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Van der Waals (vW) Josephson junctions (JJs) realized by stacking materials such as few-layered NbSe2, offers a new landscape to realize superconducting quantum devices with superior properties owing to its crystalline nature and defect-free junctions. For quantum technology, overdamped JJs are highly sought-after, whose realization demands precise control of junction capacitance by engineering the junction area using microfabrication techniques. NbSe2 is highly reactive and susceptible to damage during microfabrication processes. In this manuscript, we demonstrate both underdamped and overdamped NbSe2-NbSe2 JJs by controlling the junction area. We devise a minimally invasive microfabrication procedure, post-junction formation, to precisely control the junction area. The McCumber parameter characterizing the damping is extracted from the electrical transport measurements down to 130 mK. The results show that our sample fabrication recipe has preserved the material qualities and paved the way for the realization of scalable JJ devices on NbSe2 and similar systems., Comment: 13 pages including supplementary information

Existing evaluation frameworks for retrieval-augmented generation (RAG) systems focus on answerable queries, but they overlook the importance of appropriately rejecting unanswerable requests. In this paper, we introduce UAEval4RAG, a framework designed to evaluate whether RAG systems can handle unanswerable queries effectively. We define a taxonomy with six unanswerable categories, and UAEval4RAG automatically synthesizes diverse and challenging queries for any given knowledge base with unanswered ratio and acceptable ratio metrics. We conduct experiments with various RAG components, including retrieval models, rewriting methods, rerankers, language models, and prompting strategies, and reveal hidden trade-offs in performance of RAG systems. Our findings highlight the critical role of component selection and prompt design in optimizing RAG systems to balance the accuracy of answerable queries with high rejection rates of unanswerable ones. UAEval4RAG provides valuable insights and tools for developing more robust and reliable RAG systems.

This study is a continuation of previous work, which highlights the nutrient enhancement by using rice straw (RS) and pressmud (PM) on vermicomposting. Herein, we demonstrate the significant impact of Moringa oleifera derived Cu/Ni/Co oxide nanoparticles (TmONs) in conjunction with these vermicompost on the growth performance of Abelmoschus esculentus. Vermicompost produced under various combinations (T0, cow dung (CD) only; T1, 1CD:1RS; T2, 1CD:1PM, and T3, 1CD:1RS:1PM) were further enriched by blending with biogenic nanoparticles. This strategic combination enhances the nutritional composition of the vermicompost, contributing to its overall effectiveness in promoting plant growth and health. Various analytical techniques, including FTIR, XRD, XPS, FESEM-EDX, TEM, and ICP-OES, were employed for comprehensive characterization. The synthesized TmONs with sizes ranging from 13 to 54 nm exhibited distinct CuO, NiO, and CoO phases. The vermicompost blended TmONs demonstrated significant improvements (P < 0.05) in seed germination (167%), coefficient velocity (67%), and vigour index (95%), while reducing the mean germination time by 41% for A. esculentus compared to the control group. The plant culture group nT3 (T3 + TmONs) showed the best growth performance. Furthermore, trace element concentrations in both soil and plant leaves were found to be below the maximum permissible limits set by WHO (1996). This investigation extends the understanding of the role played by these nanoparticles in fostering optimal conditions for plant growth and development as these micronutrients are essential components for several plant enzymes., Comment: 7+1 FIGURES

Indexing is an important step towards strong performance in retrieval-augmented generation (RAG) systems. However, existing methods organize data based on either semantic similarity (similarity) or related information (relatedness), but do not cover both perspectives comprehensively. Our analysis reveals that modeling only one perspective results in insufficient knowledge synthesis, leading to suboptimal performance on complex tasks requiring multihop reasoning. In this paper, we propose SiReRAG, a novel RAG indexing approach that explicitly considers both similar and related information. On the similarity side, we follow existing work and explore some variances to construct a similarity tree based on recursive summarization. On the relatedness side, SiReRAG extracts propositions and entities from texts, groups propositions via shared entities, and generates recursive summaries to construct a relatedness tree. We index and flatten both similarity and relatedness trees into a unified retrieval pool. Our experiments demonstrate that SiReRAG consistently outperforms state-of-the-art indexing methods on three multihop datasets (MuSiQue, 2WikiMultiHopQA, and HotpotQA), with an average 1.9% improvement in F1 scores. As a reasonably efficient solution, SiReRAG enhances existing reranking methods significantly, with up to 7.8% improvement in average F1 scores.

Knowledge graphs (KGs) generated by large language models (LLMs) are becoming increasingly valuable for Retrieval-Augmented Generation (RAG) applications that require knowledge-intensive reasoning. However, existing KG extraction methods predominantly rely on prompt-based approaches, which are inefficient for processing large-scale corpora. These approaches often suffer from information loss, particularly with long documents, due to the lack of specialized design for KG construction. Additionally, there is a gap in evaluation datasets and methodologies for ontology-free KG construction. To overcome these limitations, we propose SynthKG, a multi-step, document-level ontology-free KG synthesis workflow based on LLMs. By fine-tuning a smaller LLM on the synthesized document-KG pairs, we streamline the multi-step process into a single-step KG generation approach called Distill-SynthKG, substantially reducing the number of LLM inference calls. Furthermore, we re-purpose existing question-answering datasets to establish KG evaluation datasets and introduce new evaluation metrics. Using KGs produced by Distill-SynthKG, we also design a novel graph-based retrieval framework for RAG. Experimental results demonstrate that Distill-SynthKG not only surpasses all baseline models in KG quality -- including models up to eight times larger -- but also consistently excels in retrieval and question-answering tasks. Our proposed graph retrieval framework also outperforms all KG-retrieval methods across multiple benchmark datasets. We release the SynthKG dataset and Distill-SynthKG model publicly to support further research and development.

Evaluating retrieval-augmented generation (RAG) systems remains challenging, particularly for open-ended questions that lack definitive answers and require coverage of multiple sub-topics. In this paper, we introduce a novel evaluation framework based on sub-question coverage, which measures how well a RAG system addresses different facets of a question. We propose decomposing questions into sub-questions and classifying them into three types -- core, background, and follow-up -- to reflect their roles and importance. Using this categorization, we introduce a fine-grained evaluation protocol that provides insights into the retrieval and generation characteristics of RAG systems, including three commercial generative answer engines: You.com, Perplexity AI, and Bing Chat. Interestingly, we find that while all answer engines cover core sub-questions more often than background or follow-up ones, they still miss around 50% of core sub-questions, revealing clear opportunities for improvement. Further, sub-question coverage metrics prove effective for ranking responses, achieving 82% accuracy compared to human preference annotations. Lastly, we also demonstrate that leveraging core sub-questions enhances both retrieval and answer generation in a RAG system, resulting in a 74% win rate over the baseline that lacks sub-questions.

This manuscript proposes an optimization framework to find the tailor-made functionally graded material (FGM) profiles for thermoelastic applications. This optimization framework consists of (1) a random profile generation scheme, (2) deep learning (DL) based surrogate models for the prediction of thermal and structural quantities, and (3) a genetic algorithm (GA). From the proposed random profile generation scheme, we strive for a generic design space that does not contain impractical designs, i.e., profiles with sharp gradations. We also show that the power law is a strict subset of the proposed design space. We use a dense neural network-based surrogate model for the prediction of maximum stress, while the deep neural operator DeepONet is used for the prediction of the thermal field. The point-wise effective prediction of the thermal field enables us to implement the constraint that the metallic content of the FGM remains within a specified limit. The integration of the profile generation scheme and DL-based surrogate models with GA provides us with an efficient optimization scheme. The efficacy of the proposed framework is demonstrated through various numerical examples.

This study provides an analysis of atmospheric neutrino oscillations at the ESSnuSB far detector facility. The prospects of the two cylindrical Water Cherenkov detectors with a total fiducial mass of 540 kt are investigated over 10 years of data taking in the standard three-flavor oscillation scenario. We present the confidence intervals for the determination of mass ordering, $\theta_{23}$ octant as well as for the precisions on $\sin^2\theta_{23}$ and $|\Delta m_{31}^2|$. It is shown that mass ordering can be resolved by $3\sigma$ CL ($5\sigma$ CL) after 4 years (10 years) regardless of the true neutrino mass ordering. Correspondingly, the wrong $\theta_{23}$ octant could be excluded by $3\sigma$ CL after 4 years (8 years) in the case where the true neutrino mass ordering is normal ordering (inverted ordering). The results presented in this work are complementary to the accelerator neutrino program in the ESSnuSB project., Comment: 14 pages, 7 figures and 2 tables, accepted for publication in the Journal of High Energy Physics

In this paper, we study a non-minimal gauged $U(1)_{L_{\mu}-L_{\tau}}$ model, where we add two complex singlet scalars, three right-handed Majorana neutrinos (RHN), and a vector-like dark fermion to the Standard Model (SM), all non-trivially charged under the extra gauge symmetry. The model offers an easy resolution to the muon $(g-2)$ anomaly, which fixes the scale of spontaneous symmetry breaking. Furthermore, the two-zero minor structure in the RHN mass matrix provides successful predictions for neutrino oscillation parameters, including the Dirac phase. The extended scalar sector can easily induce first-order phase transitions. We identify all possible phase transition patterns in the three-dimensional field space. We quantify the associated gravitational waves from the sound wave source and demonstrate that the signatures can be observed in future space-based experiments. We find that strong first-order phase transitions require large values of scalar quartic couplings which constrain the scalar dark matter (DM) relic density to a maximum of $10^{-2}$ and $10^{-5}$ when we consider the DM direct detection bound. Nonetheless, the model successfully explains the DM relic density via contribution from the vector-like dark fermion. We show the allowed range of the model parameters that can address all the beyond SM issues targeted in this study.

Josephson scanning tunneling microscopy (JSTM) is a powerful probe of the local superconducting order parameter, but studies have been largely limited to cases where superconducting sample and superconducting tip both have the same gap symmetry -- either s-wave or d-wave. It has been generally assumed that in an ideal $s$-to-$d$ JSTM experiment the critical current would vanish everywhere, as expected for ideal $c$-axis planar junctions. We show here that this is not the case. Employing first-principles Wannier functions for Bi$_2$Sr$_2$CaCu$_2$O$_{8+\delta}$, we develop a scheme to compute Josephson critical current ($I_{c}$) and quasiparticle tunneling current measured by JSTM with sub-angstrom resolution. We demonstrate that the critical current for tunneling between an s-wave tip and a superconducting cuprate sample has largest magnitude above O sites and it vanishes above Cu sites. $I_{c}$ changes sign under $\pi/2$-rotation and its average over a unit cell vanishes, as a direct consequence of the $d$-wave gap symmetry in cuprates. Further, we show that $I_{c}$ is strongly suppressed in the close vicinity of a Zn-like impurity owing to suppression of the superconducting order parameter. More interestingly, $I_{c}$ acquires non-vanishing values above the Cu sites near the impurity. The critical current modulations produced by the impurity occur at characteristic wavevectors distinct from the quasiparticle interference (QPI) analogue. Furthermore, the quasiparticle tunneling spectra in the JSTM set-up shows coherence peaks and impurity-induced resonances shifted by the $s$-wave tip gap. We discuss similarities and differences in JSTM observables and conventional STM observables, making specific predictions that can be tested in future JSTM experiments., Comment: 6 pages, 4 figures

Neutrino oscillation experiments provide a unique window in exploring several new physics scenarios beyond the standard three flavour. One such scenario is quantum decoherence in neutrino oscillation which tends to destroy the interference pattern of neutrinos reaching the far detector from the source. In this work, we study the decoherence in neutrino oscillation in the context of the ESSnuSB experiment. We consider the energy-independent decoherence parameter and derive the analytical expressions for P$_{\mu e}$ and P$_{\mu \mu}$ probabilities in vacuum. We have computed the capability of ESSnuSB to put bounds on the decoherence parameters namely, $\Gamma_{21}$ and $\Gamma_{32}$ and found that the constraints on $\Gamma_{21}$ are competitive compared to the DUNE bounds and better than the most stringent LBL ones from MINOS/MINOS+. We have also investigated the impact of decoherence on the ESSnuSB measurement of the Dirac CP phase $\delta_{\rm CP}$ and concluded that it remains robust in the presence of new physics., Comment: 30 pages, 9 figures, 2 tables, Version accepted for publication in JHEP

Operator-based neural network architectures such as DeepONets have emerged as a promising tool for the surrogate modeling of physical systems. In general, towards operator surrogate modeling, the training data is generated by solving the PDEs using techniques such as Finite Element Method (FEM). The computationally intensive nature of data generation is one of the biggest bottleneck in deploying these surrogate models for practical applications. In this study, we propose a novel methodology to alleviate the computational burden associated with training data generation for DeepONets. Unlike existing literature, the proposed framework for data generation does not use any partial differential equation integration strategy, thereby significantly reducing the computational cost associated with generating training dataset for DeepONet. In the proposed strategy, first, the output field is generated randomly, satisfying the boundary conditions using Gaussian Process Regression (GPR). From the output field, the input source field can be calculated easily using finite difference techniques. The proposed methodology can be extended to other operator learning methods, making the approach widely applicable. To validate the proposed approach, we employ the heat equations as the model problem and develop the surrogate model for numerous boundary value problems.

The booming success of LLMs initiates rapid development in LLM agents. Though the foundation of an LLM agent is the generative model, it is critical to devise the optimal reasoning strategies and agent architectures. Accordingly, LLM agent research advances from the simple chain-of-thought prompting to more complex ReAct and Reflection reasoning strategy; agent architecture also evolves from single agent generation to multi-agent conversation, as well as multi-LLM multi-agent group chat. However, with the existing intricate frameworks and libraries, creating and evaluating new reasoning strategies and agent architectures has become a complex challenge, which hinders research investigation into LLM agents. Thus, we open-source a new AI agent library, AgentLite, which simplifies this process by offering a lightweight, user-friendly platform for innovating LLM agent reasoning, architectures, and applications with ease. AgentLite is a task-oriented framework designed to enhance the ability of agents to break down tasks and facilitate the development of multi-agent systems. Furthermore, we introduce multiple practical applications developed with AgentLite to demonstrate its convenience and flexibility. Get started now at: \url{https://github.com/SalesforceAIResearch/AgentLite}., Comment: preprint. Library is available at https://github.com/SalesforceAIResearch/AgentLite

Ideal summarization models should generalize to novel summary-worthy content without remembering reference training summaries by rote. However, a single average performance score on the entire test set is inadequate in determining such model competencies. We propose a fine-grained evaluation protocol by partitioning a test set based on the lexical similarity of reference test summaries with training summaries. We observe up to a 5x (1.2x) difference in ROUGE-2 (entity recall) scores between the subsets with the lowest and highest similarity. Next, we show that such training repetitions also make a model vulnerable to rote learning, reproducing data artifacts such as factual errors, especially when reference test summaries are lexically close to training summaries. Consequently, we propose to limit lexical repetitions in training summaries during both supervised fine-tuning and likelihood calibration stages to improve the performance on novel test cases while retaining average performance. Our automatic and human evaluations on novel test subsets and recent news articles show that limiting lexical repetitions in training summaries can prevent rote learning and improve generalization., Comment: EMNLP 2023-Findings

Cellular actin dynamics result from collective action of hundreds of regulatory proteins, majority of which target actin filaments at their barbed ends. Three key actin binding proteins - profilin, cofilin and twinfilin individually depolymerize filament barbed ends. Notwithstanding recent leaps in our understanding of their individual action, how they collectively regulate filament dynamics remains an open question. In absence of direct and simultaneous visualization of these proteins at barbed ends, gaining mechanistic insights has been challenging. We have here investigated multicomponent dynamics of profilin, cofilin and twinfilin using a hybrid approach that combines high throughput single filament experiments with theory. We discovered that while twinfilin competes with profilin, it promotes binding of cofilin to filament sides. Interestingly, contrary to previous expectations, we found that profilin and cofilin can simultaneously bind the same filament barbed end resulting in its accelerated depolymerization. Our study reveals that pair-wise interactions can effectively capture depolymerization dynamics in simultaneous presence of all three proteins. We thus believe that our approach of employing a theory-experiment dialog can potentially help decipher multicomponent regulation of actin dynamics., Comment: Primary research paper

In this paper we study non-standard interactions mediated by a scalar field (SNSI) in the context of ESSnuSB experiment. In particular we study the capability of ESSnuSB to put bounds on the SNSI parameters and also study the impact of SNSI in the measurement of the leptonic CP phase $\delta_{\rm CP}$. Existence of SNSI modifies the neutrino mass matrix and this modification can be expressed in terms of three diagonal real parameters ($\eta_{ee}$, $\eta_{\mu\mu}$ and $\eta_{\tau\tau}$) and three off-diagonal complex parameters ($\eta_{e \mu}$, $\eta_{e\tau}$ and $\eta_{\mu\tau}$). Our study shows that the upper bounds on the parameters $\eta_{\mu\mu}$, $\eta_{\tau\tau}$ and $\eta_{\mu\tau}$ depend upon how $\Delta m^2_{31}$ is minimized in the theory. However, this is not the case when one tries to measure the impact of SNSI on $\delta_{\rm CP}$. Further, we show that the CP sensitivity of ESSnuSB can be completely lost for certain values of $\eta_{ee}$ and $\eta_{\mu\tau}$ for which the appearance channel probability becomes independent of $\delta_{\rm CP}$., Comment: 14 pages, 6 figures, 2 tables, Version accepted for publication in Phys. Rev. D

Observing matter effects in atmospheric neutrinos travelling through the entire mantle and core of the Earth is a promising way of enhancing our understanding of Earth's density structure. In that context we study the prospects of Earth tomography with the ICAL detector at the India-based Neutrino Observatory. While this experiment is smaller in size in comparison to some of the other bigger detectors being proposed, it is the only neutrino experiment with charge-identification sensitivity. In particular, ICAL can see matter effects separately in neutrinos and antineutrinos. This has been seen to enhance ICAL's sensitivity to earth matter effects and hence the mass ordering sensitivity for both normal and inverted mass orderings. It is therefore, pertinent to see if the ICAL sensitivity to earth tomography is competitive or better with respect to other experiments, especially for the inverted mass ordering, where other experiments suffer reduced sensitivity. We present the sensitivity of ICAL to earth tomography by taking into consideration both the Earth's mass constraint as well as the hydrostatic equilibrium constraints., Comment: 14 pages, 12 figures, 10 tables

Previous research in multi-document news summarization has typically concentrated on collating information that all sources agree upon. However, the summarization of diverse information dispersed across multiple articles about an event remains underexplored. In this paper, we propose a new task of summarizing diverse information encountered in multiple news articles encompassing the same event. To facilitate this task, we outlined a data collection schema for identifying diverse information and curated a dataset named DiverseSumm. The dataset includes 245 news stories, with each story comprising 10 news articles and paired with a human-validated reference. Next, to enable consistent automatic evaluation, we conducted a comprehensive analysis to pinpoint the position and verbosity biases when utilizing Large Language Model (LLM)-based metrics for evaluating the coverage and faithfulness of summaries. Through correlation analyses, we outline the best practices for effectively using automatic LLM-based metrics on the DiverseSumm dataset. Finally, we study how LLMs summarize multiple news articles by analyzing which type of diverse information LLMs are capable of identifying. Our analyses suggest that despite the extraordinary capabilities of LLMs in single-document summarization, the proposed task remains a complex challenge for them mainly due to their limited coverage, with GPT-4 only able to cover under 40% of the diverse information on average., Comment: NAACL 2024

Large Language Models (LLMs) have become ubiquitous across various domains, transforming the way we interact with information and conduct research. However, most high-performing LLMs remain confined behind proprietary walls, hindering scientific progress. Most open-source LLMs, on the other hand, are limited in their ability to support longer sequence lengths, which is a key requirement for many tasks that require inference over an input context. To address this, we have trained XGen, a series of 7B parameter models on up to 8K sequence length for up to 1.5T tokens. We have also finetuned the XGen models on public-domain instructional data, creating their instruction-tuned counterparts (XGen-Inst). We open-source our models for both research advancements and commercial applications. Our evaluation on standard benchmarks shows that XGen models achieve comparable or better results when compared with state-of-the-art open-source LLMs. Our targeted evaluation on long sequence modeling tasks shows the benefits of our 8K-sequence models over 2K-sequence open-source LLMs.