Miracle-dz/UniFAR-FTUs huggingface dataset:
The Galactic Distribution of Infrared Dark Clouds

CS (2-1) measurements toward a large sample of fourth Galactic quadrant infrared dark clouds (IRDCs) were made with the Australia Telescope National Facility Mopra telescope in order to establish their kinematic distances and Galactic distribution. Due to its large critical density, CS unambiguously separates the dense IRDCs from more diffuse giant molecular clouds. The fourth-quadrant IRDCs show a pronounced peak in their radial galactocentric distribution at R = 6 kpc. The first-quadrant IRDC distribution (traced by 13CO emission) also shows a peak, but at a galactocentric radius of R = 5 kpc rather than 6 kpc. This disparity in the peak galactocentric radius suggests that IRDCs trace a spiral arm which lies closer to the Sun in the fourth quadrant. Indeed, the deduced IRDC distribution matches the location of the Scutum-Centaurus arm in Milky Way models dominated by two spiral arms. Since, in external galaxies, OB stars form primarily in spiral arms, the association of IRDCs with a Milky Way spiral arm supports the idea that high-mass stars form in IRDCs. The first-quadrant IRDC distribution also reveals a second peak near the solar circle, possibly due to the fact that 13CO could trace somewhat lower density IRDCs. The reliability of the MSX IRDC catalog by Simon and coworkers is estimated by using the CS detection rate of IRDC candidates. The overall reliability is at least 58%, and increases to near 100% for high contrasts, Galactic longitudes within ~30° of the Galactic center, and large mid-IR backgrounds. A significant fraction of our IRDC sample (14%) showed two CS velocity components, which probably represent two distinct IRDCs along the same line of sight.
Far field subwavelength imaging of magnetic patterns

Far field imaging of subwavelength magnetic objects in real time is a very challenging issue. We propose an original solution based on a planar array of closely spaced split ring resonators. Hybridization between the resonators of such metalens induces subwavelength modes with different frequencies. Thanks to these high Q resonating modes, Purcell like effect allows an evanescent source, close to the metalens, to emit waves that can be collected efficiently in the far field. We present the first microwave experimental demonstration of such metalens to image of a subwavelength magnetic pattern. Numerical simulation shows that this approach is still valid at THz frequencies.
Critical dimension metrology by through-focus scanning optical microscopy beyond the 22 nm node

We present results using simulations and experiments to demonstrate metrological applications of the through-focus scanning optical microscopy (TSOM) down to features at and well below the International Technology Roadmap for Semiconductors' 22 nm node. The TSOM method shows the ability to detect sub-nanometer, three-dimensional shape variations such as line height, sidewall angle, width, and pitch in fins of fin-shaped field effect transistor structures using conventional optical microscopes. In addition, the method requires targets substantially smaller than the conventional target size. These results provide insight into the applicability of TSOM for economical critical dimension and yield enhancement metrology.
The plasma wake field excitation: Recent developments from thermal to quantum regime

Abstract To describe the transverse nonlinear and collective self-consistent interaction of a long relativistic electron or positron beam with an unmagnetized plasma, a pair of coupled nonlinear differential equations were proposed by Fedele and Shukla in 1992 (Fedele, R. and Shukla, P. K. 1992a Phys. Rev. A 45, 4045). They were obtained within the quantum-like description provided by the thermal wave model and the theory of plasma wake field excitation. The pair of equations comprises a 2D Schrödinger-like equation for a complex wave function (whose squared modulus is proportional to beam density) and a Poisson-like equation for the plasma wake potential. The dispersion coefficient of the Schrödinger-like equation is proportional to the beam thermal emittance. More recently, Fedele–Shukla equations have been further applied to magnetized plasmas, and solutions were found in the form of nonlinear vortex states and ring solitons. They have been also applied to plasma focusing problems and extended from thermal to quantum regimes. We present here a review of the original approach, and subsequent developments.
THE SPIRAL STRUCTURE OF OUR GALAXY. I.

We are presently passing through a very interesting phase in the study of the spiral structure of our Galaxy. Observational data have been accumulating at a good rate in both the optical and the radio-astro nomical fields, and there have been some very interesting new theoretical developments. We shall here be concerned principally with a survey of the needs for future optical work, but, to set the stage right, we shall refer on several occasions to theoretical developments and to parallel radio-astronomical research.
Assimilating GPS radio occultation measurements with two‐dimensional bending angle observation operators

Four‐dimensional variational (4D‐Var) assimilation forecast impact experiments were conducted with CHAMP radio occultation measurements from 1st June to 31st July 2004. The CHAMP bending angles were assimilated with one‐dimensional (1D) and two‐dimensional (2D) bending angle observation operators, enabling the potential benefits of the latter to be investigated. Two computationally affordable 2D bending angle observation operators were considered. The first can be viewed as a straightforward generalisation of the 1D operator. The second is based on a fourth order Runge–Kutta solution of the differential equations defining the ray‐path through the refracting atmosphere.
Interaction of low-velocity plane jets with buoyant convection adjacent to heated vertical surfaces

The interaction between the natural convection adjacent to a flat heated isothermal vertical surface and a low-velocity coolant plane jet situated at and aligned with the leading edge is numerically studied. For a given Rayleigh number, there exists a narrow range of low Reynolds number in which the jet flow detaches from the surface. The heat transfer rate for the mixed convection flow in this range of Reynolds number is drastically reduced. The fundamental physical phenomena of fluid flow and convective heat transfer in this complex interaction are explained.
On the causality-preservation capabilities of generative modelling

Modeling lies at the core of both the financial and the insurance industry for a wide variety of tasks. The rise and development of machine learning and deep learning models have created many opportunities to improve our modeling toolbox. Breakthroughs in these fields often come with the requirement of large amounts of data. Such large datasets are often not publicly available in finance and insurance, mainly due to privacy and ethics concerns. This lack of data is currently one of the main hurdles in developing better models. One possible option to alleviating this issue is generative modeling. Generative models are capable of simulating fake but realistic-looking data, also referred to as synthetic data, that can be shared more freely. Generative Adversarial Networks (GANs) is such a model that increases our capacity to fit very high-dimensional distributions of data. While research on GANs is an active topic in fields like computer vision, they have found limited adoption within the human sciences, like economics and insurance. Reason for this is that in these fields, most questions are inherently about identification of causal effects, while to this day neural networks, which are at the center of the GAN framework, focus mostly on high-dimensional correlations. In this paper we study the causal preservation capabilities of GANs and whether the produced synthetic data can reliably be used to answer causal questions. This is done by performing causal analyses on the synthetic data, produced by a GAN, with increasingly more lenient assumptions. We consider the cross-sectional case, the time series case and the case with a complete structural model. It is shown that in the simple cross-sectional scenario where correlation equals causation the GAN preserves causality, but that challenges arise for more advanced analyses.
FEASTS: IGM Cooling Triggered by Tidal Interactions through the Diffuse H i Phase around NGC 4631

We use the single-dish radio telescope Five-hundred meter Aperture Spherical Telescope (FAST) to map the H i in the tidally interacting NGC 4631 group with a resolution of 3. ′24 (7 kpc), reaching a 5σ column density limit of 1017.9 cm−2 assuming a line width of 20 km s−1. Taking the existing interferometric H i image from the Hydrogen Accretion in LOcal GAlaxieS project of Westerbork Synthesis Radio Telescope as a reference, we are able to identify and characterize a significant excess of large-scale, low-density, and diffuse H i in the group. This diffuse H i extends more than 120 kpc across, and accounts for more than one-fourth of the total H i detected by FAST in and around the galaxy NGC 4631. In the region of the tidal tails, the diffuse H i has a typical column density above 1019.5 cm−2, and is highly turbulent with a velocity dispersion of around 50 km s−1. It increases in column density with the dense H i, and tends to be associated with the kinematically hotter part of the dense H i. Through simple modeling, we find that the majority of the diffuse H i in the tail region is likely to induce cooling out of the hot intragalactic medium (IGM) instead of evaporating or being radiatively ionized. Given these relations of gas in different phases, the diffuse H i may represent a condensing phase of the IGM. Ongoing and past active tidal interactions may have produced the wide-spreading H i distribution, and triggered the gas accretion to NGC 4631 through the phase of the diffuse H i.
Knowledge-guided Causal Intervention for Weakly-supervised Object Localization

Previous weakly-supervised object localization (WSOL) methods aim to expand activation map discriminative areas to cover the whole objects, yet neglect two inherent challenges when relying solely on image-level labels. First, the ``entangled context'' issue arises from object-context co-occurrence (\eg, fish and water), making the model inspection hard to distinguish object boundaries clearly. Second, the ``C-L dilemma'' issue results from the information decay caused by the pooling layers, which struggle to retain both the semantic information for precise classification and those essential details for accurate localization, leading to a trade-off in performance. In this paper, we propose a knowledge-guided causal intervention method, dubbed KG-CI-CAM, to address these two under-explored issues in one go. More specifically, we tackle the co-occurrence context confounder problem via causal intervention, which explores the causalities among image features, contexts, and categories to eliminate the biased object-context entanglement in the class activation maps. Based on the disentangled object feature, we introduce a multi-source knowledge guidance framework to strike a balance between absorbing classification knowledge and localization knowledge during model training. Extensive experiments conducted on several benchmark datasets demonstrate the effectiveness of KG-CI-CAM in learning distinct object boundaries amidst confounding contexts and mitigating the dilemma between classification and localization performance.
Tunable GaAs Laser in an External Dispersive Cavity

Tunability of the cw radiation from a GaAs injection laser in an external dispersive cavity was achieved over a range of 150 A about a center wavelength of 8525 A at 77°K. The emitted radiation was analyzed with a Fabry‐Perot etalon. Under certain conditions single‐mode operation with cw output power as large as 17 mW was observed. Half‐width of such modes was estimated to be less than 350 MHz.
Distributed Machine Learning for UAV Swarms: Computing, Sensing, and Semantics

The unmanned aerial vehicle (UAV) swarms have shown great potential to serve next-generation communication networks with their extraordinary flexibility, affordability, and the ability to collaboratively and autonomously provide Line-of-Sight (LoS) services. However, autonomous collaboration under wireless dynamics is challenging. Distributed learning (DL) provides a chance for the UAV swarms to operate intelligently under sophisticated dynamics, such that they can be applied to wireless communication service scenarios, as well as applications including multidirectional remote surveillance, and target tracking. In this survey, we first introduce several popular DL frameworks that are capable of managing a UAV swarm, these include federated learning (FL), multiagent reinforcement learning (MARL), distributed inference (DI), and split learning (SL). We also present a comprehensive overview of how these DL frameworks manage UAV swarms in regard to trajectory design, power control, wireless resource allocation, user assignment, perception, and satellite–drone integration. Then, we present several state-of-the-art applications of UAV swarms in wireless communication systems, such as reconfigurable intelligent surfaces (RISs), virtual reality (VR), and semantic communications (SemComs), and discuss the problems and challenges that DL-enabled UAV swarms can solve in these applications. Finally, we describe open problems of using DL in UAV swarms and future research directions of DL-enabled UAV swarms. In summary, this survey provides a concise survey of various DL applications for UAV swarms in extensive scenarios.
Ranking Differential Privacy

Rankings are widely collected in various real-life scenarios, leading to the leakage of personal information such as users' preferences on videos or news. To protect rankings, existing works mainly develop privacy protection on a single ranking within a set of ranking or pairwise comparisons of a ranking under the $\epsilon$-differential privacy. This paper proposes a novel notion called $\epsilon$-ranking differential privacy for protecting ranks. We establish the connection between the Mallows model (Mallows, 1957) and the proposed $\epsilon$-ranking differential privacy. This allows us to develop a multistage ranking algorithm to generate synthetic rankings while satisfying the developed $\epsilon$-ranking differential privacy. Theoretical results regarding the utility of synthetic rankings in the downstream tasks, including the inference attack and the personalized ranking tasks, are established. For the inference attack, we quantify how $\epsilon$ affects the estimation of the true ranking based on synthetic rankings. For the personalized ranking task, we consider varying privacy preferences among users and quantify how their privacy preferences affect the consistency in estimating the optimal ranking function. Extensive numerical experiments are carried out to verify the theoretical results and demonstrate the effectiveness of the proposed synthetic ranking algorithm.
PanopticPartFormer++: A Unified and Decoupled View for Panoptic Part Segmentation

Panoptic Part Segmentation (PPS) unifies panoptic and part segmentation into one task. Previous works utilize separate approaches to handle things, stuff, and part predictions without shared computation and task association. We aim to unify these tasks at the architectural level, designing the first end-to-end unified framework, Panoptic-PartFormer. Moreover, we find the previous metric PartPQ biases to PQ. To handle both issues, we first design a meta-architecture that decouples part features and things/stuff features, respectively. We model things, stuff, and parts as object queries and directly learn to optimize all three forms of prediction as a unified mask prediction and classification problem. We term our model as Panoptic-PartFormer. Second, we propose a new metric Part-Whole Quality (PWQ), better to measure this task from pixel-region and part-whole perspectives. It also decouples the errors for part segmentation and panoptic segmentation. Third, inspired by Mask2Former, based on our meta-architecture, we propose Panoptic-PartFormer++ and design a new part-whole cross-attention scheme to boost part segmentation qualities further. We design a new part-whole interaction method using masked cross attention. Finally, extensive ablation studies and analysis demonstrate the effectiveness of both Panoptic-PartFormer and Panoptic-PartFormer++. Compared with previous Panoptic-PartFormer, our Panoptic-PartFormer++ achieves 2% PartPQ and 3% PWQ improvements on the Cityscapes PPS dataset and 5% PartPQ on the Pascal Context PPS dataset. On both datasets, Panoptic-PartFormer++ achieves new state-of-the-art results. Our models can serve as a strong baseline and aid future research in PPS. The source code and trained models will be available at~\url{https://github.com/lxtGH/Panoptic-PartFormer}.
Control and Dynamic Motion Planning for a Hybrid Air-Underwater Quadrotor: Minimizing Energy Use in a Flooded Cave Environment

We present a dynamic path planning algorithm to navigate an amphibious rotor craft through a concave time-invariant obstacle field while attempting to minimize energy usage. We create a nonlinear quaternion state model that represents the rotor craft dynamics above and below the water. The 6 degree of freedom dynamics used within a layered architecture to generate motion paths for the vehicle to follow and the required control inputs. The rotor craft has a 3 dimensional map of its surroundings that is updated via limited range onboard sensor readings within the current medium (air or water). Path planning is done via PRM and D* Lite.
Things That Might Go Bump in the Night: Assessing Structure in the Binary Black Hole Mass Spectrum

Several features in the mass spectrum of merging binary black holes (BBHs) have been identified using data from the Third Gravitational Wave Transient Catalog (GWTC-3). These features are of particular interest as they may encode the uncertain mechanism of BBH formation. We assess if the features are statistically significant or the result of Poisson noise due to the finite number of observed events. We simulate catalogs of BBHs whose underlying distribution does not have the features of interest, apply the analysis previously performed on GWTC-3, and determine how often such features are spuriously found. We find that one of the features found in GWTC-3, the peak at ∼35 M ☉, cannot be explained by Poisson noise alone: peaks as significant occur in 1.7% of catalogs generated from a featureless population. This peak is therefore likely to be of astrophysical origin. The data is suggestive of an additional significant peak at ∼10 M ☉, though the exact location of this feature is not resolvable with current observations. Additional structure beyond a power law, such as the purported dip at ∼14 M ☉, can be explained by Poisson noise. We also provide a publicly available package, GWMockCat, that creates simulated catalogs of BBH events with correlated measurement uncertainty and selection effects according to user-specified underlying distributions and detector sensitivities.
Very low state in PY Per in 2022

Using VSNET, VSOLJ, ASAS-SN and ATLAS observations, I found that the Z Cam star PY Per spent a long, faint low state reaching 19.1 mag at least between 2022 June and November. No dwarf nova outburst was recorded during this interval. TESS data during this low state showed two maxima in one orbital cycle and can be interpreted as an ellipsoidal modulation arising from the secondary. These observations suggest that the mass-transfer almost stopped during this low state and strengthen the identiﬁcation of PY Per as a VY Scl star. PY Per had shown an unusual outburst resembling an SU UMa-type superoutburst less than half a year before (Kato 2022, arXiv:2204.12056) and these phenomena may have been physically related. In Kato (2022)
GRB 200829A: External-shock Origin of the Very Early Prompt Emission?

Long-duration GRB 200829A was detected by Fermi-GBM and Swift-BAT/XRT, and then rapidly observed by other ground-based telescopes. It has a weak γ-ray emission in the very early phase and is followed by a bright spiky γ-ray emission pulse. The radiation spectrum of the very early emission is best fitted by a power-law function with index ∼−1.7. However, the bright spiky γ-ray pulse, especially the time around the peak, exhibits a distinct two-component radiation spectrum, i.e., Band function combined with a blackbody radiation spectrum. We infer the photospheric properties and reveal a medium magnetization at a photospheric position by adopting the initial size of the outflow as r 0 = 109 cm. It implies that the Band component in this pulse may be formed during the dissipation of the magnetic field. The power-law radiation spectra found in the very early prompt emission may imply the external-shock origination of this phase. Then, we perform the Markov Chain Monte Carlo method fitting on the light curves of this burst, where the jet corresponding to the γ-ray pulse at around 20 s is used to refresh the external shock. It is shown that the light curves of the very early phase and X-ray afterglow after 40 s, involving the X-ray bump at around 100 s, can be well modeled in the external-shock scenario. For the obtained initial outflow, we estimate the minimum magnetization factor of the jet based on the fact that the photospheric emission of this jet is missed in the very early phase.
Remote Trigger of Deep Convection by Cold Outflow over the Taiwan Strait in the Mei-Yu Season: A Modeling Study of the 8 June 2007 Case

AbstractIn this study, the heavy-rainfall event over central Taiwan during the mei-yu season on 8 June 2007 is investigated, with an emphasis on the triggering mechanism for the deep convection that produced the rain. Observations indicate that there existed two lines of forcing with convection prior to the rain: one over the northern Taiwan Strait along the mei-yu front and the other over the southern Taiwan Strait. Yet, the convection in question developed over the central strait between these two lines, in an unstable environment with strong westerly vertical wind shear. This motivated the authors to carry out the present study.The Cloud-Resolving Storm Simulation (CReSS) of Nagoya University was used and the event was reproduced at a horizontal grid size of 2 km, including the initiation of new convection over the central strait at the correct location and time. The model results suggest a crucial role played by the series of active, persistent, and propagating storms in the southern strait (along the.
Black hole greybody factors from Korteweg–de Vries integrals: Computation

It has recently been shown that the dynamics of perturbed non-rotating black holes (BHs) admits an infinite number of symmetries that are generated by the flow of the Korteweg-de Vries (KdV) equation. These symmetries lead to an infinite number of conserved quantities that can be obtained as integrals of differential polynomials in the potential appearing in the gauge-invariant master equations describing the BH perturbations, the KdV integrals. These conserved quantities are the same for all the possible potentials, which means that they are invariant under Darboux transformations, and they fully determine the BHs transmission amplitudes, or greybody factors, via a moment problem. In this paper we introduce a new semi-analytical method to obtain the greybody factors associated with BH scattering processes by solving the moment problem using only the KdV integrals. The method is based on the use of Pad\'e approximants and we check it first by comparing with results from the case of a P\"oschl-Teller potential, for which we have analytical expressions for the greybody factors. Then, we apply it to the case of a Schwarzschild BH and compare with results from computations based on the Wentzel-Kramers-Brillouin (WKB) approximation. It turns out that the new method provides accurate results for the BH greybody factors for all frequencies. The method is also computationally very efficient.