This work underscores the broader potential of agentification to revolutionize software tools across various domains.
E-commerce has revolutionized retail, yet its traditional workflows remain inefficient, with significant resource costs tied to product design and inventory. This paper introduces a novel system deployed at Alibaba that uses AI-generated items (AIGI) to address these challenges with personalized text-to-image generation for e-commerce product design. AIGI enables an innovative business mode called "sell it before you make it", where merchants can design fashion items and generate photorealistic images with digital models based on textual descriptions. Only when the items have received a certain number of orders, do the merchants start to produce them, which largely reduces reliance on physical prototypes and thus accelerates time to market. For such a promising application, we identify the underlying key scientific challenge, i.e., capturing users' group-level personalized preferences towards multiple generated images. To this end, we propose a Personalized Group-Level Preference Alignment Framework for Diffusion Models (PerFusion). We first design PerFusion Reward Model for user preference estimation with a feature-crossing-based personalized plug-in. Then we develop PerFusion wit
Drug addiction is a complex and pervasive global challenge that continues to pose significant public health concerns. Traditional approaches to anti-addiction drug discovery have struggled to deliver effective therapeutics, facing high attrition rates, long development timelines, and inefficiencies in processing large-scale data. Artificial intelligence (AI) has emerged as a transformative solution to address these issues. Using advanced algorithms, AI is revolutionizing drug discovery by enhancing the speed and precision of key processes. This review explores the transformative role of AI in the pipeline for anti-addiction drug discovery, including data collection, target identification, and compound optimization. By highlighting the potential of AI to overcome traditional barriers, this review systematically examines how AI addresses critical gaps in anti-addiction research, emphasizing its potential to revolutionize drug discovery and development, overcome challenges, and advance more effective therapeutic strategies.
MindCraft is a modern platform designed to revolutionize education in rural India by leveraging Artificial Intelligence (AI) to create personalized learning experiences, provide mentorship, and foster resource-sharing. In a country where access to quality education is deeply influenced by geography and socio economic status, rural students often face significant barriers in their educational journeys. MindCraft aims to bridge this gap by utilizing AI to create tailored learning paths, connect students with mentors, and enable a collaborative network of educational resources that transcends both physical and digital divides. This paper explores the challenges faced by rural students, the transformative potential of AI, and how MindCraft offers a scalable, sustainable solution for equitable education system. By focusing on inclusivity, personalized learning, and mentorship, MindCraft seeks to empower rural students, equipping them with the skills, knowledge, and opportunities needed to thrive in an increasingly digital world. Ultimately, MindCraft envisions a future in which technology not only bridges educational gaps but also becomes the driving force for a more inclusive and empow
Light is the fundamental medium through which we perceive the world around us. In the modern era, light can not only be used in its raw form but can also be used as a versatile tool. Generally, light fields carry energy and momentum (both linear and angular). Due to the transfer of linear momentum from light to matter, the radiation pressure is exerted, whereas, the intrinsic spin angular momentum (SAM) is associated with the polarization states of light. Light fields embedded with optical orbital angular momentum (OAM) -- also known as optical vortices or phase singular beams -- have truly revolutionized the field of optics and extended our basic understanding of the light-matter interaction process across various scales. Optical vortices -- spatially characterized by the presence of twisted phase fronts and a central intensity null -- have found a myriad of applications starting from microparticle trapping and manipulation to microscopy, optical communication, and quantum information science, among others. Here, we revisit some of the fundamental concepts on optical vortices and discuss extensively on how this new dimension of light i.e., the OAM, has been exploited in both linea
Integrating blockchain technology into healthcare systems presents a transformative approach to documenting, storing, and accessing electronic health records (EHRs). This research introduces a novel blockchain-based EHR system designed to significantly enhance security, scalability, and accessibility compared to existing solutions. Current systems primarily utilize SHA-256 for security and either IPFS or centralized storage, which, while effective, have limitations in providing comprehensive data integrity and security. The proposed system leverages a hybrid security algorithm combining Argon2 and AES and integrates a hybrid storage and consensus mechanism utilizing IPFS and PBFT. This multifaceted approach ensures robust encryption, efficient consensus, and high fault tolerance. Furthermore, the system incorporates Multi-Factor Authentication (MFA) to safeguard against unauthorized access. It utilizes advanced blockchain tools like MetaMask, Ganache, and Truffle to facilitate seamless interaction with the decentralized network. Simulation results demonstrate that this system offers superior protection against data breaches and enhances operational efficiency. Specifically, the pro
Onboarding newcomers is vital for the sustainability of open-source software (OSS) projects. To lower barriers and increase engagement, OSS projects have dedicated experts who provide guidance for newcomers. However, timely responses are often hindered by experts' busy schedules. The recent rapid advancements of AI in software engineering have brought opportunities to leverage AI as a substitute for expert mentoring. However, the potential role of AI as a comprehensive mentor throughout the entire onboarding process remains unexplored. To identify design strategies of this ``AI mentor'', we applied Design Fiction as a participatory method with 19 OSS newcomers. We investigated their current onboarding experience and elicited 32 design strategies for future AI mentor. Participants envisioned AI mentor being integrated into OSS platforms like GitHub, where it could offer assistance to newcomers, such as ``recommending projects based on personalized requirements'' and ``assessing and categorizing project issues by difficulty''. We also collected participants' perceptions of a prototype, named ``OSSerCopilot'', that implemented the envisioned strategies. They found the interface useful
The rapid urbanization of cities and increasing vehicular congestion have posed significant challenges to traffic management and safety. This study explores the transformative potential of artificial intelligence (AI) and machine vision technologies in revolutionizing traffic systems. By leveraging advanced surveillance cameras and deep learning algorithms, this research proposes a system for real-time detection of vehicles, traffic anomalies, and driver behaviors. The system integrates geospatial and weather data to adapt dynamically to environmental conditions, ensuring robust performance in diverse scenarios. Using YOLOv8 and YOLOv11 models, the study achieves high accuracy in vehicle detection and anomaly recognition, optimizing traffic flow and enhancing road safety. These findings contribute to the development of intelligent traffic management solutions and align with the vision of creating smart cities with sustainable and efficient urban infrastructure.
The cattle industry has been a major contributor to the economy of many countries, including the US and Canada. The integration of Artificial Intelligence (AI) has revolutionized this sector, mirroring its transformative impact across all industries by enabling scalable and automated monitoring and intervention practices. AI has also introduced tools and methods that automate many tasks previously performed by human labor with the help of computer vision, including health inspections. Among these methods, pose estimation has a special place; pose estimation is the process of finding the position of joints in an image of animals. Analyzing the pose of animal subjects enables precise identification and tracking of the animal's movement and the movements of its body parts. By summarizing the video and imagery data into movement and joint location using pose estimation and then analyzing this information, we can address the scalability challenge in cattle management, focusing on health monitoring, behavioural phenotyping and welfare concerns. Our study reviews recent advancements in pose estimation methodologies, their applicability in improving the cattle industry, existing challenges
The development of Large Language Models (LLMs) has revolutionized QA across various industries, including the database domain. However, there is still a lack of a comprehensive benchmark to evaluate the capabilities of different LLMs and their modular components in database QA. To this end, we introduce DQABench, the first comprehensive database QA benchmark for LLMs. DQABench features an innovative LLM-based method to automate the generation, cleaning, and rewriting of evaluation dataset, resulting in over 200,000 QA pairs in English and Chinese, separately. These QA pairs cover a wide range of database-related knowledge extracted from manuals, online communities, and database instances. This inclusion allows for an additional assessment of LLMs' Retrieval-Augmented Generation (RAG) and Tool Invocation Generation (TIG) capabilities in the database QA task. Furthermore, we propose a comprehensive LLM-based database QA testbed DQATestbed. This testbed is highly modular and scalable, with basic and advanced components such as Question Classification Routing (QCR), RAG, TIG, and Prompt Template Engineering (PTE). Moreover, DQABench provides a comprehensive evaluation pipeline that co
Autonomous vehicle (AV) technology is transforming the landscape of transportation bypromising safer, more efficient, and sustainable mobilitysolutions. In recent years, significant advancements in AI, machine learning, sensor fusion, and vehicle-to-everything(V2X)communicationhavepropelledthedevelopmentoffullyautonomous vehicles. This paper explores the cutting-edge technologies driving the evolution of AVs,thechallengesfacedintheirdeployment,andthepotentialsocietal,economic,and regulatory impacts. It highlights the key innovations in perception systems, decision-making algorithms, and infrastructure integration, as well as the emerging trends towards Level 4 and Level 5 autonomy. The paper also discusses future directions, including ethical considerations and the roadmap to mass adoption of autonomous mobility. Ultimately, the integrationofautonomousvehicles into globaltransportation systems is expected to revolutionize urban planning, reduce traffic accidents, and significantlyloweremissions,pavingthewayforasmarterandmoresustainablefuture.
The rapid advancement of artificial intelligence (AI) techniques has opened up new opportunities to revolutionize various fields, including operations research (OR). This survey paper explores the integration of AI within the OR process (AI4OR) to enhance its effectiveness and efficiency across multiple stages, such as parameter generation, model formulation, and model optimization. By providing a comprehensive overview of the state-of-the-art and examining the potential of AI to transform OR, this paper aims to inspire further research and innovation in the development of AI-enhanced OR methods and tools. The synergy between AI and OR is poised to drive significant advancements and novel solutions in a multitude of domains, ultimately leading to more effective and efficient decision-making.