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In this paper, we developed a spectral emulator based on the Mapping Nearby Galaxies at Apache Point Observatory Stellar Library (MaStar) and a grouping optimization strategy to estimate effective temperature (T_eff), surface gravity (log g), metallicity ([Fe/H]) and the abundance of alpha elements with respect to iron ([alpha/Fe]) for O-M-type stars within the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) low-resolution spectra. The primary aim is to use a rapid spectral-fitting method, specifically the spectral emulator with the grouping optimization strategy, to create a comprehensive catalog for stars of all types within LAMOST, addressing the shortcomings in parameter estimations for both cold and hot stars present in the official LAMOST AFGKM-type catalog. This effort is part of our series of studies dedicated to establishing an empirical spectral library for LAMOST. Experimental results demonstrate that our method is effectively applicable to parameter prediction for LAMOST, with the single-machine processing time within $70$ hr. We observed that the internal error dispersions for T_eff, log g, [Fe/H], and [alpha/Fe] across different spectral types lie within the ranges of $15-594$ K, $0.03-0.27$ dex, $0.02-0.10$ dex, and $0.01-0.04$ dex, respectively, indicating a good consistency. A comparative analysis with external data highlighted deficiencies in the official LAMOST catalog and issues with MaStar parameters, as well as potential limitations of our method in processing spectra with strong emission lines and bad pixels. The derived atmospheric parameters as a part of this work are available at https://nadc.china-vo.org/res/r101402/ ., Comment: 34 pages, 28 Figures, published in ApJS

The MUltiplexed Survey Telescope (MUST) is a 6.5-meter telescope under development. Dedicated to highly-multiplexed, wide-field spectroscopic surveys, MUST observes over 20,000 targets simultaneously using 6.2-mm pitch positioning robots within a ~5 deg2 field of view. MUST aims to carry out the first Stage-V spectroscopic survey in the 2030s to map the 3D Universe with over 100 million galaxies and quasars, spanning from the nearby Universe to redshift z~5.5, corresponding to around 1 billion years after the Big Bang. To cover this extensive redshift range, we present an initial conceptual target selection algorithm for different types of galaxies, from local bright galaxies, luminous red galaxies, and emission line galaxies to high-redshift (2 < z < 5.5) Lyman-break galaxies. Using Fisher forecasts, we demonstrate that MUST can address fundamental questions in cosmology, including the nature of dark energy, test of gravity theories, and investigations into primordial physics. This is the first paper in the series of science white papers for MUST, with subsequent developments focusing on additional scientific cases such as galaxy and quasar evolution, Milky Way physics, and dynamic phenomena in the time-domain Universe., Comment: To be submitted to SCPMA

Large Language Models (LLMs) have shown considerable promise in code generation. However, the automation sector, especially in motion control, continues to rely heavily on manual programming due to the complexity of tasks and critical safety considerations. In this domain, incorrect code execution can pose risks to both machinery and personnel, necessitating specialized expertise. To address these challenges, we introduce MCCoder, an LLM-powered system designed to generate code that addresses complex motion control tasks, with integrated soft-motion data verification. MCCoder enhances code generation through multitask decomposition, hybrid retrieval-augmented generation (RAG), and self-correction with a private motion library. Moreover, it supports data verification by logging detailed trajectory data and providing simulations and plots, allowing users to assess the accuracy of the generated code and bolstering confidence in LLM-based programming. To ensure robust validation, we propose MCEVAL, an evaluation dataset with metrics tailored to motion control tasks of varying difficulties. Experiments indicate that MCCoder improves performance by 11.61% overall and by 66.12% on complex tasks in MCEVAL dataset compared with base models with naive RAG. This system and dataset aim to facilitate the application of code generation in automation settings with strict safety requirements. MCCoder is publicly available at https://github.com/MCCodeAI/MCCoder.

2D metal halide perovskites have enabled significant stability improvements in perovskite devices, particularly in resistance to moisture. However, some 2D perovskites are even more susceptible to photooxidation compared to 3D perovskites. This is particularly true for more commonly investigated Ruddlesden-Popper (RP) perovskites that exhibit increased susceptibility to photoinduced degradation compared to Dion-Jacobson (DJ) perovskites. Comparisons between different RP and DJ perovskites reveal that this phenomenon cannot be explained by commonly proposed differences in superoxide ion generation, interlayer distance and lattice structural rigidity differences. Instead, the resistance to photooxidation of DJ perovskites can be attributed to decreased likelihood of double deprotonation events (compared to single deprotonation events in RP perovskites) required for the loss of organic cations and the perovskite decomposition. Consequently, DJ perovskites are less susceptible to oxidative degradation (both photo- and electrochemically induced), which leads to improved operational stability of solar cells based on these materials., Comment: Main text: 19 pages, 6 figures, supplementary information: 62 pages, 47 figures

Quantum error correction provides a path to reach practical quantum computing by combining multiple physical qubits into a logical qubit, where the logical error rate is suppressed exponentially as more qubits are added. However, this exponential suppression only occurs if the physical error rate is below a critical threshold. In this work, we present two surface code memories operating below this threshold: a distance-7 code and a distance-5 code integrated with a real-time decoder. The logical error rate of our larger quantum memory is suppressed by a factor of $\Lambda$ = 2.14 $\pm$ 0.02 when increasing the code distance by two, culminating in a 101-qubit distance-7 code with 0.143% $\pm$ 0.003% error per cycle of error correction. This logical memory is also beyond break-even, exceeding its best physical qubit's lifetime by a factor of 2.4 $\pm$ 0.3. We maintain below-threshold performance when decoding in real time, achieving an average decoder latency of 63 $\mu$s at distance-5 up to a million cycles, with a cycle time of 1.1 $\mu$s. To probe the limits of our error-correction performance, we run repetition codes up to distance-29 and find that logical performance is limited by rare correlated error events occurring approximately once every hour, or 3 $\times$ 10$^9$ cycles. Our results present device performance that, if scaled, could realize the operational requirements of large scale fault-tolerant quantum algorithms., Comment: 10 pages, 4 figures, Supplementary Information

Despite recent progress in enhancing the efficacy of Open-Domain Continual Learning (ODCL) in Vision-Language Models (VLM), failing to (1) correctly identify the Task-ID of a test image and (2) use only the category set corresponding to the Task-ID, while preserving the knowledge related to each domain, cannot address the two primary challenges of ODCL: forgetting old knowledge and maintaining zero-shot capabilities, as well as the confusions caused by category-relatedness between domains. In this paper, we propose a simple yet effective solution: leveraging intra-domain category-aware prototypes for ODCL in CLIP (DPeCLIP), where the prototype is the key to bridging the above two processes. Concretely, we propose a training-free Task-ID discriminator method, by utilizing prototypes as classifiers for identifying Task-IDs. Furthermore, to maintain the knowledge corresponding to each domain, we incorporate intra-domain category-aware prototypes as domain prior prompts into the training process. Extensive experiments conducted on 11 different datasets demonstrate the effectiveness of our approach, achieving 2.37% and 1.14% average improvement in class-incremental and task-incremental settings, respectively.

Super-resolution (SR) models in cosmological simulations use deep learning (DL) to rapidly supplement low-resolution (LR) runs with statistically correct, fine details. The SR technique preserves large-scale structures by conditioning on a low-resolution (LR) version of the simulation. On smaller scales, the generative deep learning (DL) process is stochastic, resulting in numerous possible SR realizations, each with unique small-scale structures. Validation of SR then relies on making sure that a specific statistic of interest is accurately reproduced by comparing SR and high resolution (HR) runs. In this study, we develop an emulator designed to reproduce the individual small-scale structures of an HR simulation as closely as possible. We process an SR realization alongside a specific High-Resolution Initial Condition (HRIC), transforming the SR output to emulate the results of a full simulation with that HRIC. By comparing visualizations, individual halo measures and cross-correlating Fourier modes we show that the emulated SR runs closely align with the corresponding HR simulation, even on length scales an order of magnitude smaller than the LR run. Additionally, small halos are trained to match the HR simulation, and the subhalo mass function is more accurately reproduced. These results show the promise of this method for generating numerous fast and accurate simulations and mock observations for large galaxy surveys., Comment: 16 pages, 15 figures

We present a field-level emulator for large-scale structure, capturing the cosmology dependence and the time evolution of cosmic structure formation. The emulator maps linear displacement fields to their corresponding nonlinear displacements from N-body simulations at specific redshifts. Designed as a neural network, the emulator incorporates style parameters that encode dependencies on $\Omega_{\rm m}$ and the linear growth factor $D(z)$ at redshift $z$. We train our model on the six-dimensional N-body phase space, predicting particle velocities as the time derivative of the model's displacement outputs. This innovation results in significant improvements in training efficiency and model accuracy. Tested on diverse cosmologies and redshifts not seen during training, the emulator achieves percent-level accuracy on scales of $k\sim~1~{\rm Mpc}^{-1}~h$ at $z=0$, with improved performance at higher redshifts. We compare predicted structure formation histories with N-body simulations via merger trees, finding consistent merger event sequences and statistical properties.

Due to the high noise caused by solar background radiation, the existing satellite-based free-space quantum key distribution (QKD) experiments are mainly carried out at night, hindering the establishment of a practical all-day real-time global-scale quantum network. Given that the 3-5 {\mu}m mid-infrared (MIR) band has extremely low solar background radiation and strong scattering resistance, it is one of the ideal bands for free-space quantum communication. Here, firstly, we report on the preparation of a high-quality MIR (3370 nm) and telecom band (1555 nm) two-color polarization-entangled photon source, then we use this source to realize a principle QKD based on free-space and fiber hybrid channels in a laboratory. The theoretical analysis clearly shows that a long-distance QKD over 500 km of free-space and 96 km of fiber hybrid channels can be reached simultaneously. This work represents a significant step toward developing all-day global-scale quantum communication networks., Comment: 24 pages, 9 figures

Polarimetric imaging, a technique that captures the invisible polarization-related properties of given materials, has broad applications from fundamental physics to advanced fields such as target recognition, stress detection, biomedical diagnosis and remote sensing. The introduction of quantum sources into classical imaging systems has demonstrated distinct advantages, yet few studies have explored their combination with polarimetric imaging. In this study, we present a quantum polarimetric imaging system that integrates polarization-entangled photon pairs into a polarizer-sample-compensator-analyzer (PSRA)-type polarimeter. Our system visualizes the birefringence properties of a periodical-distributed anisotropic material under decreasing illumination levels and diverse disturbing light sources. Compared to the classical system, the quantum approach reveals the superior sensitivity and robustness in low-light conditions, particularly useful in biomedical studies where the low illumination and non-destructive detection are urgently needed. The study also highlights the nonlocality of entangled photons in birefringence measurement, indicating the potential of quantum polarimetric system in the remote sensing domain.

The ability to quantify how well an action is carried out, also known as action quality assessment (AQA), has attracted recent interest in the vision community. Unfortunately, prior methods often ignore the score rubric used by human experts and fall short of quantifying the uncertainty of the model prediction. To bridge the gap, we present RICA^2 - a deep probabilistic model that integrates score rubric and accounts for prediction uncertainty for AQA. Central to our method lies in stochastic embeddings of action steps, defined on a graph structure that encodes the score rubric. The embeddings spread probabilistic density in the latent space and allow our method to represent model uncertainty. The graph encodes the scoring criteria, based on which the quality scores can be decoded. We demonstrate that our method establishes new state of the art on public benchmarks, including FineDiving, MTL-AQA, and JIGSAWS, with superior performance in score prediction and uncertainty calibration. Our code is available at https://abrarmajeedi.github.io/rica2_aqa/, Comment: Accepted at European Conference on Computer Vision (ECCV) 2024

Context. A large fraction of Asymptotic Giant Branch (AGB) stars develop carbon-rich atmospheres during their evolution. Based on their color and luminosity, these carbon stars can be easily distinguished from many other kinds of stars. However, numerous G, K, and M giants also occupy the same region as carbon stars on the HR diagram. Despite this, their spectra exhibit differences, especially in the prominent CN molecular bands. Target. We aim to distinguish carbon stars from other kinds of stars using $Gaia$'s XP spectra, while providing attributional interpretations of key features necessary for identification, and even discovering additional new spectral key features. Method. We propose a classification model named `GaiaNet', an improved one-dimensional convolutional neural network specifically designed for handling $Gaia$'s XP spectra. We utilized the SHAP interpretability model to determine SHAP values for each feature in a spectrum, enabling us to explain the output of the `GaiaNet' model and provide further meaningful analysis. Compared to four traditional machine-learning methods, the `GaiaNet' model exhibits an average classification accuracy improvement of approximately 0.3% on the validation set, with the highest accuracy reaching 100%. Utilizing the SHAP model, we present a clear spectroscopic heatmap highlighting molecular band absorption features primarily distributed around CN773.3 and CN895.0, and summarize five key feature regions for carbon star identification. Upon applying the trained classification model to the CSTAR sample with Gaia `xp_sampled_mean' spectra, we obtained 451 new candidate carbon stars as a by-product., Comment: 23 pages, 22 figures

Scalar-tensor gravity, exemplified by Brans-Dicke (BD) gravity, introduces additional scalar polarization modes that contribute scalar radiation alongside tensor modes. We conduct a comprehensive analysis of how gravitational wave generation and propagation effects under Brans-Dicke gravity are encoded into the astrophysical stochastic gravitational wave background (AGWB). We perform end-to-end analyses of realistic populations of simulated coalescing binary systems to generate AGWB mock data with third-generation gravitational wave detectors and conducted a complete Bayesian analysis for the first time. We find the uncertainties in the population properties of binary black holes (BBH) significantly affect the ability to constrain BD gravity. Under the most favorable conditions, the upper limit may suggest $\omega_{\rm BD} > 816$. Furthermore, we explore the detectability of potential scalar backgrounds arising from binary neutron star (BNS) mergers, setting upper limits on scalar backgrounds expected to be two orders of magnitude lower than the total background contributed by both BBH and BNS in one year of observational data. We conclude that for ambiguous populations, employing waveform matching with individual sources provides a more robust approach to constrain Brans-Dicke gravity. However, the future detection of a potential scalar background within the AGWB could provide significant support for gravity theories beyond General Relativity., Comment: 26 pages, 6 figures, and 2 tables

Many recent advances in metal halide perovskite solar cell (PSC) performance are attributed to surface treatments which passivate interfacial trap states, minimise charge recombination and boost photovoltages. Surprisingly, these photovoltages exceed the cells' built-in potentials, often with large energetic offsets reported between the perovskite and transport layer semiconductor band edges - contradicting standard photovoltaic design principles. Here we show that this tolerance to energetic offsets results from mixed ionic/electronic conduction in the perovskite layer. Combining drift-diffusion simulations with experiments probing the current-voltage performance of PSCs as a function of ion distribution, we demonstrate that electrostatic redistribution of ionic charge reduces surface recombination currents at steady-state, increasing the photovoltage by tens to hundreds of millivolts. Thus, mobile ions can reduce the sensitivity of photovoltage to energetic misalignments at perovskite/transport layer interfaces, benefitting overall efficiency. Building on these insights, we show how photovoltaic design principles are modified to account for mobile ions.

The cosmic expansion rate can be directly measured with gravitational-wave (GW) data of the compact binary mergers by jointly constraining the mass function of the population and the cosmological model via the so-called spectral sirens. Such a method relies on the features in the mass functions, which may originate from some individual subpopulations, and hence become blurred/indistinct due to the superposition of different subpopulations. In this work we propose a novel approach to constrain the cosmic expansion rate with subpopulations of GW events, named multi-spectral sirens. The advantage of the multi-spectral sirens compared to the traditional spectral sirens is demonstrated by the simulation with the mock data. The application of this approach to the GWTC-3 data yields $H_0=73.3^{+29.9}_{-25.6}~{\rm Mpc}^{-1}~{\rm km}~{\rm s}^{-1}$ (median and symmetric 68.3\% credible interval), which is about 19\% tighter than the result inferred with the traditional spectral sirens utilizing a PowerLaw+Peak mass function. The incorporation of the bright standard siren GW170817 with a uniform prior in [10,200] (log-uniform prior in [20,140]) ${\rm Mpc}^{-1}~{\rm km}~{\rm s}^{-1}$ gives $H_0=71.1^{+15.0}_{-7.5}~(70.3^{+12.9}_{-7.1})~{\rm Mpc}^{-1}~{\rm km}~{\rm s}^{-1}$ (68.3\% confidence level), corresponding to an improvement of $\sim26\%$ (23\%) with respect to the measurement from sole GW170817., Comment: 17 pages, 11 figures, 2 tables; Accepted for publication in ApJ

The spins of binary black holes (BBHs) measured from gravitational waves carry notable information of the formation pathways. Here we propose a quantity "dimensionless net spin" ($\chi_{\rm N}$), which is related to the sum of angular momentum of component black holes in the system, to provide a novel perspective to study the origin(s) of BBHs. By performing hierarchical Bayesian inference on $\chi_{\rm N}$, we find strong evidence that the marginal distribution of this quantity can be better fitted by two Gaussian components rather than one: there is a narrow peak at $\chi_{\rm N} \sim 0.15$ and another extended peak at $\chi_{\rm N} \sim 0.47$. We also find that the rapidly spinning systems likely dominate the high-mass end of the population and they evolve with redshift much quicker. These findings bring new challenges to the field binary scenario, and suggest that dynamical process should plays a key role in forming high total mass BBHs., Comment: Submitted on 25 April 2024

The standard cosmological model, with its six independent parameters, successfully describes our observable Universe. One of these parameters, the optical depth to reionization $\tau_\mathrm{reio}$, represents the scatterings that Cosmic Microwave Background (CMB) photons will experience after decoupling from the primordial plasma as the intergalactic medium transitions from neutral to ionized. $\tau_\mathrm{reio}$ depends on the neutral hydrogen fraction $x_\mathrm{HI}$, which, in turn, should theoretically depend on cosmology. We present a novel method to establish the missing link between cosmology and reionization timeline using symbolic regression. We discover the timeline has a universal shape well described by the Gompertz mortality law, applicable to any cosmology within our simulated data. Unlike the conventional tanh prescription, our model is asymmetric in time and a good fit to astrophysical constraints on $x_\mathrm{HI}$. By combining CMB with astrophysical data and marginalizing over astrophysics, we treat $\tau_\mathrm{reio}$ as a derived parameter, tightening its constraint to $<3\%$. This approach reduces the error on the amplitude of the primordial fluctuations by a factor of 2.3 compared to Planck's PR3 constraint and provides a commanding constraint on the ionization efficiency $\zeta_\mathrm{UV} = 26.9^{+2.1}_{-2.5}$. We expect further improvements in the near term as reionization constraints increase and our understanding of reionization advances., Comment: Correctly accounting for ionization efficiency enables us to place leading constraints by combining our universal asymmetric reionization model with CMB and damping wing constraints on $x_\mathrm{HI}$. By marginalizing over $\zeta_\mathrm{UV}$, we achieve powerful constraints on $A_\mathrm{s}$ and $\tau_\mathrm{reio}$, with uncertainties around 3% for the former

The analyses of the next generation cosmological surveys demand an accurate, efficient, and differentiable method for simulating the universe and its observables across cosmological volumes. We present Hamiltonian ray tracing (HRT) -- the first post-Born (accounting for lens-lens coupling and without relying on the Born approximation), three-dimensional (without assuming the thin-lens approximation), and on-the-fly (applicable to any structure formation simulations) ray tracing algorithm based on the Hamiltonian formalism. HRT performs symplectic integration of the photon geodesics in a weak gravitational field, and can integrate tightly with any gravity solver, enabling co-evolution of matter particles and light rays with minimal additional computations. We implement HRT in the particle-mesh library $\texttt{pmwd}$, leveraging hardware accelerators such as GPUs and automatic differentiation capabilities based on $\texttt{JAX}$. When tested on a point-mass lens, HRT achieves sub-percent accuracy in deflection angles above the resolution limit across both weak and moderately strong lensing regimes. We also test HRT in cosmological simulations on the convergence maps and their power spectra., Comment: 26 pages, 8 figures; as published in JCAP

To a simple polarized hyperplane arrangement (not necessarily cyclic) $\mathbb{V}$, one can associate a stopped Liouville manifold (equivalently, a Liouville sector) $\left(M(\mathbb{V}),\xi\right)$, where $M(\mathbb{V})$ is the complement of finitely many hyperplanes in $\mathbb{C}^d$, obtained as the complexifications of the real hyperplanes in $\mathbb{V}$. The Liouville structure on $M(\mathbb{V})$ comes from a very affine embedding, and the stop $\xi$ is determined by the polarization. In this article, we study the symplectic topology of $\left(M(\mathbb{V}),\xi\right)$. In particular, we prove that their partially wrapped Fukaya categories are generated by Lagrangian submanifolds associated to the bounded and feasible chambers of $\mathbb{V}$. A computation of the Fukaya $A_\infty$-algebra of these Lagrangians then enables us to identity these wrapped Fukaya categories with the $\mathbb{G}_m^d$-equivariant hypertoric convolution algebras $\widetilde{B}(\mathbb{V})$ associated to $\mathbb{V}$. This confirms a conjecture of Lauda-Licata-Manion (arXiv:2009.03981) and provides evidence for the general conjecture of Lekili-Segal (arXiv:2304.10969) on the equivariant Fukaya categories of symplectic manifolds with Hamiltonian torus actions., Comment: Fix graphical issues, add some references and expand some of the expositions in section 3. Comments welcome!

Recent advancements in wireless perception technologies, including mmWave, WiFi, and acoustics, have expanded their application in human motion tracking and health monitoring. They are promising alternatives to traditional camera-based perception systems, thanks to their efficacy under diverse conditions or occlusions, and enhanced privacy. However, the integration of deep learning within this field introduces new challenges such as the need for extensive training data and poor model generalization, especially with sparse and noisy wireless point clouds. As a remedy, data augmentation is one solution well-explored in other deep learning fields, but they are not directly applicable to the unique characteristics of wireless signals. This motivates us to propose a custom data augmentation framework, WixUp, tailored for wireless perception. Moreover, we aim to make it a general framework supporting various datasets, model architectures, sensing modalities, and tasks; while previous wireless data augmentation or generative simulations do not exhibit this generalizability, only limited to certain use cases. More specifically, WixUp can reverse-transform lossy coordinates into dense range profiles using Gaussian mixture and probability tricks, making it capable of in-depth data diversity enhancement; and its mixing-based method enables unsupervised domain adaptation via self-training, allowing training of the model with no labels from new users or environments in practice. In summary, our extensive evaluation experiments show that WixUp provides consistent performance improvement across various scenarios and outperforms the baselines.

Cluster-scale strong lensing is a powerful tool for exploring the properties of dark matter and constraining cosmological models. However, due to the complex parameter space, pixelized strong lens modeling in galaxy clusters is computationally expensive, leading to the point-source approximation of strongly lensed extended images, potentially introducing systematic biases. Herein, as the first paper of the ClUsteR strong Lens modelIng for the Next-Generation observations (CURLING) program, we use lensing ray-tracing simulations to quantify the biases and uncertainties arising from the point-like image approximation for JWST-like observations. Our results indicate that the approximation works well for reconstructing the total cluster mass distribution, but can bias the magnification measurements near critical curves and the constraints on the cosmological parameters, the total matter density of the Universe $\Omega_{\rm m}$, and dark energy equation of state parameter $w$. To mitigate the biases, we propose incorporating the extended surface brightness distribution of lensed sources into the modeling. This approach reduces the bias in magnification from 46.2 per cent to 0.09 per cent for $\mu \sim 1000$. Furthermore, the median values of cosmological parameters align more closely with the fiducial model. In addition to the improved accuracy, we also demonstrate that the constraining power can be substantially enhanced. In conclusion, it is necessary to model cluster-scale strong lenses with pixelized multiple images, especially for estimating the intrinsic luminosity of highly magnified sources and accurate cosmography in the era of high-precision observations., Comment: 12 pages, 8 figures

We investigate formation channels for merging binary black holes (BBHs) in GWTC-3, with a dedicated semiparametric population model. The model first describes or excludes a high-spin (with magnitudes of $\sim0.7$) and high-mass (ranging in $\sim 20-80M_{\odot}$) subpopulation, which was identified by previous works and can be interpreted as hierarchical mergers. We find that the rest of BBH population can be categorized into two subpopulations with different mass and mass-ratio distributions, as indicated by a Bayes factor of $\ln\mathcal{B}=1.8$. One subpopulation, characterized by nearly aligned spins and consistent with isolated-field formation, likely dominates the 10-solar-mass peak in the primary-mass function. The other subpopulation, with isotropic spins and consistent with the dynamical channels, shows a stronger preference for symmetric pairing, and mainly contributes to the 35-solar-mass peak in the primary-mass function. Note that the Bayes factor is not high enough with the currently available data, so that the case of a single population is still acceptable. Additionally, we compare the mass distributions between merging BBHs and black holes (BHs) in high-mass X-ray binaries (HMXBs). We find that the primary mass of the aligned subpopulation is slightly lighter than those of the HMXB BHs, while the isotropic subpopulation is consistent with the HMXB BHs, if the power-law index of its mass function is shifted by 2, as indicated by the dynamical formation channels. However, the spin magnitudes of both subpopulations are significantly smaller than those of the HMXB BHs., Comment: 31 pages, 21 figures, ApJ accepted

Multi-modality magnetic resonance imaging data with various sequences facilitate the early diagnosis, tumor segmentation, and disease staging in the management of nasopharyngeal carcinoma (NPC). The lack of publicly available, comprehensive datasets limits advancements in diagnosis, treatment planning, and the development of machine learning algorithms for NPC. Addressing this critical need, we introduce the first comprehensive NPC MRI dataset, encompassing MR axial imaging of 277 primary NPC patients. This dataset includes T1-weighted, T2-weighted, and contrast-enhanced T1-weighted sequences, totaling 831 scans. In addition to the corresponding clinical data, manually annotated and labeled segmentations by experienced radiologists offer high-quality data resources from untreated primary NPC.

Temporal grounding of text descriptions in videos is a central problem in vision-language learning and video understanding. Existing methods often prioritize accuracy over scalability -- they have been optimized for grounding only a few text queries within short videos, and fail to scale up to long videos with hundreds of queries. In this paper, we study the effect of cross-modal fusion on the scalability of video grounding models. Our analysis establishes late fusion as a more cost-effective fusion scheme for long-form videos with many text queries. Moreover, it leads us to a novel, video-centric sampling scheme for efficient training. Based on these findings, we present SnAG, a simple baseline for scalable and accurate video grounding. Without bells and whistles, SnAG is 43% more accurate and 1.5x faster than CONE, a state of the art for long-form video grounding on the challenging MAD dataset, while achieving highly competitive results on short videos., Comment: Accepted to CVPR 2024. Code available at https://github.com/fmu2/snag_release

We present a method for reconstructing 3D shape of arbitrary Lambertian objects based on measurements by miniature, energy-efficient, low-cost single-photon cameras. These cameras, operating as time resolved image sensors, illuminate the scene with a very fast pulse of diffuse light and record the shape of that pulse as it returns back from the scene at a high temporal resolution. We propose to model this image formation process, account for its non-idealities, and adapt neural rendering to reconstruct 3D geometry from a set of spatially distributed sensors with known poses. We show that our approach can successfully recover complex 3D shapes from simulated data. We further demonstrate 3D object reconstruction from real-world captures, utilizing measurements from a commodity proximity sensor. Our work draws a connection between image-based modeling and active range scanning and is a step towards 3D vision with single-photon cameras.

Acoustic sensing manifests great potential in various applications that encompass health monitoring, gesture interface and imaging by leveraging the speakers and microphones on smart devices. However, in ongoing research and development in acoustic sensing, one problem is often overlooked: the same speaker, when used concurrently for sensing and other traditional applications (like playing music), could cause interference in both making it impractical to use in the real world. The strong ultrasonic sensing signals mixed with music would overload the speaker's mixer. To confront this issue of overloaded signals, current solutions are clipping or down-scaling, both of which affect the music playback quality and also sensing range and accuracy. To address this challenge, we propose CoPlay, a deep learning based optimization algorithm to cognitively adapt the sensing signal. It can 1) maximize the sensing signal magnitude within the available bandwidth left by the concurrent music to optimize sensing range and accuracy and 2) minimize any consequential frequency distortion that can affect music playback. In this work, we design a deep learning model and test it on common types of sensing signals (sine wave or Frequency Modulated Continuous Wave FMCW) as inputs with various agnostic concurrent music and speech. First, we evaluated the model performance to show the quality of the generated signals. Then we conducted field studies of downstream acoustic sensing tasks in the real world. A study with 12 users proved that respiration monitoring and gesture recognition using our adapted signal achieve similar accuracy as no-concurrent-music scenarios, while clipping or down-scaling manifests worse accuracy. A qualitative study also manifests that the music play quality is not degraded, unlike traditional clipping or down-scaling methods.

Four wave mixing (FWM) is an important way to generate supercontinuum and frequency combs in the mid-infrared band. Here, we obtain simultaneous synthetic FWM in the visible and mid-infrared bands by cascading quadratic nonlinear processes in a periodically poled lithium niobate crystal (PPLN), which has a 110dB(at 3000nm) higher conversion efficiency than the FWM directly generated by third-order susceptibilities in bulk PPLN crystals. A general model of this process is developed that is in full agreement with the experimental verifications. The frequency difference between the new frequency components can be freely tuned by changing the frequency difference of the dual pump lasers. Furthermore, by increasing the conversion bandwidth and efficiency of the cascaded processes, it is feasible to generate frequency combs in three bands the visible, near-infrared and mid-infrared bands simultaneously through high-order cascaded processes. This work opens up a new avenue toward free-tuning multiband frequency comb generation with multi-octaves frequency spanning, which will have significant applications in fields such as mid-infrared gas sensing, lidar and precision spectroscopy.

Entanglement enables many promising applications in quantum technology. Devising new generation methods and harnessing entanglement are prerequisites for practical applications. Here we realize a distinct polarization-entangled source by simultaneously achieving type-0 and type-I backward quasi-phase matching (BQPM) through spontaneous parametric down-conversion in a single bulk crystal, which is different from all previous entangled-source configurations. Pumping the crystal with a single polarized beam generates a non-maximally polarization-entangled state, which can be further projected to a maximal Bell state with a pair of Brewster windows. Hong-Ou-Mandel interference experiments are done on polarization-degenerate photon pairs for both type-0 and type-I BQPM processes for the first time. The emitted photons in both processes have a bandwidth as narrow as 15.7 GHz. The high quality of this source is characterized by various methods. The rather simple configuration, narrow bandwidth, and high entanglement quality make the source very promising for many quantum information tasks.

A high-quality narrowband polarization-entangled source in the telecom band is preferred to avoid frequency dispersion for long-distance transmission in optical fibers and to efficiently couple with telecom band quantum memories. Here, we report narrowband, telecom-band, polarization-entangled photon pair generation based on the superposition of single-longitudinal-mode photon pairs from two monolithic nonlinear crystal cavities in a passively stable interferometer based on beam displacers. The photon pairs generated from the cavities exhibit a high coincidence to accidental coincidence ratio of 20000 and a bandwidth below 500 MHz. Two-photon polarization interference, Bell-inequality, and quantum state tomography are performed to indicate the high quality of the entangled source. The current configuration demonstrates greater stability than traditional free space cavity-enhanced polarization-entangled state generation, which is promising for quantum communication applications.