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With “blessings” from the original Jibo founders, iKairos is a wearable or desk-mounted “AI journal” that turns your family moments into AI images and video
Gravitational wave astronomy is rapidly advancing with the development of new observatories, leading to an increasing volume and complexity of data. This trend places growing pressure on classical data analysis methods and motivates the exploration of quantum approaches. In this work, we introduce a quantum matched filtering framework for gravitational-wave detection based on the Long algorithm, marking its first application to the gravitational-wave data analysis. Numerical simulations show that the proposed approach preserves the quadratic speedup of quantum search while exhibiting significantly improved robustness, thereby overcoming key limitations of the Grover-algorithm based methods.
LISA Pathfinder (LPF) is the technology demonstration mission for the future Laser Interferometer Space Antenna (LISA). Besides the science interferometric channel, LPF is equipped with numerous auxiliary channels that monitor the instrument and environment disturbances, some of which contain information correlated with noise in the science channel. In this work, we investigate cross-talk noise subtraction in LPF using a frequency-domain transfer-function approach, in which the coherence between the science channel and auxiliary channels is used to estimate and remove correlated noise. The effectiveness of this method is first validated using simulated science and auxiliary channel data with a common disturbance. The simulation shows that the subtraction performance strongly depends on the auxiliary-channel noise level, providing a practical framework for determining the auxiliary-channel noise requirement needed to achieve a desired subtraction performance. This method is then used to analyze 9 auxiliary channels in publicly available LPF telemetry data. In the cross-talk dominated frequency band from $1\times10^{-2}$ to $6\times10^{-2}\,\mathrm{Hz}$, this method achieves cross-ta
A Conditional Variational Autoencoder (CVAE) model is employed for parameter inference on gravitational waves (GW) signals of massive black hole binaries, considering joint observations with a network of three space-based GW detectors. Our experiments show that the trained CVAE model can estimate the posterior distribution of source parameters in approximately one second, while the standard Bayesian sampling method, utilizing parallel computation across 16 CPU cores, takes an average of 20 hours for a GW signal instance. However, the sampling distributions from CVAE exhibit lighter tails, appearing broader when compared to the standard Bayesian sampling results. By using CVAE results to constrain the prior range for Bayesian sampling, the sampling time is reduced by a factor of $\sim$6 while maintaining the similar precision of the Bayesian results.
Robust speaker verification under noisy conditions remains an open challenge. Conventional deep learning methods learn a robust unified speaker representation space against diverse background noise and achieve significant improvement. In contrast, this paper presents a noise-conditioned mixture-ofexperts framework that decomposes the feature space into specialized noise-aware subspaces for speaker verification. Specifically, we propose a noise-conditioned expert routing mechanism, a universal model based expert specialization strategy, and an SNR-decaying curriculum learning protocol, collectively improving model robustness and generalization under diverse noise conditions. The proposed method can automatically route inputs to expert networks based on noise information derived from the inputs, where each expert targets distinct noise characteristics while preserving speaker identity information. Comprehensive experiments demonstrate consistent superiority over baselines
Convolutional neural networks (CNNs) have become widely adopted in gravitational wave (GW) detection pipelines due to their ability to automatically learn hierarchical features from raw strain data. However, the physical meaning of these learned features remains underexplored, limiting the interpretability of such models. In this work, we propose a hybrid architecture that combines a CNN-based feature extractor with a random forest (RF) classifier to improve both detection performance and interpretability. Unlike prior approaches that directly connect classifiers to CNN outputs, our method introduces four physically interpretable metrics - variance, signal-to-noise ratio (SNR), waveform overlap, and peak amplitude - computed from the final convolutional layer. These are jointly used with the CNN output in the RF classifier to enable more informed decision boundaries. Tested on long-duration strain datasets, our hybrid model outperforms a baseline CNN model, achieving a relative improvement of 21\% in sensitivity at a fixed false alarm rate of 10 events per month. Notably, it also shows improved detection of low-SNR signals (SNR $\le$ 10), which are especially vulnerable to misclass
Matched filtering is a common method for detecting gravitational waves. However, the computational costs of searching large template banks limit the efficiency of classical algorithms when searching for massive black hole binary (MBHB) systems. This work explores the application of a quantum matched filtering algorithm based on Grover's algorithm to MBHB signals. Under certain simplifying assumptions, quantum approach can reduce the computational complexity from $O(N)$ to $O(\sqrt{N})$ theoretically, where $N$ is the size of the template bank. Simulated results illustrate the potential reduction in computational costs, while also showing that the performance can degrade in some cases due to instability of the algorithm. These findings reveal both the potential and the limitations of quantum search for MBHB signals, pointing to the importance of developing more robust and stable search strategies alongside realistic modeling in future work.
Learning robust speaker representations under noisy conditions presents significant challenges, which requires careful handling of both discriminative and noise-invariant properties. In this work, we proposed an anchor-based stage-wise learning strategy for robust speaker representation learning. Specifically, our approach begins by training a base model to establish discriminative speaker boundaries, and then extract anchor embeddings from this model as stable references. Finally, a copy of the base model is fine-tuned on noisy inputs, regularized by enforcing proximity to their corresponding fixed anchor embeddings to preserve speaker identity under distortion. Experimental results suggest that this strategy offers advantages over conventional joint optimization, particularly in maintaining discrimination while improving noise robustness. The proposed method demonstrates consistent improvements across various noise conditions, potentially due to its ability to handle boundary stabilization and variation suppression separately.
Massive black hole binaries (MBHBs) and other sources within the frequency band of spaceborne gravitational wave observatories like the Laser Interferometer Space Antenna (LISA), Taiji and Tianqin pose unique challenges, as gaps and glitches during the years-long observation lead to both loss of information and spectral leakage. We propose a novel data imputation strategy based on Kalman filter and smoother to mitigate gap-induced biases in parameter estimation. Applied to a scenario where traditional windowing and smoothing technique introduce significant biases, our method mitigates the biases and demonstrates lower computational cost compared to existing data augmentation techniques such as noise inpainting. This framework presents a new gap treatment approach that balances robustness and efficiency for space-based gravitational wave data analysis.
In this work, we systematically investigate the capability of space-based gravitational wave detectors in constraining parameters of non-tensor polarization modes. Using Bayesian inference and Fisher Information Matrix methods, we analyze gravitational wave signals from the inspiral phase of supermassive binary black hole mergers. By starting with time-domain signals and applying Fourier transforms, we avoid the use of the stationary phase approximation. We found an asymmetry in the estimation of the vector-mode parameter $α_x$ at inclination angles $ι= 0$ and $ι= π$, which has not been explicitly pointed out in previous studies. We also observe strong correlations between scalar-mode parameters, $α_b$ and $α_l$, which currently limit their independent estimation. These findings underscore the importance of using complete inspiral-merger-ringdown waveforms to enhance the ability to distinguish the non-tensor polarization modes. Finally, we employ a new LISA-Taiji network configuration, in which the orientation of spacecrafts of Taiji maintains a fixed phase offset relative to these of LISA. Under the adiabatic approximation and the assumption of equal arms, this phase is found to h
With the rapid growth of the aviation industry, there is a need for a large number of flight crew. How to select the right pilots in a cost-efficient manner has become an important research question. In the current study, twenty-three pilots were recruited from China Eastern Airlines, and 23 novices were from the community of Tsinghua University. A novel approach incorporating machine learning and virtual reality technology was applied to distinguish features between these participants with different flight skills. Results indicate that SVM with the MIC feature selection method consistently achieved the highest prediction performance on all metrics with an Accuracy of 0.93, an AUC of 0.96, and an F1 of 0.93, which outperforms four other classifier algorithms and two other feature selection methods. From the perspective of feature selection methods, the MIC method can select features with a nonlinear relationship to sampling labels, instead of a simple filter-out. Our new implementation of the SVM + MIC algorithm outperforms all existing pilot selection algorithms and perhaps provides the first implementation based on eye tracking and flight dynamics data. This study's VR simulation
Timely and effective load shedding in power systems is critical for maintaining supply-demand balance and preventing cascading blackouts. To eliminate load shedding bias against specific regions in the system, optimization-based methods are uniquely positioned to help balance between economic and fairness considerations. However, the resulting optimization problem involves complex constraints, which can be time-consuming to solve and thus cannot meet the real-time requirements of load shedding. To tackle this challenge, in this paper we present an efficient machine learning algorithm to enable millisecond-level computation for the optimization-based load shedding problem. Numerical studies on both a 3-bus toy example and a realistic RTS-GMLC system have demonstrated the validity and efficiency of the proposed algorithm for delivering fairness-aware and real-time load shedding decisions.
The fabrication of whispering gallery lasers (WGL) is used to experimentally evaluate the evaporation rate (mol/$μ$m) and ratio (mol/mol) of erbium and silica lost from a doped fiber during heating. Fixed lengths of doped silica fiber are spliced to different lengths of undoped fiber and then evaporated by feeding into the focus of a CO$_{2}$ laser. During evaporation, erbium ions are precipitated in the doped silica fiber to control the erbium concentration in the remaining SiO$_2$, which is melted into a microsphere. By increasing the length of the undoped section, a critical point is reached where effectively no ions remain in the glass microsphere. The critical point is found using the lasing spectra of the whispering gallery modes in microspheres with equal sizes. From the critical point, it is estimated that, for a given CO$_{2}$ laser power, $6.36 \times 10^{-21}$~mol of Er$^{3+}$ is lost during the evaporation process for every cubic micron of silica fiber. This is equivalent to $1.74 \times 10^{-7}$~mol of Er$^{3+}$ lost per mol of SiO$_{2}$ evaporated. This result facilitates the control of the doping concentration in WGLs and provides insight into the kinetics of laser-i
Taiji program is a space mission aiming to detect gravitational waves in the low frequency band. Taiji-1 is the first technology demonstration satellite of the Taiji Program in Space, with the gravitational reference sensor (GRS) serving as one of its key scientific payloads. For accurate accelerometer measurements, the test-mass center of the GRS must be positioned precisely at the center of gravity of the satellite to avoid measurement disturbances caused by angular acceleration and gradient. Due to installation and measurement errors, fuel consumption during in-flight phase, and other factors, the offset between the test-mass center and the center-of-mass (COM) of the satellite can be significant, degrading the measurement accuracy of the accelerometer. Therefore, the offset needs to be estimated and controlled within the required range by the center-of-mass adjustment mechanism during the satellite's lifetime. In this paper, we present a novel method, the Extended Kalman Filter combined with Rauch-Tung-Striebel Smoother, to estimate the offset, while utilizing the chi-square test to eliminate outliers. Additionally, the nonlinear Least Squares estimation algorithm is employed a
Recently, deep neural network (DNN)-based physical layer communication techniques have attracted considerable interest. Although their potential to enhance communication systems and superb performance have been validated by simulation experiments, little attention has been paid to the theoretical analysis. Specifically, most studies in the physical layer have tended to focus on the application of DNN models to wireless communication problems but not to theoretically understand how does a DNN work in a communication system. In this paper, we aim to quantitatively analyze why DNNs can achieve comparable performance in the physical layer comparing with traditional techniques, and also drive their cost in terms of computational complexity. To achieve this goal, we first analyze the encoding performance of a DNN-based transmitter and compare it to a traditional one. And then, we theoretically analyze the performance of DNN-based estimator and compare it with traditional estimators. Third, we investigate and validate how information is flown in a DNN-based communication system under the information theoretic concepts. Our analysis develops a concise way to open the "black box" of DNNs in
A giant planet had been hiding inside one of astronomy’s most closely studied planetary systems, concealed by a bright disk of cosmic dust。 Webb revealed Beta Pictoris d through atmospheric traces of carbon monoxide, water vapor, and methane, showcasing a promising new method for finding elusive exoplanets
Researchers are applying evolutionary theory to cancer by changing treatments before tumors have time to develop resistance。 Mathematical models suggest that rapid, carefully timed switches between multiple therapies could improve cure rates
Scientists say new technologies have reopened the debate over whether Mars could someday be terraformed, turning a once impossible idea into a serious research topic。 Before anyone tries to reshape the Red Planet, though, researchers say we must understand the risks, including what might be lost if Mars already harbors its own forms of life
NASA has chosen 41 commercial technology projects that could solve critical challenges for future missions to the Moon and Mars。 From powering lunar outposts to protecting spacecraft from Moon dust, the innovations are designed to push both space exploration and the commercial space economy forward