Pearson Education, Inc. Author Note Correspondence concerning this article should be addressed to Lee Becker. Pearson affiliated authors can be reached at .@pearson.com. Sarah Hughes can be reached at sarah.hughes1@pearson.com. Joshua Southerland can be reached at josh.southerland@pearson.com In this study, we introduce the automated detection of inauthentic, templated responses (AuDITR) task, describe a machine learning-based approach to this task and illustrate the importance of regularly updating these models in production. Introduction English language proficiency (ELP) tests carry exceptionally high stakes because of how they influence access to employment, education and national residency status.

Subtrait (latent-trait components) assessment presents a promising path toward enhancing transparency of automated writing scores. We prototype explainability and subtrait scoring with generative language models and show modest correlation between human subtrait and trait scores, and between automated and human subtrait scores. Our approach provides details to demystify scores for educators and students.

As a result of continuous advances in Music Information Retrieval (MIR) technology, generating and distributing music has become more diverse and accessible. In this context, interest in music intellectual property protection is increasing to safeguard individual music copyrights. In this work, we propose a system for detecting music plagiarism by combining various MIR technologies. We developed a music segment transcription system that extracts musically meaningful segments from audio recordings to detect plagiarism across different musical formats. With this system, we compute similarity scores based on multiple musical features that can be evaluated through comprehensive musical analysis. Our approach demonstrated promising results in music plagiarism detection experiments, and the proposed method can be applied to real-world music scenarios. We also collected a Similar Music Pair (SMP) dataset for musical similarity research using real-world cases. The dataset are publicly available.

Distributed and federated learning are essential paradigms for training models across decentralized data sources while preserving privacy, yet communication overhead remains a major bottleneck. This dissertation explores strategies to improve communication efficiency, focusing on model compression, local training, and personalization. We establish a unified framework for biased and unbiased compression operators with convergence guarantees, then propose adaptive local training strategies that incorporate personalization to accelerate convergence and mitigate client drift. In particular, Scafflix balances global and personalized objectives, achieving superior performance under both IID and non-IID settings. We further introduce privacy-preserving pruning frameworks that optimize sparsity while minimizing communication costs, with Cohort-Squeeze leveraging hierarchical aggregation to reduce cross-device overhead. Finally, SymWanda, a symmetric post-training pruning method, enhances robustness under high sparsity and maintains accuracy without retraining. Extensive experiments on benchmarks and large-scale language models demonstrate favorable trade-offs among accuracy, convergence, and communication, offering theoretical and practical insights for scalable, efficient distributed learning.

Concept drift is a major problem in online learning due to its impact on the predictive performance of data stream mining systems. Recent studies have started exploring data streams from different sources as a strategy to tackle concept drift in a given target domain. These approaches make the assumption that at least one of the source models represents a concept similar to the target concept, which may not hold in many real-world scenarios. In this paper, we propose a novel approach called Multi-source mApping with tRansfer LearnIng for Non-stationary Environments (MARLINE). MARLINE can benefit from knowledge from multiple data sources in non-stationary environments even when source and target concepts do not match. This is achieved by projecting the target concept to the space of each source concept, enabling multiple source sub-classifiers to contribute towards the prediction of the target concept as part of an ensemble. Experiments on several synthetic and real-world datasets show that MARLINE was more accurate than several state-of-the-art data stream learning approaches.

Federated Learning (FL) is a novel, multidisciplinary Machine Learning paradigm where multiple clients, such as mobile devices, collaborate to solve machine learning problems. Initially introduced in Kone{č}n{ý} et al. (2016a,b); McMahan et al. (2017), FL has gained further attention through its inclusion in the National AI Research and Development Strategic Plan (2023 Update) of the United States (Science and on Artificial Intelligence, 2023). The FL training process is inherently decentralized and often takes place in less controlled settings compared to data centers, posing unique challenges distinct from those in fully controlled environments. In this thesis, we identify five key challenges in Federated Learning and propose novel approaches to address them. These challenges arise from the heterogeneity of data and devices, communication issues, and privacy concerns for clients in FL training. Moreover, even well-established theoretical advances in FL require diverse forms of practical implementation to enhance their real-world applicability. Our contributions advance FL algorithms and systems, bridging theoretical advancements and practical implementations. More broadly, our work serves as a guide for researchers navigating the complexities of translating theoretical methods into efficient real-world implementations and software. Additionally, it offers insights into the reverse process of adapting practical implementation aspects back into theoretical algorithm design. This reverse process is particularly intriguing, as the practical perspective compels us to examine the underlying mechanics and flexibilities of algorithms more deeply, often uncovering new dimensions of the algorithms under study.

Generative artificial intelligence (GenAI) represent an emerging paradigm within artificial intelligence, with applications throughout the medical enterprise. Assessing GenAI applications necessitates a comprehensive understanding of the clinical task and awareness of the variability in performance when implemented in actual clinical environments. Presently, a prevalent method for evaluating the performance of generative models relies on quantitative benchmarks. Such benchmarks have limitations and may suffer from train-to-the-test overfitting, optimizing performance for a specified test set at the cost of generalizability across other task and data distributions. Evaluation strategies leveraging human expertise and utilizing cost-effective computational models as evaluators are gaining interest. We discuss current state-of-the-art methodologies for assessing the performance of GenAI applications in healthcare and medical devices.

--T oken communications (T okCom) is an emerging generative semantic communication concept that reduces transmission rates by using context and multimodal large language model (MLLM)-based token processing, with tokens serving as universal semantic units across modalities. In this paper, we propose a semantic multiple access scheme in the token domain, referred to as token domain multiple access (T oDMA), where a large number of devices share a token codebook and a modulation codebook for source and channel coding, respectively. Specifically, each transmitter first tokenizes its source signal and modulate each token to a codeword. At the receiver, compressed sensing is employed first to detect active tokens and the corresponding channel state information (CSI) from the superposed signals. Then, the source token sequences are reconstructed by clustering the token-associated CSI across multiple time slots. In case of token collisions, some active tokens cannot be assigned and some positions in the reconstructed token sequences are empty. We propose to use pre-trained MLLMs to leverage the context, predict masked tokens, and thus mitigate token collisions. Simulation results demonstrate the effectiveness of the proposed T oDMA framework for both text and image transmission tasks, achieving significantly lower latency compared to context-unaware orthogonal communication schemes, while also delivering superior distortion and perceptual quality compared to state-of-the-art context-unaware non-orthogonal communication methods. The rise of multimodal large language models (MLLMs) marks a significant breakthrough in artificial intelligence (AI), combining the strengths of large language models (LLMs) with the ability to process and integrate different modalities of data--such as text, images, video, and audio [2]. MLLMs, such as GPT -4 Omni [3], BLIP-2 [4], LLaV a [5], and others, enable models to handle tasks that require understanding across different modalities, such as generating descriptive captions for images, answering questions based on visual content, or even creating high-quality multimodal content. Part of the work was accepted by IEEE INFOCOM 2025 Workshop [1]. D. G und uz is with the Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, U.K. (email: d.gunduz@imperial.ac.uk).

Video shows conservative activist Charlie Kirk speaking to a crowd of hundreds on the campus of Utah Valley University on Wednesday. Then a single shot rang out, and students fled in every direction. The 31-year-old influencer and Trump ally was rushed to hospital but pronounced dead later. 'We love you, you will always be with us', says father of Minneapolis shooting victim Fletcher Merkel, 8, was one of two children killed in Wednesday's shooting at Annunciation Catholic School in Minneapolis. The Garnet wildfire in Fresno County has scorched nearly 14,000 acres (5,665 hectares) and remains uncontained.

The actor, well known for his roles in the Die Hard franchise, was diagnosed with frontotemporal dementia - a condition his family publicly disclosed in 2023. Emma Heming Willis spoke with CBS Mornings host Gayle King about her husband and her new book, which she hopes will help fellow caregivers. 'We love you, you will always be with us', says father of Minneapolis shooting victim Fletcher Merkel, 8, was one of two children killed in Wednesday's shooting at Annunciation Catholic School in Minneapolis. The Garnet wildfire in Fresno County has scorched nearly 14,000 acres (5,665 hectares) and remains uncontained. The BBC's Tom Bateman spoke with Patrick Scallen who lives near the Annunciation Church and ran towards the sound of gunfire.