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We present an investigation into the rotation and stellar activity of four fully convective M dwarf `twin' wide binaries. Components in each pair have (1) astrometry confirming they are common-proper-motion binaries, (2) Gaia $BP$, $RP$, and 2MASS $J$, $H$, and $K_s$ magnitudes matching within 0.10 mag, and (3) presumably the same age and composition. We report long-term photometry, rotation periods, multi-epoch H$\alpha$ equivalent widths, X-ray luminosities, time series radial velocities, and speckle observations for all components. Although it might be expected for the twin components to have matching magnetic attributes, this is not the case. Decade-long photometry of GJ 1183 AB indicates consistently higher spot activity on A than B, a trend matched by A appearing 58$\pm$9% stronger in $L_X$ and 26$\pm$9% stronger in H$\alpha$ on average -- this is despite similar rotation periods of A=0.86d and B=0.68d, thereby informing the range in activity for otherwise identical and similarly-rotating M dwarfs. The young $\beta$ Pic Moving Group member 2MA 0201+0117 AB displays a consistently more active B component that is 3.6$\pm$0.5 times stronger in $L_X$ and 52$\pm$19% stronger in H$\alpha$ on average, with distinct rotation at A=6.01d and B=3.30d. Finally, NLTT 44989 AB displays remarkable differences with implications for spindown evolution -- B has sustained H$\alpha$ emission while A shows absorption, and B is $\geq$39$\pm$4 times stronger in $L_X$, presumably stemming from the surprisingly different rotation periods of A=38d and B=6.55d. The last system, KX Com, has an unresolved radial velocity companion, and is therefore not a twin system., Comment: 40 pages, 17 figures, 6 tables, submitted to AJ on 2024-09-20

Kerr-Newman black holes in a de Sitter space have the limit of rotating Nariai black holes with the near-horizon geometry of a warped ${\rm dS}_3 \times {\rm S}^1/Z_2$ when the black hole horizon and the cosmological horizon coincide or approach close to each other. We study the effect of rotation on the emission of charges in the near-extremal rotating charged Nariai black hole and compare it to those from the near-extremal Nariai black hole and near-extremal Kerr-Newman black hole in de Sitter space. The emission has an exponential amplification for charges with high energy and becomes catastrophic when the two horizons are very close to each together. The angular momentum of black holes decreases the mean number of charges by a factor not by an order. We observe a catastrophic emission of boson condensation for charges with the effective energy equal to the chemical potential in the spacelike outer region of the cosmological horizon. Further, the rotating charged Nariai black holes can evolve into singular spacetimes with a naked singularity by the Schwinger pair production., Comment: 11 pages, 6 figures. arXiv admin note: text overlap with arXiv:2309.00218

Electroencephalography (EEG) is a critical tool in neuroscience and clinical practice for monitoring and analyzing brain activity. Traditional neural network models, such as EEGNet, have achieved considerable success in decoding EEG signals but often struggle with the complexity and high dimensionality of the data. Recent advances in quantum computing present new opportunities to enhance machine learning models through quantum machine learning (QML) techniques. In this paper, we introduce Quantum-EEGNet (QEEGNet), a novel hybrid neural network that integrates quantum computing with the classical EEGNet architecture to improve EEG encoding and analysis, as a forward-looking approach, acknowledging that the results might not always surpass traditional methods but it shows its potential. QEEGNet incorporates quantum layers within the neural network, allowing it to capture more intricate patterns in EEG data and potentially offering computational advantages. We evaluate QEEGNet on a benchmark EEG dataset, BCI Competition IV 2a, demonstrating that it consistently outperforms traditional EEGNet on most of the subjects and other robustness to noise. Our results highlight the significant potential of quantum-enhanced neural networks in EEG analysis, suggesting new directions for both research and practical applications in the field., Comment: 7 pages, 4 figures

Quantum private query (QPQ) is the quantum version for symmetrically private retrieval. However, the user privacy in QPQ is generally guarded in the non-realtime and cheat sensitive way. That is, the dishonest database holder's cheating to elicit user privacy can only be discovered after the protocol is finished (when the user finds some errors in the retrieved database item). Such delayed detection may cause very unpleasant results for the user in real-life applications. Current efforts to protect user privacy in realtime in existing QPQ protocols mainly use two techniques, i.e., adding an honesty checking on the database or allowing the user to reorder the qubits. We reexamine these two kinds of QPQ protocols and find neither of them can work well. We give concrete cheating strategies for both participants and show that honesty checking of inner participant should be dealt more carefully in for example the choosing of checking qubits. We hope such discussion can supply new concerns when detection of dishonest participant is considered in quantum multi-party secure computations.

Decoding images from non-invasive electroencephalographic (EEG) signals has been a grand challenge in understanding how the human brain process visual information in real-world scenarios. To cope with the issues of signal-to-noise ratio and nonstationarity, this paper introduces a MUltimodal Similarity-keeping contrastivE learning (MUSE) framework for zero-shot EEG-based image classification. We develop a series of multivariate time-series encoders tailored for EEG signals and assess the efficacy of regularized contrastive EEG-Image pretraining using an extensive visual EEG dataset. Our method achieves state-of-the-art performance, with a top-1 accuracy of 19.3% and a top-5 accuracy of 48.8% in 200-way zero-shot image classification. Furthermore, we visualize neural patterns via model interpretation, shedding light on the visual processing dynamics in the human brain. The code repository for this work is available at: https://github.com/ChiShengChen/MUSE_EEG., Comment: 19 pages, 14 figures

We present the optical photometric variability of 32 planet-hosting M dwarfs within 25 parsecs over timescales of months to decades. The primary goal of this project, ATLAS -- A Trail to Life Around Stars, is to follow the trail to life by revealing nearby M dwarfs with planets that are also "quiet", which may make them more amiable to habitability. There are 69 reported exoplanets orbiting the 32 stars discussed here, providing a rich sample of worlds for which environmental evaluations are needed. We examine the optical flux environments of these planets over month-long timescales for 23 stars observed by TESS, and find that 17 vary by less than 1% ($\sim$11 mmag). All 32 stars are being observed at the CTIO/SMARTS 0.9 m, with a median duration of 19.1 years of optical photometric data in the $VRI$ bands. We find over these extended timescales that six stars show optical flux variations less than 2%, 25 vary from 2--6% ($\sim$22-67 mmag), and only one, Proxima Centauri, varies by more than 6%. Overall, LHS 1678 exhibits the lowest optical variability levels measured over all timescales examined, thereby providing one of the most stable photometric environments among planets reported around M dwarfs within 25 parsecs. More than 600 of the nearest M dwarfs are being observed at the 0.9 m in the RECONS program that began in 1999, and many more planet hosts will undoubtedly be revealed, providing more destinations to be added to the ATLAS sample in the future., Comment: Published in the Astronomical Journal. 27 pages, 12 figures, 2 tables

Steady-state visual-evoked potential (SSVEP)-based brain-computer interfaces (BCIs) offer a non-invasive means of communication through high-speed speller systems. However, their efficiency heavily relies on individual training data obtained during time-consuming calibration sessions. To address the challenge of data insufficiency in SSVEP-based BCIs, we present SSVEP-DAN, the first dedicated neural network model designed for aligning SSVEP data across different domains, which can encompass various sessions, subjects, or devices. Our experimental results across multiple cross-domain scenarios demonstrate SSVEP-DAN's capability to transform existing source SSVEP data into supplementary calibration data, significantly enhancing SSVEP decoding accuracy in scenarios with limited calibration data. We envision SSVEP-DAN as a catalyst for practical SSVEP-based BCI applications with minimal calibration. The source codes in this work are available at: https://github.com/CECNL/SSVEP-DAN.

The Cholinergic Pathways Hyperintensities Scale (CHIPS) is a visual rating scale used to assess the extent of cholinergic white matter hyperintensities in T2-FLAIR images, serving as an indicator of dementia severity. However, the manual selection of four specific slices for rating throughout the entire brain is a time-consuming process. Our goal was to develop a deep learning-based model capable of automatically identifying the four slices relevant to CHIPS. To achieve this, we trained a 4-class slice classification model (BSCA) using the ADNI T2-FLAIR dataset (N=150) with the assistance of ResNet. Subsequently, we tested the model's performance on a local dataset (N=30). The results demonstrated the efficacy of our model, with an accuracy of 99.82% and an F1-score of 99.83%. This achievement highlights the potential impact of BSCA as an automatic screening tool, streamlining the selection of four specific T2-FLAIR slices that encompass white matter landmarks along the cholinergic pathways. Clinicians can leverage this tool to assess the risk of clinical dementia development efficiently., Comment: 8 pages, 2 figures, 2 tables

In this paper, an improved version of QTS (Quantum-inspired Tabu Search) has been proposed, which enhances the utilization of population information, called "amplitude-ensemble" QTS (AE-QTS). This makes AE-QTS more similar to the real quantum search algorithm, Grover Search Algorithm, in abstract concept, while keeping the simplicity of the algorithm. Later, we demonstrate the AE-QTS on the classical combinatorial optimization 0/1 knapsack problem. Experimental results show that the AE-QTS outperforms other algorithms, including the QTS, by at least an average of 20% in all cases and even by 30% in some cases. Even as the problem complexity increases, the quality of the solutions found by our method remains superior to that of the QTS. These results prove that our method has better search performance., Comment: 7 pages, 7 figures

Using both the in-out formalism and the monodromy method, we study the emission of charges from near-extremal charged Nariai black holes with the black hole event and cosmological horizons close to each other, whose near-horizon geometry is $\mathrm{dS}_2 \times \mathrm{S}^2$. The emission becomes catastrophic for a charge with energy greater than its chemical potential, whose leading exponential factor increases inversely proportional to the separation of two horizons. This effect may prevent near-extremal Nariai black holes with large charges that evaporate dominantly through the charge emission from evolving to black holes with a naked singularity, in analog to near-extremal RN-dS black holes that have the Breitenlohner-Friedman bound, below which they become stable against Hawking radiation and Schwinger effect of charge emission. The near-extremal Nariai black holes with small charges, which are close to near-extremal Schwarzschild-dS black holes, emit dominantly charge-neutral particles and evolve to black holes with increasing charge to mass ratio. We illuminate the origin of the catastrophic emission in the phase-integral formulation and monodromy method by comparing near-extremal charged Nariai black holes with near-extremal RN-dS black holes., Comment: v2: revised version including corrections of mistakes and more physical discussions, v3: with a new diagram and more discussions

In this study, we address the interpretability issue in complex, black-box Machine Learning models applied to sequence data. We introduce the Model-Based tree Hidden Semi-Markov Model (MOB-HSMM), an inherently interpretable model aimed at detecting high mortality risk events and discovering hidden patterns associated with the mortality risk in Intensive Care Units (ICU). This model leverages knowledge distilled from Deep Neural Networks (DNN) to enhance predictive performance while offering clear explanations. Our experimental results indicate the improved performance of Model-Based trees (MOB trees) via employing LSTM for learning sequential patterns, which are then transferred to MOB trees. Integrating MOB trees with the Hidden Semi-Markov Model (HSMM) in the MOB-HSMM enables uncovering potential and explainable sequences using available information., Comment: Published as a workshop paper at IEEE MIPR 2023

Since identifying the gap in the H-R Diagram (HRD) marking the transition between partially and fully convective interiors, a unique type of slowly pulsating M dwarf has been proposed. These unstable M dwarfs provide new laboratories in which to understand how changing interior structures can produce potentially observable activity at the surface. In this work, we report the results of the largest high-resolution spectroscopic H$\alpha$ emission survey to date spanning this transition region, including 480 M dwarfs observed using the CHIRON spectrograph at CTIO/SMARTS 1.5-m. We find that M dwarfs with H$\alpha$ in emission are almost entirely found 0 to 0.5 magnitude above the top edge of the gap in the HRD, whereas effectively no stars in and below the gap show emission. Thus, the top edge of the gap marks a relatively sharp activity transition, and there is no anomalous H$\alpha$ activity for stars in the gap. We also identify a new region at 10.3 $

Goal 1 of the National Academies of Science, Engineering and Mathematics Exoplanet Science Strategy is "to understand the formation and evolution of planetary systems as products of the process of star formation, and characterize and explain the diversity of planetary system architectures, planetary compositions, and planetary environments produced by these processes", with the finding that "Current knowledge of the demographics and characteristics of planets and their systems is substantially incomplete." One significant roadblock to our ongoing efforts to improve our demographics analyses is the lack of comprehensive meta-data accompanying published exoplanet surveys. The Exoplanet Program Analysis Group (ExoPAG) Science Interest Group 2: Exoplanet Demographics has prepared this document to provide guidance to survey architects, authors, referees and funding agencies as to the most valuable such data products for five different exoplanet detection techniques - transit, radial velocity, direct imaging, microlensing and astrometry. We find that making these additional data easily available would greatly enhance the community's ability to perform robust, reproducible demographics analyses, and make progress on achieving the most important goals identified by the exoplanet and wider astronomical community., Comment: 21 pages, final report after community feedback addressed