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This research study investigates the effectiveness of a pedagogical model (an interventional framework) of contextualised instruction through Reverso Context, a comprehensive language and translation tool, in developing the translation skills of senior EFL student teachers. The study employs a quasi-experimental one-group pre-post design, with a translation pre-post test administered to measure expected progress resulting from the intervention. The participants consisted of 50 senior EFL student teachers affiliated with Faculty of Education, Assiut University, Egypt, who were randomly selected from the main population. The research began by identifying the translation skills that senior EFL student teachers required in the context of their pre-service teacher education programme. Based on this, an interventional framework (model) that employed contextualised instruction through Reverso Context was designed and administered to the target participants. The results indicate a significant improvement in the participants' translation skills following the intervention. This suggests that contextualised instruction through Reverso Context could effectively enhance EFL student teachers' translation skills, thereby contributing to their general language proficiency and professional development. [This paper was published in "Sohag University International Journal of Educational Research (SUIJER)" v11 2025.]

The article "Some Important Facts about Language Teaching/Learning: Basics and Assumptions" by Dr. Mahmoud M. S. Abdallah explores the foundational elements of language teaching and learning. It emphasizes the importance of language proficiency for personal, social, and professional success, highlighting the cognitive and societal benefits of bilingualism and multilingualism. The article discusses various approaches and methodologies in language teaching, such as Communicative Language Teaching (CLT), which focuses on meaningful communication and interaction. It also addresses the role of teachers as facilitators and the significance of using authentic materials and technology in language instruction. The article examines underlying assumptions in language teaching, such as the importance of grammar instruction and the role of input and output in language acquisition. Additionally, it emphasizes the need for learner autonomy and culturally responsive teaching practices. The article concludes by outlining principles of effective language teaching and addressing challenges and controversies in the field, such as the role of native language, the optimal age for language learning, and the impact of technology. Overall, the article provides a comprehensive guide to understanding the complexities of language teaching and learning, offering valuable insights for educators, policymakers, and language learners.

The landscape of educational research necessitates sophisticated approaches to scholarly communication, demanding researchers to adeptly navigate diverse methodological traditions. This article provides systematic guidelines for academic writing that transcend methodological boundaries, offering practical strategies for crafting clear, ethical, and impactful scholarly work. Emphasizing the importance of structure, style, and scholarly conventions, the article addresses the complexities of quantitative, qualitative, and mixed-methods research. Key guidelines include adherence to ethical standards, clarity and conciseness in communication, and proper citation practices to maintain academic integrity. The article also offers insights into adapting dissertations and theses into journal articles, highlighting the challenges and opportunities for early-career researchers. As educational research evolves, the need for effective academic writing remains paramount, ensuring that valuable insights are shared and built upon within the scholarly community. By fostering excellence in academic writing, researchers can significantly contribute to the advancement of educational theory and practice.

The global status of English as a lingua franca necessitates effective English language teaching (TEFL) to meet the demands of the global economy and social development. This article explores key principles for effective TEFL, grounded in a sophisticated theoretical framework that integrates cognitive, sociocultural, and constructivist perspectives. It emphasizes the importance of understanding learner needs, focusing on meaningful communication, and implementing Communicative Language Teaching (CLT) and Task-Based Language Teaching (TBLT). Differentiated instruction and technology integration are highlighted as essential strategies to cater to diverse learner profiles and enhance engagement. The article also underscores the significance of cultural awareness in language instruction, advocating for the inclusion of cultural elements to prepare students for real-world interactions. Effective assessment practices, combining formative and summative approaches, are crucial for monitoring progress and informing instruction. Continuous professional development (CPD) is emphasized as vital for teachers to remain current and effective, involving action research, participation in professional learning communities, and reflective teaching practices. By adhering to these principles, educators can create dynamic and inclusive classrooms that promote successful language acquisition.

Dr Mahmoud M. S. Abdallah's article, "The New Territory of Educational Research in TESOL/TEFL: What Novice Researchers Should Know", provides a comprehensive guide for novice researchers in Teaching English to Speakers of Other Languages (TESOL) and Teaching English as a Foreign Language (TEFL). It emphasises the importance of research in enhancing teaching practices, informing educational policies, and understanding second language acquisition (SLA). It defines research as a systematic, cyclical process aimed at discovering new knowledge or validating existing knowledge. The article explores various research approaches, including qualitative, quantitative, mixed methods, and action research. Qualitative research is highlighted for its ability to explore human experiences through methods like ethnography, case studies, and discourse analysis. Quantitative research is presented for testing hypotheses and generalising findings through experimental, quasi-experimental, and survey research. Mixed methods research combines both approaches for a holistic understanding, while action research empowers teachers to become researchers in their own classrooms. The article introduces essential research terminologies, such as variables, hypotheses, data, reliability, validity, and generalisability. It outlines the research process, from identifying a problem to writing the report, emphasising the importance of thorough literature reviews, clear research questions, appropriate research designs, data collection and analysis, and findings interpretation. The article encourages TESOL/TEFL educators to embrace research as part of their professional identity, becoming informed consumers and producers of knowledge. It provides criteria for selecting research topics, including relevance, originality, feasibility, personal interest, potential impact, alignment with current educational contexts, and interdisciplinary potential. In conclusion, the article equips novice researchers with the knowledge and tools necessary to engage in rigorous and impactful research, contributing to the continuous improvement of language education.

The literature review is a pivotal component in educational research studies, serving as a bridge between existing knowledge and the research being undertaken. This article examines the multifaceted role and function of the literature review from a pragmatic perspective, highlighting its significance in advancing knowledge and informing educational practice. A comprehensive literature review establishes the research territory by situating the study within a broader intellectual context, demonstrating the researcher's command of the field, and illuminating the path for their own investigation. It justifies the research undertaking by identifying gaps, inconsistencies, or unanswered questions in the existing literature, thereby establishing the rationale for the study. Moreover, the literature review shapes the research problem and questions by critically evaluating previous studies and refining research focus. It informs methodological choices by showcasing the strengths and weaknesses of different research designs and analytical approaches. Even after data collection and analysis, the literature review provides a framework for interpreting the study's findings, placing them in dialogue with existing knowledge. By embracing a pragmatic lens, researchers can harness the power of the literature review to produce impactful and relevant research that contributes to both theoretical understanding and practical application in educational contexts. The article concludes by emphasizing the dynamic and essential nature of the literature review, which evolves throughout the research process and enhances the relevance, rigour, and potential impact of the study on educational practice and policy.

This article explores the concept of the post-methods era in language teaching, which advocates for moving beyond rigid, prescriptive methodologies. The author argues that no single method can address the diverse needs of all learners and contexts. Instead, teachers should adopt an eclectic and context-sensitive approach, drawing on a range of methods to suit specific situations. Key characteristics of this era include a focus on the learner, the importance of context, authenticity in language use, and the teacher as a reflective practitioner. The article highlights the limitations of traditional methods and the influence of sociolinguistics and psycholinguistics in shaping modern language teaching. Practical implications for teachers include informed eclecticism, needs analysis, materials development, action research, and continuous professional development. While the post-methods approach offers flexibility and adaptability, it also presents challenges, particularly for novice teachers and in systems reliant on traditional methods. The article concludes that ongoing research and dialogue are essential for refining effective language teaching practices in this era.

This research study aims to explore the current state of EFL student teacher education programmes in preparing teachers to teach English to students with special educational needs (SEN) in Egypt and other Arab-speaking countries, and subsequently, to propose a framework for preparing English as a foreign language (EFL) teachers to teach students with SEN in Egypt based on experiences in Egypt and other Arab-speaking countries. This involves identifying the main challenges and needs of EFL student teachers and their educators in relation to teaching English to students with SEN in Egypt based on experiences in other Arab-speaking countries along with the existing policies and practices that support or hinder the implementation of inclusive education for EFL learners with SEN in Arab-speaking countries in general and Egypt in particular. The proposed framework is based on a review of the literature on inclusive education (IE) and foreign language teaching for SEN learners, as well as semi-structured interviews with 50 EFL teacher educators from seven Egyptian universities. The study identifies three main components of the framework: (1) developing general and pedagogical knowledge and skills of EFL teachers to cater for the diverse needs of SEN learners, (2) enhancing self-efficacy beliefs and positive attitudes of EFL teachers towards IE and SEN learners, and (3) incorporating relevant information and communication technology (ICT) tools to support EFL teaching and learning for SEN learners. The paper also discusses the challenges and implications of implementing the framework in the Arab context, especially in Egypt, and suggests directions for future research. [This paper was published in "CDELT Occasional Papers in the Development of English Education" v88 n1 p287-316 2024.]

The field of English Language Teaching (ELT) and Learning is constantly evolving, influenced by advancements in educational theory, technology, and global trends. This article aims to explore several key issues and concepts in ELT, focusing on modern approaches and challenges faced by educators and learners alike. I will examine topics such as contextualised teaching and learning, functional linguistics, web-based new literacies and new literacy practices, situated language learning, lifelong learning, cognitive apprenticeship, online professional development, and the challenges specific to teaching English as a Foreign Language (EFL) in the Egyptian context.

This research study explores the potential of a pedagogical model of self-regulated learning supported with Artificial Intelligence (AI) chatbots to enhance self-expression and reflective writing skills for novice EFL student teachers at Faculty of Education, Assiut University. The study adopted a pre-post quasi-experimental design, that starts with the identification of the necessary self-expression and reflective writing skills for the target participants (50 fresh EFL student teachers at Assiut University who were purposively selected using a screening questionnaire based on their basic IT literacy skills). A pre-test was administered to assess their initial skill levels in self-expression and reflective writing. Then, an intervention was implemented in the form of a pedagogical model designed around the principles of self-regulated learning and situated language learning, which guided the use of AI chatbots (Bing, ChatGPT, and Google Bard). This model was initially piloted on a small sample (n = 10) of EFL student teachers to check validity and reliability and then experimented with the research participants for 8 weeks during the first semester of the academic year 2023/24. Following the intervention, a post-test was conducted to measure the participants' levels of self-expression and reflective writing skills after being exposed to the interventional model, aiming to identify any improvements gained from the intervention. The results indicated a positive effect with noticeable enhancements in the EFL student teachers' skills. This suggests the potential effectiveness of the model in fostering self-expression and reflective writing skills and developing EFL student teachers' general language proficiency and IT literacy. [This paper was published in "Academic Journal of Faculty of Education, Assiut University" v40 n9 p1-50 2024.]

Although the use of social networks, especially Facebook, has become a common practice among educational communities in Egypt, studies have yet to explore the online practices of in-service (language) teachers as part of Continuing Professional Development (hereafter CPD). Therefore, this study investigated English language teachers' informal online professional development that may include some types of language learning for improving their pedagogical content knowledge. It also explored differences between teachers' perceived use of Facebook as a venue of CPD. This study employed a mixed-method design, collecting qualitative and quantitative data via content analysis and a questionnaire. The participants were 180 English-language Egyptian in-service teachers. The qualitative analysis of teacher's posts demonstrates that CPD informal groups, as maintained by Egyptian EFL teachers, display different aspects of professional knowledge. Results showed that the data yielded seven main categories: General Pedagogical Knowledge (PK), Content Knowledge (CK), L2 Pedagogical Content Knowledge, Knowledge of L2 Learners, Knowledge of Educational context and Knowledge of (professional) self and miscellaneous topics. Besides, ANOVA's Welch test indicated significant differences in the means of the sum scores of the questionnaire as well as two sub-domains (PK and CK) among primary, preparatory and secondary teachers in some aspects, such as pronunciation and vocabulary. This study concludes that CPD Facebook groups provide wide options and spaces for continuous learning and informal study for EFL teachers. Pedagogical implications and recommendations for further research were suggested. [This paper was published in "CDELT Occasional Papers in the Development of English Education" v87 2024.]

The article presents a blended dialogic socio-constructivist pedagogy for language learning, emphasizing the importance of both individual and social aspects of learning. It argues that technology alone does not transform pedagogy; rather, it is the approach to technology that matters. The pedagogy combines personal freedom for reflection and individual assignments with social interactions like pair work and group discussions for collaborative knowledge construction. The progression from controlled, face-to-face activities to freer, online ones aids in consolidating new ideas and mastering language skills. The article supports its approach with literature on socio-constructivism, highlighting the synergy between individual constructivism and socio-culturalism. It draws on theories by Vygotsky and Piaget, advocating for a dialogic approach that extends learning beyond the classroom through web-mediated environments. This approach is particularly beneficial for English Language Learners (ELL), moving language learning from isolated mental functioning to real-world communication and fostering a participatory metaphor for language learning. The article concludes that blended learning, which combines web technologies with traditional pedagogies, is crucial for engaging learners in meaningful dialogue and practice.

Non-orthogonal multiple access (NOMA) is widely recognized for enhancing the energy and spectral efficiency through effective radio resource sharing. However, uplink NOMA systems face greater challenges than their downlink counterparts, as their bit error rate (BER) performance is hindered by an inherent error floor due to error propagation caused by imperfect successive interference cancellation (SIC). This paper investigates BER performance improvements enabled by reconfigurable intelligent surfaces (RISs) in multi-user uplink NOMA transmission. Specifically, we propose a novel RIS-assisted uplink NOMA design, where the RIS phase shifts are optimized to enhance the received signal amplitudes while mitigating the phase rotations induced by the channel. To achieve this, we first develop an accurate channel model for the effective user channels, which facilitates our BER analysis. We then introduce a channel alignment scheme for a two-user scenario, enabling efficient SIC-based detection and deriving closed-form BER expressions. We further extend the analysis to a generalized setup with an arbitrary number of users and modulation orders for quadrature amplitude modulation signaling. Using the derived BER expressions, we develop an optimized uplink NOMA power allocation (PA) scheme that minimizes the average BER while satisfying the user transmit power constraints. It will be shown that the proposed NOMA detection scheme, in conjunction with the optimized PA strategy, eliminate SIC error floors at the base station. The theoretical BER expressions are validated using simulations, which confirms the effectiveness of the proposed design in eliminating BER floors.

Estimating the cardinality of the output of a query is a fundamental problem in database query processing. In this article, we overview a recently published contribution that casts the cardinality estimation problem as linear optimization and computes guaranteed upper bounds on the cardinality of the output for any full conjunctive query. The objective of the linear program is to maximize the joint entropy of the query variables and its constraints are the Shannon information inequalities and new information inequalities involving $\ell_p$-norms of the degree sequences of the join attributes. The bounds based on arbitrary norms can be asymptotically lower than those based on the $\ell_1$ and $\ell_\infty$ norms, which capture the cardinalities and respectively the max-degrees of the input relations. They come with a matching query evaluation algorithm, are computable in exponential time in the query size, and are provably tight when each degree sequence is on one join attribute., Comment: Also in SIGMOD Record, March 2025

Analyzing large volumes of case law to uncover evolving legal principles, across multiple cases, on a given topic is a demanding task for legal professionals. Structured topical reports provide an effective solution by summarizing key issues, principles, and judgments, enabling comprehensive legal analysis on a particular topic. While prior works have advanced query-based individual case summarization, none have extended to automatically generating multi-case structured reports. To address this, we introduce LexGenie, an automated LLM-based pipeline designed to create structured reports using the entire body of case law on user-specified topics within the European Court of Human Rights jurisdiction. LexGenie retrieves, clusters, and organizes relevant passages by topic to generate a structured outline and cohesive content for each section. Expert evaluation confirms LexGenie's utility in producing structured reports that enhance efficient, scalable legal analysis.

Evolutionary Algorithms (EAs) employ random or simplistic selection methods, limiting their exploration of solution spaces and convergence to optimal solutions. The randomness in performing crossover or mutations may limit the model's ability to evolve efficiently. This paper introduces Preference-Aligned Individual Reciprocity (PAIR), a novel selection approach leveraging Large Language Models to emulate human-like mate selection, thereby introducing intelligence to the pairing process in EAs. PAIR prompts an LLM to evaluate individuals within a population based on genetic diversity, fitness level, and crossover compatibility, guiding more informed pairing decisions. We evaluated PAIR against a baseline method called LLM-driven EA (LMEA), published recently. Results indicate that PAIR significantly outperforms LMEA across various TSP instances, achieving lower optimality gaps and improved convergence. This performance is especially noticeable when combined with the flash thinking model, demonstrating increased population diversity to escape local optima. In general, PAIR provides a new strategy in the area of in-context learning for LLM-driven selection in EAs via sophisticated preference modelling, paving the way for improved solutions and further studies into LLM-guided optimization.

We introduce Phi-4-Mini and Phi-4-Multimodal, compact yet highly capable language and multimodal models. Phi-4-Mini is a 3.8-billion-parameter language model trained on high-quality web and synthetic data, significantly outperforming recent open-source models of similar size and matching the performance of models twice its size on math and coding tasks requiring complex reasoning. This achievement is driven by a carefully curated synthetic data recipe emphasizing high-quality math and coding datasets. Compared to its predecessor, Phi-3.5-Mini, Phi-4-Mini features an expanded vocabulary size of 200K tokens to better support multilingual applications, as well as group query attention for more efficient long-sequence generation. Phi-4-Multimodal is a multimodal model that integrates text, vision, and speech/audio input modalities into a single model. Its novel modality extension approach leverages LoRA adapters and modality-specific routers to allow multiple inference modes combining various modalities without interference. For example, it now ranks first in the OpenASR leaderboard to date, although the LoRA component of the speech/audio modality has just 460 million parameters. Phi-4-Multimodal supports scenarios involving (vision + language), (vision + speech), and (speech/audio) inputs, outperforming larger vision-language and speech-language models on a wide range of tasks. Additionally, we experiment to further train Phi-4-Mini to enhance its reasoning capabilities. Despite its compact 3.8-billion-parameter size, this experimental version achieves reasoning performance on par with or surpassing significantly larger models, including DeepSeek-R1-Distill-Qwen-7B and DeepSeek-R1-Distill-Llama-8B., Comment: 39 pages

With the rapidly growing availability of machine-learned interatomic potential (MLIP) models for chemistry, much current research focuses on the development of generally applicable and ``foundational'' MLIPs. An important question in this context is whether, and how well, such models can transfer from one application domain to another. Here, we assess this transferability for an MLIP model at the interface of materials and molecular chemistry. Specifically, we study GO-MACE-23, a model designed for the extended covalent network of graphene oxide, and quantify its zero-shot performance for small, isolated molecules and chemical reactions outside its direct scope--in direct comparison with a state-of-the-art model which has been trained in-domain. Our work provides quantitative insight into the transfer and generalisation ability of graph-neural-network potentials and, more generally, makes a step towards the more widespread applicability of MLIPs in chemistry.

Background and Purpose: Glioma segmentation is crucial for clinical decisions and treatment planning. Uncertainty quantification methods, including conformal prediction (CP), can enhance segmentation models reliability. This study aims to use CP in glioma segmentation. Methods: We used the UCSF and UPenn glioma datasets, with the UCSF dataset split into training (70%), validation (10%), calibration (10%), and test (10%) sets, and the UPenn dataset divided into external calibration (30%) and external test (70%) sets. A UNet model was trained, and its optimal threshold was set to 0.5 using prediction normalization. To apply CP, the conformal threshold was selected based on the internal/external calibration nonconformity score, and CP was subsequently applied to the internal/external test sets, with coverage reported for all. We defined the uncertainty ratio (UR) and assessed its correlation with the Dice score coefficient (DSC). Additionally, we categorized cases into certain and uncertain groups based on UR and compared their DSC. We also evaluate the correlation between UR and DSC of the BraTS fusion model segmentation (BFMS), and compare DSC in the certain and uncertain subgroups. Results: The base model achieved a DSC of 0.8628 and 0.8257 on the internal and external test sets, respectively. The CP coverage was 0.9982 for the internal test set and 0.9977 for the external test set. Statistical analysis showed a significant negative correlation between UR and DSC for test sets (p<0.001). UR was also linked to significantly lower DSCs in the BFMS (p<0.001). Additionally, certain cases had significantly higher DSCs than uncertain cases in test sets and the BFMS (p<0.001). Conclusion: CP effectively quantifies uncertainty in glioma segmentation. Using CONSeg improves the reliability of segmentation models and enhances human-computer interaction., Comment: 15 pages, 2 figures, 4 tables, 9 supplementary figures

Ensuring fairness in machine learning, particularly in human-centric applications, extends beyond algorithmic bias to encompass fairness in privacy, specifically the equitable distribution of privacy risk. This is critical in federated learning (FL), where decentralized data necessitates balanced privacy preservation across clients. We introduce FinP, a framework designed to achieve fairness in privacy by mitigating disproportionate exposure to source inference attacks (SIA). FinP employs a dual approach: (1) server-side adaptive aggregation to address unfairness in client contributions in global model, and (2) client-side regularization to reduce client vulnerability. This comprehensive strategy targets both the symptoms and root causes of privacy unfairness. Evaluated on the Human Activity Recognition (HAR) and CIFAR-10 datasets, FinP demonstrates ~20% improvement in fairness in privacy on HAR with minimal impact on model utility, and effectively mitigates SIA risks on CIFAR-10, showcasing its ability to provide fairness in privacy in FL systems without compromising performance.

We show that in systems with highly degenerate energy spectra, such as the 2D transverse-field Ising model (2DTFIM) in the strong-field limit, quantum chaos can emerge in finite systems for arbitrary small perturbations. In this regime, the presence of extensive quasi-conserved quantities can prevent finite systems from becoming ergodic. We study the ensuing transition to ergodicity in a family of models that includes the 2DTFIM, in which the onset of ergodic behavior exhibits universality and occurs for perturbation strengths that decrease polynomially with increasing system size. We discuss the behaviors of quantum chaos indicators, such as level spacing statistics and bipartite entanglement, and of the fidelity susceptibilities and spectral functions across the transitions., Comment: 8 pages, 6 figures

Mode-locking mechanisms are key resources in nonlinear optical phenomena, such as micro-ring solitonic states, and have transformed metrology, precision spectroscopy, and optical communication. However, despite significant efforts, mode-locking has not been demonstrated in the independently tunable multi-timescale regime. Here, we vastly expand the nonlinear mode-locking toolbox into multi-timescale synchronization on a chip. We use topological photonics to engineer a 2D lattice of hundreds of coupled silicon nitride ring resonators capable of hosting nested mode-locked states with a fast (near 1 THz) single-ring and a slow (near 3 GHz) topological super-ring timescales. We demonstrate signatures of multi-timescale mode-locking including quadratic distribution of the pump noise with the two-time azimuthal mode dimensions, as expected by mode-locking theory. Our observations are further corroborated by direct signatures of the near-transform-limit repetition beats and the formation of the temporal pattern on the slow timescale. Moreover, we show that these exotic properties of edge-confined mode-locked states are in sharp contrast to bulk and single-ring counterparts and establish a clear pathway for their identification. Our unprecedented demonstration of mode-locking in topological combs unlocks the implementation of lattice-scale synchronization and independently tunable multi-timescale mode-locking phenomena, also the exploration of the fundamental nonlinearity-topology interplay on a chip.

The rapid advancement of Generative Artificial Intelligence (GenAI) has introduced new opportunities for transforming higher education, particularly in fields that require analytical reasoning and regulatory compliance, such as cybersecurity management. This study presents a structured framework for integrating GenAI tools into cybersecurity education, demonstrating their role in fostering critical thinking, real-world problem-solving, and regulatory awareness. The implementation strategy followed a two-stage approach, embedding GenAI within tutorial exercises and assessment tasks. Tutorials enabled students to generate, critique, and refine AI-assisted cybersecurity policies, while assessments required them to apply AI-generated outputs to real-world scenarios, ensuring alignment with industry standards and regulatory requirements. Findings indicate that AI-assisted learning significantly enhanced students' ability to evaluate security policies, refine risk assessments, and bridge theoretical knowledge with practical application. Student reflections and instructor observations revealed improvements in analytical engagement, yet challenges emerged regarding AI over-reliance, variability in AI literacy, and the contextual limitations of AI-generated content. Through structured intervention and research-driven refinement, students were able to recognize AI strengths as a generative tool while acknowledging its need for human oversight. This study further highlights the broader implications of AI adoption in cybersecurity education, emphasizing the necessity of balancing automation with expert judgment to cultivate industry-ready professionals. Future research should explore the long-term impact of AI-driven learning on cybersecurity competency, as well as the potential for adaptive AI-assisted assessments to further personalize and enhance educational outcomes., Comment: 30 pages

We introduce a novel multilingual hierarchical corpus annotated for entity framing and role portrayal in news articles. The dataset uses a unique taxonomy inspired by storytelling elements, comprising 22 fine-grained roles, or archetypes, nested within three main categories: protagonist, antagonist, and innocent. Each archetype is carefully defined, capturing nuanced portrayals of entities such as guardian, martyr, and underdog for protagonists; tyrant, deceiver, and bigot for antagonists; and victim, scapegoat, and exploited for innocents. The dataset includes 1,378 recent news articles in five languages (Bulgarian, English, Hindi, European Portuguese, and Russian) focusing on two critical domains of global significance: the Ukraine-Russia War and Climate Change. Over 5,800 entity mentions have been annotated with role labels. This dataset serves as a valuable resource for research into role portrayal and has broader implications for news analysis. We describe the characteristics of the dataset and the annotation process, and we report evaluation results on fine-tuned state-of-the-art multilingual transformers and hierarchical zero-shot learning using LLMs at the level of a document, a paragraph, and a sentence., Comment: 23 pages, 12 figures. Submitted to ACL Rolling Review (ARR)

In this study, we report a first experimental demonstration of pulse compression by a gradual refractive index front moving in a periodically modulated silicon waveguide, the so-called optical push broom effect. Optical push broom captures and confines the input signal pulse in a faster propagating refractive index front, driven by a pump pulse. This is a spatio-temporal analogue of light trapping in a tapered plasmonic waveguide where light is continuously changing its wavevector approaching zero group velocity and, thus, stopped without reflection. Here the signal is accelerated by the front until the signal velocity matches the front velocity, thus stopping the light in respect to the front. We employ the slowly varying envelope approximation to model this phenomenon. Notably, we well reproduced the experimental frequency shift at the output corresponding to the temporal delay at the input.

We investigate the emergence of intuitive physics understanding in general-purpose deep neural network models trained to predict masked regions in natural videos. Leveraging the violation-of-expectation framework, we find that video prediction models trained to predict outcomes in a learned representation space demonstrate an understanding of various intuitive physics properties, such as object permanence and shape consistency. In contrast, video prediction in pixel space and multimodal large language models, which reason through text, achieve performance closer to chance. Our comparisons of these architectures reveal that jointly learning an abstract representation space while predicting missing parts of sensory input, akin to predictive coding, is sufficient to acquire an understanding of intuitive physics, and that even models trained on one week of unique video achieve above chance performance. This challenges the idea that core knowledge -- a set of innate systems to help understand the world -- needs to be hardwired to develop an understanding of intuitive physics., Comment: 24 pages,14 figures, 5 tables

Prior studies have shown that distinguishing text generated by large language models (LLMs) from human-written one is highly challenging, and often no better than random guessing. To verify the generalizability of this finding across languages and domains, we perform an extensive case study to identify the upper bound of human detection accuracy. Across 16 datasets covering 9 languages and 9 domains, 19 annotators achieved an average detection accuracy of 87.6%, thus challenging previous conclusions. We find that major gaps between human and machine text lie in concreteness, cultural nuances, and diversity. Prompting by explicitly explaining the distinctions in the prompts can partially bridge the gaps in over 50% of the cases. However, we also find that humans do not always prefer human-written text, particularly when they cannot clearly identify its source.

For an elliptic CW-complex $X$, we denote its group of self-homotopy equivalences as $\E(X)$, and its subgroup consisting of elements inducing the identity on the homology groups as $\E_{*}(X)$. This paper aims to investigate how the homology groups of $X$ influence the finiteness of $\E(X)$ and $\E_{*}(X)$.

We probabilistically analyze the performance of the arithmetic coding algorithm under a probability model for binary data in which a message is received by a coder from a source emitting independent equally distributed bits, with 1 occurring with probability $p\in(0,1)$ and 0 occurring with probability $1-p$. We establish a functional equation for the bivariate moment generating function for the two ends of the final interval delivered by the algorithm. Via the method of moments, we show that the transmitted message converges in distribution to the standard continuous uniform random variable on the interval [0,1]. It is remarkable that the limiting distribution is the same for all $p$, indicating robustness in the performance of arithmetic coding across an entire family of bit distributions. The nuance with $p$ appears only in the rate of convergence.

The complexities inherent to leukemia, multifaceted cancer affecting white blood cells, pose considerable diagnostic and treatment challenges, primarily due to reliance on laborious morphological analyses and expert judgment that are susceptible to errors. Addressing these challenges, this study presents a refined, comprehensive strategy leveraging advanced deep-learning techniques for the classification of leukemia subtypes. We commence by developing a hierarchical label taxonomy, paving the way for differentiating between various subtypes of leukemia. The research further introduces a novel hierarchical approach inspired by clinical procedures capable of accurately classifying diverse types of leukemia alongside reactive and healthy cells. An integral part of this study involves a meticulous examination of the performance of Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) as classifiers. The proposed method exhibits an impressive success rate, achieving approximately 90\% accuracy across all leukemia subtypes, as substantiated by our experimental results. A visual representation of the experimental findings is provided to enhance the model's explainability and aid in understanding the classification process., Comment: 9 pages, 11 figures

In the contemporary era of intelligent connectivity, Affective Computing (AC), which enables systems to recognize, interpret, and respond to human behavior states, has become an integrated part of many AI systems. As one of the most critical components of responsible AI and trustworthiness in all human-centered systems, explainability has been a major concern in AC. Particularly, the recently released EU General Data Protection Regulation requires any high-risk AI systems to be sufficiently interpretable, including biometric-based systems and emotion recognition systems widely used in the affective computing field. Existing explainable methods often compromise between interpretability and performance. Most of them focus only on highlighting key network parameters without offering meaningful, domain-specific explanations to the stakeholders. Additionally, they also face challenges in effectively co-learning and explaining insights from multimodal data sources. To address these limitations, we propose a novel and generalizable framework, namely the Attention-Guided Concept Model (AGCM), which provides learnable conceptual explanations by identifying what concepts that lead to the predictions and where they are observed. AGCM is extendable to any spatial and temporal signals through multimodal concept alignment and co-learning, empowering stakeholders with deeper insights into the model's decision-making process. We validate the efficiency of AGCM on well-established Facial Expression Recognition benchmark datasets while also demonstrating its generalizability on more complex real-world human behavior understanding applications.

We explore the structural, magnetic, vibrational and optical band gap properties under varying pressures. By integrating first-principles calculations with experimental techniques, including Raman spectroscopy, photoluminescence (PL), uniaxial pressure studies (thermal expansion), and magnetization measurements, we unveil the intricate pressure-induced transformations in Fe-doped CrCl3, shedding light on its structural, electronic, and magnetic evolution. At ambient pressure, Raman spectra confirm all expected Raman-active modes, which exhibit blue shifts with increasing pressure. The PL measurements demonstrate an optical bandgap of 1.48 eV at ~0.6 GPa, with a progressive increase in the bandgap under pressure, transitioning slower above 6 GPa due to an isostructural phase transition. Magnetization results under pressure shows two competing magnetic components (FM and AFM) at ambient conditions, where at the lowest temperature and applied field, the FM component dominates. The presence of competing FM and AFM energy scales is confirmed by Grueneisen analysis of the thermal expansion and their uniaxial pressure dependence is determined. The experimental findings agree with theoretical results based on Density functional theory (DFT). In the experiments, we observe a pressure-enhanced ferromagnetic interlayer coupling that is followed by the stabilization of antiferromagnetic ordering, due to weakened direct interlayer interactions. Above 1.2 GPa the FM component of the magnetism is gone in the experimental observations, which is also in good agreement with DFT based theory. The findings reported here underscore the potential of CrCl3 for use in pressure-tunable magnetic and optoelectronic applications, where, e.g., the delicate balance between FM and AFM configurations could have potential for sensor applications., Comment: 10 pages, 11 figures

Brain activity translation into human language delivers the capability to revolutionize machine-human interaction while providing communication support to people with speech disability. Electronic decoding reaches a certain level of achievement yet current EEG-to-text decoding methods fail to reach open vocabularies and depth of meaning and individual brain-specific variables. We introduce a special framework which changes conventional closed-vocabulary EEG-to-text decoding approaches by integrating subject-specific learning models with natural language processing methods to resolve detection obstacles. This method applies a deep representation learning approach to extract important EEG features which allow training of neural networks to create elaborate sentences that extend beyond original data content. The ZuCo dataset analysis demonstrates that research findings achieve higher BLEU, ROUGE and BERTScore performance when compared to current methods. The research proves how this framework functions as an effective approach to generate meaningful and correct texts while understanding individual brain variations. The proposed research aims to create a connection between open-vocabulary Text generation systems and human brain signal interpretation for developing efficacious brain-to-text systems. The research produces interdisciplinary effects through innovative assistive technology development and personalized communication systems which extend possibilities for human-computer interaction in various settings., Comment: 21 pages, 11 figures, and 6 tables

Cardinality estimation is the problem of estimating the size of the output of a query, without actually evaluating the query. The cardinality estimator is a critical piece of a query optimizer, and is often the main culprit when the optimizer chooses a poor plan. This paper introduces LpBound, a pessimistic cardinality estimator for multijoin queries (acyclic or cyclic) with selection predicates and group-by clauses. LpBound computes a guaranteed upper bound on the size of the query output using simple statistics on the input relations, consisting of $\ell_p$-norms of degree sequences. The bound is the optimal solution of a linear program whose constraints encode data statistics and Shannon inequalities. We introduce two optimizations that exploit the structure of the query in order to speed up the estimation time and make LpBound practical. We experimentally evaluate LpBound against a range of traditional, pessimistic, and machine learning-based estimators on the JOB, STATS, and subgraph matching benchmarks. Our main finding is that LpBound can be orders of magnitude more accurate than traditional estimators used in mainstream open-source and commercial database systems. Yet it has comparable low estimation time and space requirements. When injected the estimates of LpBound, Postgres derives query plans at least as good as those derived using the true cardinalities.

This paper presents two industry-grade datasets captured during an 8-hour continuous operation of the manufacturing assembly line at the Future Factories Lab, University of South Carolina, on 08/13/2024. The datasets adhere to industry standards, covering communication protocols, actuators, control mechanisms, transducers, sensors, and cameras. Data collection utilized both integrated and external sensors throughout the laboratory, including sensors embedded within the actuators and externally installed devices. Additionally, high-performance cameras captured key aspects of the operation. In a prior experiment [1], a 30-hour continuous run was conducted, during which all anomalies were documented. Maintenance procedures were subsequently implemented to reduce potential errors and operational disruptions. The two datasets include: (1) a time-series analog dataset, and (2) a multi-modal time-series dataset containing synchronized system data and images. These datasets aim to support future research in advancing manufacturing processes by providing a platform for testing novel algorithms without the need to recreate physical manufacturing environments. Moreover, the datasets are open-source and designed to facilitate the training of artificial intelligence models, streamlining research by offering comprehensive, ready-to-use resources for various applications and projects.

Efficient online learning requires seamless access to diverse resources such as videos, code repositories, documentation, and general web content. This poster paper introduces early-stage work on a Multi-Agent Retrieval-Augmented Generation (RAG) System designed to enhance learning efficiency by integrating these heterogeneous resources. Using specialized agents tailored for specific resource types (e.g., YouTube tutorials, GitHub repositories, documentation websites, and search engines), the system automates the retrieval and synthesis of relevant information. By streamlining the process of finding and combining knowledge, this approach reduces manual effort and enhances the learning experience. A preliminary user study confirmed the system's strong usability and moderate-high utility, demonstrating its potential to improve the efficiency of knowledge acquisition.

Activation functions are fundamental elements of deep learning architectures as they significantly influence training dynamics. ReLU, while widely used, is prone to the dying neuron problem, which has been mitigated by variants such as LeakyReLU, PReLU, and ELU that better handle negative neuron outputs. Recently, self-gated activations like GELU and Swish have emerged as state-of-the-art alternatives, leveraging their smoothness to ensure stable gradient flow and prevent neuron inactivity. In this work, we introduce the Gompertz Linear Unit (GoLU), a novel self-gated activation function defined as $\mathrm{GoLU}(x) = x \, \mathrm{Gompertz}(x)$, where $\mathrm{Gompertz}(x) = e^{-e^{-x}}$. The GoLU activation leverages the asymmetry in the Gompertz function to reduce variance in the latent space more effectively compared to GELU and Swish, while preserving robust gradient flow. Extensive experiments across diverse tasks, including Image Classification, Language Modeling, Semantic Segmentation, Object Detection, Instance Segmentation, and Diffusion, highlight GoLU's superior performance relative to state-of-the-art activation functions, establishing GoLU as a robust alternative to existing activation functions., Comment: 8 pages, excluding references and appendix

This paper presents a novel AI-based smart traffic management system de-signed to optimize traffic flow and reduce congestion in urban environments. By analysing live footage from existing CCTV cameras, this approach eliminates the need for additional hardware, thereby minimizing both deployment costs and ongoing maintenance expenses. The AI model processes live video feeds to accurately count vehicles and assess traffic density, allowing for adaptive signal control that prioritizes directions with higher traffic volumes. This real-time adaptability ensures smoother traffic flow, reduces congestion, and minimizes waiting times for drivers. Additionally, the proposed system is simulated using PyGame to evaluate its performance under various traffic conditions. The simulation results demonstrate that the AI-based system out-performs traditional static traffic light systems by 34%, leading to significant improvements in traffic flow efficiency. The use of AI to optimize traffic signals can play a crucial role in addressing urban traffic challenges, offering a cost-effective, scalable, and efficient solution for modern cities. This innovative system represents a key advancement in the field of smart city infra-structure and intelligent transportation systems.

The history of art has seen significant shifts in the manner in which artworks are created, making understanding of creative processes a central question in technical art history. In the Renaissance and Early Modern period, paintings were largely produced by master painters directing workshops of apprentices who often contributed to projects. The masters varied significantly in artistic and managerial styles, meaning different combinations of artists and implements might be seen both between masters and within workshops or even individual canvases. Information on how different workshops were managed and the processes by which artworks were created remains elusive. Machine learning methods have potential to unearth new information about artists' creative processes by extending the analysis of brushwork to a microscopic scale. Analysis of workshop paintings, however, presents a challenge in that documentation of the artists and materials involved is sparse, meaning external examples are not available to train networks to recognize their contributions. Here we present a novel machine learning approach we call pairwise assignment training for classifying heterogeneity (PATCH) that is capable of identifying individual artistic practice regimes with no external training data, or "ground truth." The method achieves unsupervised results by supervised means, and outperforms both simple statistical procedures and unsupervised machine learning methods. We apply this method to two historical paintings by the Spanish Renaissance master, El Greco: The Baptism of Christ and Christ on the Cross with Landscape, and our findings regarding the former potentially challenge previous work that has assigned the painting to workshop members. Further, the results of our analyses create a measure of heterogeneity of artistic practice that can be used to characterize artworks across time and space., Comment: main text: 16 pages, 6 figures; SI: 7 pages, 3 figures; v2: minor typo corrections, higher resolution figures

The extensive use of GPUs in cloud computing and the growing need for multitenancy have driven the development of innovative solutions for efficient GPU resource management. Multi-Instance GPU (MIG) technology from NVIDIA enables shared GPU usage in cloud data centers by providing isolated instances. However, MIG placement rules often lead to fragmentation and suboptimal resource utilization. In this work, we formally model the MIG-enabled VM placement as a multi-objective Integer Linear Programming (ILP) problem aimed at maximizing request acceptance, minimizing active hardware usage, and reducing migration overhead. Building upon this formulation, we propose GRMU, a multi-stage placement framework designed to address MIG placement challenges. GRMU performs intra-GPU migrations for defragmentation of a single GPU and inter-GPU migrations for consolidation and resource efficiency. It also employs a quota-based partitioning approach to allocate GPUs into two distinct baskets: one for large-profile workloads and another for smaller-profile workloads. Each basket has predefined capacity limits, ensuring fair resource distribution and preventing large-profile workloads from monopolizing resources. Evaluations on a real-world Alibaba GPU cluster trace reveal that GRMU improves acceptance rates by 22%, reduces active hardware by 17%, and incurs migration for only 1% of MIG-enabled VMs, demonstrating its effectiveness in minimizing fragmentation and improving resource utilization.

We consider unified dark sector models in which the fluid can collapse and cluster into halos, allowing for hierarchical structure formation to proceed as in standard cosmology. We show that both background evolution and linear perturbations tend towards those in $\LCDM$ as the clustered fraction $f \rightarrow 1$. We confront such models with various observational datasets, with emphasis on the relatively well motivated standard Chaplygin gas. We show that the strongest constraints come from secondary anisotropies in the CMB spectrum, which prefer models with $f \rightarrow 1$. However, as a larger Hubble constant is allowed for smaller $f$, values of $f \simeq 0.99$ (rather than tending to exact unity) are favored when late universe expansion data is included, with $f \simeq 0.97$ and $H_0 \simeq 70 {\rm km/s/Mpc}$ allowed at the 2-$\sigma$ level. Such values of $f$ imply extremely efficient clustering into nonlinear structures. They may nevertheless be compatible with clustered fractions in warm dark matter based cosmologies, which have similar minimal halo mass scales as the models considered here. Tight CMB constraints on $f$ also apply to the generalized Chaplygin gas, except for models that are already quite close to $\LCDM$, in which case all values of $0 \le f \le 1$ are allowed. In contrast to the CMB, large scale structure data, which were initially used to rule out unclustered unified dark matter models, are far less constraining. Indeed, late universe data, including the large scale galaxy distribution, prefer models that are far from $\LCDM$. But these are in tension with the CMB data., Comment: To appear in Phys. Rev. D

As they become more capable, large language models (LLMs) have continued to rapidly increase in size. This has exacerbated the difficulty in running state of the art LLMs on small, edge devices. Standard techniques advocate solving this problem through lossy compression techniques such as quantization or pruning. However, such compression techniques are lossy, and have been shown to change model behavior in unpredictable manners. We propose Huff-LLM, an \emph{end-to-end, lossless} model compression method that lets users store LLM weights in compressed format \emph{everywhere} -- cloud, disk, main memory, and even in on-chip memory/buffers. This allows us to not only load larger models in main memory, but also reduces bandwidth required to load weights on chip, and makes more efficient use of on-chip weight buffers. In addition to the memory savings achieved via compression, we also show latency and energy efficiency improvements when performing inference with the compressed model.

The aging sewer infrastructure in the U.S., covering 2.1 million kilometers, encounters increasing structural issues, resulting in around 75,000 yearly sanitary sewer overflows that present serious economic, environmental, and public health hazards. Conventional inspection techniques and deterministic models do not account for the unpredictable nature of sewer decline, whereas probabilistic methods depend on extensive historical data, which is frequently lacking or incomplete. This research intends to enhance predictive accuracy for the condition of sewer pipelines through machine learning models artificial neural networks (ANNs) and multiple linear regression (MLR) by integrating factors such as pipe age, material, diameter, environmental influences, and PACP ratings. ANNs utilized ReLU activation functions and Adam optimization, whereas MLR applied regularization to address multicollinearity, with both models assessed through metrics like RMSE, MAE, and R2. The findings indicated that ANNs surpassed MLR, attaining an R2 of 0.9066 compared to MLRs 0.8474, successfully modeling nonlinear relationships while preserving generalization. MLR, on the other hand, offered enhanced interpretability by pinpointing significant predictors such as residual buildup. As a result, pipeline degradation is driven by pipe length, age, and pipe diameter as key predictors, while depth, soil type, and segment show minimal influence in this analysis. Future studies ought to prioritize hybrid models that merge the accuracy of ANNs with the interpretability of MLR, incorporating advanced methods such as SHAP analysis and transfer learning to improve scalability in managing infrastructure and promoting environmental sustainability.

The design of spin-orbit torque properties in two-dimensional (2D) materials presents one of the challenges of modern spintronics. In this context, 2D layers involving rare-earth ions $-$ which give rise to robust magnetism, exhibit pronounced orbital polarization of the states, and carry strong spin-orbit interaction $-$ hold particular promise. Here, we investigate ferromagnetic Janus H-phase monolayers of 4$f$-Eu rare-earth dichalcogenides EuSP, EuSSe, and EuSCl using first-principles calculations. We demonstrate that all compounds exhibit significant spin-orbit torques which originate predominantly in the colossal current-induced orbital response on the Eu $f$-electrons. Moreover, we demonstrate that the corresponding orbital torques can be used to drive strong in-plane currents of orbital angular momentum with non-trivial direction of orbital polarization. Our findings promote $f$-orbital-based 2D materials as a promising platform for in-plane orbital pumping and spin-orbit torque applications, and motivate further research on educated design of orbital properties for orbitronics with 2D materials., Comment: 13 pages, 4 figures

Quantum measurements with feed-forward are crucial components of fault-tolerant quantum computers. We show how the error rate of such a measurement can be directly estimated by fitting the probability that successive randomly compiled measurements all return the ideal outcome. Unlike conventional randomized benchmarking experiments and alternative measurement characterization protocols, all the data can be obtained using a single sufficiently large number of successive measurements. We also prove that generalized Pauli fidelities are invariant under randomized compiling and can be combined with the error rate to characterize the underlying errors up to a gauge transformation that introduces an ambiguity between errors happening before or after measurements., Comment: 6 pages + appendix, comments welcome

Objectives: Lung cancer poses a significant global health challenge, necessitating improved prognostic methods for personalized treatment. This study introduces a censor-aware semi-supervised learning (SSL) framework that integrates clinical and imaging data, addressing biases in traditional models handling censored data. Methods: We analyzed clinical, PET and CT data from 199 lung cancer patients from public and local data respositories, focusing on overall survival (OS) time as the primary outcome. Handcrafted (HRF) and Deep Radiomics features (DRF) were extracted after preprocessing using ViSERA software and were combined with clinical features (CF). Feature dimensions were optimized using Principal Component Analysis (PCA), followed by the application of supervised learning (SL) and SSL. SSL incorporated pseudo-labeling of censored data to improve performance. Seven regressors and three hazard ratio survival analysis (HRSA) algorithms were optimized using five-fold cross-validation, grid search and external test bootstrapping. Results: For PET HRFs, SSL reduced the mean absolute error (MAE) by 26.5%, achieving 1.55 years with PCA+decision tree regression, compared to SL's 2.11 years with PCA+KNNR (p<0.05). Combining HRFs (CT_HRF) and DRFs from CT images using SSL+PCA+KNNR achieved an MAE of 2.08 years, outperforming SL's 2.26 years by 7.96% (p<0.05). In HRSA, CT_HRF applied to PCA+Component Wise Gradient Boosting Survival Analysis achieved an external c-index of 0.65, effectively differentiating high- and low-risk groups. Conclusions: We demonstrated that the SSL strategy significantly outperforms SL across PET, CT, and CF. As such, censor-aware SSL applied to HRFs from PET images significantly improved survival prediction performance by 26.5% compared to the SL approach., Comment: 11 pages, 4 Figures and 4 Tables