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Energy management decreases energy expenditures and consumption while simultaneously increasing energy efficiency, reducing carbon emissions, and enhancing operational performance. Smart grids are a type of sophisticated energy infrastructure that increase the generation and distribution of electricity's sustainability, dependability, and efficiency by utilizing digital communication technologies. They combine a number of cutting-edge techniques and technology to improve energy resource management. A large amount of research study on the topic of smart grids for energy management has been completed in the last several years. The authors of the present study want to cover a number of topics, including smart grid benefits and components, technical developments, integrating renewable energy sources, using artificial intelligence and data analytics, cybersecurity, and privacy. Smart Grids for Energy Management are an innovative field of study aiming at tackling various difficulties and magnifying the efficiency, dependability, and sustainability of energy systems, including: 1) Renewable sources of power like solar and wind are intermittent and unpredictable 2) Defending smart grid system from various cyber-attacks 3) Incorporating an increasing number of electric vehicles into the system of power grid without overwhelming it. Additionally, it is proposed to use AI and data analytics for better performance on the grid, reliability, and energy management. It also looks into how AI and data analytics can be used to optimize grid performance, enhance reliability, and improve energy management. The authors will explore these significant challenges and ongoing research. Lastly, significant issues in this field are noted, and recommendations for further work are provided.

We investigate the pressure-driven structural and electronic evolution of PdSe\(_2\) using powder X-ray diffraction, Raman spectroscopy, and first-principles calculations. Beyond 2.3 GPa, suppression of the Jahn-Teller distortion induces in-plane lattice expansion and metallization. Around 4.8 GPa, the interlayer \(d_{z^2}-\pi^*\) orbital hybridization drives the dimensional crossover, facilitating the transformation from the 2D distorted to a 3D undistorted pyrite phase. Above 9 GPa, a novel phase emerges, characterized by octahedral distortions in the $d$ orbitals of Pd. Structural analysis suggests the presence of marcasite (\(Pnnm\)) or arsenopyrite (\(P2_1/c\)) phase with orthorhombic and monoclinic configurations, respectively. Furthermore, the observed phonon anomaly and electronic structure modifications, including the emergence of flat bands in the high-pressure phases, elucidate the fundamental mechanisms underlying the previously reported exotic superconductivity with an enhanced critical temperature. These results highlight the pivotal role of dimensional crossover and structural transitions in tuning the electronic properties of puckered materials, providing pathways for novel functionalities.

The precise one-shot characterisation of operational tasks in classical and quantum information theory relies on different forms of smooth entropic quantities. A particularly important connection is between the hypothesis testing relative entropy and the smoothed max-relative entropy, which together govern many operational settings. We first strengthen this connection into a type of equivalence: we show that the hypothesis testing relative entropy is equivalent to a variant of the smooth max-relative entropy based on the information spectrum divergence, which can be alternatively understood as a measured smooth max-relative entropy. Furthermore, we improve a fundamental lemma due to Datta and Renner that connects the different variants of the smoothed max-relative entropy, introducing a modified proof technique based on matrix geometric means and a tightened gentle measurement lemma. We use the unveiled connections and tools to strictly improve on previously known one-shot bounds and duality relations between the smooth max-relative entropy and the hypothesis testing relative entropy, sharpening also bounds that connect the max-relative entropy with R\'enyi divergences., Comment: 24+7 pages

Understanding the solvation structure of electrolytes is crucial for optimizing the performance and stability of lithium-ion batteries. Novel solvents are essential for enhancing electrolyte structure and ensuring better integration with modern electrode systems. However, there are limited studies focused on fluorinated solvent-based electrolytes. Herein, we report a new weakly solvated ether electrolyte (WSEE) composed of a pure fluorinated ether solvent, which results in an anion-rich solvation structure even at a low salt concentration of 1M. To explore this, we selected the advanced fluorinated solvent 2,2-Difluoroethyl Methyl Ether (FEME) and compared it with Dipropyl Ether (DPE), Ethylene Carbonate (EC), and Diethyl Carbonate (DEC). The prepared electrolyte systems include DPE with 1M, 1.8M, and 4M LiFSI, FEME with 1M, 1.8M, and 4M LiFSI, and a 1:1 vol% EC/DEC mixture containing 1M LiPF6. In this work, we comprehensively investigate the Li+ solvation structures in these electrolytes using Molecular Dynamics (MD) simulations and Density Functional Theory (DFT) calculations. Our computational findings indicate the presence of large ion aggregates (AGGs) in each DPE- and FEME-based electrolyte, while SSIPs (68%) are the dominant species in the mixed EC/DEC electrolyte. Notably, the formation of large ion aggregates is more pronounced in FEME-based electrolytes. We find that, similar to DPE, FEME solvent also exhibits weak solvating power across all examined salt concentrations. Furthermore, the quantum mechanical features of the Li+ solvation structures in DPE+1.8M LiFSI, FEME+1.8M LiFSI, and EC/DEC+1M LiPF6 electrolytes have been analyzed in detail using DFT calculations. We anticipate that this study will provide valuable insights into the Li+ solvation structures in DPE, FEME, and EC/DEC electrolytes, where the ether-based electrolytes exhibit closely similar properties., Comment: For the associated GIF file, see: https://github.com/mana121/SolvationStructure/blob/main/results/previewGIF.md

We present measurements of large-scale cosmic microwave background (CMB) E-mode polarization from the Cosmology Large Angular Scale Surveyor (CLASS) 90 GHz data. Using 115 det-yr of observations collected through 2024 with a variable-delay polarization modulator, we achieved a polarization sensitivity of $78\,\mathrm{\mu K\,arcmin}$, comparable to Planck at similar frequencies (100 and 143 GHz). The analysis demonstrates effective mitigation of systematic errors and addresses challenges to large-angular-scale power recovery posed by time-domain filtering in maximum-likelihood map-making. A novel implementation of the pixel-space transfer matrix is introduced, which enables efficient filtering simulations and bias correction in the power spectrum using the quadratic cross-spectrum estimator. Overall, we achieved an unbiased time-domain filtering correction to recover the largest angular scale polarization, with the only power deficit, arising from map-making non-linearity, being characterized as less than $3\%$. Through cross-correlation with Planck, we detected the cosmic reionization at $99.4\%$ significance and measured the reionization optical depth $\tau=0.053^{+0.018}_{-0.019}$, marking the first ground-based attempt at such a measurement. At intermediate angular scales ($\ell>30$), our results, both independently and in cross-correlation with Planck, remain fully consistent with Planck's measurements., Comment: 24 pages, 19 figures, 3 tables; submitted to ApJ

As deep neural networks (DNNs) become increasingly common, concerns about their robustness do as well. A longstanding problem for deployed DNNs is their behavior in the face of unfamiliar inputs; specifically, these models tend to be overconfident and incorrect when encountering out-of-distribution (OOD) examples. In this work, we present a topological approach to characterizing OOD examples using latent layer embeddings from DNNs. Our goal is to identify topological features, referred to as landmarks, that indicate OOD examples. We conduct extensive experiments on benchmark datasets and a realistic DNN model, revealing a key insight for OOD detection. Well-trained DNNs have been shown to induce a topological simplification on training data for simple models and datasets; we show that this property holds for realistic, large-scale test and training data, but does not hold for OOD examples. More specifically, we find that the average lifetime (or persistence) of OOD examples is statistically longer than that of training or test examples. This indicates that DNNs struggle to induce topological simplification on unfamiliar inputs. Our empirical results provide novel evidence of topological simplification in realistic DNNs and lay the groundwork for topologically-informed OOD detection strategies., Comment: 24 pages, 13 figures

Clustering large high-dimensional datasets with diverse variable is essential for extracting high-level latent information from these datasets. Here, we developed an unsupervised clustering algorithm, we call "Village-Net". Village-Net is specifically designed to effectively cluster high-dimension data without priori knowledge on the number of existing clusters. The algorithm operates in two phases: first, utilizing K-Means clustering, it divides the dataset into distinct subsets we refer to as "villages". Next, a weighted network is created, with each node representing a village, capturing their proximity relationships. To achieve optimal clustering, we process this network using a community detection algorithm called Walk-likelihood Community Finder (WLCF), a community detection algorithm developed by one of our team members. A salient feature of Village-Net Clustering is its ability to autonomously determine an optimal number of clusters for further analysis based on inherent characteristics of the data. We present extensive benchmarking on extant real-world datasets with known ground-truth labels to showcase its competitive performance, particularly in terms of the normalized mutual information (NMI) score, when compared to other state-of-the-art methods. The algorithm is computationally efficient, boasting a time complexity of O(N*k*d), where N signifies the number of instances, k represents the number of villages and d represents the dimension of the dataset, which makes it well suited for effectively handling large-scale datasets., Comment: Software available at https://villagenet.streamlit.app/

We study the aging of colloidal gels using light microscopy movies of depletion gels from the International Space Station. Under such microgravity conditions, we observe a slowdown in particle dynamics consistent with gel aging. Stronger attractive forces promote the formation of thicker gel strands over time. The samples are bidisperse, composed of particles with a size ratio 1.2. Larger particles experience stronger depletion forces, which lead to a large first-neighbor peak in the pair correlation function $g(r)$ due to the prevalence of large-large particle contacts. As the gel ages, small mobile particles are incorporated into the gel structure. The changes in gel structure correlate with a slow power-law decay in particle motion, observed over nearly two orders of magnitude of time scales in microgravity experiments. Additionally, through complementary ground-based experiments, we compare two-dimensional (2D) and three-dimensional (3D) images of depletion colloidal gels. While microgravity gel data are limited to 2D projections, ground-based data establish a correspondence between the 2D and 3D $g(r)$ peak heights. Our results provide new insights into how colloidal gels age in the absence of gravitational collapse., Comment: Study of microscopy movies from the NASA PSI database (originally from T. Kodger and M. Lynch)

The rapid spread of fake news presents a significant global challenge, particularly in low-resource languages like Bangla, which lack adequate datasets and detection tools. Although manual fact-checking is accurate, it is expensive and slow to prevent the dissemination of fake news. Addressing this gap, we introduce BanFakeNews-2.0, a robust dataset to enhance Bangla fake news detection. This version includes 11,700 additional, meticulously curated fake news articles validated from credible sources, creating a proportional dataset of 47,000 authentic and 13,000 fake news items across 13 categories. In addition, we created a manually curated independent test set of 460 fake and 540 authentic news items for rigorous evaluation. We invest efforts in collecting fake news from credible sources and manually verified while preserving the linguistic richness. We develop a benchmark system utilizing transformer-based architectures, including fine-tuned Bidirectional Encoder Representations from Transformers variants (F1-87\%) and Large Language Models with Quantized Low-Rank Approximation (F1-89\%), that significantly outperforms traditional methods. BanFakeNews-2.0 offers a valuable resource to advance research and application in fake news detection for low-resourced languages. We publicly release our dataset and model on Github to foster research in this direction.

In English literature, the 19th century witnessed a significant transition in styles, themes, and genres. Consequently, the novels from this period display remarkable diversity. This paper explores these variations by examining the evolution of term usage in 19th century English novels through the lens of information retrieval. By applying a query expansion-based approach to a decade-segmented collection of fiction from the British Library, we examine how related terms vary over time. Our analysis employs multiple standard metrics including Kendall's tau, Jaccard similarity, and Jensen-Shannon divergence to assess overlaps and shifts in expanded query term sets. Our results indicate a significant degree of divergence in the related terms across decades as selected by the query expansion technique, suggesting substantial linguistic and conceptual changes throughout the 19th century novels., Comment: Accepted at JCDL 2024

Although the star formation process has been studied for decades, many important aspects of the physics involved remain unsolved. Recent advancement of instrumentation in the infrared, far-infrared and sub-millimetre wavelength regimes have contributed to a significantly improved understanding of processes in the interstellar medium (ISM) leading to star formation. The future of research on the ISM and star formation looks exciting with instruments like the JWST, ALMA, etc., already contributing to the topic by gathering high-resolution high-sensitivity data and with several larger ground- and space-bound facilities either being planned or constructed. India has a sizable number of astronomers engaged in research on topics related to the ISM and star formation. In this white paper invited by the Astronomical Society of India to prepare a vision document for Indian astronomy, we review the Indian contributions to the global understanding of the star formation process and suggest areas that require focused efforts both in creating observing facilities and in theoretical front in India, in order to improve the impact of our research in the coming decades., Comment: Accepted for publication in The Journal of Astrophysics and Astronomy Special Issue: Indian Astronomy in the Global Context: A compendium of white papers submitted towards the Astronomical Society of India Vision Document (2024)

We study the interaction of two massive particles with a quantised gravitational field in its vacuum state using two different position observables: (i) a frame-dependent coordinate separation and (ii) a frame-independent geodesic separation. For free particles, (i) leads to purely unitary dynamics but (ii) leads to dissipation. For two particles coupled through a linear spring, (i) and (ii) lead to different cut-off dependences in the frequency shift harmonic ladder of the differential motional mode. Our findings highlight the subtle consequences of different position observables at the interface of quantum mechanics and gravity., Comment: 6 pages, 2 figures

Cosmic strings are powerful witnesses to cosmic events including any period of early matter domination. If such a period of matter domination was catalysed by metastable, long-lived particles, then there will be complementary signals to ascertain the nature of dark sector in experiments detecting primordial features in the gravitational wave (GW) power spectrum and laboratory searches for long-lived particles. We give explicit examples of global and local U(1) gauge extended dark sectors to demonstrate such a complementarity as the union of the two experiments reveals more information about the dark sector than either experiment. Demanding that Higgs-portal long-lived scalar be looked for, in various experiments such as DUNE, FASER, FASER-II, MATHUSLA, SHiP, we identify the parameter space which leads to complementary observables for GW detectors such as LISA and ET., Comment: 23 pages + references, 6 figures, comments are welcome

Continuous gravitational waves (CWs) emission from neutron stars carries information about their internal structure and equation of state, and it can provide tests of General Relativity. We present a search for CWs from a set of 45 known pulsars in the first part of the fourth LIGO--Virgo--KAGRA observing run, known as O4a. We conducted a targeted search for each pulsar using three independent analysis methods considering the single-harmonic and the dual-harmonic emission models. We find no evidence of a CW signal in O4a data for both models and set upper limits on the signal amplitude and on the ellipticity, which quantifies the asymmetry in the neutron star mass distribution. For the single-harmonic emission model, 29 targets have the upper limit on the amplitude below the theoretical spin-down limit. The lowest upper limit on the amplitude is $6.4\!\times\!10^{-27}$ for the young energetic pulsar J0537-6910, while the lowest constraint on the ellipticity is $8.8\!\times\!10^{-9}$ for the bright nearby millisecond pulsar J0437-4715. Additionally, for a subset of 16 targets we performed a narrowband search that is more robust regarding the emission model, with no evidence of a signal. We also found no evidence of non-standard polarizations as predicted by the Brans-Dicke theory., Comment: main paper: 12 pages, 6 figures, 4 tables

While recent advancements in foundation models have significantly impacted machine learning, rigorous tests on the performance of time series foundation models (TSFMs) remain largely underexplored. This paper presents an empirical study evaluating the zero-shot, long-horizon forecasting abilities of several leading TSFMs over two synthetic datasets constituting noisy periodic time series. We assess model efficacy across different noise levels, underlying frequencies, and sampling rates. As benchmarks for comparison, we choose two statistical techniques: a Fourier transform (FFT)-based approach and a linear autoregressive (AR) model. Our findings demonstrate that while for time series with bounded periods and higher sampling rates, TSFMs can match or outperform the statistical approaches, their forecasting abilities deteriorate with longer periods, higher noise levels, lower sampling rates and more complex shapes of the time series.

Using a combined Dynamical Mean Field Theory and Hartree (DMFT+H) calculation we study the transport and optical properties of the 8-band heavy fermion model for Twisted Bilayer Graphene (TBG) in the normal state. We find resistive states around integer fillings which resemble the ones observed in transport experiments. From a Drude fitting of the low frequency optical conductivity, we extract a very strongly doping-dependent Drude weight and scattering rate, resetting at the integers. For most dopings, particularly above the integers, the Drude scattering rate is high but notably smaller than that of the local electrons. This highlights the important role of itinerant electrons in the transport properties, despite their limited spectral weight on the flat bands. At far infrared frequencies, the optical conductivity exhibits cascades characterized by highly asymmetric resets of the intensity and oscillations in the interband peak frequencies., Comment: 6 pages article including 3 multiple figures + 6 pages Supplementary Information including 5 multiple figures. v2 contains a few changes in the references or how we quote them with respect to v1, as well as a small clarification

An investigation of high transverse momentum (high-$p_T$) photon triggered jets in proton-proton ($p$-$p$) and ion-ion ($A$-$A$) collisions at $\sqrt{s_{NN}} = 0.2$ and $5.02~\mathrm{TeV}$ is carried out, using the multistage description of in-medium jet evolution. Monte Carlo simulations of hard scattering and energy loss in heavy-ion collisions are performed using parameters tuned in a previous study of the nuclear modification factor ($R_{AA}$) for inclusive jets and high-$p_T$ hadrons. We obtain a good reproduction of the experimental data for photon triggered jet $R_{AA}$, as measured by the ATLAS detector, the distribution of the ratio of jet to photon $p_T$ ($X_{\rm J \gamma}$), measured by both CMS and ATLAS, and the photon-jet azimuthal correlation as measured by CMS. We obtain a moderate description of the photon triggered jet $I_{AA}$, as measured by STAR. A noticeable improvement in the comparison is observed when one goes beyond prompt photons and includes bremsstrahlung and decay photons, revealing their significance in certain kinematic regions, particularly at $X_{J\gamma} > 1$. Moreover, azimuthal angle correlations demonstrate a notable impact of non-prompt photons on the distribution, emphasizing their role in accurately describing experimental results. This work highlights the success of the multistage model of jet modification to straightforwardly predict (this set of) photon triggered jet observables. This comparison, along with the role played by non-prompt photons, has important consequences on the inclusion of such observables in a future Bayesian analysis., Comment: 16 pages, 10 figures

We present a fully analytical model of hybridization between magnon, and phonons observed experimentally in magneto-Raman scattering in van der Waals (vdW) antiferromagnets (AFM). Here, the representative material, FePS3, has been shown to be a quasi-two-dimensional-Ising antiferromagnet, with additional features of spin-phonon coupling in the Raman spectra emerging below the N\'eel temperature (TN) of approximately 120 K. Using magneto-Raman spectroscopy as an optical probe of magnetic structure, we show that one of these Raman-active modes in the magnetically ordered state is a magnon with a frequency of 3.7 THz (~ 122 cm-1). In addition, one magnon band and three phonon bands are coupled via the magneto-elastic coupling evidenced by anti-crossing in the complete spectra. We consider a simple model involving only in-plane nearest neighbor exchange couplings (designed to give rise to a similar magnetic structure) and perpendicular anisotropy in presence of an out-of-plane magnetic field. Exact diagonalization of the Hamiltonian leads to energy bands which show that the interaction term gives rise to avoided crossings between the hybridized magnon and phonon branches. Realizing magnon-phonon coupling in two-dimensional (2D) AFMs is important for the verification of the theoretical predictions on exotic quantum transport phenomena like spin-caloritronics, topological magnonics, etc., Comment: 5 pages, 2 figures

This paper presents a pilot study introducing a multimodal fusion framework for the detection and analysis of bridge defects, integrating Non-Destructive Evaluation (NDE) techniques with advanced image processing to enable precise structural assessment. By combining data from Impact Echo (IE) and Ultrasonic Surface Waves (USW) methods, this preliminary investigation focuses on identifying defect-prone regions within concrete structures, emphasizing critical indicators such as delamination and debonding. Using geospatial analysis with alpha shapes, fusion of defect points, and unified lane boundaries, the proposed framework consolidates disparate data sources to enhance defect localization and facilitate the identification of overlapping defect regions. Cross-verification with adaptive image processing further validates detected defects by aligning their coordinates with visual data, utilizing advanced contour-based mapping and bounding box techniques for precise defect identification. The experimental results, with an F1 score of 0.83, demonstrate the potential efficacy of the approach in improving defect localization, reducing false positives, and enhancing detection accuracy, which provides a foundation for future research and larger-scale validation. This preliminary exploration establishes the framework as a promising tool for efficient bridge health assessment, with implications for proactive structural monitoring and maintenance., Comment: Accepted by IEEE Big Data 2024

AI deployed in the real-world should be capable of autonomously adapting to novelties encountered after deployment. Yet, in the field of continual learning, the reliance on novelty and labeling oracles is commonplace albeit unrealistic. This paper addresses a challenging and under-explored problem: a deployed AI agent that continuously encounters unlabeled data - which may include both unseen samples of known classes and samples from novel (unknown) classes - and must adapt to it continuously. To tackle this challenge, we propose our method COUQ "Continual Open-world Uncertainty Quantification", an iterative uncertainty estimation algorithm tailored for learning in generalized continual open-world multi-class settings. We rigorously apply and evaluate COUQ on key sub-tasks in the Continual Open-World: continual novelty detection, uncertainty guided active learning, and uncertainty guided pseudo-labeling for semi-supervised CL. We demonstrate the effectiveness of our method across multiple datasets, ablations, backbones and performance superior to state-of-the-art., Comment: Manuscript Under Review (full-length); Related 4-page manuscripts accepted at Neurips 2024 Non-Archival Workshops https://sites.google.com/view/continual-fomo-workshop and https://imol-workshop.github.io/

Crack segmentation plays a crucial role in ensuring the structural integrity and seismic safety of civil structures. However, existing crack segmentation algorithms encounter challenges in maintaining accuracy with domain shifts across datasets. To address this issue, we propose a novel deep network that employs incremental training with unsupervised domain adaptation (UDA) using adversarial learning, without a significant drop in accuracy in the source domain. Our approach leverages an encoder-decoder architecture, consisting of both domain-invariant and domain-specific parameters. The encoder learns shared crack features across all domains, ensuring robustness to domain variations. Simultaneously, the decoder's domain-specific parameters capture domain-specific features unique to each domain. By combining these components, our model achieves improved crack segmentation performance. Furthermore, we introduce BuildCrack, a new crack dataset comparable to sub-datasets of the well-established CrackSeg9K dataset in terms of image count and crack percentage. We evaluate our proposed approach against state-of-the-art UDA methods using different sub-datasets of CrackSeg9K and our custom dataset. Our experimental results demonstrate a significant improvement in crack segmentation accuracy and generalization across target domains compared to other UDA methods - specifically, an improvement of 0.65 and 2.7 mIoU on source and target domains respectively., Comment: Accepted at ICPR 2024. Details and code can be accessed from https://crackuda.github.io

Recent anomalies in $B$-meson decays, such as deviations in $R_{D^{(*)}}$ and $B\to K\nu{\bar\nu}$, suggest possible lepton flavor universality violation and new exotic interactions. In this work, we explore these anomalies within a non-minimal SU(5) grand unified theory (GUT) framework, which introduces a 45-dimensional Higgs representation predicting exotic scalar particles, including the leptoquark $R_2$ and diquark $S_6$. The $R_2$ leptoquark addresses charged current anomalies in $b\to c\tau\nu$ transitions, the $S_6$ diquark contributes to nonleptonic neutral current processes, such as $B\to K\pi$ while at the loop level, the exchange of a leptoquark and diquark contributes to $B\to K\nu{\bar\nu}$ offering solutions to longstanding puzzles., Comment: 36 pages, 3 figures

The rapid development of the semiconductor industry and the ubiquity of electronic devices have led to a significant increase in the counterfeiting of integrated circuits (ICs). This poses a major threat to public health, the banking industry, and military defense sectors that are heavily reliant on electronic systems. The electronic physically unclonable functions (PUFs) are widely used to authenticate IC chips at the unit level. However, electronic PUFs are limited by their requirement for IC chips to be in working status for measurements and their sensitivity to environmental variations. This paper proposes using optical PUFs for IC chip authentication by leveraging the unique microscopic structures of the packaging surface of individual IC chips. The proposed method relies on color images of IC chip surfaces acquired using a flatbed scanner or mobile camera. Our initial study reveals that these consumer-grade imaging devices can capture meaningful physical features from IC chip surfaces. We then propose an efficient, lightweight verification scheme leveraging specular-reflection-based features extracted from videos, achieving an equal error rate (EER) of 0.0008. We conducted factor, sensitivity, and ablation studies to understand the detailed characteristics of the proposed lightweight verification scheme. This work is the first to apply the optical PUF principle for the authentication of IC chips and has the potential to significantly enhance the security of the semiconductor supply chain.

The hot and dense QCD matter, known as the Quark-Gluon Plasma (QGP), is explored through heavy-ion collision experiments at the LHC and RHIC. Jets and heavy flavors, produced from the initial hard scattering, are used as hard probes to study the properties of the QGP. Recent experimental observations on jet quenching and heavy-flavor suppression have strengthened our understanding, allowing for fine-tuning of theoretical models in hard probes. The second conference, HOT QCD Matter 2024, was organized to bring the community together for discussions on key topics in the field. This article comprises 15 sections, each addressing various aspects of hard probes in relativistic heavy-ion collisions, offering a snapshot of current experimental observations and theoretical advancements. The article begins with a discussion on memory effects in the quantum evolution of quarkonia in the quark-gluon plasma, followed by an experimental review, new insights on jet quenching at RHIC and LHC, and concludes with a machine learning approach to heavy flavor production at the Large Hadron Collider., Comment: Compilation of the 15 contributions in Hard Probes presented at the second 'Hot QCD Matter 2024 Conference' held from July 1-3, 2024, organized by IIT Mandi, India

Low-cost air pollution sensor networks are increasingly being deployed globally, supplementing sparse regulatory monitoring with localized air quality data. In some areas, like Baltimore, Maryland, there are only few regulatory (reference) devices but multiple low-cost networks. While there are many available methods to calibrate data from each network individually, separate calibration of each network leads to conflicting air quality predictions. We develop a general Bayesian spatial filtering model combining data from multiple networks and reference devices, providing dynamic calibrations (informed by the latest reference data) and unified predictions (combining information from all available sensors) for the entire region. This method accounts for network-specific bias and noise (observation models), as different networks can use different types of sensors, and uses a Gaussian process (state-space model) to capture spatial correlations. We apply the method to calibrate PM$_{2.5}$ data from Baltimore in June and July 2023 -- a period including days of hazardous concentrations due to wildfire smoke. Our method helps mitigate the effects of preferential sampling of one network in Baltimore, results in better predictions and narrower confidence intervals. Our approach can be used to calibrate low-cost air pollution sensor data in Baltimore and any other areas with multiple low-cost networks.

In recent years, several proposals that leverage principles from condensed matter and high-energy physics for engineering laser arrays have been put forward. The most important among these concepts are topology, which enables the construction of robust zero-mode laser devices, and supersymmetry (SUSY), which holds the potential for achieving phase locking in laser arrays. In this work, we show that the relation between supersymmetric coupled bosonic and fermionic oscillators on one side, and bipartite networks (and hence chiral symmetry) on another side can be exploited together with non-Hermitian engineering for building one- and two-dimensional laser arrays with in-phase synchronization. To demonstrate our strategy, we present a concrete design starting from the celebrated Su-Schrieffer-Heeger (SSH) model to arrive at a SUSY laser structure that enjoys two key advantages over those reported in previous works. Firstly, the design presented here features a near-uniform geometry for both the laser array and supersymmetric reservoir (i.e. the widths and distances between the cavity arrays are almost the same). Secondly, the uniform field distribution in the presented structure leads to a far-field intensity that scales as N^2 where N is the number of lasing elements. Taken together, these two features can enable the implementation of higher-power laser arrays that are easy to fabricate, and hence provide a roadmap for pushing the frontier of SUSY laser arrays beyond the proof-of-concept phase., Comment: 10 pages, 9 figures

The eigenstates of one-dimensional Hermitian and non-Hermitian tight-binding systems (in the presence/absence of quasiperiodic potential) and an external electric field undergo complete localization with equally spaced eigenenergies, known as the Wannier-Stark (WS) localization. In this work, we demonstrate that when the electric field is slowly modulated with time, new non-trivial phases with multiple mobility edges emerge in place of WS localized phase, which persists up to a certain strength of the non-Hermiticity. On the other hand, for a large driving frequency, we retrieve the usual sharp delocalization-localization transition to the usual (no WS) localized phase, similar to the static non-Hermitian Aubry-Andr\'e-Harper type without any electric field. This vanishing of WS localization can be attributed solely to the time-periodic drive and occurs irrespective of the non-Hermiticity. Interestingly, under the open boundary condition (OBC), we find that contrary to the undriven systems where an external electric field destroys the SE completely, the SE appears in certain regime of the parameter space when the electric field is temporally driven. This appearance of SE is closely related to the absence of extended unitarity. In addition, in the presence of the drive, the skin states are found to be multifractal, contrary to its usual nature in such non-Hermitian systems. An in-depth understanding about the behavior of the states in the driven system is established from the long-time dynamics of an initial excitation., Comment: 15 pages, 19 figures

Motivated by the Iowa Fluoride Study (IFS) dataset, which comprises zero-inflated multi-level ordinal responses on tooth fluorosis, we develop an estimation scheme leveraging generalized estimating equations (GEEs) and James-Stein shrinkage. Previous analyses of this cohort study primarily focused on caries (count response) or employed a Bayesian approach to the ordinal fluorosis outcome. This study is based on the expanded dataset that now includes observations for age 23, whereas earlier works were restricted to ages 9, 13, and/or 17 according to the participants' ages at the time of measurement. The adoption of a frequentist perspective enhances the interpretability to a broader audience. Over a choice of several covariance structures, separate models are formulated for the presence (zero versus non-zero score) and severity (non-zero ordinal scores) of fluorosis, which are then integrated through shared regression parameters. This comprehensive framework effectively identifies risk or protective effects of dietary and non-dietary factors on dental fluorosis., Comment: 33 pages, 1 figure. Added Acknowledgment section and fixed page numbering

In the ever-evolving landscape of natural language processing and information retrieval, the need for robust and domain-specific entity linking algorithms has become increasingly apparent. It is crucial in a considerable number of fields such as humanities, technical writing and biomedical sciences to enrich texts with semantics and discover more knowledge. The use of Named Entity Disambiguation (NED) in such domains requires handling noisy texts, low resource settings and domain-specific KBs. Existing approaches are mostly inappropriate for such scenarios, as they either depend on training data or are not flexible enough to work with domain-specific KBs. Thus in this work, we present an unsupervised approach leveraging the concept of Group Steiner Trees (GST), which can identify the most relevant candidates for entity disambiguation using the contextual similarities across candidate entities for all the mentions present in a document. We outperform the state-of-the-art unsupervised methods by more than 40\% (in avg.) in terms of Precision@1 across various domain-specific datasets., Comment: Accepted in EMNLP-2024

In the field of continual learning, relying on so-called oracles for novelty detection is commonplace albeit unrealistic. This paper introduces CONCLAD ("COntinuous Novel CLAss Detector"), a comprehensive solution to the under-explored problem of continual novel class detection in post-deployment data. At each new task, our approach employs an iterative uncertainty estimation algorithm to differentiate between known and novel class(es) samples, and to further discriminate between the different novel classes themselves. Samples predicted to be from a novel class with high-confidence are automatically pseudo-labeled and used to update our model. Simultaneously, a tiny supervision budget is used to iteratively query ambiguous novel class predictions, which are also used during update. Evaluation across multiple datasets, ablations and experimental settings demonstrate our method's effectiveness at separating novel and old class samples continuously. We will release our code upon acceptance.

AI deployed in many real-world use cases should be capable of adapting to novelties encountered after deployment. Here, we consider a challenging, under-explored and realistic continual adaptation problem: a deployed AI agent is continuously provided with unlabeled data that may contain not only unseen samples of known classes but also samples from novel (unknown) classes. In such a challenging setting, it has only a tiny labeling budget to query the most informative samples to help it continuously learn. We present a comprehensive solution to this complex problem with our model "CUAL" (Continual Uncertainty-aware Active Learner). CUAL leverages an uncertainty estimation algorithm to prioritize active labeling of ambiguous (uncertain) predicted novel class samples while also simultaneously pseudo-labeling the most certain predictions of each class. Evaluations across multiple datasets, ablations, settings and backbones (e.g. ViT foundation model) demonstrate our method's effectiveness. We will release our code upon acceptance.

We report a sequential two-step vapor deposition process for growing mixed-dimensional van der Waals (vdW) materials, specifically Te nanowires (1D) and MoS$_2$ (2D), on a single SiO$_2$ wafer. Our growth technique offers a unique potential pathway to create large scale, high-quality, defect-free interfaces. The assembly of samples serves a twofold application: first, the as-prepared heterostructures (Te NW/MoS$_2$) provide insights into the atomically thin depletion region of a 1D/2D vdW diode, as revealed by electrical transport measurements and density functional theory-based quantum transport calculations. The charge transfer at the heterointerface is confirmed using Raman spectroscopy and Kelvin probe force microscopy (KPFM). We also observe modulation of the rectification ratio with varying applied gate voltage. Second, the non-hybrid regions on the substrate, consisting of the as-grown individual Te nanowires and MoS$_2$ microstructures, are utilized to fabricate separate p- and n-FETs, respectively. Furthermore, the ionic liquid gating helps to realize low-power CMOS inverter and all basic logic gate operations using a pair of n- and p- field-effect transistors (FETs) on Si/SiO$_2$ platform. This approach also demonstrates the potential for unifying diode and CMOS circuits on a single platform, opening opportunities for integrated analog and digital electronics.

Photon-subtracted two-mode squeezed vacuum states, a significant quantum resource, exhibit intricate correlations and unique quantum properties. In this work, we propose a theoretical yet experimentally feasible model to engineer these states using a waveguide trimer. Our study uncovers distinct characteristics of the photon-subtracted state depending on whether an even or odd number of photons is extracted, shedding light on the subtle relationship between quantum state manipulation and the parity of the number of subtracted photons. Furthermore, our integrated device facilitates the generation of multiphoton states with tunable correlations, offering significant potential for applications in quantum-enhanced technologies.

Although hydrogen generation by water electrolysis is the cheapest of all other available sources, water splitting still occurs with sluggish kinetics. It is a challenging barrier for H2 production on a large scale. Moreover, research is still underway to understand the oxygen evolution reaction (OER) and design the catalysts with improved OER performance. Herein, we report the synthesis, characterization, and OER performance of iron-doped copper oxide (CuO) as low-cost catalysts for water oxidation. The OER occurs at about 1.49 V versus the RHE with a Tafel slope of 69 mV/dec in a 1 M KOH solution. The overpotential of 338 mV at 10 mA/cm2 is among the lowest compared with other copper-based materials. The catalyst can deliver a stable current density of >10 mA/cm2 for more than 10 hours. Additionally, wastewater treatment, particularly synthetic dye wastewater, is vital for preventing water scarcity and adverse effects on human health and ecotoxicology. The as-synthesized catalysts are also utilized for Fenton-like photo-degradation under low-power visible household LED lights toward the most commonly industrially used simulated Methylene blue dye wastewater. Almost complete degradation of the MB dye has been achieved within 50 minutes of visible light irradiation with a first-order rate constant of 0.0973/min. This dual functionality feature can open new pathways as a non-noble, highly efficient, and robust catalyst for OER and wastewater treatments.

STARR (STAnford Research Repository) is a clinical research support ecosystem that supports basic science research, population health research and translational research at Stanford University. STARR consists of raw and analysis ready multi-modal data, and tools for cohort analysis and self service data access. STARR data is accessible on secure shared computing systems for ad hoc analysis. Also present is a suite of services on top of STARR, that allow researchers access to complex purpose built data cuts, common data models and software solutions. This manuscript is a research resource description and describes the evolution of STARR Tools that are used to offer self-service access to detailed clinical data for research purposes to researchers at Stanford Medicine, along with a framework used to ensure that data acquired via the self-service tools is handled in compliance with all applicable regulations and rules.

Many environmental, energy, and industrial processes involve the flow of polymer solutions in three-dimensional (3D) porous media where fluid is confined to navigate through complex pore space geometries. As polymers are transported through the tortuous pore space, elastic stresses accumulate, leading to the onset of unsteady flow fluctuations above a threshold flow rate. How does pore space geometry influence the development and features of this elastic instability? Here, we address this question by directly imaging polymer solution flow in microfabricated 3D ordered porous media with precisely controlled geometries consisting of simple-cubic (SC) or body-centered cuboid (BC) arrays of spherical grains. In both cases, we find that the flow instability is generated at stagnation points arising at the contacts between grains rather than at the polar upstream/downstream grain surfaces, as is the case for flow around a single grain. The characteristics of the flow instability are strongly dependent on the unit cell geometry: in SC packings, the instability manifests through the formation of time-dependent, fluctuating 3D eddies, whereas in BC packings, it manifests as continual fluctuating 'wobbles' and crossing in the flow pathlines. Despite this difference, we find that characteristics of the transition from steady to unsteady flow with increasing flow rate have commonalities across geometries. Moreover, for both packing geometries, our data indicate that extensional flow-induced polymeric stresses generated by contact-associated stagnation points are the primary contributor to the macroscopic resistance to flow across the entire medium. Altogether, our work highlights the pivotal role of inter-grain contacts -- which are typically idealized as discrete points and therefore overlooked, but are inherent in most natural and engineered media -- in shaping elastic instabilities in porous media.

Magnetic reconnection is a ubiquitous process in plasma physics, driving rapid and energetic events such as coronal mass ejections. Reconnection between magnetic fields with arbitrary shear can be decomposed into an anti-parallel, reconnecting component, and a non-reconnecting guide-field component which is parallel to the reconnecting electric field. This guide field modifies the structure of the reconnection layer and the reconnection rate. We present results from experiments on the MAIZE pulsed-power generator (500 kA peak current, 200 ns rise-time) which use two exploding wire arrays, tilted in opposite directions, to embed a guide field in the plasma flows with a relative strength $b\equiv B_g/B_{rec}=\text{0, 0.4, or 1}$. The reconnection layers in these experiments have widths which are less than the ion skin depth, $d_i=c/\omega_{pi}$, indicating the importance of the Hall term, which generates a distinctive quadrupolar magnetic field structure along the separatrices of the reconnection layer. Using laser imaging interferometry, we observe quadrupolar structures in the line-integrated electron density, consistent with the interaction of the embedded guide field with the quadrupolar Hall field. Our measurements extend over much larger length scales ($40 d_i$) at higher $\beta$ ($\sim 1$) than previous experiments, providing an insight into the global structure of the reconnection layer., Comment: 12 pages, 9 figures. Submitted to Physics of Plasmas for review

In this paper, we compute sharp bounds for the higher topological complexity of many planar polygon spaces having small genetic codes. In most of these cases we show that the $k$-th higher topological complexity of planar polygon spaces is either $km$ or $km+1$, where $m$ is their dimension.

Decentralized Finance (DeFi) and smart contracts are the next generation and fast-growing market for quick and safe interaction between lenders and borrowers. However for maintaining a streamline ecosystem it is necessary to understand the risk associated with the particular user under consideration. In this paper we have developed 'On Chain Credit Risk Score' of a wallet is an answer to quantifying the risk of the particular wallet, namely the probability that the particular wallet may have a loan liquidated. 'On Chain Credit Risk Score (OCCR Score)' of wallets, will help lending borrowing protocols and other DeFi institutes to understand the risk involved in giving out loans to a wallet and may change the Loan-to-Value (LTV) Ratio and subsequently the Liquidation Threshold (LT) if required.

We present a mapping of the nonlinear, electrostatic Vlasov equation with Krook type collision operators, discretized on a (1 + 1) dimensional grid, onto a recent Carleman linearization based quantum algorithm for solving ordinary differential equations (ODEs) with quadratic nonlinearities. We show that the quantum algorithm is guaranteed to converge only when the plasma parameters take unphysical values. This is due to the high level of dissipation in the ODE system required for convergence, that far exceeds the physical dissipation effect provided by the Krook operator. Additionally, we derive upper bounds for the query- and gate complexities of the quantum algorithm in the limit of large grid sizes. We conclude that these are polynomially larger than the time complexity of the corresponding classical algorithms. We find that this is mostly due to the dimension, sparsity and norm of the Carleman linearized evolution matrix.

Blue-tilted Gravitational Waves (BGWs) have been proposed as a potential candidate for the cosmic gravitational waves detected by Pulsar Timing Arrays (PTA). In the standard cosmological framework, BGWs are constrained in their frequency range by the Big Bang Nucleosynthesis (BBN) limit on GW amplitude, which precludes their detection at interferometer scales. However, introducing a phase of early matter domination dilutes BGWs at higher frequencies, ensuring compatibility with both the BBN and LIGO constraints on stochastic GWs. This mechanism allows BGWs to align with PTA data while producing a distinct and testable GW signal across a broad frequency spectrum. Ultralight Primordial Black Holes (PBHs) could provide the required early matter-dominated phase to support this process. Interpreted through the lens of BGWs, the PTA results offer a way to constrain the parameter space of a new scenario involving modified Hawking radiation, known as the ``memory burden" effect, associated with ultralight PBHs. This interpretation can be further probed by high-frequency GW detectors. Specifically, we demonstrate that PBHs as light as $10^{2-3}~{\rm g}$ can leave detectable imprints on BGWs at higher frequencies while remaining consistent with PTA observations., Comment: 20 pages, 7 figures, comments are welcome

We show that the use of 1,3-diaminopropane (DAP) as a chemical modifier at the perovskite/electron-transport layer (ETL) interface enhances the power conversion efficiency (PCE) of 1.7 eV bandgap FACs mixed-halide perovskite single-junction cells, primarily by boosting the open-circuit voltage (VOC) from 1.06 V to 1.15 V. Adding a post-processing annealing step after C60 evaporation, further improves the fill factor (FF) by 20% from the control to the DAP + post-annealing devices. Using hyperspectral photoluminescence microscopy, we demonstrate that annealing helps improve compositional homogeneity at the top and bottom interfaces of the solar cell, which prevents detrimental bandgap pinning in the devices and improves C60 adhesion. Using time-of-flight secondary ion mass spectrometry, we show that DAP reacts with formamidinium present near the surface of the perovskite lattice to form a larger molecular cation, 1,4,5,6-tetrahydropyrimidinium (THP) that remains at the interface. Combining the use of DAP and the annealing of C60 interface, we fabricate Si-perovskite tandems with PCE of 25.29%, compared to 23.26% for control devices. Our study underscores the critical role of chemical reactivity and thermal post-processing of the C60/Lewis-base passivator interface in minimizing device losses and advancing solar-cell performance of wide-bandgap mixed-cation mixed-halide perovskite for tandem application.

Text-to-image diffusion models, such as Stable Diffusion, have shown exceptional potential in generating high-quality images. However, recent studies highlight concerns over the use of unauthorized data in training these models, which may lead to intellectual property infringement or privacy violations. A promising approach to mitigate these issues is to apply a watermark to images and subsequently check if generative models reproduce similar watermark features. In this paper, we examine the robustness of various watermark-based protection methods applied to text-to-image models. We observe that common image transformations are ineffective at removing the watermark effect. Therefore, we propose RATTAN, that leverages the diffusion process to conduct controlled image generation on the protected input, preserving the high-level features of the input while ignoring the low-level details utilized by watermarks. A small number of generated images are then used to fine-tune protected models. Our experiments on three datasets and 140 text-to-image diffusion models reveal that existing state-of-the-art protections are not robust against RATTAN.

Many biological and synthetic systems are suspensions of oriented, actively-moving components. Unlike in passive suspensions, the interplay between orientational order, active flows, and interactions with boundaries gives rise to fascinating new phenomena in such active suspensions. Here, we examine the paradigmatic example of a surface-attached drop of an active suspension (an "active drop"), which has so far only been studied in the idealized limit of thin drops. We find that such surface-attached active drops can exhibit a wide array of stable steady-state shapes and internal flows that are far richer than those documented previously, depending on boundary conditions and the strength of active stresses. Our analysis uncovers quantitative principles to predict and even rationally control the conditions under which these different states arise -- yielding design principles for next-generation active materials., Comment: 9 pages, 4 figures

We present the in-lab and on-sky performance for the upgraded 90 GHz focal plane of the Cosmology Large Angular Scale Surveyor (CLASS), which had four of its seven detector wafers updated during the austral winter of 2022. The update aimed to improve the transition-edge-sensor (TES) stability and bias range and to realize the high optical efficiency of the sensor design. Modifications included revised circuit terminations, electrical contact between the TES superconductor and the normal metal providing the bulk of the bolometer's heat capacity, and additional filtering on the TES bias lines. The upgrade was successful: 94% of detectors are stable down to 15% of the normal resistance, providing a wide overlapping range of bias voltages for all TESs on a wafer. The median telescope efficiency improved from $0.42^{+0.15}_{-0.22}$ to $0.60^{+0.10}_{-0.32}$ (68% quantiles). For the four upgraded wafers alone, median telescope efficiency increased to $0.65^{+0.06}_{-0.06}$. Given our efficiency estimate for the receiver optics, this telescope efficiency implies a detector efficiency exceeding $0.90$. The overall noise-equivalent temperature of the 90 GHz focal plane improved from 19 $\mu$K$\sqrt{s}$ to 11.3 $\mu$K$\sqrt{s}$., Comment: 18 pages, 10 figures.Submitted to ApJS

Routing and Spectrum Assignment (RSA) represents a significant challenge within Elastic Optical Networks (EONs), particularly in dynamic traffic scenarios where the network undergoes continuous changes. Integrating multiple modulation formats transforms it into Routing Modulation Level and Spectrum Assignment (RMLSA) problem, thereby making it more challenging. Traditionally, addressing the RSA problem involved identifying a fixed number of paths and subsequently allocating spectrum among them. Numerous heuristic and metaheuristic approaches have been proposed for RSA using this two-step methodology. However, solving for routing and assignment of spectrum independently is not recommended due to their interdependencies and their impact on resource utilization, fragmentation and bandwidth blocking probability. In this paper, we propose a novel approach to solve the RMLSA problem jointly in dynamic traffic scenarios, inspired by Ant Colony Optimization (ACO). This approach involves augmenting the network into an Auxiliary Graph and transforming conventional ACO into a constraint-based ACO variant that adapts to the constraints of EONs. This adaptation also includes an adaptive initiation process and an aggressive termination strategy aimed at achieving faster convergence. Moreover, we have introduced a novel objective/fitness function, to minimize average network fragmentation while ensuring optimal spectrum resource utilization, thereby reducing overall blocking probability., Comment: 11 pages, 10 figures

The majority of atomic nuclei have deformed shapes and nearly all these shapes are symmetric with respect to reflection. There are only a few reflection asymmetric pear-shaped nuclei that have been found in actinide and lanthanide regions, which have static octupole deformation. These nuclei possess an intrinsic electric dipole moment due to the shift between the center of charge and the center of mass. This manifests in the enhancement of the electric dipole transition rates. In this article, we report on the measurement of the lifetimes of the high spin levels of the two alternate parity bands in $^{100}$Ru through the Doppler Shift Attenuation Method. The estimated electric dipole transition rates have been compared with the calculated transition rates using the triaxial projected shell model without octupole deformation, and are found to be an order of magnitude enhanced. Thus, the observation of seven inter-leaved electric dipole transitions with enhanced rates establish $^{100}$Ru as possibly the first octupole deformed nucleus reported in the A $\approx$ 100 mass region.

The study of $\bar{B} \to D^* \tau {\bar{\nu}}_\tau$ angular distribution can be used to obtain information about new physics (or beyond the Standard Model) couplings, which are motivated by various $B$ anomalies. However, the inability to measure precisely the three-momentum of the $\tau$ lepton hinders such measurements, as the tau decay contains one or more undetected neutrinos. Here, we present a measurable angular distribution of $\bar{B} \to D^* \tau {\bar{\nu}}_\tau$ by considering the additional decay $\tau \to \ell \nu_\tau \bar{\nu}_\ell$, where $\ell \in \{ e , \mu \}$. The full process used is $\bar{B} \to D^* (\to D \pi) \tau (\to \ell \nu_\tau \bar{\nu}_\ell) \bar{\nu}_\tau$, in which only the $\ell$ and $D^*$ are reconstructed. A fit to the experimental angular distribution of this process can be used to extract information on new physics parameters. To demonstrate the feasibility of this approach, we generate simulated data for this process and perform a sensitivity study to obtain the expected statistical errors on new physics parameters from experiments in the near future. We obtain a sensitivity of the order of 5% for the right-handed current and around 6% for the tensor current. In addition, we use the recent lattice QCD data on $B \to D^*$ form factors and obtain correlations between form factors and new physics parameters., Comment: 38 pages, 8 figures; new version: added references, corrected typos

Electron-magnon coupling reveals key insights into the interfacial properties between non-magnetic metals and magnetic insulators, influencing charge transport and spin dynamics. Here, we present temperature-dependent Raman spectroscopy and magneto-transport measurements of few-layer graphene (FLG)/antiferromagnetic FePS\(_3\) heterostructures. The magnon mode in FePS\(_3\) softens below 40 K, and effective magnon stiffness decreases with cooling. Magnetotransport measurements show that FLG exhibits negative magnetoresistance (MR) in the heterostructure at low fields (\(\pm 0.2 \, \text{T}\)), persisting up to 100 K; beyond this, MR transitions to positive. Notably, as layer thickness decreases, the coupling strength at the interface reduces, leading to a suppression of negative MR. Additionally, magnetodielectric measurements in the FLG/FePS\(_3\)/FLG heterostructure show an upturn at temperatures significantly below ($T_\text{N}$), suggesting a role for the magnon mode in capacitance, as indicated by hybridization between magnon and phonon bands in pristine FePS\(_3\) \textit{via} magnetoelastic coupling.