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Large language models (LLMs) have achieved remarkable performance in recent years but are fundamentally limited by the underlying training data. To improve models beyond the training data, recent works have explored how LLMs can be used to generate synthetic data for autonomous self-improvement. However, successive steps of self-improvement can reach a point of diminishing returns. In this work, we propose a complementary approach towards self-improvement where finetuning is applied to a multiagent society of language models. A group of language models, all starting from the same base model, are independently specialized by updating each one using data generated through multiagent interactions among the models. By training each model on independent sets of data, we illustrate how this approach enables specialization across models and diversification over the set of models. As a result, our overall system is able to preserve diverse reasoning chains and autonomously improve over many more rounds of fine-tuning than single-agent self-improvement methods. We quantitatively illustrate the efficacy of the approach across a wide suite of reasoning tasks., Comment: 22 pages, 13 figures, 7 tables; Project page at https://llm-multiagent-ft.github.io/

We modeled the kinematics of the Small Magellanic Cloud (SMC) by analyzing the proper motion (PM) from Gaia DR3 of nine different stellar populations, which include young main sequence (MS) stars (< 2 Gyr), red giant branch stars, red clump stars, red giants with line-of-sight velocities, and three groups of star clusters. This analysis was carried out using a robust Markov Chain Monte Carlo method to derive up to 7 kinematic parameters. We trace the evolution from a non-rotating flattened elliptical system as mapped by the old population to a rotating highly stretched disk structure as denoted by the young MS stars and clusters (< 400 Myr). We estimated that the inclination, i (~ 58$^\circ$ to 82$^\circ$) decreases and the position angle, $\Theta$ (~ 180$^\circ$ to 240$^\circ$) increases with age. We estimated an asymptotic velocity of ~ 49 - 89 km s$^{-1}$ with scale-radius of ~ 6 - 9 kpc for the young MS populations with velocity dispersion of ~ 11 km s$^{-1}$, suggesting a rotation-supported disk structure. Our models estimate a line-of-sight extension of ~ 30 kpc, in agreement with observations. We identified four regions of the SMC showing anomalies in the residual PM, the East Anomaly (EA), South East Anomaly (SEA), South Anomaly (SA), and West Anomaly (WA). The SEA appears like an infalling feature and is identified for the first time. The tidal imprints observed in the residual PM of the SMC suggest that its evolution is considerably shaped by the recent interaction with the Large Magellanic Cloud., Comment: Accepted for publication in ApJ. 18 pages, 13 figures, 2 tables

Since the launch of ChatGPT in late 2022, the capacities of Large Language Models and their evaluation have been in constant discussion and evaluation both in academic research and in the industry. Scenarios and benchmarks have been developed in several areas such as law, medicine and math (Bommasani et al., 2023) and there is continuous evaluation of model variants. One area that has not received sufficient scenario development attention is journalism, and in particular journalistic sourcing and ethics. Journalism is a crucial truth-determination function in democracy (Vincent, 2023), and sourcing is a crucial pillar to all original journalistic output. Evaluating the capacities of LLMs to annotate stories for the different signals of sourcing and how reporters justify them is a crucial scenario that warrants a benchmark approach. It offers potential to build automated systems to contrast more transparent and ethically rigorous forms of journalism with everyday fare. In this paper we lay out a scenario to evaluate LLM performance on identifying and annotating sourcing in news stories on a five-category schema inspired from journalism studies (Gans, 2004). We offer the use case, our dataset and metrics and as the first step towards systematic benchmarking. Our accuracy findings indicate LLM-based approaches have more catching to do in identifying all the sourced statements in a story, and equally, in matching the type of sources. An even harder task is spotting source justifications.

To reduce costs and delays related to developing new and effective drugs, there is a critical need for improved human liver tissue models. Here we describe an approach for 3D bioprinting functional human liver tissue models, in which we fabricate disc-shaped structures (discoids) 200 {\mu}m in thickness and 1-3 mm in diameter, embedded in a highly permeable support medium made from packed microgels. We demonstrate that the method is precise, accurate, and scalable; up to 100 tissues per hour can be manufactured with a variability and error in diameter of about 4%. Histologic and immunohistochemical evaluation of printed discs reveal self-organization, cell cohesion, and key liver marker expression. During the course of 3-4 weeks in culture, the tissues stably synthesize albumin and urea at high levels, outperforming spheroid tissue models. We find the tissues express more than 100 genes associated with molecular absorption, distribution, metabolism, and excretion (ADME) at levels within the range of human liver. The liver tissue models exhibit enzymatic formation of metabolites after exposure to multiple test compounds. Together, these results demonstrate the promise of 3D printed discoids for pharmacological and toxicological applications., Comment: 34 pages, 7 figures, 2 supplementary figures

We present ARCAS (Automated Root Cause Analysis System), a diagnostic platform based on a Domain Specific Language (DSL) built for fast diagnostic implementation and low learning curve. Arcas is composed of a constellation of automated troubleshooting guides (Auto-TSGs) that can execute in parallel to detect issues using product telemetry and apply mitigation in near-real-time. The DSL is tailored specifically to ensure that subject matter experts can deliver highly curated and relevant Auto-TSGs in a short time without having to understand how they will interact with the rest of the diagnostic platform, thus reducing time-to-mitigate and saving crucial engineering cycles when they matter most. This contrasts with platforms like Datadog and New Relic, which primarily focus on monitoring and require manual intervention for mitigation. ARCAS uses a Large Language Model (LLM) to prioritize Auto-TSGs outputs and take appropriate actions, thus suppressing the costly requirement of understanding the general behavior of the system. We explain the key concepts behind ARCAS and demonstrate how it has been successfully used for multiple products across Azure Synapse Analytics and Microsoft Fabric Synapse Data Warehouse., Comment: 13 pages, 6 figures

The implementation of 5G and the future deployment of 6G necessitate the utilization of optical networks that possess substantial capacity and exhibit minimal latency. The dynamic arrival and departure of connection requests in optical networks result in particular central links experiencing more traffic and congestion than non-central links. The occurrence of congested links leads to service blocking despite the availability of resources within the network, restricting the efficient utilization of network resources. The available algorithms in the literature that aim to balance load among network links offer a trade-off between blocking performance and algorithmic complexity, thus increasing service provisioning time. This work proposes a dynamic routing-based congestion-aware routing, modulation, core, and spectrum assignment (RMCSA) algorithm for space division multiplexing elastic optical networks (SDM-EONs). The algorithm finds alternative candidate paths based on real-time link occupancy metrics to minimize blocking due to link congestion under dynamic traffic scenarios. As a result, the algorithm reduces the formation of congestion hotspots in the network owing to link-betweenness centrality. We have performed extensive simulations using two realistic network topologies to compare the performance of the proposed algorithm with relevant RMCSA algorithms available in the literature. The simulation results verify the superior performance of our proposed algorithm compared to the benchmark Yen's K-shortest paths and K-Disjoint shortest paths RMCSA algorithms in connection blocking ratio and spectrum utilization efficiency. To expedite the route-finding process, we present a novel caching strategy that allows the proposed algorithm to demonstrate a much-reduced service delay time compared to the recently developed adaptive link weight-based load-balancing RMCSA algorithm., Comment: The preliminary work was presented at ONDM 2023 conference. https://doi.org/10.23919/ONDM57372.2023.10144866

The elastic moduli of tissues are connected to their states of health and function. The epithelial monolayer is a simple, minimal, tissue model that is often used to gain understanding of mechanical behavior at the cellular or multi-cellular scale. Here we investigate how the elastic modulus of Madin Darby Canine Kidney (MDCK) cells depends on their packing density. Rather than measuring elasticity at the sub-cellular scale with local probes, we characterize the monolayer at the multi-cellular scale, as one would a thin slab of elastic material. We use a micro-indentation system to apply gentle forces to the apical side of MDCK monolayers, applying a normal force to approximately 100 cells in each experiment. In low-density confluent monolayers, we find that the elastic modulus decreases with increasing cell density. At high densities, the modulus appears to plateau. This finding will help guide our understanding of known collective behaviors in epithelial monolayers and other tissues where variations in cell packing density are correlated with cell motion., Comment: 13 pages, 4 figures

With the advent of smartphones, there has been a recent increase in the use of capacitive touch sensing for various Human Machine Interfaces (HMI). Capacitive-based touch sensing provides higher flexibility and cost-effectiveness than, methodologies such as resistive-based touch sensing. However, Capacitive-based touch sensing is more prone to disturbances such as Electromagnetic interference (EMI) and noise due to temperature variation. This effect becomes more dominating as the sensing excitation frequency increases. Traditional capacitance to digital circuits, such as sigma-delta capacitive sensing, requires multiple clock cycles to measure sensing capacitance, thus necessitating higher frequency operation. In turn, this produces challenges in Electromagnetic Emission while also increasing its susceptibility to EMI, such as false or ghost touch due to exposure of the sensing electrodes to various frequency electric fields. This paper discusses the conducted electromagnetic emission behavior of an external excitation-frequency independent self-oscillating capacitance-to-time converter, where sensing is done with a single clock cycle, and discusses radiated Electromagnetic emission of the touch sensing electrode. The proposed approach is suitable for touch-sensing applications, mainly when used in a noisy EMI environment, such as inside a vehicle within the Automotive industry., Comment: 4 pages, 8 figures

The symmetric teleparallel theory offers an alternative gravitational formulation which can elucidate events in the early and late universe without requiring the physical existence of dark matter or dark energy. In this formalism, $f(Q, C)$ gravity has been recently introduced by incorporating the boundary term $C$ with the non-metricity scalar $Q$. In this paper, we develop the theory of cosmological scalar perturbation for $f(Q, C)$ gravity, and retrieve that of $f(\mathring{R})$ and $f(Q)$ gravity from our result. The analysis assumes a model-independent approach within these theories that adheres to the conventional continuity equation at the background level. We derive the density contrast equation by employing some standard cosmological approximations, where the $f(Q, C)$ theory is encoded in the effective Newtonian constant $G_{eff}$. Finally, we derive the evolution equation of density growth $f_g$.

Molecular clouds fragment under the action of supersonic turbulence & gravity which results in a scale-free hierarchical distribution of star formation (SF) within galaxies. Recent studies suggest that the hierarchical distribution of SF in nearby galaxies shows a dependence on host galaxy properties. In this context, we study the nature of hierarchical SF from a few tens of pc up to several kpc in 4 nearby spiral galaxies NGC1566, NGC5194, NGC5457 & NGC7793, by leveraging the large FoV & high resolution FUV+NUV observations from the UltraViolet Imaging Telescope (UVIT). Using the two-point correlation function, we infer that the young star-forming clumps (SFCs) in the galaxies are arranged in a fractal-like hierarchical distribution, but only up to a maximum scale ($l_{corr}$) & it ranges from 0.5 kpc to 3.1 kpc. The flocculent spiral NGC7793 has $\sim$5 times smaller $l_{corr}$ than the 3 grand design spirals, possibly due to its lower mass, low pressure environment & lack of strong spiral arms. $l_{corr}$ being much smaller than the galaxy size suggests that the SF hierarchy does not extend to the full galaxy size & it is likely an effect set by multiple physical mechanisms in the galaxy. The hierarchical distribution of SFCs dissipates within 10 to 50 Myr, signifying their migration away from their birthplaces over time. Our results suggest that the global hierarchical properties of SF in galaxies are not universal & significant variations exist in the local & global hierarchy parameters of a galaxy. This study also demonstrates the capabilities of UVIT in characterizing the SF hierarchy in nearby galaxies. In the future, a bigger sample can be employed to further understand the role of large-scale galaxy properties (morphology, environment) & physical processes (feedback, turbulence, shear & ISM conditions) on determining the non-universal hierarchical properties of SF in galaxies., Comment: 18 pages, 11 figures, Accepted for publication in Astronomy and Astrophysics (A&A), after language correction

Osteoporosis, a prevalent condition among the aging population worldwide, is characterized by diminished bone mass and altered bone structure, increasing susceptibility to fractures. It poses a significant and growing global public health challenge over the next decade. Diagnosis typically involves Dual-energy X-ray absorptiometry to measure bone mineral density, yet its mass screening utility is limited. The Singh Index (SI) provides a straightforward, semi-quantitative means of osteoporosis diagnosis through plain hip radiographs, assessing trabecular patterns in the proximal femur. Although cost-effective and accessible, manual SI calculation is time-intensive and requires expertise. This study aims to automate SI identification from radiographs using machine learning algorithms. An unlabelled dataset of 838 hip X-ray images from Indian adults aged 20-70 was utilized. A custom convolutional neural network architecture was developed for feature extraction, demonstrating superior performance in cluster homogeneity and heterogeneity compared to established models. Various clustering algorithms categorized images into six SI grade clusters, with comparative analysis revealing only two clusters with high Silhouette Scores for promising classification. Further scrutiny highlighted dataset imbalance and emphasized the importance of image quality and additional clinical data availability. The study suggests augmenting X-ray images with patient clinical data and reference images, alongside image pre-processing techniques, to enhance diagnostic accuracy. Additionally, exploring semi-supervised and self-supervised learning methods may mitigate labelling challenges associated with large datasets.

Transitions between solid-like and fluid-like states in living tissues have been found in steps of embryonic development and in stages of disease progression. Our current understanding of these transitions has been guided by experimental and theoretical investigations focused on how motion becomes arrested with increased mechanical coupling between cells, typically as a function of packing density or cell cohesiveness. However, cells actively respond to externally applied forces by contracting after a time delay, so it is possible that at some packing densities or levels of cell cohesiveness, mechanical coupling stimulates cell motion instead of suppressing it. Here we report our findings that at low densities and within multiple ranges of cell cohesiveness, cell migration speeds increase with these measures of mechanical coupling. Our observations run counter to our intuition that cell motion will be suppressed by increasingly packing or sticking cells together and may provide new insight into biological processes involving motion in dense cell populations., Comment: 5 figures; supplementary information

Investigating students' thinking in classroom tasks, particularly in science and engineering, is essential for improving educational practices and advancing student learning. In this context, the notion of Ways of Thinking (WoT) has gained traction in STEM education, offering a framework to explore how students approach and solve interdisciplinary problems. Building on our earlier studies and contributing to ongoing discussions on WoT frameworks, this paper introduces a new WoT framework: Ways of Thinking in Engineering Design based Physics (WoT4EDP). WoT4EDP integrates five key elements: design, science, mathematics, metacognitive reflection, and computational thinking within an undergraduate introductory physics laboratory. This framework offers a novel perspective by emphasizing how these interconnected elements work together to foster deeper learning and holistic problem-solving in Engineering Design based projects. A key takeaway is that this framework serves as a practical tool for educators and researchers to design, implement, and analyze interdisciplinary STEM activities in physics classrooms. We describe the development of WoT4EDP, situate it within the broader landscape of undergraduate STEM education, and provide detailed characterizations of its components. Additionally, we compare WoT4EDP with two contemporary frameworks: Dalal et al. (2021) and English (2023), to glean insights that enhance its application and promote interdisciplinary thinking. This paper is the first of a two-part series. In the upcoming second part, we will demonstrate the application of the WoT4EDP framework, showcasing how it can be used to analyze student thinking in real-world, ED-based physics projects., Comment: 26 pages, 3 figures Section 4F is now expanded with more details. Abstract slightly expanded

We present the Brain Treebank, a large-scale dataset of electrophysiological neural responses, recorded from intracranial probes while 10 subjects watched one or more Hollywood movies. Subjects watched on average 2.6 Hollywood movies, for an average viewing time of 4.3 hours, and a total of 43 hours. The audio track for each movie was transcribed with manual corrections. Word onsets were manually annotated on spectrograms of the audio track for each movie. Each transcript was automatically parsed and manually corrected into the universal dependencies (UD) formalism, assigning a part of speech to every word and a dependency parse to every sentence. In total, subjects heard over 38,000 sentences (223,000 words), while they had on average 168 electrodes implanted. This is the largest dataset of intracranial recordings featuring grounded naturalistic language, one of the largest English UD treebanks in general, and one of only a few UD treebanks aligned to multimodal features. We hope that this dataset serves as a bridge between linguistic concepts, perception, and their neural representations. To that end, we present an analysis of which electrodes are sensitive to language features while also mapping out a rough time course of language processing across these electrodes. The Brain Treebank is available at https://BrainTreebank.dev/, Comment: 36 pages, 17 figures; Accepted at NeurIPS Dataset and Benchmarks 2024

Deep neural networks (DNNS) excel at learning from static datasets but struggle with continual learning, where data arrives sequentially. Catastrophic forgetting, the phenomenon of forgetting previously learned knowledge, is a primary challenge. This paper introduces EXponentially Averaged Class-wise Feature Significance (EXACFS) to mitigate this issue in the class incremental learning (CIL) setting. By estimating the significance of model features for each learned class using loss gradients, gradually aging the significance through the incremental tasks and preserving the significant features through a distillation loss, EXACFS effectively balances remembering old knowledge (stability) and learning new knowledge (plasticity). Extensive experiments on CIFAR-100 and ImageNet-100 demonstrate EXACFS's superior performance in preserving stability while acquiring plasticity.

Solutions of the wave equations for time-independent disordered media can exhibit Anderson localization where instead of wave propagation we observe their localization around different points in space. Photonic time crystals are spatially homogeneous media in which the refractive index changes periodically in time, leading to the formation of bands in the wave number domain. By analogy to Anderson localization in space, one might expect that the presence of temporal disorder in photonic time crystals would lead to Anderson localization in the time domain. Here, we show that indeed periodic modulations of the refractive index with the addition of temporal disorder lead to Anderson localization in time, where an electromagnetic field can emerge from the temporally modulated medium at a certain moment in time and then decay exponentially over time. Thus, we are dealing with a situation where, in a fluctuating three-dimensional medium, the birth and death of waves can occur, and the mechanism of this phenomenon corresponds to Anderson localization., Comment: 4 pages + supplementary material, 3 figures

We demonstrate that architectures which traditionally are considered to be ill-suited for a task can be trained using inductive biases from another architecture. Networks are considered untrainable when they overfit, underfit, or converge to poor results even when tuning their hyperparameters. For example, plain fully connected networks overfit on object recognition while deep convolutional networks without residual connections underfit. The traditional answer is to change the architecture to impose some inductive bias, although what that bias is remains unknown. We introduce guidance, where a guide network guides a target network using a neural distance function. The target is optimized to perform well and to match its internal representations, layer-by-layer, to those of the guide; the guide is unchanged. If the guide is trained, this transfers over part of the architectural prior and knowledge of the guide to the target. If the guide is untrained, this transfers over only part of the architectural prior of the guide. In this manner, we can investigate what kinds of priors different architectures place on untrainable networks such as fully connected networks. We demonstrate that this method overcomes the immediate overfitting of fully connected networks on vision tasks, makes plain CNNs competitive to ResNets, closes much of the gap between plain vanilla RNNs and Transformers, and can even help Transformers learn tasks which RNNs can perform more easily. We also discover evidence that better initializations of fully connected networks likely exist to avoid overfitting. Our method provides a mathematical tool to investigate priors and architectures, and in the long term, may demystify the dark art of architecture creation, even perhaps turning architectures into a continuous optimizable parameter of the network., Comment: Under Review; 24 pages, 9 figures; Project page and code is at https://untrainable-networks.github.io/

Open clusters (OCs) in the Galaxy are excellent probes for tracing the structure and evolution of the Galactic disk. We present an updated catalog of parameters for 1,145 OCs, estimated using the Gaia DR3 data earlier listed in Cantat-Gaudin et al. (2020). This sample is complemented by 3,677 OCs from the catalog by Hunt & Reffert (2023). Using the Galaxy potential and the space velocities, orbits of 4,006 OCs were computed. We provide a catalog with orbital parameters such as eccentricity, perigalactic and apogalactic distance, and the maximum vertical height traced by OCs from the Galactic disk. The OCs were found to be distributed between 5-16 kpc from the Galactic center, with older OCs showing a radially extended distribution. The low number of old OCs in the inner Solar circle region likely suggests their destruction in this area. We derive a quantitative expression for the dependency of the maximum vertical height (Z_max) OCs can reach with the cluster's age and Galactocentric radius for the first time. The young and intermediate-age OCs show similar values of Z_max till 9 kpc, with the latter group having higher values beyond. OCs older than 1 Gyr show larger values of Z_max at all Galactocentric radii and significantly larger values beyond 9 kpc. Higher values of Z_max are found in the third Galactic quadrant, suggesting a link between these higher values and the Galactic warp. This sample shows that young OCs are also involved in the diagonal ridge formation in the solar neighborhood.

The Small Magellanic Cloud (SMC) is an irregular dwarf galaxy that has recently undergone an interaction with the Large Magellanic Cloud. The young massive stars in the SMC formed in the disturbed low-metallicity environment are important targets in astrophysics. We present a catalog of $\sim$ 76,800 far ultraviolet (FUV) sources towards the SMC detected using the Ultra Violet Imaging Telescope (UVIT) onboard AstroSat. We created an FUV catalog with $\sim$ 62900 probable SMC members which predominantly comprise main-sequence, giant, and subgiant stars. We selected 4 young populations (Young 1, Young 2, Young 3, and Blue Loop (BL) stars) identified from the Gaia optical color-magnitude diagram to study the morphology and kinematics of the young SMC using this catalog. We detect a clumpy morphology with a broken bar, a shell-like structure, and the inner SMC Wing for the 4 stellar populations. The eastern region and the northeastern regions are mainly populated by Young 1, 2, and 3. The central region predominantly has the Young 2 and 3 populations, whereas the SW has BL stars, Young 2 and 3. The 2-D kinematic study using proper motion (PM) reveals that Young 2 and 3 populations show two kinematically distinct sub-populations with low and high PM dispersion, whereas the Young 1 and BL stars show two kinematically distinct populations with low dispersion. Our analysis points to a kinematic disturbance along the RA direction for stars younger than $\sim$ 150 Myr located in the eastern region, with no significant disturbance along the Declination., Comment: 14 figures, 6 tables, Accepted for publication in the Astronomical Journal

The assembly of galaxy clusters is understood to be a hierarchical process with a continuous accretion of galaxies over time, which increases the cluster size and mass. Late-type galaxies that fall into clusters can undergo ram-pressure stripping, forming extended gas tails within which star formation can happen. The number, location, and tail orientations of such galaxies provide clues about the galaxy infall process, the assembly of the cluster over time, and the consequences of infall for galaxy evolution. Here, we utilise the $\sim$ 0.5 degree diameter circular field of the Ultraviolet Imaging Telescope to image six galaxy clusters at z < 0.06 that are known to contain jellyfish galaxies. We searched for stripping candidates in the ultraviolet images of these clusters, which revealed 54 candidates showing signs of unilateral extra-planar emission, due to ram-pressure stripping. Seven candidates had already been identified as likely stripping based on optical B-band imaging. We identified 47 new candidates through UV imaging. Spectroscopic redshift information is available for 39 of these candidate galaxies, of which 19 are associated with six clusters. The galaxies with spectroscopic redshifts that are not part of the clusters appear to be within structures at different redshifts identified as additional peaks in the redshift distribution of galaxies, indicating that they might be ram-pressure stripped or disturbed galaxies in other structures along the line of sight. We examine the orbital history of these galaxies based on their location in the position-velocity phase-space diagram and explore a possible connection to the orientation of the tail direction among cluster member candidates. The tails of confirmed cluster member galaxies are found to be oriented away from the cluster centre., Comment: Accepted for publication in A&A

Reform documents advocate for innovative pedagogical strategies to enhance student learning. A key innovation is the integration of science and engineering practices through Engineering Design (ED)-based physics laboratory tasks, where students tackle engineering design problems by applying physics principles. While this approach has its benefits, research shows that students do not always effectively apply scientific concepts, but instead rely on trial-and-error approaches, and end up 'gadgeteering' their way to a solution. This leads to what is commonly referred to as the "design-science gap" -- that students do not always consciously apply science concepts while solving a design problem. However, as obvious as the notion of a `gap' may appear, there seems to exist no consensus on the definitions of `design' and `science', further complicating the understanding of this `gap'. This qualitative study addresses the notion of the design-science gap by examining student-groups' discussions and written lab reports from a multi-week ED-based undergraduate introductory physics laboratory task. Building on our earlier studies, we developed and employed a nuanced, multi-layered coding scheme inspired by the Gioia Framework to characterize `design thinking' and `science thinking'. We discuss how student-groups engage in various aspects of design and how they apply concepts physics principles to solve the problem. In the process, we demonstrate the interconnectedness of students' design thinking and science thinking. We advocate for the usage of the term "design-science connection" as opposed to "design-science gap" to deepen both design and scientific thinking. Our findings offer valuable insights for educators in design-based science education., Comment: 1. Some changes made in Sections: 1, 2A, 2B, 2E, 4A, 4E (i), 4E (ii), 5D (ii), 5G, 5I; Nothing removed, but there's significantly more detailing now. 2. Caption 5E has been slightly modified. 3. Tables 4 and 5 have been expanded (extra column added). 5. A new Section 9 added to outline Future Work

Periodic driving of systems of particles can create crystalline structures in time. Such systems can be used to study solid-state physics phenomena in the time domain. In addition, it is possible to engineer the wave-number band structure of optical systems and to realize photonic time crystals by periodic temporal modulation of the material properties of the electromagnetic wave propagation medium. We introduce here a versatile averaged-permittivity approach which empowers emulating various condensed matter phases in the time dimension in a traveling wave resonator. This is achieved by utilizing temporal modulation of permittivity within a small segment of the resonator and the spatial shape of the segment. The required frequency and depth of the modulation are experimentally achievable, opening a pathway for research into the practical realisation of crystalline structures in time utilising microwave and optical systems., Comment: 4 pages + supplemental materials, 3 figures

In the realms of the internet of vehicles (IoV) and intelligent transportation systems (ITS), software defined vehicular networks (SDVN) and edge computing (EC) have emerged as promising technologies for enhancing road traffic efficiency. However, the increasing number of connected autonomous vehicles (CAVs) and EC-based applications presents multi-domain challenges such as inefficient traffic flow due to poor CAV coordination and flow-table overflow in SDVN from increased connectivity and limited ternary content addressable memory (TCAM) capacity. To address these, we focus on a data-driven approach using virtualization technologies like digital twin (DT) to leverage real-time data and simulations. We introduce a DT design and propose two data-driven solutions: a centralized decision support framework to improve traffic efficiency by reducing waiting times at roundabouts and an approach to minimize flow-table overflow and flow re-installation by optimizing flow-entry lifespan in SDVN. Simulation results show the decision support framework reduces average waiting times by 22% compared to human-driven vehicles, even with a CAV penetration rate of 40%. Additionally, the proposed optimization of flow-table space usage demonstrates a 50% reduction in flow-table space requirements, even with 100% penetration of connected vehicles., Comment: 7 pages, 9 figures, conference paper

Laboratory research is a complex, collaborative process that involves several stages, including hypothesis formulation, experimental design, data generation and analysis, and manuscript writing. Although reproducibility and data sharing are increasingly prioritized at the publication stage, integrating these principles at earlier stages of laboratory research has been hampered by the lack of broadly applicable solutions. Here, we propose that the workflow used in modern software development offers a robust framework for enhancing reproducibility and collaboration in laboratory research. In particular, we show that GitHub, a platform widely used for collaborative software projects, can be effectively adapted to organize and document all aspects of a research project's lifecycle in a molecular biology laboratory. We outline a three-step approach for incorporating the GitHub ecosystem into laboratory research workflows: 1. designing and organizing experiments using issues and project boards, 2. documenting experiments and data analyses with a version control system, and 3. ensuring reproducible software environments for data analyses and writing tasks with containerized packages. The versatility, scalability, and affordability of this approach make it suitable for various scenarios, ranging from small research groups to large, cross-institutional collaborations. Adopting this framework from a project's outset can increase the efficiency and fidelity of knowledge transfer within and across research laboratories. An example GitHub repository based on the above approach is available at https://github.com/rasilab/github_demo., Comment: 13 pages, 6 figures

At first light, the Thirty Meter Telescope (TMT) near-infrared (NIR) instruments will be fed by a multiconjugate adaptive optics instrument known as the Narrow Field Infrared Adaptive Optics System (NFIRAOS). NFIRAOS will use six laser guide stars to sense atmospheric turbulence in a volume corresponding to a field of view of 2', but natural guide stars (NGSs) will be required to sense tip/tilt and focus. To achieve high sky coverage (50% at the north Galactic pole), the NFIRAOS client instruments use NIR on-instrument wavefront sensors that take advantage of the sharpening of the stars by NFIRAOS. A catalog of guide stars with NIR magnitudes as faint as 22 mag in the J band (Vega system), covering the TMT-observable sky, will be a critical resource for the efficient operation of NFIRAOS, and no such catalog currently exists. Hence, it is essential to develop such a catalog by computing the expected NIR magnitudes of stellar sources identified in deep optical sky surveys using their optical magnitudes. This paper discusses the generation of a partial NIR Guide Star Catalog (IRGSC), similar to the final IRGSC for TMT operations. The partial catalog is generated by applying stellar atmospheric models to the optical data of stellar sources from the Panoramic Survey Telescope and Rapid Response System (Pan-STARRS) optical data and then computing their expected NIR magnitudes. We validated the computed NIR magnitudes of the sources in some fields by using the available NIR data for those fields. We identified the remaining challenges of this approach. We outlined the path for producing the final IRGSC using the Pan-STARRS data. We have named the Python code to generate the IRGSC as irgsctool, which generates a list of NGS for a field using optical data from the Pan-STARRS 3pi survey and also a list of NGSs having observed NIR data from the UKIRT Infrared Deep Sky Survey if they are available.

State of the art methods for target tracking with sensor management (or controlled sensing) are model-based and are obtained through solutions to Partially Observable Markov Decision Process (POMDP) formulations. In this paper a Reinforcement Learning (RL) approach to the problem is explored for the setting where the motion model for the object/target to be tracked is unknown to the observer. It is assumed that the target dynamics are stationary in time, the state space and the observation space are discrete, and there is complete observability of the location of the target under certain (a priori unknown) sensor control actions. Then, a novel Markov Decision Process (MDP) rather than POMDP formulation is proposed for the tracking problem with controlled sensing, which is termed as Track-MDP. In contrast to the POMDP formulation, the Track-MDP formulation is amenable to an RL based solution. It is shown that the optimal policy for the Track-MDP formulation, which is approximated through RL, is guaranteed to track all significant target paths with certainty. The Track-MDP method is then compared with the optimal POMDP policy, and it is shown that the infinite horizon tracking reward of the optimal Track-MDP policy is the same as that of the optimal POMDP policy. In simulations it is demonstrated that Track-MDP based RL leads to a policy that can track the target with high accuracy.

In this article, we calculate the $S$-matrix elements for the process $e^{+} e^{-}\rightarrow t \bar{t}$ mediated by SM photon, $Z$ boson and an additional $Z^{'}$ boson indicating the contribution from new physics. We calculate the amplitude square using two component spinor formalism and four component spinor formalism and show the equivalence of the results using the two formalisms. We also establish the relations between the couplings of $Z^{'}$ boson to fermions in the two component spinor formalism and four component spinor formalism.

We report the discovery of a barium blue straggler star (BSS) in M67, exhibiting enhancements in slow neutron-capture ($s$-) process elements. Spectroscopic analysis of two BSSs (WOCS\,9005 \& WOCS\,1020) and 4 stars located near the main-sequence turn-off using GALAH spectra, showed that WOCS\,9005 has a significantly high abundance of the s-process elements ([Ba/Fe] = 0.75$\pm$0.08, [Y/Fe] = 1.09$\pm$0.07, [La/Fe] = 0.65$\pm$0.06). The BSS (WOCS\,9005) is a spectroscopic binary with a known period, eccentricity and a suspected white dwarf (WD) companion with a kinematic mass of 0.5 M$_\odot$. The first `sighting' of the WD in this barium BSS is achieved through multi-wavelength spectral energy distribution (SED) with the crucial far-UV data from the UVIT/\textit{AstroSat}. The parameters of the hot and cool companions are derived using binary fits of the SED using two combinations of models, yielding a WD with T$_{eff}$ in the range 9750--15250 K. Considering the kinematic mass limit, the cooling age of the WD is estimated as $\sim$ 60 Myr. The observed enhancements are attributed to a mass transfer (MT) from a companion asymptotic giant branch star, now a WD. We estimate the accreted mass to be 0.15 M$_{\odot}$, through wind accretion, which increased the envelope mass from 0.45 M$_{\odot}$. The detection of chemical enhancement, as well as the sighting of WD in this system have been possible due to the recent MT in this binary, as suggested by the young WD., Comment: Accepted for publication in The Astrophysical Journal Letters. 12 pages, 5 figures

Edge Artificial Intelligence (AI) incorporates a network of interconnected systems and devices that receive, cache, process, and analyze data in close communication with the location where the data is captured with AI technology. Recent advancements in AI efficiency, the widespread use of Internet of Things (IoT) devices, and the emergence of edge computing have unlocked the enormous scope of Edge AI. Edge AI aims to optimize data processing efficiency and velocity while ensuring data confidentiality and integrity. Despite being a relatively new field of research from 2014 to the present, it has shown significant and rapid development over the last five years. This article presents a systematic literature review for Edge AI to discuss the existing research, recent advancements, and future research directions. We created a collaborative edge AI learning system for cloud and edge computing analysis, including an in-depth study of the architectures that facilitate this mechanism. The taxonomy for Edge AI facilitates the classification and configuration of Edge AI systems while examining its potential influence across many fields through compassing infrastructure, cloud computing, fog computing, services, use cases, ML and deep learning, and resource management. This study highlights the significance of Edge AI in processing real-time data at the edge of the network. Additionally, it emphasizes the research challenges encountered by Edge AI systems, including constraints on resources, vulnerabilities to security threats, and problems with scalability. Finally, this study highlights the potential future research directions that aim to address the current limitations of Edge AI by providing innovative solutions., Comment: Preprint Version Accepted for Publication in Springer Cluster Computing, 2024

Modern climate projections often suffer from inadequate spatial and temporal resolution due to computational limitations, resulting in inaccurate representations of sub-grid processes. A promising technique to address this is the Multiscale Modeling Framework (MMF), which embeds a kilometer-resolution cloud-resolving model within each atmospheric column of a host climate model to replace traditional convection and cloud parameterizations. Machine learning (ML) offers a unique opportunity to make MMF more accessible by emulating the embedded cloud-resolving model and reducing its substantial computational cost. Although many studies have demonstrated proof-of-concept success of achieving stable hybrid simulations, it remains a challenge to achieve near operational-level success with real geography and comprehensive variable emulation that includes, for example, explicit cloud condensate coupling. In this study, we present a stable hybrid model capable of integrating for at least 5 years with near operational-level complexity, including real geography, seasonality, explicit cloud condensate predictions, and land coupling. Our model demonstrates skillful online performance in metrics such as 5-year zonal mean biases compared to previous MMF emulation studies. The monthly error against reference MMF simulations with the same initial condition approaches the fundamental predictability limit. Key factors contributing to our online performance include an expressive U-Net architecture, additional input features that include large-scale forcings and convection memory, and physical thermodynamic constraints for microphysics. With microphysical constraints mitigating unrealistic cloud formation, our work is the first to demonstrate realistic multi-year cloud condensate climatology under the MMF framework. Our work showcases ML parameterization's potential for operational-level climate simulations., Comment: 31 pages, 7 figures in the main text, 4 figures in appendix. This version is a minor editorial update from the previous version 2

We use (multi)modal deep neural networks (DNNs) to probe for sites of multimodal integration in the human brain by predicting stereoencephalography (SEEG) recordings taken while human subjects watched movies. We operationalize sites of multimodal integration as regions where a multimodal vision-language model predicts recordings better than unimodal language, unimodal vision, or linearly-integrated language-vision models. Our target DNN models span different architectures (e.g., convolutional networks and transformers) and multimodal training techniques (e.g., cross-attention and contrastive learning). As a key enabling step, we first demonstrate that trained vision and language models systematically outperform their randomly initialized counterparts in their ability to predict SEEG signals. We then compare unimodal and multimodal models against one another. Because our target DNN models often have different architectures, number of parameters, and training sets (possibly obscuring those differences attributable to integration), we carry out a controlled comparison of two models (SLIP and SimCLR), which keep all of these attributes the same aside from input modality. Using this approach, we identify a sizable number of neural sites (on average 141 out of 1090 total sites or 12.94%) and brain regions where multimodal integration seems to occur. Additionally, we find that among the variants of multimodal training techniques we assess, CLIP-style training is the best suited for downstream prediction of the neural activity in these sites., Comment: ICML 2024; 23 pages, 11 figures

The interactions between the Magellanic Clouds significantly affect the shape and distribution of the young stellar population, particularly in the periphery of the Small Magellanic Cloud (SMC). We present the first far-UV (FUV) map of the north-east SMC-Shell region using the Ultra Violet Imaging Telescope (UVIT) onboard AstroSat. The detected FUV stars are combined with Gaia Early Data Release 3 data to create a FUV-optical catalog of ~ 14,400 stars. FUV-optical colour-magnitude diagrams are used along with isochrones to estimate the stellar ages. The detected stars are formed in multiple episodes. We identified two episodes of star formation (~ 60 and ~ 260 Myr ago) where the episode at ~ 260 Myr is linked to the recent interaction with the Large Magellanic Cloud (LMC) and the episode at ~ 60 Myr is linked to the pericentric passage of the SMC around our Galaxy. The median proper motion (PM) and velocity dispersion are found to be similar to the SMC main body, indicating that this region has not experienced significant tidal effects. The FUV stellar surface density and the dispersion in PM suggest an extent of the inner SMC in the north-east direction to be around 2.2 deg. We detect arm-like and arc-like structures in the FUV stellar density map, and their kinematics appear to be similar to the SMC main body. These extended outer features are the spatial stellar overdensities formed over multiple episodes of star formation, but without apparent kinematic distinction., Comment: 14 pages, 11 figures, 1 appendix figure, 3 tables, Accepted for publication in Monthly Notices of the Royal Astronomical Society (MNRAS)

Evolutionary pathways of binary systems are vastly different from single stellar evolution, and thus, there is a need to quantify their frequency and diversity. Open clusters are the best test-bed to unveil the secrets of binary populations due to their coeval nature. And the availability of multi-wavelength data in recent years has been critical in characterising the binary population. NGC 752 is a solar metallicity, intermediate-age open cluster located at 460 pc. In this work, we aim to identify the optically subluminous white dwarfs in NGC 752 and identify the illusive blue lurkers by association. We used multiwavelength photometry from Astrosat/UVIT, swift/UVOT, Gaia DR3 and other archival surveys to analyse the colour-magnitude diagrams and spectral energy distributions of 37 cluster members. We detected eight white dwarfs as companions to cluster members. Four of the systems are main sequence stars with extremely low mass white dwarfs as their companions. Two are these main sequence stars are also fast rotators. The presence of low mass white dwarfs and high rotation signals a past mass transfer, and we classified the four main sequence stars as blue lurkers. The binary fraction in NGC 752 was estimated to be 50--70%, and it shows that the contribution of optically undetected stars is crucial in quantifying the present-day binary fraction., Comment: 7 pages, 3 figures, accepted in Astronomy & Astrophysics

We present a self-supervised framework that learns population-level codes for arbitrary ensembles of neural recordings at scale. We address two key challenges in scaling models with neural time-series data: sparse and variable electrode distribution across subjects and datasets. The Population Transformer (PopT) stacks on top of pretrained representations and enhances downstream decoding by enabling learned aggregation of multiple spatially-sparse data channels. The pretrained PopT lowers the amount of data required for downstream decoding experiments, while increasing accuracy, even on held-out subjects and tasks. Compared to end-to-end methods, this approach is computationally lightweight and more interpretable, while still retaining competitive performance. We further show how our framework is generalizable to multiple time-series embeddings and neural data modalities. Beyond decoding, we interpret the pretrained PopT and fine-tuned models to show how they can be used to extract neuroscience insights from massive amounts of data. We release our code as well as a pretrained PopT to enable off-the-shelf improvements in multi-channel intracranial data decoding and interpretability., Comment: 19 pages, 11 figures, submitted to ICLR 2025

We introduce randomized algorithms to Clifford's Geometric Algebra, generalizing randomized linear algebra to hypercomplex vector spaces. This novel approach has many implications in machine learning, including training neural networks to global optimality via convex optimization. Additionally, we consider fine-tuning large language model (LLM) embeddings as a key application area, exploring the intersection of geometric algebra and modern AI techniques. In particular, we conduct a comparative analysis of the robustness of transfer learning via embeddings, such as OpenAI GPT models and BERT, using traditional methods versus our novel approach based on convex optimization. We test our convex optimization transfer learning method across a variety of case studies, employing different embeddings (GPT-4 and BERT embeddings) and different text classification datasets (IMDb, Amazon Polarity Dataset, and GLUE) with a range of hyperparameter settings. Our results demonstrate that convex optimization and geometric algebra not only enhances the performance of LLMs but also offers a more stable and reliable method of transfer learning via embeddings.

Nanocomposites comprising of high surface area adsorption materials and nanosized transition metals have emerged as a promising strategy for hydrogen storage application due to their inherent ability to store atomic and molecular forms of hydrogen by invoking mechanisms like physisorption and spillover mechanism or Kubas interaction. The potential use of these materials for both transport and stationary applications depends on reaching the ultimate storage capacity and scalability. In addition to achieving good hydrogen storage capacity, it is also vital to explore novel and efficient synthesis routes to control the microstructure. Herein, a direct and simple thermal decomposition technique is reported to synthesize carbon-based nanocomposites, where nickel nanoparticles are dispersed in a porous carbon matrix. The structure, morphology, composition and nature of bonding in the samples were investigated using transmission electron microscopy, scanning electron microscopy, energy dispersive spectroscopy, X-ray diffraction and Raman spectroscopy. Sorption-desorption isotherms were used to study the hydrogen storage capacity of the nanocomposites at a moderate H2 pressure of 20 bar. Among the various nanocomposites examined, the best obtained storage capacity was 0.73 wt.% (against 0.11 wt.% for pure carbon sample) at 298 K with reversible cyclability. It is shown that the uniform dispersion of catalytic nanoparticles along with a high surface area carbon matrix helps in the enhancement of hydrogen storage capacity by a factor of 6.5 times over pure carbon., Comment: 23 pages, 9 figures