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The key challenge in advancing multivalent-ion batteries lies in finding suitable intercalation hosts. Open-tunnel oxides, featuring one-dimensional channels or nanopores, show promise for enabling effective ion transport. However, the vast range of compositional possibilities renders traditional experimental and quantum-based methods impractical for large-scale studies. This work presents a generative AI framework that uses the Crystal Diffusion Variational Autoencoder (CDVAE) and a fine-tuned Large Language Model (LLM) to expedite the discovery of stable open-tunneled oxide materials for multivalent-ion batteries. By combining machine learning with data mining techniques, five promising transition metal oxide (TMO) structures are generated. These structures, known for forming open-tunnel oxide frameworks, are structurally validated through Density Functional Theory (DFT). The results show that the generated structures have lower formation energies compared to similar compositions in the Materials Project (MP) database, indicating improved thermodynamic stability. Additionally, the graph-based M3GNet model is employed to relax further generated structures, providing a more computationally efficient alternative to DFT. Machine learning-based predictions of formation energy, band gap, and energy above the hull refine the selection process, leading to the identification of materials with significant potential for real-world battery applications. This research demonstrates the power of generative AI in rapidly exploring the vast chemical space of TMOs, offering a new approach to discovering stable open-tunnel oxides for multivalent-ion batteries. The results highlight the potential of this approach to contribute to more sustainable energy storage technologies, addressing the growing concerns surrounding the scarcity of lithium., Comment: 11 pages, 10 figures

Using medium-band imaging from the newly released Merian Survey, we conduct a nonparametric morphological analysis of H$\alpha$ emission maps and stellar continua for a sample of galaxies with $8\lesssim\log (M_\star/M_\odot) < 10.3$ at $0.064

We present the Merian Survey, an optical imaging survey optimized for studying the physical properties of bright star-forming dwarf galaxies. Merian is carried out with two medium-band filters ($N708$ and $N540$, centered at $708$ and $540$ nm), custom-built for the Dark Energy Camera (DECam) on the Blanco telescope. Merian covers $\sim 750\,\mathrm{deg}^2$ of equatorial fields, overlapping with the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) wide, deep, and ultra-deep fields. When combined with the HSC-SSP imaging data ($grizy$), the new Merian DECam medium-band imaging allows for photometric redshift measurements via the detection of H$\rm\alpha$ and [OIII] line emission flux excess in the $N708$ and $N540$ filters, respectively, at $0.06

In this work, we share the insights for achieving state-of-the-art quality in our text-to-image anime image generative model, called Illustrious. To achieve high resolution, dynamic color range images, and high restoration ability, we focus on three critical approaches for model improvement. First, we delve into the significance of the batch size and dropout control, which enables faster learning of controllable token based concept activations. Second, we increase the training resolution of images, affecting the accurate depiction of character anatomy in much higher resolution, extending its generation capability over 20MP with proper methods. Finally, we propose the refined multi-level captions, covering all tags and various natural language captions as a critical factor for model development. Through extensive analysis and experiments, Illustrious demonstrates state-of-the-art performance in terms of animation style, outperforming widely-used models in illustration domains, propelling easier customization and personalization with nature of open source. We plan to publicly release updated Illustrious model series sequentially as well as sustainable plans for improvements.

The current paradigm for the co-evolution of galaxies and their supermassive black holes postulates that dust-obscured active galactic nuclei (AGNs) represent a transitional phase towards a more luminous and unobscured state. However, our understanding of dusty AGNs and their host galaxies at early cosmic times is inadequate due to observational limitations. Here, we present JWST observations of CID-931, an X-ray-detected AGN at a spectroscopic redshift of $z_{\rm spec}=4.91$. Multiband NIRCam imaging from the COSMOS-Web program reveals an unresolved red core, similar to JWST-discovered dusty AGNs. Strikingly, the red core is surrounded by at least eight massive star-forming clumps spread over $1.\!\!^{\prime\prime}6 \approx 10~{\rm kpc}$, each of which has a stellar mass of $10^9-10^{10}M_\odot$ and $\sim0.1-1~{\rm kpc}$ in radius. The whole system amounts to $10^{11}M_\odot$ in stellar mass, higher than typical star-forming galaxies at the same epoch. In this system, gas inflows and/or complex merger events may trigger clump formation and AGN activity thus leading to the rapid formation of a massive galaxy hosting a supermassive black hole. Future follow-up observations will provide new insights into the evolution of the galaxy-black hole relationship during such transitional phases in the early universe., Comment: 13 pages, 10 figures, submitted to PASJ

Nodal line semimetals, a class of topological quantum materials, exhibit a variety of novel phenomena due to their properties, such as bands touching on a one-dimensional line or a ring in the Brillouin zone and drumhead-like surface states. In addition, these semimetals are protected by the combined space-inversion and time-reversal ($\mathcal{PT}$) symmetry. In this study, we investigate the longitudinal DC conductivity of the Dirac nodal line semimetals for the broken $\mathcal{PT}$-symmetric system by the mass term. Here, using the quantum kinetic technique, we find the intrinsic (field-driven) and extrinsic (scattering-driven) contributions to the total DC conductivity due to interband effects. Interestingly, the resulting intrinsic conductivity is the Fermi sea contribution, while the extrinsic stems from the Fermi surface contribution. We show that at low chemical potential, the extrinsic part contributes more and dominates over the traditional Drude intraband term, while at the high chemical potential, the intrinsic conductivity contributes. Furthermore, the total DC response due to interband effects saturates at high chemical potential and its strength decreases with increasing mass value. Our findings suggest that the extrinsic contributions are rich enough to understand the overall feature of the response for the three-dimensional system., Comment: Accepted for publication in the Physical Review B

A unique aspect of human visual understanding is the ability to flexibly interpret abstract concepts: acquiring lifted rules explaining what they symbolize, grounding them across familiar and unfamiliar contexts, and making predictions or reasoning about them. While off-the-shelf vision-language models excel at making literal interpretations of images (e.g., recognizing object categories such as tree branches), they still struggle to make sense of such visual abstractions (e.g., how an arrangement of tree branches may form the walls of a maze). To address this challenge, we introduce Deep Schema Grounding (DSG), a framework that leverages explicit structured representations of visual abstractions for grounding and reasoning. At the core of DSG are schemas--dependency graph descriptions of abstract concepts that decompose them into more primitive-level symbols. DSG uses large language models to extract schemas, then hierarchically grounds concrete to abstract components of the schema onto images with vision-language models. The grounded schema is used to augment visual abstraction understanding. We systematically evaluate DSG and different methods in reasoning on our new Visual Abstractions Dataset, which consists of diverse, real-world images of abstract concepts and corresponding question-answer pairs labeled by humans. We show that DSG significantly improves the abstract visual reasoning performance of vision-language models, and is a step toward human-aligned understanding of visual abstractions.

The vast pre-existing slides serve as rich and important materials to carry lecture knowledge. However, effectively leveraging lecture slides to serve students is difficult due to the multi-modal nature of slide content and the heterogeneous teaching actions. We study the problem of discovering effective designs that convert a slide into an interactive lecture. We develop Slide2Lecture, a tuning-free and knowledge-regulated intelligent tutoring system that can (1) effectively convert an input lecture slide into a structured teaching agenda consisting of a set of heterogeneous teaching actions; (2) create and manage an interactive lecture that generates responsive interactions catering to student learning demands while regulating the interactions to follow teaching actions. Slide2Lecture contains a complete pipeline for learners to obtain an interactive classroom experience to learn the slide. For teachers and developers, Slide2Lecture enables customization to cater to personalized demands. The evaluation rated by annotators and students shows that Slide2Lecture is effective in outperforming the remaining implementation. Slide2Lecture's online deployment has made more than 200K interaction with students in the 3K lecture sessions. We open source Slide2Lecture's implementation in https://anonymous.4open.science/r/slide2lecture-4210/.

The integration of fine-scale multispectral imagery with deep learning models has revolutionized land use and land cover (LULC) classification. However, the atmospheric effects present in Top-of-Atmosphere sensor measured Digital Number values must be corrected to retrieve accurate Bottom-of-Atmosphere surface reflectance for reliable analysis. This study employs look-up-table-based radiative transfer simulations to estimate the atmospheric path reflectance and transmittance for atmospherically correcting high-resolution CARTOSAT-3 Multispectral (MX) imagery for several Indian cities. The corrected surface reflectance data were subsequently used in supervised and semi-supervised segmentation models, demonstrating stability in multi-class (buildings, roads, trees and water bodies) LULC segmentation accuracy, particularly in scenarios with sparsely labelled data.

A Bose-Einstein condensate in a modulated, one-dimensional, anharmonic potential can exhibit dynamical tunneling between islands of regular motion in phase space. With increasingly repulsive atomic interactions, dynamical tunneling is predicted to cease due to self-trapping [S. W\"uster et al. Phys. Rev. Lett. 109 080401 (2012)]. This suppression of tunneling oscillations is related to the same phenomenon that occurs in the two-mode dynamics of a repulsively interacting Bose-Einstein condensate in a double-well potential. Here we present a two-mode model for dynamical tunnelling based on nonlinear Floquet states and examine the range of validity of the approximation. We characterise nonlinear dynamical tunneling for different trap strengths, modulation amplitudes, and effective Planck constants. Using the linear Floquet states we derive an expression for the critical nonlinearity beyond which tunneling ceases. Finally we demonstrate the dynamical instability of selected nonlinear Floquet states and show how to initialise some Floquet states in experiments. Our detailed survey will enable experiments to target accessible parameter regimes for the study of nonlinear dynamical tunneling., Comment: 18 pages, 17 figures

Introducing superconductivity in topological materials can lead to innovative electronic phases and device functionalities. Here, we present a new strategy for quantum engineering of superconducting junctions in moire materials through direct, on-chip, and fully encapsulated 2D crystal growth. We achieve robust and designable superconductivity in Pd-metalized twisted bilayer molybdenum ditelluride (MoTe2) and observe anomalous superconducting effects in high-quality junctions across ~ 20 moire cells. Surprisingly, the junction develops enhanced, instead of weakened, superconducting behaviors, exhibiting fluctuations to a higher critical magnetic field compared to its adjacent Pd7MoTe2 superconductor. Additionally, the critical current further exhibits a striking V-shaped minimum at zero magnetic field. These features are unexpected in conventional Josephson junctions and indeed absent in junctions of natural bilayer MoTe2 created using the same approach. We discuss implications of these observations, including the possible formation of mixed even- and odd-parity superconductivity at the moire junctions. Our results also demonstrate a pathway to engineer and investigate superconductivity in fractional Chern insulators., Comment: 25 pages, 5 main article figures and 10 supplementary figures

Context: Test engineers are looking at more ways to test systems more effectively and efficiently. With recent advances in the field of AI (Artificial Intelligence), a large number of AI-powered test automation tools have emerged, which can help make testing more effective and efficient. Objective: We investigate the features provided by existing AI-based test automation tools. We also empirically evaluate the performance of two AI-based testing tools by applying them on two selected open-source Software Under Test (SUT). We empirically evaluate how the AI features can be helpful for effectiveness and efficiency of testing. We also study the limitations of the AI features in AI-based test tools. Method: To accomplish the objective, a Multivocal Literature Review (MLR) study was conducted to investigate the landscape of the AI-based test automation tools in the industry. Moreover, an empirical assessment is also conducted to empirically analyze two AI-based test automation tools by using it on two open-source projects. To determine the need of AI for selected feature, the same feature was created without the use of ML to explore its limitations which can be avoided using AI. Results: Our results are based on 55 AI-based test automation tools. Furthermore, an empirical assessment was performed by selecting two of the 55 tools analyzed as part of the MLR. Conclusion: This paper explores the potential benefits and limitations of AI-based test automation tools. The limitations explored can be used as inspiration to develop better AI-based test tools.

Foundation models have emerged as a promising approach in time series forecasting (TSF). Existing approaches either repurpose large language models (LLMs) or build large-scale time series datasets to develop TSF foundation models for universal forecasting. However, these methods face challenges due to the severe cross-domain gap or in-domain heterogeneity. This paper explores a new road to building a TSF foundation model from rich, high-quality natural images. Our key insight is that a visual masked autoencoder, pre-trained on the ImageNet dataset, can naturally be a numeric series forecaster. By reformulating TSF as an image reconstruction task, we bridge the gap between image pre-training and TSF downstream tasks. Surprisingly, without further adaptation in the time-series domain, the proposed VisionTS could achieve superior zero-shot forecasting performance compared to existing TSF foundation models. With fine-tuning for one epoch, VisionTS could further improve the forecasting and achieve state-of-the-art performance in most cases. Extensive experiments reveal intrinsic similarities between images and real-world time series, suggesting visual models may offer a ``free lunch'' for TSF and highlight the potential for future cross-modality research. Our code is publicly available at https://github.com/Keytoyze/VisionTS., Comment: v2: add more experiments

One of the greatest challenges in galaxy evolution over the last decade has been constraining the prevalence of heavily dust-obscured galaxies in the early Universe. At $z>3$, these galaxies are increasingly rare, and difficult to identify as they are interspersed among the more numerous dust-obscured galaxy population at $z=1-3$, making efforts to secure confident spectroscopic redshifts expensive, and sometimes unsuccessful. In this work, we present the Extended Mapping Obscuration to Reionization with ALMA (Ex-MORA) Survey -- a 2mm blank-field survey in the COSMOS-Web field, and the largest ever ALMA blank-field survey to-date covering 577 arcmin$^2$. Ex-MORA is an expansion of the MORA survey designed to identify primarily $z>3$ dusty, star-forming galaxies while simultaneously filtering out the more numerous $z<3$ population by leveraging the very negative $K$-correction at observed-frame 2mm. We identify 37 significant ($>$5$\sigma$) sources, 33 of which are robust thermal dust emitters. We measure a median redshift of $\langle z \rangle = 3.6^{+0.1}_{-0.2}$, with two-thirds of the sample at $z>3$, and just under half at $z>4$, demonstrating the overall success of the 2mm-selection technique. The integrated $z>3$ volume density of Ex-MORA sources is $\sim1-3\times10^{-5}$ Mpc$^{-3}$, consistent with other surveys of infrared luminous galaxies at similar epochs. We also find that techniques using rest-frame optical emission (or lack thereof) to identify $z>3$ heavily dust-obscured galaxies miss at least half of Ex-MORA galaxies. This supports the idea that the dusty galaxy population is heterogeneous, and that synergies across observatories spanning multiple energy regimes are critical to understanding their formation and evolution at $z>3$., Comment: 13 pages, 5 figures, submitted to ApJ; fully reduced mosaic will be shared upon publication

A new database of collisional rate coefficients for transitions between the rotational states of H$_2$O collided with H$_2$ background gas is developed. The goal is to expand over the other existing databases in terms of the rotational states of water (200 states are included here) and the rotational states of hydrogen (10 states). All four symmetries of ortho and para water combined with ortho, and para hydrogen are considered.The mixed quantum/classical theory of inelastic scattering implemented in the code MQCT is employed. A detailed comparison with previous databases is conducted to ensure that this approximate method is sufficiently accurate. Integration over collision energies, summation over the final states of H$_2$ and averaging over the initial states of H$_2$ is carried out to provide state-to-state, effective, and thermal rate coefficients in a broad range of temperatures.The rate coefficients for collisions with highly excited H$_2$ molecules are presented for the first time. It is found that rate coefficients for rotational transitions in H$_2$O molecules grow with the rotational excitation of H$_2$ projectiles and exceed those of the ground state H$_2$, roughly, by a factor of 2. These data enable more accurate description of water molecules in high-temperature environments, where the hydrogen molecules of background gas are rotationally excited, and the H$_2$O + H$_2$ collision energy is high. The rate coefficients presented here are expected to be accurate up to the temperature of $\sim$ 2000 K.

This study compares eight pre-trained CNNs for diagnosing keratoconus, a degenerative eye disease. A carefully selected dataset of keratoconus, normal, and suspicious cases was used. The models tested include DenseNet121, EfficientNetB0, InceptionResNetV2, InceptionV3, MobileNetV2, ResNet50, VGG16, and VGG19. To maximize model training, bad sample removal, resizing, rescaling, and augmentation were used. The models were trained with similar parameters, activation function, classification function, and optimizer to compare performance. To determine class separation effectiveness, each model was evaluated on accuracy, precision, recall, and F1-score. MobileNetV2 was the best accurate model in identifying keratoconus and normal cases with few misclassifications. InceptionV3 and DenseNet121 both performed well in keratoconus detection, but they had trouble with questionable cases. In contrast, EfficientNetB0, ResNet50, and VGG19 had more difficulty distinguishing dubious cases from regular ones, indicating the need for model refining and development. A detailed comparison of state-of-the-art CNN architectures for automated keratoconus identification reveals each model's benefits and weaknesses. This study shows that advanced deep learning models can enhance keratoconus diagnosis and treatment planning. Future research should explore hybrid models and integrate clinical parameters to improve diagnostic accuracy and robustness in real-world clinical applications, paving the way for more effective AI-driven ophthalmology tools., Comment: 14 pages, 3 tables, 27 figures

JWST has enabled detecting and spatially resolving the heavily dust-attenuated stellar populations of sub-millimeter galaxies, revealing detail that was previously inaccessible. In this work we construct a sample of 289 sub-millimeter galaxies with detailed joint ALMA and JWST constraints in the COSMOS field. Sources are originally selected using the SCUBA-2 instrument and have archival ALMA observations from various programs. Their JWST NIRCam imaging is from COSMOS-Web and PRIMER. We extract multi-wavelength photometry in a manner that leverages the unprecedented near-infrared spatial resolution of JWST, and fit the data with spectral energy distribution models to derive photometric redshifts, stellar masses, star-formation rates and optical attenuation. The sample has an average z=2.6, A_V=2.5, SFR=270 and log(M*)=11.1. There are 81 (30%) galaxies that have no previous optical/near-infrared detections, including 75% of the z>4 sub-sample (n=28). The faintest observed near-infrared sources have the highest redshifts and largest A_V=4. In a preliminary morphology analysis we find that ~10% of our sample exhibit spiral arms and 5% host stellar bars, with one candidate bar found at z>3. Finally, we find that the clustering of JWST galaxies within 10 arcseconds of a sub-mm galaxy is a factor of 2 greater than what is expected based on either random clustering or the distribution of sources around any red galaxy irrespective of a sub-mm detection., Comment: 37 pages (15 for RGBs + references), 14 figures, submitted to ApJ

I point out a sign mistake in the GHZ variant of Bell's theorem, invalidating its claim that the premisses of the EPR argument are inconsistent for systems of more than two particles in entangled quantum states., Comment: 1 page; Comment submitted to American Journal of Physics where one of the GHZ papers is published

The design and optimization of laser-Compton x-ray systems based on compact distributed charge accelerator structures can enable micron-scale imaging of disease and the concomitant production of beams of Very High Energy Electrons (VHEEs) capable of producing FLASH-relevant dose rates. The physics of laser-Compton x-ray scattering ensures that the scattered x-rays follow exactly the trajectory of the incident electrons, thus providing a route to image-guided, VHEE FLASH radiotherapy. The keys to a compact architecture capable of producing both laser-Compton x-rays and VHEEs are the use of X-band RF accelerator structures which have been demonstrated to operate with over 100 MeV/m acceleration gradients. The operation of these structures in a distributed charge mode in which each radiofrequency (RF) cycle of the drive RF pulse is filled with a low-charge, high-brightness electron bunch is enabled by the illumination of a high-brightness photogun with a train of UV laser pulses synchronized to the frequency of the underlying accelerator system. The UV pulse trains are created by a patented pulse synthesis approach which utilizes the RF clock of the accelerator to phase and amplitude modulate a narrow band continuous wave (CW) seed laser. In this way it is possible to produce up to 10 {\mu}A of average beam current from the accelerator. Such high current from a compact accelerator enables production of sufficient x-rays via laser-Compton scattering for clinical imaging and does so from a machine of "clinical" footprint. At the same time, the production of 1000 or greater individual micro-bunches per RF pulse enables > 10 nC of charge to be produced in a macrobunch of < 100 ns. The design, construction, and test of the 100-MeV class prototype system in Irvine, CA is also presented., Comment: 28 pages, 13 figures, 2 tables

Citizen science offers an opportunity for ordinary people, known as citizen scientists or citizen astronomers in the context of astronomy, to contribute to scientific research. The Pan-African Citizen Science e-Lab (PACS e-Lab) was founded to promote and engage the African public in citizen science and soft astronomy research to advance space research and exploration and enhance space education and outreach. PACS e-Lab, in collaboration with several international astronomy research, education, and outreach organizations, currently runs several projects including but not limited to asteroid search, exoplanet photometry, research writing for peer-reviewed publications, astrophoto visual development, and Amateur Radio contact with astronauts aboard the International Space Station (ARISS). Despite several challenges, the group has engaged over 600 Africans from more than 40 countries and is working towards covering the entire continent in the future. PACS e-Lab's development efforts resonate with seven United Nations Sustainable Development Goals (UN-SDGs)., Comment: 32 pages,17 figures, 2 Tables

Asteroid search is a global effort for planetary defense. The International Astronomical Search Collaboration (IASC) is the leading global educational outreach program that provides high-quality astronomical datasets to citizen scientists to discover asteroids. Since December 4, 2020, the Pan-African Citizen Science e-Lab (PACS e-Lab) has been IASC's biggest partner on the continent in recruiting and training citizen scientists in asteroid search endeavors. Over 30 asteroids have been discovered by 60 citizen scientists. About 595 citizen scientists from over 40 countries have been engaged in the project up to the time of composing this literature. The group is set to expand its endeavors to the rest of the continent and aims to engage thousands of citizen scientists., Comment: 16 pages, 10 figures, 5 Tables

Land Use Land Cover (LULC) mapping is a vital tool for urban and resource planning, playing a key role in the development of innovative and sustainable cities. This study introduces a semi-supervised segmentation model for LULC prediction using high-resolution satellite images with a vast diversity of data distributions in different areas of India. Our approach ensures a robust generalization across different types of buildings, roads, trees, and water bodies within these distinct areas. We propose a modified Cross Pseudo Supervision framework to train image segmentation models on sparsely labelled data. The proposed framework addresses the limitations of the famous 'Cross Pseudo Supervision' technique for semi-supervised learning, specifically tackling the challenges of training segmentation models on noisy satellite image data with sparse and inaccurate labels. This comprehensive approach significantly enhances the accuracy and utility of LULC mapping, providing valuable insights for urban and resource planning applications.

In this work, Machine Learning (ML) techniques have been employed to explore the highest performing single-heteronunction thermophotovoltaic cell. Initially, traditional homo junction TPV cells have been explored using ML methodologies for the optimal material combinations. ML methods have notably been devoted to analyze the importance of each parameter in the model, thereby improving the comprehension of the system's behavior and facilitating design optimization. Following this investigation, it has been found that Ge emerged as the most effective emitter layer when paired with the optimal base layer, InGaAsSb compound that possesses a direct bandgap of 0.53 eV. Subsequently, a p-Ge/n-InGaAsSb single-heterojunction TPV cell is introduced executing a device transport model featuring a p-n structure. This cell operated at black body (TBB) and cell temperatures of 1578 K and 300 K, respectively. Through meticulous optimization efforts, the performance of the TPV cell is significantly enhanced resulting in an impressive efficiency of 16.50%. This efficiency is accompanied by a short circuit current, JSC=15.53 A/cm2, an open-circuit voltage, VOC=0.47 V, and a fill factor FF=79.5%. These findings suggest that this structural configuration holds considerable promise for the development of high-performance TPV cells., Comment: 27 pages, 15 figures, 2 tables

Vertical Take-Off and Landing (VTOL) vehicles have gained immense popularity in the delivery drone market and are now being developed for passenger transportation in urban areas to efficiently enable Urban Air Mobility (UAM). UAM aims to utilize the urban airspace \hidetxt{vertical dimension} to address the problem of heavy road congestion in dense urban cities. VTOL vehicles require vertiport terminals for landing, take-off, passengers boarding or deboarding, refuelling (or charging), and maintenance. An efficient scheduling algorithm is essential to maximize the throughput of the vertiport terminal (vertiminal)\hidetxt{ as well as efficient use of airspace} while maintaining safety protocols to handle the UAM traffic. While traditional departure and taxiing operations can be applied in the context of vertiminal, specific algorithms are required for take-off and landing schedules. Unlike fixed-wing aircraft that require a runway to take-off and climb in a single direction, VTOL vehicles can approach and climb in several directions. We propose a Mixed Integer Linear Program (MILP) formulation to schedule flights for taxiing, climbing (or approaching) using multiple directions after take-off (before landing) and turnaround on gates. We also derived equations to thoroughly analyze the throughput capacity of a vertiminal considering all its core elements. We have shown that our MILP can achieve the maximum throughput obtained through the equations. Given the input parameters, our analysis can be used to analyze the capacity of a vertiminal without running any simulation, while our MILP can be used to get the most efficient schedule., Comment: Extension of conference work "Integrated Taxiing and TLOF pad Scheduling Using Different Surface Directions with Fairness Analysis" published in ITSC 2023. DOI: https://doi.org/10.1109/ITSC57777.2023.10422484

A Monge directed acyclic graph (DAG) $G$ on the nodes $1,2,\cdots,N$ has edges $\left( i,j\right) $ for $1\leq i

Language models (LMs) are increasingly used to simulate human-like responses in scenarios where accurately mimicking a population's behavior can guide decision-making, such as in developing educational materials and designing public policies. The objective of these simulations is for LMs to capture the variations in human responses, rather than merely providing the expected correct answers. Prior work has shown that LMs often generate unrealistically accurate responses, but there are no established metrics to quantify how closely the knowledge distribution of LMs aligns with that of humans. To address this, we introduce "psychometric alignment," a metric that measures the extent to which LMs reflect human knowledge distribution. Assessing this alignment involves collecting responses from both LMs and humans to the same set of test items and using Item Response Theory to analyze the differences in item functioning between the groups. We demonstrate that our metric can capture important variations in populations that traditional metrics, like differences in accuracy, fail to capture. We apply this metric to assess existing LMs for their alignment with human knowledge distributions across three real-world domains. We find significant misalignment between LMs and human populations, though using persona-based prompts can improve alignment. Interestingly, smaller LMs tend to achieve greater psychometric alignment than larger LMs. Further, training LMs on human response data from the target distribution enhances their psychometric alignment on unseen test items, but the effectiveness of such training varies across domains., Comment: Code and data: https://github.com/joyheyueya/psychometric-alignment

Data-to-text (D2T) generation aims to generate human-readable text from semi-structured data, such as tables and graphs. The recent success of D2T is largely attributed to advancements in LLMs. Despite the success of LLMs, no research has been conducted to illustrate the impact of model size on the performance of fine-tuned LLMs for D2T tasks. D2T model performance is typically assessed based on three key qualities: \textit{readability} (indicates fluency and coherence), \textit{informativeness} (measures content similarity), and \textit{faithfulness} (assesses consistency of factual information). It is currently uncertain whether increasing the size of LLMs effectively improves performance in D2T tasks across these three qualities. The objective of this study is to investigate the performance of fine-tuned LLMs in D2T tasks in terms of model size. Through extensive comparative analysis, we aim to elucidate both the advantages and limitations of scaling model sizes across five widely used D2T datasets (E2E, ViGGo, WikiTableText, DART, and WebNLG) and twelve state-of-the-art LLMs with varying sizes from five different LLM families (T5, BART, OPT, BLOOM, and Llama 2). To comprehensively cover all the three essential qualities of D2T models, we incorporate six widely recognized automatic metrics -- \textsc{BLEU}, \textsc{METEOR}, \textsc{BERTScore}, \textsc{MoverScore}, \textsc{Parent}, and \textsc{BARTScore}. We also provide an in-depth analysis of LLM performance concerning model size in the presence of source-reference divergence, a critical aspect of D2T tasks. Our investigation reveals that increasing LLM size enhances \textit{readability} and \textit{informativeness} in D2T tasks, but larger (in terms of size) LLMs may sacrifice \textit{faithfulness}. Moreover, small-sized LLMs show more resilience than larger ones when source-reference divergence is present., Comment: 30 pages

The integration of information and communication technology (ICT) has become increasingly vital across various educational fields, including physical education (PE). This study aimed to evaluate the proficiency levels of PE teachers in using various ICT applications and to examine the relationship between the perceived importance of ICT proficiency for teaching and learning, career advancement, and actual proficiency among Senior High school PE teachers in the municipality of Mexico, Pampanga. This study employed a quantitative descriptive approach. PE teachers from the municipality of Mexico, Pampanga, were selected as the respondents. This study used a two-part survey. The first section collected demographic data, such as age, gender, rank/position, and years of teaching experience, and the second section assessed ICT skill levels and the perceived importance of ICT in teaching, learning, and career progression. The results revealed that the majority of PE teachers had access to ICT resources. However, their proficiency levels with these tools varied significantly. Factors such as age, teaching experience, and professional position were found to significantly influence teachers proficiency and their perceptions of the benefits of ICT integration in PE instruction. The study provided a glimpse of the current state of ICT integration among Senior High school PE teachers in Mexico, Pampanga, Philippines. This also highlights areas of improvement. The study suggests that policymakers, administrators, and training program developers should focus on enhancing the ICT proficiency of PE teachers to improve teaching practices and student engagement. Enhancing the ICT proficiency of PE teachers is recommended to foster better teaching experiences, increase student engagement, and promote overall educational outcomes., Comment: 16 pages, 1 figure, 4 tables

Safety fine-tuning helps align Large Language Models (LLMs) with human preferences for their safe deployment. To better understand the underlying factors that make models safe via safety fine-tuning, we design a synthetic data generation framework that captures salient aspects of an unsafe input by modeling the interaction between the task the model is asked to perform (e.g., "design") versus the specific concepts the task is asked to be performed upon (e.g., a "cycle" vs. a "bomb"). Using this, we investigate three well-known safety fine-tuning methods -- supervised safety fine-tuning, direct preference optimization, and unlearning -- and provide significant evidence demonstrating that these methods minimally transform MLP weights to specifically align unsafe inputs into its weights' null space. This yields a clustering of inputs based on whether the model deems them safe or not. Correspondingly, when an adversarial input (e.g., a jailbreak) is provided, its activations are closer to safer samples, leading to the model processing such an input as if it were safe. We validate our findings, wherever possible, on real-world models -- specifically, Llama-2 7B and Llama-3 8B., Comment: Preprint

Large language models (LLMs) have shown a remarkable ability to learn and perform complex tasks through in-context learning (ICL). However, a comprehensive understanding of its internal mechanisms is still lacking. This paper explores the role of induction heads in a few-shot ICL setting. We analyse two state-of-the-art models, Llama-3-8B and InternLM2-20B on abstract pattern recognition and NLP tasks. Our results show that even a minimal ablation of induction heads leads to ICL performance decreases of up to ~32% for abstract pattern recognition tasks, bringing the performance close to random. For NLP tasks, this ablation substantially decreases the model's ability to benefit from examples, bringing few-shot ICL performance close to that of zero-shot prompts. We further use attention knockout to disable specific induction patterns, and present fine-grained evidence for the role that the induction mechanism plays in ICL., Comment: 9 pages, 7 figures; mean-ablation experiments instead of zero-ablations

Learning from multiple sensors is challenging due to spatio-temporal misalignment and differences in resolution and captured spectra. To that end, we introduce GeoWATCH, a flexible framework for training models on long sequences of satellite images sourced from multiple sensor platforms, which is designed to handle image classification, activity recognition, object detection, or object tracking tasks. Our system includes a novel partial weight loading mechanism based on sub-graph isomorphism which allows for continually training and modifying a network over many training cycles. This has allowed us to train a lineage of models over a long period of time, which we have observed has improved performance as we adjust configurations while maintaining a core backbone., Comment: IGARSS 2024 https://2024.ieeeigarss.org/view_paper.php?PaperNum=5431