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Purpose: This chapter explores the transformation of teacher education in India to enhance student-teacher interactions within virtual learning environments (VLEs). It aims to address the challenges and opportunities posed by the digital shift in education, particularly in the wake of the COVID-19 pandemic, and emphasizes the need for pre-service teachers to develop the skills and knowledge necessary for effective virtual teaching. Method: The chapter employs a comprehensive literature review, drawing on studies that highlight the benefits and challenges of VLEs. It incorporates theoretical frameworks such as Technological Pedagogical Content Knowledge (TPACK) and the Community of Inquiry (CoI) model to underscore the importance of integrating technology, pedagogy, and content knowledge. The analysis includes current data on the adoption of virtual learning platforms in India and the impact of the National Education Policy (NEP) 2020 on e-learning and virtual classrooms. The chapter also examines practical strategies for fortifying teacher education through curriculum enhancements and practical training modules. Implication: The findings underscore the urgent need to align teacher education programs with the demands of virtual pedagogy. By incorporating digital literacy training, practical virtual teaching experiences, and fostering positive attitudes toward technology, teacher education programs can better prepare pre-service teachers for the complexities of virtual interactions. This enhanced preparation is crucial for ensuring effective student engagement, motivation, and learning outcomes in VLEs. The chapter's insights contribute to the ongoing discourse on educational technology integration and provide actionable recommendations for policymakers and educators in India to bridge the gaps in virtual teacher training. [This is a chapter in: Vartika Rajput, Mohini Singh Vishen, Lohans Kumar Kalyani Kavita Vishnoi (Eds.), "Futuristics Trends in Higher Education" (pp. 129-144), 2024, Gonda, UP: Surya Multidisciplinary Publication.]

Robot learning holds tremendous promise to unlock the full potential of flexible, general, and dexterous robot systems, as well as to address some of the deepest questions in artificial intelligence. However, bringing robot learning to the level of generality required for effective real-world systems faces major obstacles in terms of data, generalization, and robustness. In this paper, we discuss how generalist robot policies (i.e., robot foundation models) can address these challenges, and how we can design effective generalist robot policies for complex and highly dexterous tasks. We propose a novel flow matching architecture built on top of a pre-trained vision-language model (VLM) to inherit Internet-scale semantic knowledge. We then discuss how this model can be trained on a large and diverse dataset from multiple dexterous robot platforms, including single-arm robots, dual-arm robots, and mobile manipulators. We evaluate our model in terms of its ability to perform tasks in zero shot after pre-training, follow language instructions from people and from a high-level VLM policy, and its ability to acquire new skills via fine-tuning. Our results cover a wide variety of tasks, such as laundry folding, table cleaning, and assembling boxes., Comment: See project website for videos: https://physicalintelligence.company/blog/pi0

Recent reports indicate that perovskite based light emitting diodes (LEDs) have achieved an external quantum efficiency (EQE) of 32% - rather an internal quantum efficiency close to 100%. Much of this improved performance is attributed to the role of excitons. While the experimental trends are encouraging, the recombination parameters estimated through extensive curve-fitting of photoluminescence (PL) transients are often not amenable to reasonable interpretations. In view of the same, through a detailed analysis of free carrier - exciton dynamics in perovskite optoelectronic materials, here we identify a coherent scheme to unambiguously back extract the relevant parameters. The model predictions compare well with the recent experimental results on perovskite LEDs with record EQE thus quantifying the role of excitons. Importantly, this work identifies a physics aware scheme for the design of experiments tailored towards consistent exploration of underlying physical mechanisms and hence could enable a synergistic optimization of process technology and device performance.

Image and video generative models that are pre-trained on Internet-scale data can greatly increase the generalization capacity of robot learning systems. These models can function as high-level planners, generating intermediate subgoals for low-level goal-conditioned policies to reach. However, the performance of these systems can be greatly bottlenecked by the interface between generative models and low-level controllers. For example, generative models may predict photorealistic yet physically infeasible frames that confuse low-level policies. Low-level policies may also be sensitive to subtle visual artifacts in generated goal images. This paper addresses these two facets of generalization, providing an interface to effectively "glue together" language-conditioned image or video prediction models with low-level goal-conditioned policies. Our method, Generative Hierarchical Imitation Learning-Glue (GHIL-Glue), filters out subgoals that do not lead to task progress and improves the robustness of goal-conditioned policies to generated subgoals with harmful visual artifacts. We find in extensive experiments in both simulated and real environments that GHIL-Glue achieves a 25% improvement across several hierarchical models that leverage generative subgoals, achieving a new state-of-the-art on the CALVIN simulation benchmark for policies using observations from a single RGB camera. GHIL-Glue also outperforms other generalist robot policies across 3/4 language-conditioned manipulation tasks testing zero-shot generalization in physical experiments., Comment: Code, model checkpoints and videos can be found at https://ghil-glue.github.io

In this paper, we consider the importance of channel measurement data from specific sites and its impact on air interface optimization and test. Currently, a range of statistical channel models including 3GPP 38.901 tapped delay line (TDL), clustered delay line (CDL), urban microcells (UMi) and urban macrocells (UMa) type channels are widely used for air interface performance testing and simulation. However, there remains a gap in the realism of these models for air interface testing and optimization when compared with real world measurement based channels. To address this gap, we compare the performance impacts of training neural receivers with 1) statistical 3GPP TDL models, and 2) measured macro-cell channel impulse response (CIR) data. We leverage our OmniPHY-5G neural receiver for NR PUSCH uplink simulation, with a training procedure that uses statistical TDL channel models for pre-training, and fine-tuning based on measured site specific MIMO CIR data. The proposed fine-tuning method achieves a 10% block error rate (BLER) at a 1.85 dB lower signal-to-noise ratio (SNR) compared to pre-training only on simulated TDL channels, illustrating a rough magnitude of the gap that can be closed by site-specific training, and gives the first answer to the question "how much can fine-tuning the RAN for site-specific channels help?", Comment: Appears in the proceedings of the First Workshop on Research and Innovation in Testing and Integration for Open Radio Access Networks (RitiRAN)

In today's machine learning world for tabular data, XGBoost and fully connected neural network (FCNN) are two most popular methods due to their good model performance and convenience to use. However, they are highly complicated, hard to interpret, and can be overfitted. In this paper, we propose a new modeling framework called cross spline net (CSN) that is based on a combination of spline transformation and cross-network (Wang et al. 2017, 2021). We will show CSN is as performant and convenient to use, and is less complicated, more interpretable and robust. Moreover, the CSN framework is flexible, as the spline layer can be configured differently to yield different models. With different choices of the spline layer, we can reproduce or approximate a set of non-neural network models, including linear and spline-based statistical models, tree, rule-fit, tree-ensembles (gradient boosting trees, random forest), oblique tree/forests, multi-variate adaptive regression spline (MARS), SVM with polynomial kernel, etc. Therefore, CSN provides a unified modeling framework that puts the above set of non-neural network models under the same neural network framework. By using scalable and powerful gradient descent algorithms available in neural network libraries, CSN avoids some pitfalls (such as being ad-hoc, greedy or non-scalable) in the case-specific optimization methods used in the above non-neural network models. We will use a special type of CSN, TreeNet, to illustrate our point. We will compare TreeNet with XGBoost and FCNN to show the benefits of TreeNet. We believe CSN will provide a flexible and convenient framework for practitioners to build performant, robust and more interpretable models.

Humans develop their grammars by making structural generalizations from finite input. We ask how filler-gap dependencies, which share a structural generalization despite diverse surface forms, might arise from the input. We explicitly control the input to a neural language model (NLM) to uncover whether the model posits a shared representation for filler-gap dependencies. We show that while NLMs do have success differentiating grammatical from ungrammatical filler-gap dependencies, they rely on superficial properties of the input, rather than on a shared generalization. Our work highlights the need for specific linguistic inductive biases to model language acquisition., Comment: accepted at CoNLL 2024

In recent times, extracting valuable information from large text is making significant progress. Especially in the current era of social media, people expect quick bites of information. Automatic text summarization seeks to tackle this by slimming large texts down into more manageable summaries. This important research area can aid in decision-making by digging out salient content from large text. With the progress in deep learning models, significant work in language models has emerged. The encoder-decoder framework in deep learning has become the central approach for automatic text summarization. This work leverages transformer-based BART model for human-like summarization which is an open-ended problem with many challenges. On training and fine-tuning the encoder-decoder model, it is tested with diverse sample articles and the quality of summaries of diverse samples is assessed based on human evaluation parameters. Further, the finetuned model performance is compared with the baseline pretrained model based on evaluation metrics like ROUGE score and BERTScore. Additionally, domain adaptation of the model is required for improved performance of abstractive summarization of dialogues between interlocutors. On investigating, the above popular evaluation metrics are found to be insensitive to factual errors. Further investigation of the summaries generated by finetuned model is done using the contemporary evaluation metrics of factual consistency like WeCheck and SummaC. Empirical results on BBC News articles highlight that the gold standard summaries written by humans are more factually consistent by 17% than the abstractive summaries generated by finetuned model., Comment: Pre-print

Intels efforts to build a practical quantum computer are focused on developing a scalable spin-qubit platform leveraging industrial high-volume semiconductor manufacturing expertise and 300 mm fabrication infrastructure. Here, we provide an overview of the design, fabrication, and demonstration of a new customized quantum test chip, which contains 12-quantum-dot spin-qubit linear arrays, code named Tunnel Falls. These devices are fabricated using immersion and extreme ultraviolet lithography (EUV), along with other standard high-volume manufacturing (HVM) processes, as well as production-level process control. We present key device features and fabrication details, as well as qubit characterization results confirming device functionality. These results corroborate our fabrication methods and are a crucial step towards scaling of extensible 2D qubit array schemes.

We present a systematic evaluation of large language models' sensitivity to argument roles, i.e., who did what to whom, by replicating psycholinguistic studies on human argument role processing. In three experiments, we find that language models are able to distinguish verbs that appear in plausible and implausible contexts, where plausibility is determined through the relation between the verb and its preceding arguments. However, none of the models capture the same selective patterns that human comprehenders exhibit during real-time verb prediction. This indicates that language models' capacity to detect verb plausibility does not arise from the same mechanism that underlies human real-time sentence processing.

Privacy is essential to fully enjoying the benefits of social media. While fear around privacy risks can sometimes motivate privacy management, the negative impact of such fear, particularly when it is perceived as unaddressable (i.e., "dysfunctional" fear), can significantly harm teen well-being. In a co-design study with 136 participants aged 13-18, we explored how teens can protect their privacy without experiencing heightened fear. We identified seven different sources of dysfunctional fear, such as `fear of a hostile environment' and `fear of overstepping privacy norms.' We also evaluated ten designs, co-created with teen participants, that address these fears. Our findings suggest that social media platforms can mitigate dysfunctional fear without compromising privacy by creating a culture where privacy protection is the norm through default privacy-protective features. However, we also found that even the most effective privacy features are not likely to be adopted unless they balance the multifaceted and diverse needs of teens. Individual teens have different needs -- for example, public and private account users have different needs -- and teens often want to enjoy the benefits they get from slightly reducing privacy and widening their social reach. Given these considerations, augmenting default privacy features by allowing them to be toggled on and off will allow individual users to choose their own balance while still maintaining a privacy-focused norm.

Spin squeezing, a form of many-body entanglement, is a crucial resource in quantum metrology and information processing. While experimentally viable protocols for generating stable spin squeezing have been proposed in quantum-optical platforms using atomic ensembles, there is growing interest in quantum hybrid solid-state systems as alternative platforms for exploring many-body quantum phenomena. In this work, we propose a scheme to generate long-lived spin squeezing in an ensemble of solid-state qubits interacting with electromagnetic noise emitted by a squeezed solid-state bath. We identify the conditions under which quantum correlations within the bath can be transferred to the qubit array, effectively driving the qubits into an entangled state, regardless of their initial configuration. To assess the experimental feasibility of our approach, we investigate the dynamics of an array of solid-state spin defects coupled to a common ferromagnetic bath, which is driven into a non-equilibrium squeezed state through its interaction with a surface acoustic wave mode. Our results demonstrate that the ensemble can exhibit steady-state spin squeezing under suitable conditions, paving the way for the generation of steady-state many-body entanglement in ensembles of solid-state spin defects., Comment: 9 pages, 2 figures

Generalizing from previous work on the integer quantum Hall effect, we construct the effective action for the analog of Laughlin states for the fractional quantum Hall effect in higher dimensions. The formalism is a generalization of the parton picture used in two spatial dimensions, the crucial ingredient being the cancellation of anomalies for the gauge fields binding the partons together. Some subtleties which exist even in two dimensions are pointed out. The effective action is obtained from a combination of the Dolbeault and Dirac index theorems. We also present expressions for some transport coefficients such as Hall conductivity and Hall viscosity for the fractional states., Comment: 27 pages

Lieb excitations are fundamental to the understanding of the low energy behaviour of many-body quantum gases. Here we study the spectral function of a Tonks-Girardeau gas in a finite sized Kronig-Penney potential and show that the Lieb-I and Lieb-II excitations can become gapped as a function of the barrier height. Moreover, we reveal the existence of a topological flat mode near the Fermi energy and at zero momentum and show that this is robust to perturbations in the system. Through a scaling analysis, we determine the divergent behaviour of the spectral function. Our results provide a significant reference for the observation and understanding of the gapped Lieb excitations and the topological flat mode of quantum gases in experimentally realistic subwavelength optical lattice potentials.

The in vitro transcription (IVT) process is a critical step in RNA production. To ensure the efficiency of RNA manufacturing, it is essential to optimize and identify its key influencing factors. In this study, multiple Gaussian Process (GP) models are used to perform efficient optimization and global sensitivity analysis (GSA). Firstly, multiple GP models were constructed using the data from multiple experimental replicates, accurately capturing the complexities of the IVT process. Then GSA was conducted to determine the dominant reaction factors, specifically the concentrations of reactants NTP and Mg across all data-driven models. Concurrently, a multi-start optimization algorithm was applied to these GP models to identify optimal operational conditions that maximize RNA yields across all surrogate models. These optimized conditions are subsequently validated through additional experimental data., Comment: This version was uploaded without full consent from all co-authors. We apologize for this oversight and are addressing it with the involved parties

LLM-as-a-Judge is a widely used method for evaluating the performance of Large Language Models (LLMs) across various tasks. We address the challenge of quantifying the uncertainty of LLM-as-a-Judge evaluations. While uncertainty quantification has been well-studied in other domains, applying it effectively to LLMs poses unique challenges due to their complex decision-making capabilities and computational demands. In this paper, we introduce a novel method for quantifying uncertainty designed to enhance the trustworthiness of LLM-as-a-Judge evaluations. The method quantifies uncertainty by analyzing the relationships between generated assessments and possible ratings. By cross-evaluating these relationships and constructing a confusion matrix based on token probabilities, the method derives labels of high or low uncertainty. We evaluate our method across multiple benchmarks, demonstrating a strong correlation between the accuracy of LLM evaluations and the derived uncertainty scores. Our findings suggest that this method can significantly improve the reliability and consistency of LLM-as-a-Judge evaluations.

Within the basis light-front quantization framework, we systematically investigate the unpolarized and longitudinally polarized double parton distributions (DPDs) of quarks inside the proton. We utilize the light-front wave functions of the proton derived in the valence sector from a Hamiltonian quantized on the light-front. The interaction terms of the Hamiltonian consist of a one-gluon exchange interaction at fixed coupling and a three-dimensional confinement potential. Our current analysis yields significant correlations of the quarks' longitudinal momenta with their transverse separation. We also demonstrate that our calculations do not support the commonly used $x-\vec{k}_\perp$ factorization of the DPDs in $x$ and $k_\perp$. Our results are qualitatively consistent with those of other phenomenological models., Comment: 14 pages, 16 figures

This proposal aims to develop more accurate federated learning (FL) methods with faster convergence properties and lower communication requirements, specifically for forecasting distributed energy resources (DER) such as renewables, energy storage, and loads in modern, low-carbon power grids. This will be achieved by (i) leveraging recently developed extensions of FL such as hierarchical and iterative clustering to improve performance with non-IID data, (ii) experimenting with different types of FL global models well-suited to time-series data, and (iii) incorporating domain-specific knowledge from power systems to build more general FL frameworks and architectures that can be applied to diverse types of DERs beyond just load forecasting, and with heterogeneous clients., Comment: Presented at the NeurIPS 2022 Tackling Climate Change with Machine Learning workshop

In today's day and age where information is rapidly spread through online platforms, the rise of fake news poses an alarming threat to the integrity of public discourse, societal trust, and reputed news sources. Classical machine learning and Transformer-based models have been extensively studied for the task of fake news detection, however they are hampered by their reliance on training data and are unable to generalize on unseen headlines. To address these challenges, we propose our novel solution, leveraging web-scraping techniques and Natural Language Inference (NLI) models to retrieve external knowledge necessary for verifying the accuracy of a headline. Our system is evaluated on a diverse self-curated evaluation dataset spanning over multiple news channels and broad domains. Our best performing pipeline achieves an accuracy of 84.3% surpassing the best classical Machine Learning model by 33.3% and Bidirectional Encoder Representations from Transformers (BERT) by 31.0% . This highlights the efficacy of combining dynamic web-scraping with Natural Language Inference to find support for a claimed headline in the corresponding externally retrieved knowledge for the task of fake news detection., Comment: Preprint, 15 pages, 7 figures

For a graph $\mathbb{Q}=(\mathbb{V},\mathbb{E})$, the transformation graphs are defined as graphs with vertex set being $\mathbb{V(Q)} \cup \mathbb{E(Q)}$ and edge set is described following certain conditions. In comparison to the structure descriptor of the original graph $\mathbb{Q}$, the topological descriptor of its transformation graphs displays distinct characteristics related to structure. Thus, a compound's transformation graphs descriptors can be used to model a variety of structural features of the underlying chemical. In this work, the concept of transformation graphs are extended giving rise to novel class of graphs, the $(r,s)$- generalised transformation graphs, whose vertex set is union of $r$ copies of $\mathbb{V(Q)}$ and $s$ copies of $\mathbb{E(Q)}$, where, $r, s \in N$ and the edge set are defined under certain conditions. Further, these class of graphs are analysed with the help of first Zagreb index. Mainly, there are eight transformation graphs based on the criteria for edge set, but under the concept of $(r,s)$- generalised transformation graphs, infinite number of graphs can be described and analysed.

Providing feedback is widely recognized as crucial for refining students' writing skills. Recent advances in language models (LMs) have made it possible to automatically generate feedback that is actionable and well-aligned with human-specified attributes. However, it remains unclear whether the feedback generated by these models is truly effective in enhancing the quality of student revisions. Moreover, prompting LMs with a precise set of instructions to generate feedback is nontrivial due to the lack of consensus regarding the specific attributes that can lead to improved revising performance. To address these challenges, we propose PROF that PROduces Feedback via learning from LM simulated student revisions. PROF aims to iteratively optimize the feedback generator by directly maximizing the effectiveness of students' overall revising performance as simulated by LMs. Focusing on an economic essay assignment, we empirically test the efficacy of PROF and observe that our approach not only surpasses a variety of baseline methods in effectiveness of improving students' writing but also demonstrates enhanced pedagogical values, even though it was not explicitly trained for this aspect., Comment: Accepted to EMNLP 2024

Exocytosis is a fundamental process related to the information exchange in the nervous and endocrine system. Among the various techniques, vesicle impact electrochemical cytometry (VIEC) has emerged as an effective method to mimic the exocytosis process and measure dynamic information about content transfer using nanoscale electrodes. In this manuscript, through analytical models and large scale simulations, we develop scaling laws for the decay time constant ($\tau$) for VIEC single-exponential transients. Specifically, our results anticipate a power law dependence of $\tau$ on the geometric and the transport parameters. This model compares very well with large scale simulations exploring the parameter space relevant for VIEC and with experimental results from literature. Remarkably, such physics based compact models could allow for novel multi-feature based self consistent strategies for back extraction of geometric and transport parameters and hence could contribute towards better statistical analysis and understanding of exocytosis transients and events., Comment: 7 pages, 5 figures

We develop improved physics-informed neural networks (PINNs) for high-order and high-dimensional power system models described by nonlinear ordinary differential equations. We propose some novel enhancements to improve PINN training and accuracy and also implement several other recently proposed ideas from the literature. We successfully apply these to study the transient dynamics of synchronous generators. We also make progress towards applying PINNs to advanced inverter models. Such enhanced PINNs can allow us to accelerate high-fidelity simulations needed to ensure a stable and reliable renewables-rich future grid., Comment: Accepted to the Tackling Climate Change with Machine Learning workshop at NeurIPS 2024

Synthetic data generation is an important application of machine learning in the field of medical imaging. While existing approaches have successfully applied fine-tuned diffusion models for synthesizing medical images, we explore potential improvements to this pipeline through feature-aligned diffusion. Our approach aligns intermediate features of the diffusion model to the output features of an expert, and our preliminary findings show an improvement of 9% in generation accuracy and ~0.12 in SSIM diversity. Our approach is also synergistic with existing methods, and easily integrated into diffusion training pipelines for improvements. We make our code available at \url{https://github.com/lnairGT/Feature-Aligned-Diffusion}., Comment: Accepted to First International Workshop on Vision-Language Models for Biomedical Applications (VLM4Bio 2024) at the 32nd ACM-Multimedia conference

Evaluation of large language model (LLM) outputs requires users to make critical judgments about the best outputs across various configurations. This process is costly and takes time given the large amounts of data. LLMs are increasingly used as evaluators to filter training data, evaluate model performance or assist human evaluators with detailed assessments. To support this process, effective front-end tools are critical for evaluation. Two common approaches for using LLMs as evaluators are direct assessment and pairwise comparison. In our study with machine learning practitioners (n=15), each completing 6 tasks yielding 131 evaluations, we explore how task-related factors and assessment strategies influence criteria refinement and user perceptions. Findings show that users performed more evaluations with direct assessment by making criteria task-specific, modifying judgments, and changing the evaluator model. We conclude with recommendations for how systems can better support interactions in LLM-assisted evaluations.

Deep Learning Recommendation Models (DLRMs) have become increasingly popular and prevalent in today's datacenters, consuming most of the AI inference cycles. The performance of DLRMs is heavily influenced by available bandwidth due to their large vector sizes in embedding tables and concurrent accesses. To achieve substantial improvements over existing solutions, novel approaches towards DLRM optimization are needed, especially, in the context of emerging interconnect technologies like CXL. This study delves into exploring CXL-enabled systems, implementing a process-in-fabric-switch (PIFS) solution to accelerate DLRMs while optimizing their memory and bandwidth scalability. We present an in-depth characterization of industry-scale DLRM workloads running on CXL-ready systems, identifying the predominant bottlenecks in existing CXL systems. We, therefore, propose PIFS-Rec, a PIFS-based scheme that implements near-data processing through downstream ports of the fabric switch. PIFS-Rec achieves a latency that is 3.89x lower than Pond, an industry-standard CXL-based system, and also outperforms BEACON, a state-of-the-art scheme, by 2.03x.

(abridged) Characterizing duty cycles of recurrent phases of dormancy and activity in supermassive black holes in active galactic nuclei is crucial in understanding impact of energy released on host galaxies and their evolution. However, identifying sources in quiescent and restarted phases is challenging. Our goal is to identify and characterize a substantial sample of radio galaxies in restarted phase and explore core prominence as a signature of this activity. We expand our prior study from a $30\,\mathrm{deg^2}$ area in Lockman Hole to a larger $424\,\mathrm{deg^2}$ region in Hobby-Eberly Telescope Dark Energy Experiment (HETDEX) field using visually selected core-dominated radio galaxies. We used 144 MHz LOFAR survey images to identify galaxies with restarting jets. By applying selection criteria, including radio core dominance, low surface brightness extended emission, spectral index properties, and morphology, we found 69 candidate restarted radio galaxies. These candidates show diverse intrinsic morphology, spanning FRI, FRII, core-with-halo, and asymmetric forms, suggesting different progenitors. Among these, nine galaxies exhibit ultra-steep spectrum extended emission combined with high radio core prominence, representing previous and current epochs of jet activity. This subset supports a model where the switch-on and switch-off mechanism occurs with fast duty cycle. The restarted candidates span radio luminosities from log$_{10}$(L$_\mathrm{144 MHz}$/$\mathrm{WHz^{-1}}$) = 23.24 to 26.80, with sizes between 88 and 1659 kpc, including 16 giant radio galaxies. Their total stellar content aligns with massive elliptical galaxies. Our findings at $z<0.4$ suggest that many restarting galaxies are not found in rich cluster environments, consistent with broader radio-galaxy population. This study confirms core prominence as an effective parameter for selecting restarted radio sources., Comment: 28 pages, 18 figures, 7 tables, accepted for publication in A&A

This paper introduces the concept of compliant vertices and compliant graphs, with a focus on the total domination degree (TDD) of a vertex in compliant graphs. The TDD is systematically calculated for various graph classes, including path graphs, cycles, book graphs, windmill graphs, wheel graphs, complete graphs, and complete bipartite graphs. The study explores inequalities involving TDD and defines total domination regular graphs. Furthermore, the TDD is analyzed in several graph operations such as union, join, composition, and corona, with a discussion on the property of the resulting graphs. The paper also examines the subdivision of complete graphs and degree splitting of path graphs. In the subsequent section, the total domination index (TDI) is introduced, and its values are calculated for different graph classes. The study concludes with bounds for the TDI across these graph classes.

Triggered by the realization that AI emulators can rival the performance of traditional numerical weather prediction models running on HPC systems, there is now an increasing number of large AI models that address use cases such as forecasting, downscaling, or nowcasting. While the parallel developments in the AI literature focus on foundation models -- models that can be effectively tuned to address multiple, different use cases -- the developments on the weather and climate side largely focus on single-use cases with particular emphasis on mid-range forecasting. We close this gap by introducing Prithvi WxC, a 2.3 billion parameter foundation model developed using 160 variables from the Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). Prithvi WxC employs an encoder-decoder-based architecture, incorporating concepts from various recent transformer models to effectively capture both regional and global dependencies in the input data. The model has been designed to accommodate large token counts to model weather phenomena in different topologies at fine resolutions. Furthermore, it is trained with a mixed objective that combines the paradigms of masked reconstruction with forecasting. We test the model on a set of challenging downstream tasks namely: Autoregressive rollout forecasting, Downscaling, Gravity wave flux parameterization, and Extreme events estimation. The pretrained model with 2.3 billion parameters, along with the associated fine-tuning workflows, has been publicly released as an open-source contribution via Hugging Face.

Quantum computers face challenges due to limited resources, particularly in cloud environments. Despite these obstacles, Variational Quantum Algorithms (VQAs) are considered promising applications for present-day Noisy Intermediate-Scale Quantum (NISQ) systems. VQAs require multiple optimization iterations to converge on a globally optimal solution. Moreover, these optimizations, known as restarts, need to be repeated from different points to mitigate the impact of noise. Unfortunately, the job scheduling policies for each VQA task in the cloud are heavily unoptimized. Notably, each VQA execution instance is typically scheduled on a single NISQ device. Given the variety of devices in the cloud, users often prefer higher-fidelity devices to ensure higher-quality solutions. However, this preference leads to increased queueing delays and unbalanced resource utilization. We propose Qoncord, an automated job scheduling framework to address these cloud-centric challenges for VQAs. Qoncordleverages the insight that not all training iterations and restarts are equal, Qoncord strategically divides the training process into exploratory and fine-tuning phases. Early exploratory iterations, more resilient to noise, are executed on less busy machines, while fine-tuning occurs on high-fidelity machines. This adaptive approach mitigates the impact of noise and optimizes resource usage and queuing delays in cloud environments. Qoncord also significantly reduces execution time and minimizes restart overheads by eliminating low-performance iterations. Thus, Qoncord offers similar solutions 17.4x faster. Similarly, it can offer 13.3% better solutions for the same time budget as the baseline., Comment: This paper has been accepted at the 2024 57th IEEE/ACM International Symposium on Microarchitecture (MICRO)

Systems equipped with modern sensing modalities such as vision and lidar gain access to increasingly high-dimensional measurements with which to enact estimation and control schemes. In this article, we examine the continuum limit of high-dimensional measurements and analyze state estimation in linear time-invariant systems with infinite-dimensional measurements but finite-dimensional states, both corrupted by additive noise. We propose a linear filter and derive the corresponding optimal gain functional in the sense of the minimum mean square error, analogous to the classic Kalman filter. By modeling the measurement noise as a wide-sense stationary random field, we are able to derive the optimal linear filter explicitly, in contrast to previous derivations of Kalman filters in distributed-parameter settings. Interestingly, we find that we need only impose conditions that are finite-dimensional in nature to ensure that the filter is asymptotically stable. The proposed filter is verified via simulation of a linearized system with a pinhole camera sensor., Comment: 15 pages, 3 Figures. This paper will appear in the IEEE Transactions on Automatic Control

The topological Hall effect (THE) has attracted great attention since it provides an important probe of the interaction between electron and topological spin textures. THE has been considered an experimental signature of the topological spin texture of skyrmions. While THE has been widely reported in chiral magnets, oxide heterostructures, and hybrid systems such as ferromagnet/heavy metal and ferromagnet/topological insulators, the study of monolayer structures is lacking, hindering the understanding of noncollinear spin textures at the atomically thin scale. Here, we show a discernible THE via proximity coupling of Fe-doped monolayer WSe2 (Fe:WSe2) synthesized using chemical vapor deposition on a Pt Hall bar. Multiple characterization methods were employed to demonstrate that Fe atoms substitutionally replace W atoms, making a two-dimensional (2D) van der Waals (vdW) dilute magnetic semiconductor (DMS) at room temperature. Distinct from the intrinsic anomalous Hall effect, we found the transverse Hall resistivity of Fe:WSe2 displaying two additional dip/peak features in the temperature-dependent measurements, consistent with the contribution of THE. The topological Hall effect is attributed to the magnetic skyrmions that emerge from the Dzyaloshinskii-Moriya interactions at the Fe:WSe2 and Pt interface. Our work shows that a DMS synthesized from 2D vdW transition metal dichalcogenides is promising for realizing magnetic skyrmions and spintronic applications.

We present an efficient momentum based perturbation scheme to evaluate polarizability tensors of small molecules and at the fraction of the computational cost compared to conventional energy based perturbation schemes. Furthermore, the simplicity of the scheme allows for the seamless integration into modern quantum chemistry codes. We apply the method to systems where the wavefunctions are described on a real-space grid and are therefore not subject to finite size basis set errors. In the grid-based scheme errors can be attributed to the resolution and the size of the grid-space. The applicability and generality of the method is exhibited by calculating polarizability tensors including the dipole-dipole and up to the quadrupole-quadrupole for a series of small molecules, representing the most common symmetry groups. By a direct comparison with standard techniques based on energy perturbation we show that the method reduces the number of explicit computations by a factor of 30. Numerical errors introduced due to the arrangement of the explicit point charges are eliminated with an extrapolation scheme to the effective zero-perturbation limit., Comment: 12 pages, 7 figures

This paper concerns the propagation of high frequency wave-beams in highly turbulent atmospheres. Using a paraxial model of wave propagation, we show in the long-distance weak-coupling regime that the wavefields are approximately described by a complex Gaussian field whose scintillation index is unity. This provides a model of the speckle formation observed in many practical settings. The main step of the derivation consists in showing that closed-form moment equations in the It\^o-Schr\"odinger regime are still approximately satisfied in the paraxial regime. The rest of the proof is then an extension of results derived in [Bal, G. and Nair, A., arXiv:2402.17107.]

Central banks are actively exploring retail central bank digital currencies (CBDCs), with the Bank of England currently in the design phase for a potential UK retail CBDC, the digital pound. In a previous paper, we defined and explored the important concept of functional consistency (which is the principle that different forms of money have the same operational characteristics) and evaluated design options to support functional consistency across digital pounds and commercial bank money, based on a set of key capabilities. In this paper, we continue to analyse the design options for supporting functional consistency and, in order to perform a detailed analysis, we focus on three key capabilities: communication between digital pound ecosystem participants, funds locking, and interoperability across digital pounds and commercial bank money. We explore these key capabilities via three payments use cases: person-to-person push payment, merchant-initiated request to pay, and lock funds and pay on physical delivery. We then present and evaluate the suitability of design options to provide the specific capabilities for each use case and draw initial insights. We conclude that a financial market infrastructure (FMI) providing specific capabilities could simplify the experience of ecosystem participants, simplify the operating platforms for both the Bank of England and digital pound Payment Interface Providers (PIPs), and facilitate the creation of innovative services. We also identify potential next steps., Comment: 77 pages, 30 figures, 10 tables

We report direct numerical simulation (DNS) results of the rough-wall channel, focusing on roughness with high $k_{rms}/k_a$ statistics but small to negative $Sk$ statistics, and we study the implications of this new dataset on rough-wall modelling. Here, $k_{rms}$ is the root-mean-square, $k_a$ is the first order moment of roughness height, and $Sk$ is the skewness. The effects of packing density, skewness and arrangement of roughness elements on mean streamwise velocity, equivalent roughness height ($z_0$) and Reynolds and dispersive stresses have been studied. We demonstrate that two-point correlation lengths of roughness height statistics play an important role in characterizing rough surfaces with identical moments of roughness height but different arrangements of roughness elements. Analysis of the present as well as historical data suggests that the task of rough-wall modelling is to identify geometric parameters that distinguish the rough surfaces within the calibration dataset. We demonstrate a novel feature selection procedure to determine these parameters. Further, since there is not a finite set of roughness statistics that distinguish between all rough surfaces, we argue that obtaining a universal rough-wall model for making equivalent sand-grain roughness ($k_s$) predictions would be challenging, and that each rough-wall model would have its applicable range. This motivates the development of group-based rough-wall models. The applicability of multi-variate polynomial regression and feedforward neural networks for building such group-based rough-wall models using the selected features has been shown., Comment: 34 pages, 29 figures

Despite the growing adoption of video processing via Internet of Things (IoT) devices due to their cost-effectiveness, transmitting captured data to nearby servers poses challenges due to varying timing constraints and scarcity of network bandwidth. Existing video compression methods face difficulties in recovering compressed data when incomplete data is provided. Here, we introduce FrameCorr, a deep-learning based solution that utilizes previously received data to predict the missing segments of a frame, enabling the reconstruction of a frame from partially received data.

Food computing is both important and challenging in computer vision (CV). It significantly contributes to the development of CV algorithms due to its frequent presence in datasets across various applications, ranging from classification and instance segmentation to 3D reconstruction. The polymorphic shapes and textures of food, coupled with high variation in forms and vast multimodal information, including language descriptions and nutritional data, make food computing a complex and demanding task for modern CV algorithms. 3D food modeling is a new frontier for addressing food-related problems, due to its inherent capability to deal with random camera views and its straightforward representation for calculating food portion size. However, the primary hurdle in the development of algorithms for food object analysis is the lack of nutrition values in existing 3D datasets. Moreover, in the broader field of 3D research, there is a critical need for domain-specific test datasets. To bridge the gap between general 3D vision and food computing research, we propose MetaFood3D. This dataset consists of 637 meticulously labeled 3D food objects across 108 categories, featuring detailed nutrition information, weight, and food codes linked to a comprehensive nutrition database. The dataset emphasizes intra-class diversity and includes rich modalities such as textured mesh files, RGB-D videos, and segmentation masks. Experimental results demonstrate our dataset's significant potential for improving algorithm performance, highlight the challenging gap between video captures and 3D scanned data, and show the strength of the MetaFood3D dataset in high-quality data generation, simulation, and augmentation., Comment: Dataset is coming soon

Demand for ADHD diagnosis and treatment is increasing significantly and the existing services are unable to meet the demand in a timely manner. In this work, we introduce a novel action recognition method for ADHD diagnosis by identifying and analysing raw video recordings. Our main contributions include 1) designing and implementing a test focusing on the attention and hyperactivity/impulsivity of participants, recorded through three cameras; 2) implementing a novel machine learning ADHD diagnosis system based on action recognition neural networks for the first time; 3) proposing classification criteria to provide diagnosis results and analysis of ADHD action characteristics., Comment: Neuroscience Applied