Search

The origin of the correlation between the effective spins ($\chi_{\rm eff}$) and mass ratios ($q$) of LIGO-Virgo-KAGRA's binary black holes (BBHs) is still an open question. Motivated by recent identification of two subpopulations of the BBHs, in this work we investigate the potential $\chi_{\rm eff}-q$ correlation for each subpopulation. Surprisingly, the $\chi_{\rm eff}$-$q$ correlation vanishes for the low-mass subpopulation if we introduce a second $\chi_{\rm eff}$ distribution for the high-mass subpopulation likely originating from hierarchical mergers. The first subpopulation has a narrow $\chi_{\rm eff}$ distribution peaking at $\sim0.05$, whose primary-mass function cuts off at $\sim 45M_{\odot}$, in agreement with first-generation BBHs. The second $\chi_{\rm eff}$ distribution is broad and peaks at $\mu_{\chi,2}=0.35^{+0.18}_{-0.22}$, consistent with the expectation of hierarchical mergers formed in the disks of active galactic nucleus (AGNs). We infer $\mu_{\chi,2}>0$ at 98.7\% credible level, and a symmetric $\chi_{\rm eff}$ distribution for the second subpopulation is disfavored by $\mathcal{B}\sim5$. However, negative values of $\chi_{\rm eff}$ are also measured, indicating that the hierarchical mergers may take place within both star clusters and AGN disks. We find a Bayes factor of $\ln\mathcal{B}=5.2$ for two distinct $\chi_{\rm eff}$ distributions relative to single $\chi_{\rm eff}$ distribution that conditioned on mass ratios. Therefore we conclude that the $\chi_{\rm eff}$-$q$ correlation in the entire population can be explained as the superposition of two subpopulations. Additionally, we suggest to use a flexible mass function to reduce the bias in $\chi_{\rm eff}$-$q$ correlation that may be introduced by model mis-specification., Comment: 13 pages, 7 figures, comments welcome!

To address the issues of insufficient robustness, unstable features, and data noise interference in existing network attack detection and identification models, this paper proposes an attack traffic detection and identification method based on temporal spectrum. First, traffic data is segmented by a sliding window to construct a feature sequence and a corresponding label sequence for network traffic. Next, the proposed spectral label generation methods, SSPE and COAP, are applied to transform the label sequence into spectral labels and the feature sequence into temporal features. Spectral labels and temporal features are used to capture and represent behavioral patterns of attacks. Finally, the constructed temporal features and spectral labels are used to train models, which subsequently detects and identifies network attack behaviors. Experimental results demonstrate that compared to traditional methods, models trained with the SSPE or COAP method improve identification accuracy by 10%, and exhibit strong robustness, particularly in noisy environments., Comment: 20 pages, 7 figures, 7 tables, 8 formulas

The 2023 Big ANN Challenge, held at NeurIPS 2023, focused on advancing the state-of-the-art in indexing data structures and search algorithms for practical variants of Approximate Nearest Neighbor (ANN) search that reflect the growing complexity and diversity of workloads. Unlike prior challenges that emphasized scaling up classical ANN search ~\cite{DBLP:conf/nips/SimhadriWADBBCH21}, this competition addressed filtered search, out-of-distribution data, sparse and streaming variants of ANNS. Participants developed and submitted innovative solutions that were evaluated on new standard datasets with constrained computational resources. The results showcased significant improvements in search accuracy and efficiency over industry-standard baselines, with notable contributions from both academic and industrial teams. This paper summarizes the competition tracks, datasets, evaluation metrics, and the innovative approaches of the top-performing submissions, providing insights into the current advancements and future directions in the field of approximate nearest neighbor search., Comment: Code: https://github.com/harsha-simhadri/big-ann-benchmarks/releases/tag/v0.3.0

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

We study the problem of multimodal physical scene understanding, where an embodied agent needs to find fallen objects by inferring object properties, direction, and distance of an impact sound source. Previous works adopt feed-forward neural networks to directly regress the variables from sound, leading to poor generalization and domain adaptation issues. In this paper, we illustrate that learning a disentangled model of acoustic formation, referred to as disentangled acoustic field (DAF), to capture the sound generation and propagation process, enables the embodied agent to construct a spatial uncertainty map over where the objects may have fallen. We demonstrate that our analysis-by-synthesis framework can jointly infer sound properties by explicitly decomposing and factorizing the latent space of the disentangled model. We further show that the spatial uncertainty map can significantly improve the success rate for the localization of fallen objects by proposing multiple plausible exploration locations.

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

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

Utilizing the non-equilibrium Green's function method, we study the local heat current flow of phonons in nanoscale ballistic graphene nanoribbon, where boundary scattering leads to the formation of atomic-scale current vortices. We further map out the atomic temperature distribution in the ribbon with B\"uttiker's probe approach. From the heat current and temperature distribution, we observe inverted temperature response in the ribbon, where the heat current direction goes from the colder to the hotter region. Moreover, we show that atomic scale defect can generate heat vortex at certain frequency, but it is averaged out when including contributions from all the phonon modes. Meanwhile, our results have recovered residual-resistivity dipole features manifested at the vicinity of local defects. These results extend the study of local heat vortex and negative temperature response in the bulk hydrodynamic regime to the atomic-scale ballistic regime, further confirming boundary scattering is crucial to generate backflow of heat current.

Previous mechanical meta-structures used for mechanical memory storage, computing and information processing are severely constrained by low information density and/or non-robust structural stiffness to stably protect the maintained information. To address these challenges, we proposed a novel reprogrammable multifunctional mechanical metastructure made by an unprecedented building block based on kinematic mechanism. The proposed meta-structure can achieve all abovementioned functionalities accompanying with high information density and promising structural stability. We attribute all these merits to the intrinsic kinematic bifurcations of structural units, which enable the periodic meta-structure with additional and independently deformable bi-stable structural segments, and multi-layered deformed configurations to significantly enlarge the available information bits. We validate the stable information storage are originated from the compatible deformations of local structural segments before and after bifurcations. We illustrated the stored information can be feasibly reprogrammed by magnetic poles. Our design strategy paves new way for creating novel functional mechanical metastuctures.

We introduce Ground-Fusion, a low-cost sensor fusion simultaneous localization and mapping (SLAM) system for ground vehicles. Our system features efficient initialization, effective sensor anomaly detection and handling, real-time dense color mapping, and robust localization in diverse environments. We tightly integrate RGB-D images, inertial measurements, wheel odometer and GNSS signals within a factor graph to achieve accurate and reliable localization both indoors and outdoors. To ensure successful initialization, we propose an efficient strategy that comprises three different methods: stationary, visual, and dynamic, tailored to handle diverse cases. Furthermore, we develop mechanisms to detect sensor anomalies and degradation, handling them adeptly to maintain system accuracy. Our experimental results on both public and self-collected datasets demonstrate that Ground-Fusion outperforms existing low-cost SLAM systems in corner cases. We release the code and datasets at https://github.com/SJTU-ViSYS/Ground-Fusion.

Recent research has focused on understanding how cooperation is fostered through various mechanisms in cognitive settings, particularly through pairwise interactions. However, real-world interactions often extend beyond simple dyads, including multiple cliques with both pairwise and higher-order interactions. These complex interactions influence how individuals perceive and adapt their strategies based on social norms. We here introduce a model that explores the evolution of collective strategies and social norms within a heterogeneous environment, encompassing both dyadic and three-body interactions. We find that social norms play a crucial role in promoting cooperation in comparison to simply imitating the most successful neighbor. We also show that the rise of prosocial norms leads to increased cooperation across various social dilemmas, often resulting in shifts from defective to cooperative behavior. Additionally, we observe that a moderate level of information privacy helps sustaining prosocial norms and curtails antisocial tendencies, even in situations where mutual defection might seem advantageous. Our research thus offers insights into the evolution of cooperation through the lens of social norm diffusion in higher-order networks., Comment: 12 pages, 9 figures

Implicit neural SLAM has achieved remarkable progress recently. Nevertheless, existing methods face significant challenges in non-ideal scenarios, such as motion blur or lighting variation, which often leads to issues like convergence failures, localization drifts, and distorted mapping. To address these challenges, we propose EN-SLAM, the first event-RGBD implicit neural SLAM framework, which effectively leverages the high rate and high dynamic range advantages of event data for tracking and mapping. Specifically, EN-SLAM proposes a differentiable CRF (Camera Response Function) rendering technique to generate distinct RGB and event camera data via a shared radiance field, which is optimized by learning a unified implicit representation with the captured event and RGBD supervision. Moreover, based on the temporal difference property of events, we propose a temporal aggregating optimization strategy for the event joint tracking and global bundle adjustment, capitalizing on the consecutive difference constraints of events, significantly enhancing tracking accuracy and robustness. Finally, we construct the simulated dataset DEV-Indoors and real captured dataset DEV-Reals containing 6 scenes, 17 sequences with practical motion blur and lighting changes for evaluations. Experimental results show that our method outperforms the SOTA methods in both tracking ATE and mapping ACC with a real-time 17 FPS in various challenging environments. Project page: https://delinqu.github.io/EN-SLAM., Comment: Accept at CVPR 2024

The mass ($M_{\rm TOV}$) and radius ($R_{\rm TOV}$) of the maximum-mass nonrotating neutron star (NS) play a crucial role in constraining the elusive equation of state of cold dense matter and in predicting the fate of remnants from binary neutron star (BNS) mergers. In this study, we introduce a novel method to deduce these parameters by examining the mergers of second-generation (2G) black holes (BHs) with NSs. These 2G BHs are assumed to originate from supramassive neutron stars (SMNSs) formed in BNS mergers. Since the properties of the remnant BHs arising from the collapse of SMNSs follow a universal relation governed by $M_{\rm TOV}$ and $R_{\rm TOV}$, we anticipate that by analyzing a series ($\sim 100$ detections) of mass and spin measurements of the 2G BHs using the third-generation ground-based gravitational-wave detectors, $M_{\rm TOV}$ and $R_{\rm TOV}$ can be determined with a precision of $\sim 0.01M_\odot$ and $\sim 0.6$ km, respectively., Comment: 11 pages, 5 figures, ApJ published

In isolated nonlinear optical waveguide arrays with bounded energy spectrum, simultaneous conservation of energy and power of the optical modes enables study of coupled thermal and particle transport in the negative temperature regime. Here, based on exact numerical simulation and rationale from Landauer formalism, we predict generic violation of the Wiedemann-Franz law in such systems. This is rooted in the spectral decoupling of thermal and power current of optical modes, and their different temperature dependence. Our work extends the study of coupled thermal and particle transport into unprecedented regimes, not reachable in natural condensed matter and atomic gas systems.

High-quality datasets can speed up breakthroughs and reveal potential developing directions in SLAM research. To support the research on corner cases of visual SLAM systems, this paper presents Ground-Challenge: a challenging dataset comprising 36 trajectories with diverse corner cases such as aggressive motion, severe occlusion, changing illumination, few textures, pure rotation, motion blur, wheel suspension, etc. The dataset was collected by a ground robot with multiple sensors including an RGB-D camera, an inertial measurement unit (IMU), a wheel odometer and a 3D LiDAR. All of these sensors were well-calibrated and synchronized, and their data were recorded simultaneously. To evaluate the performance of cutting-edge SLAM systems, we tested them on our dataset and demonstrated that these systems are prone to drift and fail on specific sequences. We will release the full dataset and relevant materials upon paper publication to benefit the research community. For more information, visit our project website at https://github.com/sjtuyinjie/Ground-Challenge.

Entity alignment (EA) aims at identifying equivalent entity pairs across different knowledge graphs (KGs) that refer to the same real-world identity. To systematically combat confirmation bias for pseudo-labeling-based entity alignment, we propose a Unified Pseudo-Labeling framework for Entity Alignment (UPL-EA) that explicitly eliminates pseudo-labeling errors to boost the accuracy of entity alignment. UPL-EA consists of two complementary components: (1) The Optimal Transport (OT)-based pseudo-labeling uses discrete OT modeling as an effective means to enable more accurate determination of entity correspondences across two KGs and to mitigate the adverse impact of erroneous matches. A simple but highly effective criterion is further devised to derive pseudo-labeled entity pairs that satisfy one-to-one correspondences at each iteration. (2) The cross-iteration pseudo-label calibration operates across multiple consecutive iterations to further improve the pseudo-labeling precision rate by reducing the local pseudo-label selection variability with a theoretical guarantee. The two components are respectively designed to eliminate Type I and Type II pseudo-labeling errors identified through our analyse. The calibrated pseudo-labels are thereafter used to augment prior alignment seeds to reinforce subsequent model training for alignment inference. The effectiveness of UPL-EA in eliminating pseudo-labeling errors is both theoretically supported and experimentally validated. The experimental results show that our approach achieves competitive performance with limited prior alignment seeds.

Integrating Global Navigation Satellite Systems (GNSS) in Simultaneous Localization and Mapping (SLAM) systems draws increasing attention to a global and continuous localization solution. Nonetheless, in dense urban environments, GNSS-based SLAM systems will suffer from the Non-Line-Of-Sight (NLOS) measurements, which might lead to a sharp deterioration in localization results. In this paper, we propose to detect the sky area from the up-looking camera to improve GNSS measurement reliability for more accurate position estimation. We present Sky-GVINS: a sky-aware GNSS-Visual-Inertial system based on a recent work called GVINS. Specifically, we adopt a global threshold method to segment the sky regions and non-sky regions in the fish-eye sky-pointing image and then project satellites to the image using the geometric relationship between satellites and the camera. After that, we reject satellites in non-sky regions to eliminate NLOS signals. We investigated various segmentation algorithms for sky detection and found that the Otsu algorithm reported the highest classification rate and computational efficiency, despite the algorithm's simplicity and ease of implementation. To evaluate the effectiveness of Sky-GVINS, we built a ground robot and conducted extensive real-world experiments on campus. Experimental results show that our method improves localization accuracy in both open areas and dense urban environments compared to the baseline method. Finally, we also conduct a detailed analysis and point out possible further directions for future research. For detailed information, visit our project website at https://github.com/SJTU-ViSYS/Sky-GVINS.

Spin and mass properties provide essential clues in distinguishing the origins of coalescing black holes (BHs). With a dedicated semiparametric population model for the coalescing binary black holes (BBHs), we identify two distinct categories of BHs among the GWTC-3 events, which is {favored over the one population scenario by} a logarithmic Bayes factor ($\ln\mathcal{B}$) of 7.5. One category, with a mass ranging from $\sim 25M_\odot$ to $\sim 80M_\odot$, is distinguished by the high spin magnitudes ($\sim0.75$) and consistent with the hierarchical merger origin. The other category, characterized by low spins, has a sharp mass cutoff at $\sim 40M_\odot$, which is natural for the stellar-collapse origin and in particular the pair-instability explosion of massive stars. We infer the local hierarchical merger rate density as $0.46^{+0.61}_{-0.24}~{\rm Gpc^{-3}yr^{-1}}$. Additionally, we find that a fraction of the BBHs has a cosine-spin-tilt-angle distribution concentrated preferentially around $1$, and the fully isotropic distribution for spin orientation is disfavored by a $\ln\mathcal{B}$ of -6.3, suggesting that the isolated field evolution channels are contributing to the total population., Comment: Main text: 6 pages and 4 figures; Supplementary: 26 pages, 31 figures, and 4 tables; this version has been accepted by Phys.Rev.Lett; doi: https://doi.org/10.1103/PhysRevLett.133.051401