Search for Continuous Gravitational Wave Signals in Pulsar Timing Residuals: A New Scalable Approach with Diffusive Nested Sampling

Detecting continuous nanohertz gravitational waves (GWs) generated by individual close binaries of supermassive black holes (CB-SMBHs) is one of the primary objectives of pulsar timing arrays (PTAs). The detection sensitivity is slated to increase significantly as the number of well-timed millisecond pulsars will increase by more than an order of magnitude with the advent of next-generation radio telescopes. Currently, the Bayesian analysis pipeline using parallel tempering Markov chain Monte Carlo has been applied in multiple studies for CB-SMBH searches, but it may be challenged by the high dimensionality of the parameter space for future large-scale PTAs. One solution is to reduce the dimensionality by maximizing or marginalizing over uninformative parameters semi-analytically, but it is not clear whether this approach can be extended to more complex signal models without making overly simplified assumptions. Recently, the method of diffusive nested (DNest) sampling shown the capability of coping with high dimensionality and multimodality effectively in Bayesian analysis. In this paper, we apply DNest to search for continuous GWs in simulated pulsar timing residuals and find that it performs well in terms of accuracy, robustness, and efficiency for a PTA including $mathcal{O}(10^2)$ pulsars. DNest also allows a simultaneous search of multiple sources elegantly, which demonstrates its scalability and general applicability. Our results show that it is convenient and also high beneficial to include DNest in current toolboxes of PTA analysis.

Improving Fake News Detection by Using an Entity-enhanced Framework to Fuse Diverse Multimodal Clues

Recently, fake news with text and images have achieved more effective diffusion than text-only fake news, raising a severe issue of multimodal fake news detection. Current studies on this issue have made significant contributions to developing multimodal models, but they are defective in modeling the multimodal content sufficiently. Most of them only preliminarily model the basic semantics of the images as a supplement to the text, which limits their performance on detection. In this paper, we find three valuable text-image correlations in multimodal fake news: entity inconsistency, mutual enhancement, and text complementation. To effectively capture these multimodal clues, we innovatively extract visual entities (such as celebrities and landmarks) to understand the news-related high-level semantics of images, and then model the multimodal entity inconsistency and mutual enhancement with the help of visual entities. Moreover, we extract the embedded text in images as the complementation of the original text. All things considered, we propose a novel entity-enhanced multimodal fusion framework, which simultaneously models three cross-modal correlations to detect diverse multimodal fake news. Extensive experiments demonstrate the superiority of our model compared to the state of the art.

Distributed Learning for Time-varying Networks: A Scalable Design

The wireless network is undergoing a trend from onnection of things to connection of intelligence. With data spread over the communication networks and computing capability enhanced on the devices, distributed learning becomes a hot topic in both industrial and academic communities. Many frameworks, such as federated learning and federated distillation, have been proposed. However, few of them takes good care of obstacles such as the time-varying topology resulted by the characteristics of wireless networks. In this paper, we propose a distributed learning framework based on a scalable deep neural network (DNN) design. By exploiting the permutation equivalence and invariance properties of the learning tasks, the DNNs with different scales for different clients can be built up based on two basic parameter sub-matrices. Further, model aggregation can also be conducted based on these two sub-matrices to improve the learning convergence and performance. Finally, simulation results verify the benefits of the proposed framework by compared with some baselines.

Towards fully ab initio simulation of atmospheric aerosol nucleation

Atmospheric aerosol nucleation contributes to around half of cloud condensation nuclei (CCN) globally and the nucleated particles can grow larger to impact air quality and consequently human health. Despite the decades efforts, the detailed nucleation mechanism is still poorly understood. The ultimate goal of theoretical understanding aerosol nucleation is to simulate nucleation in ambient condition. However, there is lack of accurate reactive force field. Here for the first time, we propose the reactive force field with good size scalability for nucleation systems based on deep neural network and further bridge the simulation in the limited box with cluster kinetics towards boosting the aerosol simulation to be fully ab initio. We found that the formation rates based on hard sphere collision rate constants tend to be underestimated. Besides, the framework here is transferable to other nucleation systems, potentially revolutionizing the atmospheric aerosol nucleation field.

Rethinking Sampling Strategies for Unsupervised Person Re-identification

Unsupervised person re-identification (re-ID) remains a challenging task. While extensive research has focused on the framework design or loss function, we show in this paper that sampling strategy plays an equally important role. We analyze the reasons for differences in performance between various sampling strategies under the same framework and loss function. We suggest that deteriorated over-fitting is an important factor causing poor performance, and enhancing statistical stability can rectify this issue. Inspired by that, a simple yet effective approach is proposed, known as group sampling, which gathers groups of samples from the same class into a mini-batch. The model is thereby trained using normalized group samples, which helps to alleviate the effects associated with a single sample. Group sampling updates the pipeline of pseudo label generation by guaranteeing that samples are more efficiently divided into the correct classes. Group sampling regulates the representation learning process, which enhances statistical stability for feature representation in a progressive fashion. Qualitative and quantitative experiments on Market-1501, DukeMTMC-reID, and MSMT17 show that group sampling improves upon state-of-the-art methods by between 3.3%~6.1%. Code has been available at https://github.com/ucas-vg/GroupSampling.

Disorder-induced chiral and helical Majorana edge modes in a two-dimensional Ammann-Beenker quasicrystal

The recent research of disorder effects on topological phases in quasicrystalline systems has received much attention. In this work, by numerically computing the (spin) Bott index and the thermal conductance, we reveal the effects of disorder on a class D chiral and a class DIII time-reversal invariant topological superconductors in a two-dimensional Ammann-Beenker tiling quasicrystalline lattice. We demonstrate that both the topologically protected chiral and helical Majorana edge modes are robust against weak disorder in the quasicrystalline lattice. More fascinating is the discovery of disorder-induced topologically nontrivial phases exhibiting chiral and helical Majorana edge modes in class D and DIII topological superconductor systems, respectively. Our findings open the door for the research on disorder-induced Majorana edge modes in quasicrystalline systems.

Perception-Aware Multi-Sensor Fusion for 3D LiDAR Semantic Segmentation

3D LiDAR (light detection and ranging) semantic segmentation is important in scene understanding for many applications, such as auto-driving and robotics. For example, for autonomous cars equipped with RGB cameras and LiDAR, it is crucial to fuse complementary information from different sensors for robust and accurate segmentation. Existing fusion-based methods, however, may not achieve promising performance due to the vast difference between the two modalities. In this work, we investigate a collaborative fusion scheme called perception-aware multi-sensor fusion (PMF) to exploit perceptual information from two modalities, namely, appearance information from RGB images and spatio-depth information from point clouds. To this end, we first project point clouds to the camera coordinates to provide spatio-depth information for RGB images. Then, we propose a two-stream network to extract features from the two modalities, separately, and fuse the features by effective residual-based fusion modules. Moreover, we propose additional perception-aware losses to measure the perceptual difference between the two modalities. Extensive experiments on two benchmark data sets show the superiority of our method. For example, on nuScenes, our PMF outperforms the state-of-the-art method by 0.8 in mIoU.

3D Object Detection for Autonomous Driving: A Survey

Autonomous driving is regarded as one of the most promising remedies to shield human beings from severe crashes. To this end, 3D object detection serves as the core basis of such perception system especially for the sake of path planning, motion prediction, collision avoidance, etc. Generally, stereo or monocular images with corresponding 3D point clouds are already standard layout for 3D object detection, out of which point clouds are increasingly prevalent with accurate depth information being provided. Despite existing efforts, 3D object detection on point clouds is still in its infancy due to high sparseness and irregularity of point clouds by nature, misalignment view between camera view and LiDAR birds eye of view for modality synergies, occlusions and scale variations at long distances, etc. Recently, profound progress has been made in 3D object detection, with a large body of literature being investigated to address this vision task. As such, we present a comprehensive review of the latest progress in this field covering all the main topics including sensors, fundamentals, and the recent state-of-the-art detection methods with their pros and cons. Furthermore, we introduce metrics and provide quantitative comparisons on popular public datasets. The avenues for future work are going to be judiciously identified after an in-deep analysis of the surveyed works. Finally, we conclude this paper.

Making Sense of Complex Sensor Data Streams

This concept paper draws from our previous research on individual grip force data collected from biosensors placed on specific anatomical locations in the dominant and non dominant hands of operators performing a robot assisted precision grip task for minimally invasive endoscopic surgery. The specificity of the robotic system on the one hand, and that of the 2D image guided task performed in a real world 3D space on the other, constrain the individual hand and finger movements during task performance in a unique way. Our previous work showed task specific characteristics of operator expertise in terms of specific grip force profiles, which we were able to detect in thousands of highly variable individual data. This concept paper is focused on two complementary data analysis strategies that allow achieving such a goal. In contrast with other sensor data analysis strategies aimed at minimizing variance in the data, it is in this case here necessary to decipher the meaning of the full extent of intra and inter individual variance in the sensor data by using the appropriate statistical analyses, as shown in the first part of this paper. Then, it is explained how the computation of individual spatio temporal grip force profiles permits detecting expertise specific differences between individual users. It is concluded that these two analytic strategies are complementary. They enable drawing meaning from thousands of biosensor data reflecting human grip performance and its evolution with training, while fully taking into account their considerable inter and intra individual variability.

Accretion-modified Stars in Accretion Disks of Active Galactic Nuclei: Gravitational Wave Bursts and Electromagnetic Counterparts from Merging Stellar Black Hole Binaries

The recent advanced LIGO/Virgo detections of gravitational waves (GWs) from stellar binary black hole (BBH) mergers, in particular GW190521, which is potentially associated with a quasar, have stimulated renewed interest in active galactic nuclei (AGNs) as factories of merging BBHs. Compact objects evolving from massive stars are unavoidably enshrouded by a massive envelope to form accretion-modified stars (AMSs) in the dense gaseous environment of a supermassive black hole (SMBH) accretion disk. We show that most AMSs form binaries due to gravitational interaction with each other during radial migration in the SMBH disk, forming BBHs inside the AMS. When a BBH is born, its orbit is initially governed by the tidal torque of the SMBH. Bondi accretion onto BBH at a hyper-Eddington rate naturally develops and then controls the evolution of its orbits. We find that Bondi accretion leads to efficient removal of orbital angular momentum of the binary, whose final merger produces a GW burst. Meanwhile, the Blandford-Znajek mechanism pumps the spin energy of the merged BH to produce an electromagnetic counterpart (EMC). Moreover, hyper-Eddington accretion onto the BBH develops powerful outflows and triggers a Bondi explosion, which manifests itself as a EMC of the GW burst, depending on the viscosity of the accretion flow. Thermal emission from Bondi sphere appears as one of EMCs. BBHs radiate GWs with frequencies $sim 10^{2},$Hz, which are accessible to LIGO.