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The Commonwealth of the Northern Mariana Islands Public School System requested a systematic review of Tier 2 literacy interventions for students in grades K-3. This review defines a Tier 2 literacy intervention as a supplemental instructional program for students who require support in addition to the Tier 1 core reading program. Of the 267 studies on Tier 2 literacy interventions identified, 20 met What Works Clearinghouse 5.0 standards with or without reservations. Two interventions--Reading Recovery and Literacy First--had strong evidence of positive effects (as defined by the Every Student Succeeds Act) on students' literacy skills. One additional intervention--Achieve3000--had moderate evidence of positive effects. This report includes an evidence and gap map and a supplemental matrix that highlights implementation strategies used in each intervention.

We present broadband X-ray observations of the High Mass X-ray Binary (HMXB) pulsar SMC X-2, using concurrent NuSTAR and NICER observations during its 2022 outburst. The source is found to be spinning with a period of 2.37281(3) s. We confirm the existence of cyclotron resonant scattering feature (CRSF) at 31 keV in addition to the iron emission line in the X-ray continuum of the source. Spectral analysis performed with the physical bulk and thermal Comptonization model indicates that the bulk Comptonization dominates the thermal Comptonization. Using phase-resolved spectroscopy, we have investigated the variations of the spectral parameters relative to pulse phase that may be due to the complex structure of magnetic field of the pulsar or the impact of the emission geometry. It is observed that the spectral parameters exhibit significant variabilities relative to the pulsed phase. Time-resolved spectroscopy is employed to examine the evolution of the continuum and changes in the spectral characteristics. Measurements of luminosity along with variations in cyclotron line energy and photon index suggest that the source may be accreting in the super-critical regime., Comment: Accepted for Publication in New Astronomy

The study of causal structure in complex systems has gained increasing attention, with many recent studies exploring causal networks that capture cause-effect relationships across diverse fields. Despite increasing empirical evidence linking causal structures to network topological correlations, the mechanisms underlying the emergence of these correlations in causal networks remain poorly understood. In this work, we propose a general growth framework for causal networks, incorporating two key types of correlations: causal and dynamic. We analytically demonstrate that degree correlations emerge as a consequence of marginal dependencies on these correlations. Our theoretical predictions align quantitatively with empirical data from four large-scale innovation networks. Our theory not only sheds light on the origins of topological correlations but also provides a general framework for understanding correlated growth across causal systems.

Hydrogen is considered an alternative source of energy to fossil fuels for the fulfilment of current energy demands. Photocatalysis initiates the hydrogen evolution reaction which is believed to be the greenest approach to produce hydrogen through clean, safe, and environmentally friendly methods. In this Review, we focus mainly on the comprehensive analysis of the 2D and 3D bulk materials on the basis of their superior photocatalytic activities. However, several literatures have reported the superiority of 2D material over the bulk counterpart in terms of photocatalytic performance owing to their ultrathin layered structures, offer a higher surface-to-volume ratio, flexibility, large active sites for incoming H2O molecules, etc. We have thoroughly analysed the drawbacks of various hydrogen production methods focusing on the photocatalysis mechanism and the processes of evolution of hydrogen. In addition to this, a short overview of the various solid-state materials for photocatalysis that have been developed so far and their mechanisms are discussed. Lastly, we have discussed the recent developments in 2D materials and their composites as promising photocatalysts.

Recently, a new magnetic phase, termed altermagnetism, has caught the attention of the magnetism and spintronics community. This newly discovered magnetic phenomenon differs from traditional ferromagnetism and antiferromagnetic. It generally lacks net magnetization and is characterized by unusual non-relativistic spin-splitting and broken time-reversal symmetry. This leads to novel transport properties such as the anomalous Hall effect, the crystal Nernst effect, and spin-dependent phenomena that cannot be fully explained by traditional magnetic theories. Spin-dependent phenomena such as spin currents, spin-splitter torques, and high-frequency dynamics emerge as key characteristics in altermagnets. This paper reviews the main aspects pertaining to altermagnets by providing an overview of theoretical investigations and experimental realizations. We discuss the most recent developments in altermagnetism, its comparison to other magnetic orders, and future prospects for exploiting its unique properties in next-generation devices.

Large Language Models (LLMs) based on transformer architectures have revolutionized a variety of domains, with tokenization playing a pivotal role in their pre-processing and fine-tuning stages. In multilingual models, particularly those tailored for Indic languages, effective tokenization is crucial for optimizing performance. This paper presents a comprehensive evaluation of tokenizers used by 12 LLMs across all 22 official languages of India, with a focus on comparing the efficiency of their tokenization processes. We employed the Normalized Sequence Length (NSL) as a key metric in our analysis. Our findings reveal that the SUTRA tokenizer outperforms all other models, including several Indic-specific models, excelling in 14 languages. Notable insights include the SUTRA tokenizer's superior handling of Indic languages, GPT-4o's advancement over its predecessor GPT-4 in processing Indian languages, and the limited performance of Project Indus in certain languages. This study underscores the critical importance of developing targeted tokenization strategies for multilingual and Indic-centric models, laying the groundwork for future improvements in tokenizer design to enhance linguistic coverage and model efficiency.

In this paper, we obtain a restricted decomposition formula for interpolated multiple zeta values using t-stuffle product. We then derive a recursive formula of t-stuffle product, which also provides a route to the same formula. In both cases, combinatorial description of t-stuffle product is our basic tool. We also provide alternative proofs by mathematical induction for some of the results., Comment: 22 Pages

Centaurus X-3 is a persistent high-mass X-ray binary with the long-term light curve from the source exhibiting orbit-to-orbit intensity variations with no apparent superorbital periodicity. We used $\sim$13.5 years of MAXI/GSC data to study the long-term behaviour of X-ray absorption caused by the stellar wind from the companion star and any absorbing structures present in the binary. We used orbital-phase-resolved spectroscopy to study the variation in the photoelectric absorption along the line of sight of the source for both the intensity-averaged data and intensity-resolved data after dividing all the data binned with orbital period into three intensity levels. We find an asymmetric variation in the photoelectric absorption along the line of sight across an orbit of the source. The orbital-phase-resolved spectra show a clear increase in photoelectric absorption after $\phi_\text{orb}\sim$ 0.5, which deviates from a spherically symmetric stellar wind model. The flux of Cen X-3 shows significant variation between consecutive orbits. An intensity-resolved spectral analysis of the source was performed, followed by an intensity-resolved and orbital-phase-resolved spectral analysis, which showed that at the medium and high intensity levels, the orbital-phase-resolved photoelectric absorption is slightly asymmetric with respect to mid-phase ($\phi_\text{orb}=$ 0.5). The asymmetry is very pronounced at the lowest intensity level and cannot be explained by a spherically symmetric wind from the companion star. The differences in the orbital phase-dependence of absorption for different intensity levels suggest that the presence of an accretion wake, photoionization wake, or tidal stream is more prominent at a lower intensity level for Centaurus X-3 than at a higher intensity level., Comment: Accepted for publication in Astronomy and Astrophysics

This paper introduces a centralized, open-source dataset repository designed to advance NLP and NMT for Assamese, a low-resource language. The repository, available at GitHub, supports various tasks like sentiment analysis, named entity recognition, and machine translation by providing both pre-training and fine-tuning corpora. We review existing datasets, highlighting the need for standardized resources in Assamese NLP, and discuss potential applications in AI-driven research, such as LLMs, OCR, and chatbots. While promising, challenges like data scarcity and linguistic diversity remain. The repository aims to foster collaboration and innovation, promoting Assamese language research in the digital age., Comment: 6 pages, 1 table, 1 figure

Evidence of chirality was observed at the Fe metal center in Fe(III) spin crossover coordination salts [Fe(qsal)2][ Ni(dmit)2] and [Fe(qsal)2](TCNQ)2 from X-ray absorption spectroscopy at the Fe 2p3/2 core threshold. This indicates the formation of chiral domains that influence the octahedral coordination on the Fe core., Comment: 5 pages, 4 figures. Submitted for publication in JPCM

In today's digital age, cyberspace has become integral to daily life, however it has also led to an increase in cybercriminal activities. This paper explores cybercrime trends and highlights the need for cybercrime awareness (cyberawareness) to mitigate vulnerabilities. The study also examines Indian statistics on cybercrime. We review the existing literature on cybercrime and cybersecurity, focusing on various types of cybercrimes and their impacts. We present a list of 31 technical as well as non-technical solutions considering that a "common man" may not be technologically aware. Common man solutions, considering that they are not technologically updated. Expanding the list of solutions and validating their effectiveness in cyber threats can be the future scope of the research.

In this paper, we have gone through different AI-Based frameworks used for various educational tasks like digital customized assignment allotment and performance monitoring, identifying slow-learners and fast-learners, etc. application describes a novel invention, digital screen-integrated tables, designed specifically for educational settings. The tables feature integrated digital screens controlled by a central processing unit (CPU), enabling synchronized display of educational content such as textbooks, presentations, exam questions, and interactive learning materials. Additionally, the invention facilitates the collection of student performance data during classroom activities and assessments. The gathered data is utilized for analysis using machine learning models to identify patterns and trends in student learning behaviours. By leveraging machine learning algorithms, educators can ascertain whether a student is a fast learner or a slow learner, based on which, the teacher can allocate more resources to the slow learners. This innovative approach aims to address the evolving needs of modern classrooms by providing a dynamic and data-driven learning environment. The unique integration of digital screens into traditional classroom furniture represents a significant advancement in educational technology. This patent filing encompasses the design, functionality, and method of operation of the digital screen-integrated tables, emphasizing their innovative features and applications in educational institutions.

Training of a tokenizer plays an important role in the performance of deep learning models. This research aims to understand the performance of tokenizers in five state-of-the-art (SOTA) large language models (LLMs) in the Assamese language of India. The research is important to understand the multi-lingual support for a low-resourced language such as Assamese. Our research reveals that the tokenizer of SUTRA from Two AI performs the best with an average Normalized Sequence Length (NSL) value of 0.45, closely followed by the tokenizer of GPT-4o from Open AI with an average NSL value of 0.54, followed by Gemma 2, Meta Llama 3.1, and Mistral Large Instruct 2407 with an average NSL value of 0.82, 1.4, and 1.48 respectively.

In many critical Machine Learning applications, such as autonomous driving and medical image diagnosis, the detection of out-of-distribution (OOD) samples is as crucial as accurately classifying in-distribution (ID) inputs. Recently Outlier Exposure (OE) based methods have shown promising results in detecting OOD inputs via model fine-tuning with auxiliary outlier data. However, most of the previous OE-based approaches emphasize more on synthesizing extra outlier samples or introducing regularization to diversify OOD sample space, which is rather unquantifiable in practice. In this work, we propose a novel and straightforward method called Margin bounded Confidence Scores (MaCS) to address the nontrivial OOD detection problem by enlarging the disparity between ID and OOD scores, which in turn makes the decision boundary more compact facilitating effective segregation with a simple threshold. Specifically, we augment the learning objective of an OE regularized classifier with a supplementary constraint, which penalizes high confidence scores for OOD inputs compared to that of ID and significantly enhances the OOD detection performance while maintaining the ID classification accuracy. Extensive experiments on various benchmark datasets for image classification tasks demonstrate the effectiveness of the proposed method by significantly outperforming state-of-the-art (S.O.T.A) methods on various benchmarking metrics. The code is publicly available at https://github.com/lakpa-tamang9/margin_ood, Comment: 10 pages, 5 figures, IEEE Conference in Data Mining 2024

From a comparison of energy dispersive analyses of X-rays (EDAX), the known molecular stoichiometry and X-ray photoemission spectroscopy (XPS), it is evident that the Fe(III) spin crossover salt [Fe(qsal)2Ni(dmit)2] has a preferential surface termination with the Ni(dmit)2 moiety. This preferential surface termination leads to a significant surface to bulk core level shift for the Ni 2p X-ray photoemission core level, not seen in the corresponding Fe 2p core level spectra. Inverse photoemission spectroscopy (IPES), thus provides some indication of the density of states resulting from the dmit2 = 1,3-dithiol-2-thione-4,5-dithiolato ligand unoccupied molecular orbitals., Comment: 5 pages, 4 figures, submitted for publication in JPCM

Objectives: This study aims to assess the impact of domain shift on chest X-ray classification accuracy and to analyze the influence of ground truth label quality and demographic factors such as age group, sex, and study year. Materials and Methods: We used a DenseNet121 model pretrained MIMIC-CXR dataset for deep learning-based multilabel classification using ground truth labels from radiology reports extracted using the CheXpert and CheXbert Labeler. We compared the performance of the 14 chest X-ray labels on the MIMIC-CXR and Veterans Healthcare Administration chest X-ray dataset (VA-CXR). The VA-CXR dataset comprises over 259k chest X-ray images spanning between the years 2010 and 2022. Results: The validation of ground truth and the assessment of multi-label classification performance across various NLP extraction tools revealed that the VA-CXR dataset exhibited lower disagreement rates than the MIMIC-CXR datasets. Additionally, there were notable differences in AUC scores between models utilizing CheXpert and CheXbert. When evaluating multi-label classification performance across different datasets, minimal domain shift was observed in unseen datasets, except for the label "Enlarged Cardiomediastinum." The study year's subgroup analyses exhibited the most significant variations in multi-label classification model performance. These findings underscore the importance of considering domain shifts in chest X-ray classification tasks, particularly concerning study years. Conclusion: Our study reveals the significant impact of domain shift and demographic factors on chest X-ray classification, emphasizing the need for improved transfer learning and equitable model development. Addressing these challenges is crucial for advancing medical imaging and enhancing patient care.

The compilation and analysis of radiological images poses numerous challenges for researchers. The sheer volume of data as well as the computational needs of algorithms capable of operating on images are extensive. Additionally, the assembly of these images alone is difficult, as these exams may differ widely in terms of clinical context, structured annotation available for model training, modality, and patient identifiers. In this paper, we describe our experiences and challenges in establishing a trusted collection of radiology images linked to the United States Department of Veterans Affairs (VA) electronic health record database. We also discuss implications in making this repository research-ready for medical investigators. Key insights include uncovering the specific procedures required for transferring images from a clinical to a research-ready environment, as well as roadblocks and bottlenecks in this process that may hinder future efforts at automation.

We report the X-ray characteristics of the persistent X-ray pulsar 4U 1626-67 using simultaneous NuSTAR and NICER observations. The X-ray pulsar 4U 1626-67 has just encountered a torque reversal in 2023 and is presently in the spin-down state. We have examined the temporal and spectral characteristics of the source during its ongoing spin-down episode. The pulse profiles of the source are characterized by multiple substructures at lower energies and a wide asymmetric single-peaked structure at higher energies. The pulse fraction follows an overall increasing trend with energy. We confirm the existence of mHz quasi-periodic oscillation (QPO) exclusively during the current spin-down phase in all the observations. The source is spinning down at 0.00045(4)\; s\; $yr^{-1}$. The broadband spectrum during this phase is described by empirical NPEX model and a soft blackbody component with kT $\sim$ 0.25 keV. In addition to the iron emission line, we also confirm the presence of cyclotron line at $\sim$ 36 keV. The source flux continues to decrease during the current spin-down phase, and the corresponding luminosity $\sim$ (3.3-4.9)\;$\times 10^{36}\; ergs\; s^{-1}$ lies in the intermediate range of accreting X-ray pulsars that may be associated with a hybrid accretion geometry. The magnetic field strengths estimated using the cyclotron line measurements and QPO frequency are consistent. The evolution of the spectral parameters relative to the pulsed phase is examined using phase-resolved spectroscopy., Comment: Accepted for publication in Monthly Notices of the Royal Astronomical Society, 9 Pages, 8 figures, 3 tables