Class Of Sources Research Articles

From mouse to human: Accessing the biochemistry of vision in vivo by two-photon excitation.

The eye is an ideal organ for imaging by a multi-photon excitation approach, because ocular tissues such as the sclera, cornea, lens and neurosensory retina, are highly transparent to infrared (IR) light. The interface between the retina and the retinal pigment epithelium (RPE) is especially informative, because it reflects the health of the visual (retinoid) cycle and its changes in response to external stress, genetic manipulations, and drug treatments. Vitamin A-derived retinoids, like retinyl esters, are natural fluorophores that respond to multi-photon excitation with near IR light, bypassing the filter-like properties of the cornea, lens, and macular pigments. Also, during natural aging some retinoids form bisretinoids, like diretinoid-pyridiniumethanolamine (A2E), that are highly fluorescent. These bisretinoids appear to be elevated concurrently with aging. Vitamin A-derived retinoids and bisretinoidss are detected by two-photon ophthalmoscopy (2PO), using a new class of light sources with adjustable spatial, temporal, and spectral properties. Furthermore, the two-photon (2P) absorption of IR light by the visual pigments in rod and cone photoreceptors can initiate visual transduction by cis-trans isomerization of retinal, enabling parallel functional studies. Recently we overcame concerns about safety, data interpretation and complexity of the 2P-based instrumentation, the major roadblocks toward advancing this modality to the clinic. These imaging and retina-function assessment advancements have enabled us to conduct the first 2P studies with humans.

A catalogue of high-mass X-ray binaries in the Galaxy: from the INTEGRAL to theGaiaera

Context.High-mass X-ray binaries (HMXBs) are a particular class of high-energy sources that require multi-wavelength observational efforts to be properly characterised. New identifications and the refinement of previous measurements are regularly published in the literature by independent teams of researchers and might, when they are collected in a catalogue, offer a tool for facilitating further studies of HMXBs.Aims.We update previous instances of HMXB catalogues in the Galaxy and provide the community easy access to the most complete set of observables on Galactic HMXBs. In addition to the fixed version that is available in Vizier, we also aim to host and maintain a dynamic version that can be updated upon request from users. Any modification will be logged in this version.Methods.Using previous HMXB catalogues supplemented by listings of hard X-ray sources detected in the past 20 yr, we produced a base set of HMXBs and candidates by means of identifier and sky coordinate cross matches. We queried in Simbad for unreferenced HMXBs. We searched for as many hard X-ray, soft X-ray, optical, and infrared counterparts to the HMXBs as we could in well-known catalogues and compiled their coordinates. Each HMXB was subjected to a meticulous search in the literature to find relevant measurements and the original reference.Results.We provide a catalogue of 152 HMXBs in the Galaxy with their best known coordinates, the spectral type of the companion star, systemic radial velocities, component masses, orbital period, eccentricity, and spin period when available. We also provide the coordinates and identifiers for each counterpart we found from hard X-rays to the near-infrared, including 111 counterparts from the recentGaiaDR3 catalogue.

Pollution characteristics and ecological risks of trace elements in PM2.5 over three COALESCE network sites - Bhopal, Mesra, and Mysuru, India

Time-synchronized, 24 h integrated PM2.5 trace element (TE) measurements made as a part of the COALESCE project (Venkataraman et al., 2020) at Bhopal, Mesra, and Mysore during all of 2019 were analyzed in this study. The concentrations of 15 key elements ranged between 0.05 ng m−3 and 50 μg m−3 across the study sites. Pronounced seasonal variation of elements from multiple source classes showed that the crustal origin elements (Al, Si, Fe, Ti, and Ca) peaked during the pre-monsoon season, while the anthropogenic activities driven element (P, S, K, V, Mn, Cu, Zn, and Pb) concentrations increased during the winter and post-monsoon seasons. Spearman correlation coupled with hierarchical clustering separated the matrix of elements into three common clusters at all sites, corresponding to crustal sources, combustion and biomass burning emissions, and industrial/non-exhaust vehicular emissions, respectively. Furthermore, episodes of metal pollution throughout the year were examined using characteristic radar charts of TEs to identify the association between TE sources and poor air quality. For example, maximum metal pollution in Bhopal occurred during the post-monsoon season, attributable to biomass burning, dust storms, industrial and non-exhaust vehicular emissions. Finally, an ecological risk assessment revealed that the risk index was higher than the threshold value of 600 for all heavy metals at all sites. Pb, Cu, and Zn were the top contributors to ‘extremely high risk’ amongst all heavy metals. Overall, the results show that although TE concentrations at all three locations were much lower than in other urban locations in India, the risk from heavy metals to the ecosystem (and likely to human health) cannot be ignored. The findings warrant a full source apportionment of fine PM to better identify TE-rich source contributions and future studies to examine the atmospheric processing and eco-system uptake of TEs.

ANALISIS KETERAMPILAN BERBICARA SISWA PADA TEMA 6 ENERGI DAN PERUBAHANNYA SUBTEMA 1 SUMBER ENERGI KELAS 3 SD NEGERI 2 BANGOAN

The speaking skills intended in this study are the skills to express the articulation sounds or language sounds to express, express, or convey one's ideas, thoughts and feelings to be understood by others. The purpose of this study is to describe the data on how the analysis of students' speaking skills on the theme of energy 6 and the changes in sub-theme 1 energy source class III SD Negeri 2 Bangoan. The research approach used is a qualitative approach and the type of research used is descriptive. Related to data collection methods using interviews, tests and documentation. Interview and test methods are used to obtain information about students' speaking skills. Data analysis uses data reduction, data presentation, and drawing conclusions. The results of this study revealed that the sample taken by the researcher was one male student and one female student said male students were more skilled at speaking than female students

Adversarial Auxiliary Weighted Subdomain Adaptation for Open-Set Deep Transfer Bridge Damage Diagnosis.

Deep learning models have been widely used in data-driven bridge structural damage diagnosis methods in recent years. However, these methods require training and test datasets to satisfy the same distribution, which is difficult to satisfy in practice. Domain adaptation transfer learning is an efficient method to solve this problem. Most of the current domain adaptation methods focus on close-set scenarios with the same classes in the source and target domains. However, in practical applications, new damage caused by long-term degradation often makes the target and source domains dissimilar in the class space. For such challenging open-set scenarios, existing domain adaptation methods will be powerless. To effectively solve the above problems, an adversarial auxiliary weighted subdomain adaptation algorithm is proposed for open-set scenarios. Adversarial learning is introduced to proposed an adversarial auxiliary weighting scheme to reflect the similarity of target samples with source classes. It effectively distinguishes unknown damage from known states. This paper further proposes a multi-channel multi-kernel weighted local maximum mean discrepancy metric (MCMK-WLMMD) to capture the fine-grained transferable information for conditional distribution alignment (sub-domain alignment). Extensive experiments on transfer tasks between three bridges verify the effectiveness of the algorithm in open-set scenarios.

Information seeking by experimentation: Trying something out to discover what happens

AbstractExperimentation is the process of trying something out to discover what happens. It is a widespread information practice, yet often bypassed in information‐behavior research. This article argues that experimentation complements prior knowledge, documents, and people as an important fourth class of information sources. Relative to the other classes, the distinguishing characteristics of experimentation are that it is a personal—as opposed to interpersonal—source and that it provides “backtalk.” When the information seeker tries something out and then attends to the resulting situation, it is as though the materials of the situation talk back: They provide the information seeker with a situated and direct experience of the consequences of the tried‐out options. In this way, experimentation involves obtaining information by creating it. It also involves turning material and behavioral processes into information interactions. Thereby, information seeking by experimentation is important to practical information literacy and extends information‐behavior research with new insights on the interrelations between creating and seeking information.

Universal source-free domain adaptation method for cross-domain fault diagnosis of machines

Cross-domain machinery fault diagnosis aims to transfer enriched diagnosis knowledge from a labeled source domain to a new unlabeled target domain. Most existing methods assume that the prior information on the fault modes of the target domain is known in advance. However, in engineering practice, prior knowledge of fault modes is rare in a new domain, in which there may be only partial source fault modes or some new fault modes. Furthermore, up to the present, almost all existing cross-domain fault diagnosis methods require the labeled source data during the model training process, which restricts their deployment on certain devices with limited computing resources. To this end, we propose a universal source-free domain adaptation method that can handle cross-domain fault diagnosis scenarios without access to the source data and is free of explicit assumptions about the target fault modes. More specifically, we develop a convolutional network with a Transformer as the attention module to extract discriminative feature information from the source data and then send the model and parameters to the target domain. In target domain training, we first propose a supervised contrastive learning strategy based on source class prototypes, which utilizes high-confident predictions to achieve source-free domain alignment and class alignment. Then, we also introduce a threshold-based entropy max–min loss to further align known class samples in the target domain or reject target outlier samples as an unknown class. Furthermore, we introduce self-supervised learning to further learn feature representations of the target domain to reduce the previous misclassification. A series of experiments on two rotating machine datasets demonstrate the effectiveness and practicability of the proposed method.

Star-forming galaxies provide a larger contribution to the isotropic gamma-ray background than misaligned active galactic nuclei

The total extragalactic γ-ray flux provides a powerful probe into the origin and evolution of the highest energy processes in our universe. An important component of this emission is the isotropic γ-ray background (IGRB), composed of sources that cannot be individually resolved by current experiments. Previous studies have determined that the IGRB can be dominated by either misaligned active galactic nuclei (mAGN) or star-forming galaxies (SFGs). However, these analyses are limited, because they have utilized binary source classifications and examined only one source class at a time. We perform the first combined joint-likelihood analysis that simultaneously correlates the γ-ray luminosity of extragalactic objects with both star-formation and mAGN activity. We find that SFGs produce 48+33 -20% of the total IGRB at 1 GeV and 56+40 -23% of the total IGRB at 10 GeV. The contribution of mAGN is more uncertain, but can also be significant. Future work to quantify the radio and infrared properties of nearby galaxies could significantly improve these constraints.

A multifrequency characterization of the extragalactic hard X-ray sky

Context. Nowadays, we know that the origin of the cosmic X-ray background (CXB) is mainly due to the integrated emission of active galactic nuclei. Therefore, in order to obtain a precise estimate of the contribution of different source classes to the CXB, it is crucial to achieve full characterization of the hard-X ray sky. Aims. We present a multifrequency analysis of all sources listed in the third release of the Palermo Swift-BAT hard X-ray catalog (3PBC) with the goal of (i) identifying and classifying the largest number of sources adopting multifrequency criteria, with particular emphasis on extragalactic populations and (ii) extracting sources belonging to the class of Seyfert galaxies to present here the release of the second version of the Turin-SyCAT. Methods. We outline a classification scheme based on radio, infrared (IR), and optical criteria that allows us to distinguish between unidentified and unclassified hard X-ray sources, as well as to classify those sources belonging to the Galactic and the extragalactic populations. Results. Our revised version of the 3PBC lists 1176 classified, 820 extragalactic, and 356 Galactic sources, as well as 199 unclassified and 218 unidentified sources. According to our analysis, the hard X-ray sky is mainly populated by Seyfert galaxies and blazars. For the blazar population, we report trends between the hard X-ray and the gamma-ray emissions based on the fact that a large fraction of them also have a counterpart detected by the Fermi satellite. These trends are all in agreement with the expectations of inverse Compton models which are widely adopted to explain the blazar broadband emission. For the Seyfert galaxies, we present the second version of the Turin-SyCAT, including a total of 633 Seyfert galaxies, with 282 new sources corresponding to an increase of ~80% with respect to the previous release. Comparing the hard X-ray and the infrared emissions of Seyfert galaxies, we confirm that there is no clear difference between the flux distribution of the infrared-to-hard X-ray flux ratio of Seyfert galaxies Type 1 and Type 2. However, there is a significant trend between the mid-IR flux and hard X-ray flux, confirming previous statistical results in the literature. Conclusions. We provide two catalog tables. The first is the revised version of the 3PBC catalog based on our multifrequency analyses. The second catalog table is a release of the second version of the Turin-SyCAT catalog. Finally, we highlight that extensive soft X-ray data are already available in the form of the Swift archive which can be used to search for potential counterparts of unidentified hard X-ray sources. All these datasets will be reduced and analyzed in a forthcoming analysis to determine the precise position of low-energy counterparts in the 0.5–10 keV energy range for 3PBC sources that can be targets of future optical spectroscopic campaigns; this is necessary to obtain their precise classification.

From Big to Small: Adaptive Learning to Partial-Set Domains.

Domain adaptation targets at knowledge acquisition and dissemination from a labeled source domain to an unlabeled target domain under distribution shift. Still, the common requirement of identical class space shared across domains hinders applications of domain adaptation to partial-set domains. Recent advances show that deep pre-trained models of large scale endow rich knowledge to tackle diverse downstream tasks of small scale. Thus, there is a strong incentive to adapt models from large-scale domains to small-scale domains. This paper introduces Partial Domain Adaptation (PDA), a learning paradigm that relaxes the identical class space assumption to that the source class space subsumes the target class space. First, we present a theoretical analysis of partial domain adaptation, which uncovers the importance of estimating the transferable probability of each class and each instance across domains. Then, we propose Selective Adversarial Network (SAN and SAN++) with a bi-level selection strategy and an adversarial adaptation mechanism. The bi-level selection strategy up-weighs each class and each instance simultaneously for source supervised training, target self-training, and source-target adversarial adaptation through the transferable probability estimated alternately by the model. Experiments on standard partial-set datasets and more challenging tasks with superclasses show that SAN++ outperforms several domain adaptation methods.

Limits on the non-thermal emission of the WR–WR system Apep

Colliding-wind binaries (CWBs) constitute an emerging class of γ-ray sources powered by strong, dense winds in massive stellar systems. The most powerful of them are those binaries hosting a Wolf-Rayet (WR) star. Following the recent discovery of Apep – the closest known Galactic WR–WR binary – we discuss the non-detection of its putative high-energy emission by the Fermi Large Area Telescope (Fermi-LAT) in this Letter. The limits reported in the GeV regime can be used to set a lower limit on the magnetic field pressure density within the shocked wind-collision region (WCR), and to exclude Apep as a bright γ-ray emitting binary. Given that this WR–WR system is the most luminous CWB identified until now at radio wavelengths, this result proves unambiguously that non-thermal synchrotron emission is not a suitable identifier for the subset of γ-ray emitters in this class of particle accelerators. Rather, Apep could be an interesting case of study for magnetic field amplification in shocked stellar winds.

Identifying the 3FHL Catalog. VI. Results of the 2019 Gemini Optical Spectroscopy

Active galactic nuclei with their relativistic jets pointed toward the observer are a class of luminous gamma-ray sources commonly known as blazars. The study of this source class is essential to unveil the physical processes powering these extreme jets, to understand their cosmic evolution, as well as to indirectly probe the extragalactic background light. To do so, however, one needs to correctly classify and measure a redshift for a large sample of these sources. The Third Fermi–LAT Catalog of High-Energy Sources (3FHL) contains 1212 blazars detected at energies greater than 10 GeV. However, ∼25% of these sources are unclassified and ∼56% lack redshift information. To increase the optical completeness of blazars in the 3FHL catalog, we devised an optical spectroscopic follow-up campaign using 4 m and 8 m telescopes. In this paper, we present the results of the last part of this campaign, where we observed 5 blazars using the 8 m Gemini-S telescope in Chile. We report all the 5 sources to be classified as BL Lacs, a redshift lower limit for 2 sources, and featureless spectra for the remaining 3 sources. We also performed a one-zone leptonic fit to the two sources with the redshift lower limits.

Convolutional Transformer-Based Few-Shot Learning for Cross-Domain Hyperspectral Image Classification

In cross-domain hyperspectral image (HSI) classification, the labeled samples of the target domain are very limited, and it is a worthy attention to obtain sufficient class information from the source domain to categorize the target domain classes (both the same and new unseen classes). This article investigates this problem by employing few-shot learning (FSL) in a meta-learning paradigm. However, most existing cross-domain FSL methods extract statistical features based on convolutional neural networks (CNNs), which typically only consider the local spatial information among features, while ignoring the global information. To make up for these shortcomings, this article proposes novel convolutional transformer-based few-shot learning (CTFSL). Specifically, FSL is first performed in the classes of source and target domains simultaneously to build the consistent scenario. Then, a domain aligner is set up to map the source and target domains to the same dimensions. In addition, a convolutional transformer (CT) network is utilized to extract local-global features. Finally, a domain discriminator is executed subsequently that can not only reduce domain shift but also distinguish from which domain a feature originates. Experiments on three widely used hyperspectral image datasets indicate that the proposed CTFSL method is superior to the state-of-the-art cross-domain FSL methods and several typical HSI classification methods in terms of classification accuracy.

Foundations of plasmas as ion sources

An overview of low temperature, partially magnetized ion sources is presented. This class of devices is broadly characterized by plasma densities below 1019 m−3, electron temperatures below 100 eV, and magnetic field strength tailored such that electrons are magnetized whereas ions are not. The overarching approach is pedagogical, targeting an audience that does not necessarily have an expertise in low temperature devices. A tutorial is presented on the basic physics of operation of common ion sources including an overview of standard methods for plasma generation and acceleration. Typical diagnostics and common uses of these plasma sources are also reviewed. Special attention is given to applications in plasma propulsion and materials processing. This class of ion sources is then discussed in the context of the current state of the field, key technical and scientific challenges, and future prospects.

Table-top optical parametric chirped pulse amplifiers: past and present

The generation of power- and wavelength-scalable few optical cycle pulses remains one of the major challenges in modern laser physics. Over the past decade, the development of table-top optical parametric chirped pulse amplification-based systems was progressing at amazing speed, demonstrating excellent performance characteristics in terms of pulse duration, energy, peak power and repetition rate, which place them at the front line of modern ultrafast laser technology. At present, table-top optical parametric chirped pulse amplifiers comprise a unique class of ultrafast light sources, which currently amplify octave-spanning spectra and produce carrier-envelope phase-stable, few optical cycle pulses with multi-gigawatt to multi-terawatt peak powers and multi-watt average powers, with carrier wavelengths spanning a considerable range of the optical spectrum. This article gives an overview on the state of the art of table-top optical parametric chirped pulse amplifiers, addressing their relevant scientific and technological aspects, and provides a short outlook of practical applications in the growing field of ultrafast science.

MultI-messenger probe of Cosmic Ray Origins: MICRO project

We present the milestones achieved by the MICRO (MultI-messenger probe of Cosmic Ray Origins) project. This multi-institute project consists of a study of bursting astrophysical sources as candidate sources for ultra-high-energy cosmic rays (UHECRs). We aim at identifying source classes that correlate best with existing observational data (arrival direction distribution, energy spectrum and primary mass composition) and to constrain the origin of UHECRs on the basis of the largest datasets acquired to this day. The study of the secondary fluxes of neutrinos and gamma rays will provide a powerful test of the most suitable astrophysical scenarios. For these achievements, a public software enabling a joint fit of both composition and flux data as a function of energy and direction is being developed. In addition, a modelling of transient sources is foreseen, including the treatment of hadronic interactions within the sources, which represents a novelty for UHECR simulation codes. This contribution will highlight the first results of the project and detail upcoming software releases, which will benefit the astroparticle community at large.

Testing hadronic and photo-hadronic interactions as responsible for UHECR and neutrino fluxes from Starburst Galaxies

We test the hypothesis that starburst galaxies are the sources of ultra-high energy cosmic rays and high-energy neutrinos. The computation of interactions of ultra-high energy cosmic rays in the starburst environment as well as in the propagation to the Earth is made using a modified version of the Monte Carlo code SimProp, where hadronic processes are implemented for the first time. Taking into account a star-formationrate distribution of sources, the fluxes of ultra-high energy cosmic rays and high-energy neutrinos are computed and compared with observations, and the explored parameter space for the source characteristics is discussed. We find that, depending on the density of the gas in the source environment, spallation reactions could hide the outcome in neutrinos from photo-hadronic interactions in the source environment and in extra-galactic space. We confirm that source-propagation models constitute a promising way to improve the discrimination power of models considering only ultra-high energy cosmic rays, on the way to unveiling the source class responsible for ultra-high energy cosmic rays and high-energy neutrinos.

Accounting for systematic uncertainties in the Imaging X-ray Polarimetry Explorer (IXPE) detector response

Launched on December 9, 2021, the Imaging X-ray Polarimetry Explorer (IXPE) is the first imaging polarimeter ever flown, providing sensitivity in the 2–8 keV range, and during the 2-year initial phase of the mission will sample tens of X-ray sources among different source classes. While most of the measurements will be statistics-limited, for some of the brightest objects observed and long integration times, the systematic uncertainties in the detector response (primarily the effective area, the modulation factor and the absolute energy scale) will be important. In this contribution, we describe a framework to propagate on high-level observables (e.g.: spectro-polarimetric fit parameters) the systematic uncertainties connected with the response of the detector, that we estimate from relevant ground calibrations and from observations of celestial point sources.

Astronomical source detection in radio continuum maps with deep neural networks

Source finding is one of the most challenging tasks in upcoming radio continuum surveys with SKA precursors, such as the Evolutionary Map of the Universe (EMU) survey of the Australian SKA Pathfinder (ASKAP) telescope. The resolution, sensitivity, and sky coverage of such surveys is unprecedented, requiring new features and improvements to be made in existing source finders. Among them, reducing the false detection rate, particularly in the Galactic plane, and the ability to associate multiple disjoint islands into physical objects. To bridge this gap, we developed a new source finder, based on the Mask R-CNN object detection framework, capable of both detecting and classifying compact, extended, spurious, and poorly imaged sources in radio continuum images. The model was trained using ASKAP EMU data, observed during the Early Science and pilot survey phase, and previous radio survey data, taken with the VLA and ATCA telescopes. On the test sample, the final model achieves an overall detection completeness above 85%, a reliability of ∼65%, and a classification precision/recall above 90%. Results obtained for all source classes are reported and discussed.

Semi-supervised bidirectional alignment for Remote Sensing cross-domain scene classification

Remote sensing (RS) image scene classification has obtained increasing attention for its broad application prospects. Conventional fully-supervised approaches usually require a large amount of manually-labeled data. As more and more RS images becoming available, how to make full use of these unlabeled data is becoming an urgent topic. Semi-supervised learning, which uses a few labeled data to guide the self-training of numerous unlabeled data, is an intuitive strategy. However, it is hard to apply it to cross-dataset (i.e., cross-domain) scene classification due to the significant domain shift among different datasets. To this end, semi-supervised domain adaptation (SSDA), which can reduce the domain shift and further transfer knowledge from a fully-labeled RS scene dataset (source domain) to a limited-labeled RS scene dataset (target domain), would be a feasible solution. In this paper, we propose an SSDA method termed bidirectional sample-class alignment (BSCA) for RS cross-domain scene classification. BSCA consists of two alignment strategies, unsupervised alignment (UA) and supervised alignment (SA), both of which can contribute to decreasing domain shift. UA concentrates on reducing the distance of maximum mean discrepancy across domains, with no demand for class labels. In contrast, SA aims to achieve the distribution alignment both from source samples to the associate target class centers and from target samples to the associate source class centers, with awareness of their classes. To validate the effectiveness of the proposed method, extensive ablation, comparison, and visualization experiments are conducted on an RS-SSDA benchmark built upon four widely-used RS scene classification datasets. Experimental results indicate that in comparison with some state-of-the-art methods, our BSCA achieves the superior cross-domain classification performance with compact feature representation and low-entropy classification boundary. Our code will be available at https://github.com/hw2hwei/BSCA.