Miracle-dz/UniFAR-FTUs huggingface dataset:
Infrared Space Observatory Observations of the 53W002 Group at 6.7 Microns: In Search of the Oldest Stellar Populations at z = 2.4

We present a deep Infrared Space Observatory (ISO) observation at 6.7 μm of the 53W002 group of galaxies and active galactic nuclei (AGNs) at z = 2.4. This approximately samples the emitted K band. Faint, blue star‐forming objects are not detected, as expected from their very blue color across the emitted optical and UV spectrum. However, 53W002 itself is detected at the ∼3 σ level, with an emitted V−K color that is appropriate for a population that formed starting at z = 3.6–7.0, most likely z = 4.7. This fits with shorter wavelength data, suggesting that the more massive members of this group, which may all host AGNs, began star formation earlier in deeper potential wells than the compact Lyα emission objects. Two foreground galaxies are detected, as are several stars. One additional 6.7 μm source closely coincides with an optically faint galaxy, potentially at z = 2–3. The overall source counts are consistent (within the errors of such a small sample) with the other ISO deep fields at 6.7 μm.
M SUBDWARF SECONDARIES: A TEST OF THE METALLICITY SCALE

We present spectra of three M subdwarfs which are common proper motion companions to F or G subdwarfs of known metallicity. The assumption that the companions have the same composition allows us to test the Gizis (1997, AJ, 113, 806) M subdwarf classification system and its correspondence to metallicity. The results are in excellent agreement with the Gizis (1997) scale, thereby showing that the Allard & Hauschildt (1995, \apj, 445, 433) Extended model atmospheres agree well in the 6200 -- 7400 Angstroms region for cool metal-poor stars. We also show that the results are consistent with the main sequences of globular clusters using the Reid (1997, AJ, in press) distance scale.
Effect of La doping on microstructure and critical current density of MgB2

In the present study, La-doped MgB2 superconductors with different doping levels (Mg1−xLaxB2; x = 0.00, 0.01, 0.03 and 0.05) have been synthesized by the solid-state reaction route at ambient pressure. Effects of La doping have been investigated in relation to microstructural characteristics and superconducting properties, particularly intragrain critical current density (Jc). The microstructural characteristics of the as-synthesized Mg(La)B2 compounds were studied employing the transmission electron microscopic (TEM) technique. The TEM investigations reveal inclusion of LaB6 nanoparticles within the MgB2 grains, which provide effective flux pinning centres. The evaluation of intragrain Jc through magnetic measurements on the fine powdered version of the as-synthesized samples reveal that Jc values of the samples change significantly with the doping level. The optimum result on Jc is obtained for Mg0.97La0.03B2 at 5 K; Jc reaches ∼1.4 × 107 A cm−2 in self-field, ∼2.1 × 106 A cm−2 at 1 T, ∼2.5 × 105 A cm−2 at 2.5 T and ∼1.8 × 104 A cm−2 at 4.5 T. The highest value of intragrain Jc in the Mg0.97La0.03B2 superconductor has been attributed to the inclusion of LaB6 nanoparticles, which are capable of providing effective flux pinning centres.
On Constraining the Growth History of Massive Black Holes via Their Distribution on the Spin–Mass Plane

The spin distribution of massive black holes (MBHs) contains rich information on the MBH growth history. In this paper, we investigate the spin evolution of MBHs by assuming that each MBH experiences two-phase accretion, with an initial phase of coherent accretion via either the standard thin disk or super-Eddington disk, followed by a chaotic-accretion phase composed of many episodes with different disk orientations. If the chaotic-accretion phase is significant to the growth of an MBH, the MBH spin quickly reaches the maximum value because of the initial coherent accretion, then changes to a quasi-equilibrium state and fluctuates around a value mainly determined by the mean ratio of the disk to the MBH mass (M•) in the chaotic-accretion episodes, and further declines because of late chaotic accretion if . The turning point to this decline is determined by the equality of the disk warp radius and disk size. By matching the currently available spin measurements with mock samples generated from the two-phase model(s) on the spin–mass plane, we find that MBHs must experience significant chaotic-accretion phase with many episodes and that the mass accreted in each episode is roughly 1%–2% of M• or less. MBHs with appear to have intermediate-to-high spins (∼0.5–1), while lighter MBHs have higher spins (≳0.8). The best matches also infer that (1) the radiative efficiencies (η) of those active MBHs appear to slightly decrease with M•; however, the correlation between η and M•, if any, is weak; (2) the mean radiative efficiency of active MBHs is , consistent with the global constraints.
Emergence of large spin-charge interconversion at an oxidized Cu/W interface

Spin-orbitronic devices can integrate memory and logic by exploiting spin-charge interconversion (SCI), which is optimized by design and materials selection. In these devices, such as the magnetoelectric spin-orbit (MESO) logic, interfaces are crucial elements as they can prohibit or promote spin flow in a device as well as possess spin-orbit coupling resulting in interfacial SCI. Here, we study the origin of SCI in a Py/Cu/W lateral spin valve and quantify its efficiency. An exhaustive characterization of the interface between Cu and W electrodes uncovers the presence of an oxidized layer (WO$_x$). We determine that the SCI occurs at the Cu/WO$_x$ interface with a temperature-independent interfacial spin-loss conductance of $G_{||} \approx$ 20 $\times$ 10$^{13} \Omega^{-1}m^{-2}$ and an interfacial spin-charge conductivity $\sigma_{SC}=-$1610 $\Omega^{-1}cm^{-1}$ at 10 K ($-$830 $\Omega^{-1}cm^{-1}$ at 300 K). This corresponds to an efficiency given by the inverse Edelstein length $\lambda_{IEE}=-$0.76 nm at 10 K ($-$0.4 nm at 300 K), which is remarkably larger than in metal/metal and metal/oxide interfaces and bulk heavy metals. The large SCI efficiency at such an oxidized interface is a promising candidate for the magnetic readout in MESO logic devices.
QCD, with Strings Attached

In the nearly twenty years that have elapsed since its discovery, the gauge-gravity correspondence has become established as an efficient tool to explore the physics of a large class of strongly-coupled field theories. A brief overview is given here of its formulation and a few of its applications, emphasizing attempts to emulate aspects of the strong-coupling regime of quantum chromodynamics (QCD). To the extent possible, the presentation is self-contained, and in particular, it does not presuppose knowledge of string theory.
Instrumental variable estimation of dynamic treatment effects on a duration outcome

This paper considers identification and estimation of the causal effect of the time Z until a subject is treated on a survival outcome T. The treatment is not randomly assigned, T is randomly right censored by a random variable C and the time to treatment Z is right censored by min(T,C). The endogeneity issue is treated using an instrumental variable explaining Z and independent of the error term of the model. We study identification in a fully nonparametric framework. We show that our specification generates an integral equation, of which the regression function of interest is a solution. We provide identification conditions that rely on this identification equation. For estimation purposes, we assume that the regression function follows a parametric model. We propose an estimation procedure and give conditions under which the estimator is asymptotically normal. The estimators exhibit good finite sample properties in simulations. Our methodology is applied to find evidence supporting the efficacy of a therapy for burn-out.
Deep Reinforcement Learning for IRS Phase Shift Design in Spatiotemporally Correlated Environments

The paper studies the problem of designing the Intelligent Reﬂecting Surface (IRS) phase shifters for Multiple Input Single Output (MISO) communication systems in spatiotemporally correlated channel environments, where the destination can move within a conﬁned area. The objective is to maximize the expected sum of SNRs at the receiver over inﬁnite time horizons. The problem formulation gives rise to a Markov Decision Process (MDP). We propose a deep actor-critic algorithm that accounts for channel correlations and destination motion by constructing the state representation to include the current position of the receiver and the phase shift values and receiver positions that correspond to a window of previous time steps. The channel variability induces high frequency components on the spectrum of the underlying value function. We propose the preprocessing of the critic’s input with a Fourier kernel which enables stable value learning. Finally, we investigate the use of the destination SNR as a component of the designed MDP state, which is common practice in previous work. We provide empirical evidence that, when the channels are spatiotemporally correlated, the inclusion of the SNR in the state representation interacts with function approximation in ways that inhibit convergence.
Beads, bubbles and drops in microchannels: stability of centred position and equilibrium velocity

Abstract Understanding and predicting the dynamics of dispersed micro-objects in microfluidics is crucial in numerous natural, industrial and technological situations. In this paper, we experimentally characterized the equilibrium velocity $V$ and lateral position $\varepsilon$ of various dispersed micro-objects, such as beads, bubbles and drops, in a cylindrical microchannel over an unprecedentedly wide range of parameters. By varying the dimensionless object size ($d \in [0.1; 1]$), the viscosity ratio ($\lambda \in [10^{-2}; \infty [$), the density ratio ($\varphi \in [10^{-3}; 2]$), the Reynolds number ($Re \in [10^{-2}; 10^2]$) and the capillary number ($Ca \in [10^{-3}; 0.3]$), we offer an exhaustive parametric study exploring various dynamics from the non-deformable viscous regime to the deformable inertial regime, thus enabling us to highlight the sole and combined roles of inertia and capillary effects on lateral migration. Experiments are compared and agree well with a steady three-dimensional Navier–Stokes model for incompressible two-phase fluids, including the effects of inertia and possible interfacial deformations. This model enables us to propose a correlation for the object velocity $V$ as functions of $d$, $\varepsilon$ and $\lambda$, obtained in the ${Re}={Ca}=0$ limit, but valid for a larger range of values of ${Re}$ and ${Ca}$ delimited by the validity of the linear regime. Next, we present stability maps for the centred position showing that non-deformable objects dominated by inertial effects are only stable if large enough, typically for $d \gtrsim 0.7$, whereas deformable objects dominated by capillary effects can be stable for much smaller sizes, provided the viscosity ratio is outside the range $0.7 \lesssim \lambda \lesssim 10$, in which deformability also plays a destabilizing effect, as for inertia.
Hard Exudate Segmentation Supplemented by Super-Resolution with Multi-scale Attention Fusion Module

Hard exudates (HE) is the most specific biomarker for retina edema. Precise HE segmentation is vital for disease diagnosis and treatment, but automatic segmentation is challenged by its large variation of characteristics including size, shape and position, which makes it difficult to detect tiny lesions and lesion boundaries. Considering the complementary features between segmentation and super-resolution tasks, this paper proposes a novel hard exudates segmentation method named SSMAF with an auxiliary super-resolution task, which brings in helpful detailed features for tiny lesion and boundaries detection. Specifically, we propose a fusion module named Multi-scale Attention Fusion (MAF) module for our dual-stream framework to effectively integrate features of the two tasks. MAF first adopts split spatial convolutional (SSC) layer for multi-scale features extraction and then utilize attention mechanism for features fusion of the two tasks. Considering pixel dependency, we introduce region mutual information (RMI) loss to optimize MAF module for tiny lesions and boundary detection. We evaluate our method on two public lesion datasets, IDRiD and E-Ophtha. Our method shows competitive performance with low-resolution inputs, both quantitatively and qualitatively. On E-Ophtha dataset, the method can achieve $\ge 3$% higher dice and recall compared with the state-of-the-art methods.
Optimizing Function Layout for Mobile Applications

Function layout, also known as function reordering or function placement, is one of the most effective profile-guided compiler optimizations. By reordering functions in a binary, compilers can improve the performance of large-scale applications or reduce the compressed size of mobile applications. Although the technique has been extensively studied in the context of large-scale binaries, no study has thoroughly investigated function layout algorithms on mobile applications. In this paper we develop the first principled solution for optimizing function layouts in the mobile space. To this end, we identify two key optimization goals: reducing the compressed code size and improving the cold start-up time of a mobile application. Then we propose a formal model for the layout problem, whose objective closely matches our goals. Our novel algorithm for optimizing the layout is inspired by the classic balanced graph partitioning problem. We have carefully engineered and implemented the algorithm in an open-source compiler, LLVM. An extensive evaluation of the new method on large commercial mobile applications demonstrates significant improvements in start-up time and compressed size compared to the state-of-the-art approach.
DeepSense 6G: A Large-Scale Real-World Multi-Modal Sensing and Communication Dataset

This article presents the DeepSense 6G data-set, which is a large-scale dataset based on real-world measurements of co-existing multi-modal sensing and communication data. The Deep-Sense 6G dataset is built to advance deep learning research in a wide range of applications in the intersection of multi-modal sensing, communication, and positioning. This article provides a detailed overview of the DeepSense dataset structure, adopted testbeds, data collection and processing methodology, deployment scenarios, and example applications, with the objective of facilitating the adoption and reproducibility of multi-modal sensing and communication datasets.
How frequent are close supermassive binary black holes in powerful jet sources?

Supermassive black hole binaries may be detectable by an upcoming suite of gravitational wave experiments. Their binary nature can also be revealed by radio jets via a short-period precession driven by the orbital motion as well as the geodetic precession at typically longer periods. We have investigated Karl G. Jansky Very Large Array and Multi-Element Radio Linked Interferometer Network (MERLIN) radio maps of powerful jet sources for morphological evidence of geodetic precession. For perhaps the best-studied source, Cygnus A, we find strong evidence for geodetic precession. Projection effects can enhance precession features, for which we find indications in strongly projected sources. For a complete sample of 33 3CR radio sources, we find strong evidence for jet precession in 24 cases (73 per cent). The morphology of the radio maps suggests that the precession periods are of the order of 106-107 yr. We consider different explanations for the morphological features and conclude that geodetic precession is the best explanation. The frequently observed gradual jet angle changes in samples of powerful blazars can be explained by orbital motion. Both observations can be explained simultaneously by postulating that a high fraction of powerful radio sources have subparsec supermassive black hole binaries. We consider complementary evidence and discuss if any jetted supermassive black hole with some indication of precession could be detected as individual gravitational wave source in the near future. This appears unlikely, with the possible exception of M87.
PromptCap: Prompt-Guided Task-Aware Image Captioning

Knowledge-based visual question answering (VQA) involves questions that require world knowledge beyond the image to yield the correct answer. Large language models (LMs) like GPT-3 are particularly helpful for this task because of their strong knowledge retrieval and reasoning capabilities. To enable LM to understand images, prior work uses a captioning model to convert images into text. However, when summarizing an image in a single caption sentence, which visual entities to describe are often underspecified. Generic image captions often miss visual details essential for the LM to answer visual questions correctly. To address this challenge, we propose PromptCap (Prompt-guided image Captioning), a captioning model designed to serve as a better connector between images and black-box LMs. Different from generic captions, PromptCap takes a natural-language prompt to control the visual entities to describe in the generated caption. The prompt contains a question that the caption should aid in answering. To avoid extra annotation, PromptCap is trained by examples synthesized with GPT-3 and existing datasets. We demonstrate PromptCap's effectiveness on an existing pipeline in which GPT-3 is prompted with image captions to carry out VQA. PromptCap outperforms generic captions by a large margin and achieves state-of-the-art accuracy on knowledge-based VQA tasks (60.4% on OK-VQA and 59.6% on A-OKVQA). Zero-shot results on WebQA show that PromptCap generalizes well to unseen domains.
How to Fine-Tune Vision Models with SGD

SGD and AdamW are the two most used optimizers for fine-tuning large neural networks in computer vision. When the two methods perform the same, SGD is preferable because it uses less memory (12 bytes/parameter with momentum and 8 bytes/parameter without) than AdamW (16 bytes/parameter). However, on a suite of downstream tasks, especially those with distribution shifts, we find that fine-tuning with AdamW performs substantially better than SGD on modern Vision Transformer and ConvNeXt models. We find that large gaps in performance between SGD and AdamW occur when the fine-tuning gradients in the first"embedding"layer are much larger than in the rest of the model. Our analysis suggests an easy fix that works consistently across datasets and models: freezing the embedding layer (less than 1% of the parameters) leads to SGD with or without momentum performing slightly better than AdamW while using less memory (e.g., on ViT-L, SGD uses 33% less GPU memory). Our insights result in state-of-the-art accuracies on five popular distribution shift benchmarks: WILDS-FMoW, WILDS-Camelyon, BREEDS-Living-17, Waterbirds, and DomainNet.
Numerical Simulations and Observations of Mg ii in the Solar Chromosphere

The Mg ii h and k lines are among the best diagnostic tools of the upper solar chromosphere. This region of the atmosphere is of particular interest, as it is the lowest region of the Sun’s atmosphere where the magnetic field is dominant in the energetics and dynamics, defining its structure. While highly successful in the photosphere and lower to mid-chromosphere, numerical models have produced synthetic Mg ii lines that do not match the observations well. We present a number of large-scale models with magnetic field topologies representative of the quiet Sun, ephemeral flux regions and plage, and also models where the numerical resolution is high and where we go beyond the MHD paradigm. The results of this study show models with a much improved correspondence with IRIS observations in terms of both intensities and widths, especially underscoring the importance of chromospheric mass loading and of capturing the magnetic field topology and evolution in simulations. This comes in addition to the importance of capturing the generation of small-scale velocity fields and including nonequilibrium ionization and ion−neutral interaction effects. However, it should be noted that difficulties in achieving a good correspondence remain, especially when considering the width of Mg ii h and k lines in plage. Understanding and modeling all these effects and their relative importance is necessary in order to reproduce observed spectral features and in isolating the missing pieces necessary to fully comprehend Mg ii formation.
HARDVS: Revisiting Human Activity Recognition with Dynamic Vision Sensors

The main streams of human activity recognition (HAR) algorithms are developed based on RGB cameras which usually suffer from illumination, fast motion, privacy preservation, and large energy consumption. Meanwhile, the biologically inspired event cameras attracted great interest due to their unique features, such as high dynamic range, dense temporal but sparse spatial resolution, low latency, low power, etc. As it is a newly arising sensor, even there is no realistic large-scale dataset for HAR. Considering its great practical value, in this paper, we propose a large-scale benchmark dataset to bridge this gap, termed HARDVS, which contains 300 categories and more than 100K event sequences. We evaluate and report the performance of multiple popular HAR algorithms, which provide extensive baselines for future works to compare. More importantly, we propose a novel spatial-temporal feature learning and fusion framework, termed ESTF, for event stream based human activity recognition. It first projects the event streams into spatial and temporal embeddings using StemNet, then, encodes and fuses the dual-view representations using Transformer networks. Finally, the dual features are concatenated and fed into a classification head for activity prediction. Extensive experiments on multiple datasets fully validated the effectiveness of our model. Both the dataset and source code will be released at https://github.com/Event-AHU/HARDVS.
Do massive compact objects without event horizon exist in infinite derivative gravity

Einstein’s general theory of relativity is plagued by cosmological and black-hole type singularities Recently, it has been shown that infinite derivative, ghost free, gravity can yield nonsingular cosmological and mini-black hole solutions. In particular, the theory possesses a mass-gap determined by the scale of new physics. This paper provides a plausible argument, not a no-go theorem, based on the Area-law of gravitational entropy that within infinite derivative, ghost free, gravity nonsingular compact objects in the static limit need not have horizons.
Indistinguishable photons from an artificial atom in silicon photonics

Silicon is the ideal material for building electronic and photonic circuits at scale. Spin qubits and integrated photonic quantum technologies in silicon offer a promising path to scaling by leveraging advanced semiconductor manufacturing and integration capabilities. However, the lack of deterministic quantum light sources, two-photon gates, and spin-photon interfaces in silicon poses a major challenge to scalability. In this work, we show a new type of indistinguishable photon source in silicon photonics based on an artificial atom. We show that a G center in a silicon waveguide can generate high-purity telecom-band single photons. We perform high-resolution spectroscopy and time-delayed two-photon interference to demonstrate the indistinguishability of single photons emitted from a G center in a silicon waveguide. Our results show that artificial atoms in silicon photonics can source highly coherent single photons suitable for photonic quantum networks and processors.