This study presents a weakly supervised method for identifying faults in infrared images of substation equipment. It utilizes the Faster RCNN model for equipment identification, enhancing detection accuracy through modifications to the model's network structure and parameters. The method is exemplified through the analysis of infrared images captured by inspection robots at substations. Performance is validated against manually marked results, demonstrating that the proposed algorithm significantly enhances the accuracy of fault identification across various equipment types.

The Universe at the present epoch is found to be a network of matter over-dense and under-dense regions. To date, this picture of the Universe is best revealed through cosmological large-volume simulations and large-scale galaxy redshift surveys, in which, the most important step is the appropriate identification of structures. So far, these structures are identified using various group finding codes, mostly based on the friends-of-friends (FoF) or spherical over-density (SO) algorithms. Although, the main purpose is to identify gravitationally bound structures, surprisingly, the mass information has hardly been used effectively by these codes. Moreover, the methods used so far either constrain the over-density or use the real unstructured geometry only. Even though these are key factors in the accurate determination of structures-mass information, hardly any attempt has been made as yet to consider these important parameters together while formulating the grouping algorithms. In this paper, we present our proposed algorithm which takes care of all the above-mentioned relevant features and ensures the bound structures by means of physical quantities, mainly mass and the total energy information. We introduced a novel concept of physically relevant arm-length for each element depending on their individual gravity leading to a distinct linking length for each unique pair of elements. This proposed algorithm is thus fundamentally new that, not only able to catch the gravitationally bound, real unstructured geometry, it does identify it roughly within a predefined physically motivated density threshold. Such a thing could not be simultaneously achieved before by any of the usual FoF or SO-based methods. We also demonstrate the unique ability of the code in the appropriate identification of structures, both from large volume cosmological simulations as well as from galaxy redshift surveys.

Identifying methods to discover dual AGN has proven to be challenging. Several indirect tracers have been explored in the literature, including X/S-shaped radio morphologies and double-peaked (DP) emission lines in the optical spectra. However, the detection rates of confirmed dual AGN candidates from the individual methods remain extremely small. We search for binary black holes in a sample of six sources that exhibit both X-shaped radio morphology and DP emission lines using the VLBA. Three out of the six sources show dual VLBA compact components, making them strong candidates for binary black hole sources. In addition, we present deep uGMRT images revealing the exquisite details of the X-shaped wings in three sources. We present a detailed precession modeling analysis of these sources. The BH separations estimated from the simplistic geodetic precession model are incompatible with those estimated from emission line offsets and the VLBA separations. However, precession induced by a noncoplanar secondary black hole is a feasible mechanism for explaining the observed X-shaped radio morphologies and the black hole separations estimated from other methods. The black hole separations estimated from the double-peaked emission lines agree well with the VLBA compact component separations. Future multi-frequency VLBA observations will be critical in ruling out or confirming the binary black hole scenario in the three galaxies with dual component detections.

Herein, we report a case study in which we saw the spontaneous conversion of commercial bulk graphite into LaB6 decorated carbon nanotubes (CNTs) under normal atmospheric conditions. The feedstock graphite was used as a hollow cylindrical anode filled with LaB6 powder and partially eroded in a DC electric-arc plasma reactor in pure nitrogen atmosphere. An unusual and spontaneous deformation of the plasma-treated residual anode into a fluffy powder was seen to continue for months when left to ambient atmospheric conditions. The existence of LaB6 decorated multi-walled CNTs at large quantity was confirmed in the as-generated powder by using electron microscopy, Raman spectroscopy and x-ray diffraction. The as-synthesized CNT-based large-area field emitter showed promising field-emitting properties with a low turn-on electric field of ~1.5 V per micrometer, and a current density of ~1.17 mA per square cm at an applied electric field of 3.24 V per micrometer.

Experiments performed using micro-patterned one dimensional collision assays have allowed a precise quantitative analysis of the collective manifestation of contact inhibition locomotion (CIL) wherein, individual migrating cells reorient their direction of motion when they come in contact with other cells. Inspired by these experiments, we present a discrete, minimal 1D Active spin model that mimics the CIL interaction between cells in one dimensional channels. We analyze the emergent collective behaviour of migrating cells in such confined geometries, as well as the sensitivity of the emergent patterns to driving forces that couple to cell motion. In the absence of vacancies, akin to dense cell packing, the translation dynamics is arrested and the model reduces to an equilibrium spin model which can be solved exactly. In the presence of vacancies, the interplay of activity-driven translation, cell polarity switching, and CIL results in an exponential steady cluster size distribution. We define a dimensionless P\'eclet number Q - the ratio of the translation rate and directional switching rate of particles in the absence of CIL. While the average cluster size increases monotonically as a function of Q, it exhibits a non-monotonic dependence on CIL strength, when the Q is sufficiently high. In the high Q limit, an analytical form of average cluster size can be obtained approximately by effectively mapping the system to an equivalent equilibrium process involving clusters of different sizes wherein the cluster size distribution is obtained by minimizing an effective Helmholtz free energy for the system. The resultant prediction of exponential dependence on CIL strength of the average cluster size and Q^{1/2} dependence of the average cluster size is borne out to reasonable accuracy as long as the CIL strength is not very large.

The Universe at the present epoch is found to be a network of matter over-dense and under-dense regions. To date, this picture of the Universe is best revealed through cosmological large-volume simulations and large-scale galaxy redshift surveys, in which, the most important step is the appropriate identification of structures. So far, these structures are identified using various group finding codes, mostly based on the friends-of-friends (FoF) or spherical over-density (SO) algorithms. Although, the main purpose is to identify gravitationally bound structures, surprisingly, the mass information has hardly been used effectively by these codes. Moreover, the methods used so far either constrain the over-density or use the real unstructured geometry only. Even though these are key factors in the accurate determination of structures-mass information, hardly any attempt has been made as yet to consider these important parameters together while formulating the grouping algorithms. In this paper, we present our proposed algorithm which takes care of all the above-mentioned relevant features and ensures the bound structures by means of physical quantities, mainly mass and the total energy information. We introduced a novel concept of physically relevant arm-length for each element depending on their individual gravity leading to a distinct linking length for each unique pair of elements. This proposed algorithm is thus fundamentally new that, not only able to catch the gravitationally bound, real unstructured geometry, it does identify it roughly within a predefined physically motivated density threshold. Such a thing could not be simultaneously achieved before by any of the usual FoF or SO-based methods. We also demonstrate the unique ability of the code in the appropriate identification of structures, both from large volume cosmological simulations as well as from galaxy redshift surveys.

We study the asymptotic symmetries of near-horizon extremal BTZ black holes in higher derivative theories of gravity, such as New Massive Gravity and Topological Massive Gravity. By employing a particular boundary condition and the regularization prescription proposed earlier for the Einstein gravity, we demonstrate the existence of two centrally extended Virasoro algebras. The central charges evaluated within this framework are in agreement with their corresponding expressions evaluated at the spatial infinity. We also discuss the robustness of the regularization procedure by relating asymptotic and near-horizon geometries.

In the theoretical framework of hierarchical structure formation, galaxy clusters evolve through continuous accretion and mergers of substructures. Cosmological simulations have revealed the best picture of the Universe as a 3-D filamentary network of dark-matter distribution called the cosmic web. Galaxy clusters are found to form at the nodes of this network and are the regions of high merging activity. Such mergers being highly energetic, contain a wealth of information about the dynamical evolution of structures in the Universe. Observational validation of this scenario needs a colossal effort to identify numerous events from all-sky surveys. Therefore, such efforts are sparse in literature and tend to focus on individual systems. In this work, we present an improved search algorithm for identifying interacting galaxy clusters and have successfully produced a comprehensive list of systems from SDSS DR-17. By proposing a set of physically motivated criteria, we classified these interacting clusters into two broad classes, 'merging' and 'pre-merging/postmerging' systems. Interestingly, as predicted by simulations, we found that most cases show cluster interaction along the prominent cosmic filaments of galaxy distribution (i.e., the proxy for DM filaments), with the most violent ones at their nodes. Moreover, we traced the imprint of interactions through multi-band signatures, such as diffuse cluster emissions in radio or X-rays. Although we could not find direct evidence of diffuse emission from connecting filaments and ridges; our catalogue of interacting clusters will ease locating such faintest emissions as data from sensitive telescopes like eROSITA or SKA, becomes accessible

In this Letter, we address the {long-range interaction} between kinks and antikinks, as well as kinks and kinks, in $\varphi^{2n+4}$ field theories for $n>1$. The kink-antikink interaction is generically attractive, while the kink-kink interaction is generically repulsive. We find that the force of interaction decays with the $(\frac{2n}{n-1})$th power of their separation, and we identify the general prefactor for {\it arbitrary} $n$. Importantly, we test the resulting mathematical prediction with detailed numerical simulations of the dynamic field equation, and obtain good agreement between theory and numerics for the cases of $n=2$ ($\varphi^8$ model), $n=3$ ($\varphi^{10}$ model) and $n=4$ ($\varphi^{12}$ model).

Water dissociation is a rate limiting step in many industrially important chemical reactions. In this investigation, climbing image nudged elastic band (CINEB) method, within the framework of density functional theory, is used to report the activation energies (E a ) of water dissociation on Cu(111) surface with a vacancy. Introduction of vacancy results in a reduced coordination of the dissociated products, which facilitates their availability for reactions that involve water dissociation as an intermediate step. Activation energy for dissociation of water reduces by nearly 0.2 eV on Cu(111) surface with vacancy, in comparison with that of pristine Cu(111) surface. We also find that surface modification of the Cu upper surface is one of the possible pathways to dissociate water when the vacancy is introduced. Activation energy, and the minimum energy path (MEP) leading to the transition state remain same for various product configurations. CINEB corresponding to hydrogen gas evolution is also performed which shows that it is a two step process involving water dissociation. We conclude that the introduction of vacancy facilitates the water dissociation reaction, by reducing the activation energy by about 20%.

Understanding the star-formation properties of galaxies as a function of cosmic epoch is a critical exercise in studies of galaxy evolution. Traditionally, stellar population synthesis models have been used to obtain best fit parameters that characterise star formation in galaxies. As multiband flux measurements become available for thousands of galaxies, an alternative approach to characterising star formation using machine learning becomes feasible. In this work, we present the use of deep learning techniques to predict three important star formation properties -- stellar mass, star formation rate and dust luminosity. We characterise the performance of our deep learning models through comparisons with outputs from a standard stellar population synthesis code.

The existence of magnetostriction in bulk BiFeO3 is still a matter of investigation and it is also an issue to investigate the magnetostriction effect in nano BiFeO3. Present work demonstrates the existence of magnetostrictive strain in superparamagnetic BiFeO3 nanoparticles at room temperature and the magnetoelectric coupling properties in composite form with P(VDFTrFE). Despite few reports on the magnetostriction effect in bulk BiFeO3 evidenced by the indirect method, the direct method (strain gauge) was employed in this work to examine the magnetostriction of superparamagnetic BiFeO3. In addition, a high magnetoelectric coupling coefficient was observed by the lock-in technique for optimized BiFeO3_P(VDF-TrFE) nanocomposite film. These nanocomposite films also exhibit room-temperature multiferroic properties. These results provide aspects of material with immense potential for practical applications in spintronics and magneto-electronics applications. We report a magnetoelectric sensor using superparamagnetic BiFeO3_P(VDF-TrFE) nanocomposite film for detection of ac magnetic field.

In this paper, we introduce a novel fine-tuning technique for language models, which involves incorporating symmetric noise into the embedding process. This method aims to enhance the model's function by more stringently regulating its local curvature, demonstrating superior performance over the current method, NEFTune. When fine-tuning the LLaMA-2-7B model using Alpaca, standard techniques yield a 29.79% score on AlpacaEval. However, our approach, SymNoise, increases this score significantly to 69.04%, using symmetric noisy embeddings. This is a 6.7% improvement over the state-of-the-art method, NEFTune~(64.69%). Furthermore, when tested on various models and stronger baseline instruction datasets, such as Evol-Instruct, ShareGPT, OpenPlatypus, SymNoise consistently outperforms NEFTune. The current literature, including NEFTune, has underscored the importance of more in-depth research into the application of noise-based strategies in the fine-tuning of language models. Our approach, SymNoise, is another significant step towards this direction, showing notable improvement over the existing state-of-the-art method.

The experimental use of micropatterned quasi-1D substrates has emerged as an useful experimental tool to study the nature of cell-cell interactions and gain insight on collective behaviour of cell colonies. Inspired by these experiments, we propose an active spin model to investigate the emergent properties of the cell assemblies. The lattice gas model incorporates the interplay of self-propulsion, polarity directional switching, intra-cellular attraction, and contact Inhibition Locomotion (CIL). In the absence of vacancies, which corresponds to a confluent cell packing on the substrate, the model reduces to an equilibrium spin model which can be solved exactly. In the presence of vacancies, the clustering is controlled by a dimensionless Peclet Number, Q - the ratio of magnitude of self-propulsion rate and directional switching rate of particles. In the absence of CIL interactions, we invoke a mapping to Katz-Lebowitz-Spohn(KLS) model to determine an exact analytical form of the cluster size distribution in the limit Q << 1. In the limit of Q >> 1, the cluster size distribution exhibits an universal scaling behaviour (in an approximate sense), such that the distribution function can be expressed as a scaled function of Q, particle density and CIL interaction strength. We characterize the phase behaviour of the system in terms of contour plots of average cluster size. The average cluster size exhibit a non-monotonic dependence on CIL interaction strength, attractive interaction strength, and self-propulsion.

Context - Around the year 2000, Triton's south pole experienced an extreme summer solstice that occurs every about 650 years, when the subsolar latitude reached about 50°. Bracketing this epoch, a few occultations probed Triton's atmosphere in 1989, 1995, 1997, 2008 and 2017. A recent ground-based stellar occultation observed on 6 October 2022 provides a new measurement of Triton's atmospheric pressure which is presented here. Aims- The goal is to constrain the Volatile Transport Models (VTMs) of Triton's atmosphere that is basically in vapor pressure equilibrium with the nitrogen ice at its surface. Methods - Fits to the occultation light curves yield Triton's atmospheric pressure at the reference radius 1400 km, from which the surface pressure is induced. Results - The fits provide a pressure p_1400= 1.211 +/- 0.039 microbar at radius 1400 km (47 km altitude), from which a surface pressure of p_surf= 14.54 +/- 0.47 microbar is induced (1-sigma error bars). To within error bars, this is identical to the pressure derived from the previous occultation of 5 October 2017, p_1400 = 1.18 +/- 0.03 microbar and p_surf= 14.1 +/- 0.4 microbar, respectively. Based on recent models of Triton's volatile cycles, the overall evolution over the last 30 years of the surface pressure is consistent with N2 condensation taking place in the northern hemisphere. However, models typically predict a steady decrease in surface pressure for the period 2005-2060, which is not confirmed by this observation. Complex surface-atmosphere interactions, such as ice albedo runaway and formation of local N2 frosts in the equatorial regions of Triton could explain the relatively constant pressure between 2017 and 2022.

We propose a novel technique to enhance Knowledge Graph Reasoning by combining Graph Convolution Neural Network (GCN) with the Attention Mechanism. This approach utilizes the Attention Mechanism to examine the relationships between entities and their neighboring nodes, which helps to develop detailed feature vectors for each entity. The GCN uses shared parameters to effectively represent the characteristics of adjacent entities. We first learn the similarity of entities for node representation learning. By integrating the attributes of the entities and their interactions, this method generates extensive implicit feature vectors for each entity, improving performance in tasks including entity classification and link prediction, outperforming traditional neural network models. To conclude, this work provides crucial methodological support for a range of applications, such as search engines, question-answering systems, recommendation systems, and data integration tasks.

A set of $k$ spanning trees in a graph $G$ is called a set of \textit{completely independent spanning trees (CISTs)} if, for every pair of vertices $x$ and $y$, the paths connecting $x$ and $y$ across different trees do not share any vertices or edges, except for $x$ and \( y \) themselves. Hasunuma conjectured that every $2k$-connected graph contains exactly $k$ completely independent spanning trees (CISTs). However, P\'et\'erfalvi disproved this conjecture. When $k = 2$, the two CISTs are called a \textit{dual-CIST}. It has been shown that determining whether a graph can have $k$ CISTs is an NP-complete problem, even when $k = 2$. In $2017$, Darties et al. raised the question of whether the $6-$dimensional hypercube $Q_6$ can have three completely independent spanning trees (CISTs). This paper provides an answer to that question. In this paper, we first present a necessary condition for $k$-regular, \( k \)-connected bipartite graphs to have \( \left\lfloor \frac{k}{2} \right\rfloor \) CISTs. We also investigate that the hypercube of dimension \( n \) cannot have $\frac{n}{2}$ CISTs, which means Hasunuma's conjecture does not hold for the hypercube $Q_n$ when $n$ is an even integer \(2 < n \leq 10^7 \), except when $n = 2^r$ and \( n \in \{161038, 215326, 2568226, 3020626, 7866046, 9115426 \} \). This result also resolves a question posed by Darties et al. The construction of multiple CISTs on the underlying graph of a network has practical applications in ensuring the fault tolerance of data transmission. In this context, we also provide a construction for three completely independent spanning trees in the hypercube $Q_n$ for $n \geq 7$. Our results show that Hasunuma's conjecture holds for odd integer $n = 7$ in $Q_n$, but does not hold for even integer $n = 6$.

Fleischner introduced the idea of splitting a vertex of degree at least three in a connected graph and used the operation to characterize Eulerian graphs. Raghunathan et. al. extended the splitting operation from graphs to binary matroids. It has been studied that splitting operation, in general, may not preserve the connectedness of the binary matroid. Interestingly, it is true that the splitting matroid of a disconnected matroid may be connected. In this paper, we characterize the binary disconnected matroids whose splitting matroid is connected.

With two central galaxies engaged in a major merger and a remarkable chain of 19 young stellar superclusters wound around them in projection, the galaxy cluster SDSS J1531+3414 ($z=0.335$) offers an excellent laboratory to study the interplay between mergers, AGN feedback, and star formation. New Chandra X-ray imaging reveals rapidly cooling hot ($T\sim 10^6$ K) intracluster gas, with two "wings" forming a concave density discontinuity near the edge of the cool core. LOFAR $144$ MHz observations uncover diffuse radio emission strikingly aligned with the "wings," suggesting that the "wings" are actually the opening to a giant X-ray supercavity. The steep radio emission is likely an ancient relic of one of the most energetic AGN outbursts observed, with $4pV > 10^{61}$ erg. To the north of the supercavity, GMOS detects warm ($T\sim 10^4$ K) ionized gas that enshrouds the stellar superclusters but is redshifted up to $+ 800$ km s$^{-1}$ with respect to the southern central galaxy. ALMA detects a similarly redshifted $\sim 10^{10}$ M$_\odot$ reservoir of cold ($T\sim 10^2$ K) molecular gas, but it is offset from the young stars by $\sim 1{-}3$ kpc. We propose that the multiphase gas originated from low-entropy gas entrained by the X-ray supercavity, attribute the offset between the young stars and the molecular gas to turbulent intracluster gas motions, and suggest that tidal interactions stimulated the "beads on a string" star formation morphology.