المؤلفون: Mehra, Brijesh Singh, Kumar, Sanjeev, Dubey, Gaurav, Shyam, Ayyappan, Kumar, Ankit, Anirudh, K, Singh, Kiran, Rana, Dhanvir Singh

مصطلحات موضوعية: Condensed Matter - Materials Science

الوصف: Terahertz (THz) magnonics represent the notion of mathematical algebraic operations of magnons such as addition and subtraction in THz regime which is an emergent dissipationless ultrafast alternative to existing data processing technologies. Spin waves on antiferromagnets with a twist in spin order host such magnons in THz regime, which possess advantage of higher processing speeds, additional polarization degree of freedom and longer propagation lengths compared to that of gigahertz magnons in ferromagnets. While interaction among THz magnons is the crux of algebra operations, it requires magnetic orders with closely spaced magnon modes for easier experimental realization of their interactions. Herein, rich wealth of magnons spanning a narrow energy range of 0.4 to 10 meV is unraveled in Co4Ta2O9 using magneto-THz spectroscopy. Rare multitude of ten excitation modes, either of magnons or hybrid magnon-phonon modes is presented. Among other attributes, spin lattice interaction suggests a correlation among spin and local lattice distortion, magnetostriction, and magnetic exchange interaction signifying a THz magnetoelectric effect. This unification of structural, magnetic and dielectric facets, and their magnetic field control in a narrow spectrum unwinds the mechanism underneath the system's complexity while the manifestation of multitude of spin excitation modes is a potential source to design multiple channels in spin-wave computing based devices.

الوصول الحر: http://arxiv.org/abs/2407.00597Test

المؤلفون: Dubey, Gaurav, Mehra, Brijesh Singh, Kumar, Sanjeev, Shyam, Ayyappan, Sharma, Karan Datt, Vagadia, Megha, Rana, Dhanvir Singh

مصطلحات موضوعية: Condensed Matter - Materials Science

الوصف: The complexity of interactions between the crystal-field and unusual non-collinear spin arrangement in non-trivial magnets demands novel tools to unravel the mystery underneath. In this work, we study such interaction dynamics of crystal-field-excitations (CFE) and low-energy magnetic excitations in orthochromite TmCrO3 with controls of temperature and magnetic field using high-resolution magneto-terahertz (THz) time-domain spectroscopy. The THz energy spectrum spanning 0.5-10 meV possesses a low-frequency spin-excitation (magnon) mode and a multitude of CFE modes at 10 K, all of which uniquely embody a range of phenomena. For the magnon mode, a temperature dependence of peak frequency is induced by magnetic interactions between Tm and Cr subsystems. While a change from blue- to red-shift of peak frequency of this mode marks the magnetization reversal transition, the spin reorientation temperature and change of magnetic anisotropy are depicted by different features of field- and temperature-dependent peak frequency dynamics. The modes corresponding to CFE are robust and laden with a multitude of sub-modes which are attributes of non-trivial interactions across different transitions. These modes are suppressed only upon substitution of Tb3+ at Tm3+ site, which suggests a dominant role of single-ion anisotropy in controlling entire THz excitations spectra. Overall, this remarkable range of phenomena seen through the unique lens of all-optical THz tools provides deeper insights into the origin of magnetic phases in systems with complex interactions between rare-earth and transition metal ions and provides a multitude of a novel combination of closely spaced modes for emerging hybrid spin-wave computation.
Comment: The main copy of the manuscript includes 21 pages with 7 figures

الوصول الحر: http://arxiv.org/abs/2406.19784Test

المؤلفون: Zhang, Yunbei, Mehra, Akshay, Hamm, Jihun

مصطلحات موضوعية: Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition

الوصف: Continual Test-Time Adaptation (TTA) seeks to adapt a source pre-trained model to continually changing, unlabeled target domains. Existing TTA methods are typically designed for environments where domain changes occur gradually and can struggle in more dynamic scenarios. Inspired by the principles of online K-Means, this paper introduces a novel approach to continual TTA through visual prompting. We propose a Dynamic Prompt Coreset that not only preserves knowledge from previously visited domains but also accommodates learning from new potential domains. This is complemented by a distance-based weight updating mechanism that ensures the coreset remains current and relevant. Our approach employs a fixed model architecture alongside the coreset and an innovative updating system to effectively mitigate challenges such as catastrophic forgetting and error accumulation. Extensive testing across various benchmarks-including ImageNet-C, CIFAR100-C, and CIFAR10-C-demonstrates that our method consistently outperforms state-of-the-art (SOTA) alternatives, particularly excelling in dynamically changing environments.

الوصول الحر: http://arxiv.org/abs/2406.10737Test

المؤلفون: Mehra, Akshay, Zhang, Yunbei, Hamm, Jihun

مصطلحات موضوعية: Computer Science - Machine Learning

الوصف: Gauging the performance of ML models on data from unseen domains at test-time is essential yet a challenging problem due to the lack of labels in this setting. Moreover, the performance of these models on in-distribution data is a poor indicator of their performance on data from unseen domains. Thus, it is essential to develop metrics that can provide insights into the model's performance at test time and can be computed only with the information available at test time (such as their model parameters, the training data or its statistics, and the unlabeled test data). To this end, we propose a metric based on Optimal Transport that is highly correlated with the model's performance on unseen domains and is efficiently computable only using information available at test time. Concretely, our metric characterizes the model's performance on unseen domains using only a small amount of unlabeled data from these domains and data or statistics from the training (source) domain(s). Through extensive empirical evaluation using standard benchmark datasets, and their corruptions, we demonstrate the utility of our metric in estimating the model's performance in various practical applications. These include the problems of selecting the source data and architecture that leads to the best performance on data from an unseen domain and the problem of predicting a deployed model's performance at test time on unseen domains. Our empirical results show that our metric, which uses information from both the source and the unseen domain, is highly correlated with the model's performance, achieving a significantly better correlation than that obtained via the popular prediction entropy-based metric, which is computed solely using the data from the unseen domain.

الوصول الحر: http://arxiv.org/abs/2405.01451Test

المؤلفون: Singh, Abhishek Kumar, Mehra, Mani, Alikhanov, Anatoly A.

مصطلحات موضوعية: Mathematics - Numerical Analysis

الوصف: The least squares method provides the best-fit curve by minimizing the total squares error. In this work, we provide the modified least squares method based on the fractional orthogonal polynomials that belong to the space $M_{n}^{\lambda} := \text{span}\{1,x^{\lambda},x^{2\lambda},\ldots,x^{n\lambda}\},~\lambda \in (0,2]$. Numerical experiments demonstrate how to solve different problems using the modified least squares method. Moreover, the results show the advantage of the modified least squares method compared to the classical least squares method. Furthermore, we discuss the various applications of the modified least squares method in the fields like fractional differential/integral equations and machine learning.

الوصول الحر: http://arxiv.org/abs/2405.00382Test

المؤلفون: Mehra, Aman, Saxena, Rahul, Kim, Taeyoun, Baek, Christina, Kolter, Zico, Raghunathan, Aditi

مصطلحات موضوعية: Computer Science - Machine Learning

الوصف: Estimating the out-of-distribution performance in regimes where labels are scarce is critical to safely deploy foundation models. Recently, it was shown that ensembles of neural networks observe the phenomena ``agreement-on-the-line'', which can be leveraged to reliably predict OOD performance without labels. However, in contrast to classical neural networks that are trained on in-distribution data from scratch for numerous epochs, foundation models undergo minimal finetuning from heavily pretrained weights, which may reduce the ensemble diversity needed to observe agreement-on-the-line. In our work, we demonstrate that when lightly finetuning multiple runs from a $\textit{single}$ foundation model, the choice of randomness during training (linear head initialization, data ordering, and data subsetting) can lead to drastically different levels of agreement-on-the-line in the resulting ensemble. Surprisingly, only random head initialization is able to reliably induce agreement-on-the-line in finetuned foundation models across vision and language benchmarks. Second, we demonstrate that ensembles of $\textit{multiple}$ foundation models pretrained on different datasets but finetuned on the same task can also show agreement-on-the-line. In total, by careful construction of a diverse ensemble, we can utilize agreement-on-the-line-based methods to predict the OOD performance of foundation models with high precision.

الوصول الحر: http://arxiv.org/abs/2404.01542Test

المؤلفون: Drees, Manuel, Mehra, Rahul

مصطلحات موضوعية: High Energy Physics - Phenomenology, High Energy Physics - Experiment

الوصف: Interactions between Dark Matter (DM) and nucleons relevant for direct search experiments can be organised in a model independent manner using a Galiliean invariant, non--relativistic effective field theory (NREFT). Here one expands the interactions in powers of the momentum transfer $\vec{q}$ and DM velocity $\vec{v}$. This approach generates many operators. The potentially most important subleading operators are odd under $T$, and can thus only be present in a theory with $CP$ violating interactions. We consider two such operators, called $\mathcal{O}_{10}$ and $\mathcal{O}_{11}$ in the literature, in simplified models with neutral spin$-0$ mediators; the couplings are chosen such that the coefficient of the leading spin independent (SI) operator, which survives for $\vec{v} \rightarrow 0$, vanishes at tree level. However, it is generically induced at the next order in perturbation theory. We perform a numerical comparison of the number of scattering events between interactions involving the $T-$odd operators and the corresponding loop induced SI contributions. We find that for ''maximal'' $CP$ violation the former can dominate over the latter. However, in two of the three models we consider, an electric dipole moment of the neutron (nEDM) is induced at two--loop order. We find that the experimental bound on the nEDM typically leads to undetectably small rates induced by ${\mathcal O}_{10}$. On the other hand, the model leading to a nonvanishing coefficient of ${\mathcal O}_{11}$ does not induce an nEDM.
Comment: 49 pages, 17 figures, 4 tables

الوصول الحر: http://arxiv.org/abs/2403.02083Test

المؤلفون: Mehra, P.

المصدر: TAI Journal (A Half Yearly Technical Journal of Indian Chapter of TAI) 8(2):16-20. 2019

المؤلفون: Johnson, Isabella, Mehra, Renee, Remy, Linda, Rienks, Jennifer, Franck, Linda

المصدر: Journal of Public Health Management and Practice. 30(2)

مصطلحات موضوعية: Child, Female, Pregnancy, Humans, Parenting, Pandemics, Caregivers, Cross-Sectional Studies, COVID-19, California, Poverty

الوصف: CONTEXT: In-person home visiting programs that provide evidence-based parenting and child development support improve outcomes for low-income children and families. The COVID-19 pandemic led to a shift from primarily in-person to virtual home visiting services, and little is known about clients experience of home visiting in this context. OBJECTIVE: To describe the experience of clients in the California Work Opportunity and Responsibility to Kids (CalWORKs) Home Visiting Program (HVP) across California during the first 2 years of the pandemic. DESIGN: Three repeated cross-sectional surveys over a 2-year period. Clients free-text responses to open-ended questions were analyzed using a directed content analysis approach. SETTING: Forty-one counties in California. PARTICIPANTS: Current CalWORKs HVP clients and those who left the program in the 6 months prior to each survey. MAIN OUTCOME MEASURES: Clients experience of the CalWORKs HVP. RESULTS: Five main themes emerged: (1) benefits received from the program; (2) life challenges; (3) COVID-19-related or other program changes; (4) client dissatisfaction and suggestions for improvement; and (5) appreciation for the program. Clients valued the practical, financial, parenting, and interpersonal support provided to themselves and their children. Almost three-quarters commented on life challenges experienced during the program. Significant programmatic changes related to COVID-19 pandemic public health safety and organizational constraints impacted clients both positively and negatively. Very few clients experienced overt dissatisfaction with the program. Many clients expressed appreciation for the program, particularly the individualized and relational support offered by a consistent home visitor. CONCLUSIONS: The findings provide insights into the benefits and challenges experienced by clients receiving evidence-based home visiting services. The findings highlight the ongoing life challenges faced by clients who experience poverty, and how those challenges were exacerbated by a global pandemic. The CalWORKs HVP may buffer the substantial personal stresses clients experience related to parenting in the context of poverty and major public health challenges.

وصف الملف: application/pdf

الوصول الحر: https://escholarship.org/uc/item/85w2d46sTest

المؤلفون: Mhasakar, Manas, Sharma, Shikhar, Mehra, Apurv, Venaik, Utkarsh, Singhal, Ujjwal, Kumar, Dhruv, Mittal, Kashish

مصطلحات موضوعية: Computer Science - Human-Computer Interaction, Computer Science - Computers and Society

الوصف: In this paper, we investigate the potential of Large Language Models (LLMs) to improve English speaking skills. This is particularly relevant in countries like India, where English is crucial for academic, professional, and personal communication but remains a non-native language for many. Traditional methods for enhancing speaking skills often rely on human experts, which can be limited in terms of scalability, accessibility, and affordability. Recent advancements in Artificial Intelligence (AI) offer promising solutions to overcome these limitations. We propose Comuniqa, a novel LLM-based system designed to enhance English speaking skills. We adopt a human-centric evaluation approach, comparing Comuniqa with the feedback and instructions provided by human experts. In our evaluation, we divide the participants in three groups: those who use LLM-based system for improving speaking skills, those guided by human experts for the same task and those who utilize both the LLM-based system as well as the human experts. Using surveys, interviews, and actual study sessions, we provide a detailed perspective on the effectiveness of different learning modalities. Our preliminary findings suggest that while LLM-based systems have commendable accuracy, they lack human-level cognitive capabilities, both in terms of accuracy and empathy. Nevertheless, Comuniqa represents a significant step towards achieving Sustainable Development Goal 4: Quality Education by providing a valuable learning tool for individuals who may not have access to human experts for improving their speaking skills.
Comment: Accepted at 7th ACM SIGCAS/SIGCHI Conference of Computing and Sustainable Societies : ACM COMPASS 2024

الوصول الحر: http://arxiv.org/abs/2401.15595Test