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Recent advancements in Natural Language Processing (NLP) have highlighted the potential of sentence embeddings in measuring semantic similarity (hereafter similarity). Yet, whether this approach can be used to analyze real-world dyadic interactions and predict people's emotional experiences in response to these interactions remains largely uncharted. To bridge this gap, the present study analyzes verbal conversations of 50 married couples who engage in naturalistic 10-minute conflict and 10-minute positive conversations. Transformer-based model General Text Embeddings-Large is employed to obtain the embeddings of the utterances from each speaker. The overall similarity of the conversations is then quantified by the average cosine similarity between the embeddings of adjacent utterances. Results show that lower similarity is associated with greater positive emotional experiences in the positive (but not conflict) conversation. Follow-up analyses show that (a) findings remain stable when controlling for marital satisfaction and the number of utterance pairs and (b) the similarity measure is valid in capturing critical features of a dyadic conversation. The present study underscores the potency of sentence embeddings in understanding links between interpersonal dynamics and individual emotional experiences, paving the way for innovative applications of NLP tools in affective and relationship science.

We consider privacy amplification against quantum side information by using regular random binning as an effective extractor. For constant-type sources, we obtain error exponent and strong converse bounds in terms of the so-called quantum Augustin information. Via type decomposition, we then recover the error exponent for independent and identically distributed sources proved by Dupuis [arXiv:2105.05342]. As an application, we obtain an achievable secrecy exponent for classical-quantum wiretap channel coding in terms of the Augustin information, which solves an open problem in [IEEE Trans.~Inf.~Theory, 65(12):7985, 2019]. Our approach is to establish an operational equivalence between privacy amplification and quantum soft covering; this may be of independent interest., Comment: 10 pages, submitted to 2023 IEEE Allerton Conference

The first measurement of two-particle angular correlations for charged particles produced in $e^+e^-$ annihilation up to $\sqrt{s}$ = 209 GeV is presented. Hadronic $e^+e^-$ data, archived at center-of-mass energies ranging from 91 to 209 GeV, were collected using the ALEPH detector at LEP between 1992 and 2000. The angular correlation functions have been measured across a wide range of pseudorapidities and the full azimuth in bins of charged particle multiplicity. This is the first such measurement using LEP-II data. With LEP-II data at 91 GeV, neither the beam coordinate analysis nor the thrust coordinate analysis reveals significant long-range correlations, consistent with the finding in the previous measurement with the LEP-I sample. Results for $e^+e^-$ data at energies above 91 GeV, which allow for higher event multiplicities reaching approximately 50, are presented for the first time. A long-range near-side excess in the correlation function has been identified in the thrust axis analysis. Moreover, the two-particle correlation functions were decomposed using a Fourier series, and the resulting Fourier coefficients $v_n$ were compared with event generator outputs. In events with high multiplicity, featuring more than 50 particles, the extracted $v_2$ and $v_3$ magnitudes from the data are higher than those from the Monte Carlo reference., Comment: 37 pages, 17 figures

High-fidelity singlet-triplet state readout is essential for large-scale quantum computing. However, the widely used threshold method of comparing a mean value with the fixed threshold will limit the judgment accuracy, especially for the relaxed triplet state, under the restriction of relaxation time and signal-to-noise ratio. Here, we achieve an enhanced latching readout based on Pauli spin blockade in a Si-MOS double quantum dot device and demonstrate an average singlet-triplet state readout fidelity of 97.59% by the threshold method. We reveal the inherent deficiency of the threshold method for the relaxed triplet state classification and introduce machine learning as a relaxation-independent readout method to reduce the misjudgment. The readout fidelity for classifying the simulated single-shot traces can be improved to 99.67% by machine learning method, better than the threshold method of 97.54% which is consistent with the experimental result. This work indicates that machine learning method can be a strong potential candidate for alleviating the restrictions of stably achieving high-fidelity and high-accuracy singlet-triplet state readout in large-scale quantum computing., Comment: 11 pages,11 figures

The core-collapse supernova (CCSN) is considered one of the most energetic astrophysical events in the universe. The early and prompt detection of neutrinos before (pre-SN) and during the supernova (SN) burst presents a unique opportunity for multi-messenger observations of CCSN events. In this study, we describe the monitoring concept and present the sensitivity of the system to pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), a 20 kton liquid scintillator detector currently under construction in South China. The real-time monitoring system is designed to ensure both prompt alert speed and comprehensive coverage of progenitor stars. It incorporates prompt monitors on the electronic board as well as online monitors at the data acquisition stage. Assuming a false alert rate of 1 per year, this monitoring system exhibits sensitivity to pre-SN neutrinos up to a distance of approximately 1.6 (0.9) kiloparsecs and SN neutrinos up to about 370 (360) kiloparsecs for a progenitor mass of 30 solar masses, considering both normal and inverted mass ordering scenarios. The pointing ability of the CCSN is evaluated by analyzing the accumulated event anisotropy of inverse beta decay interactions from pre-SN or SN neutrinos. This, along with the early alert, can play a crucial role in facilitating follow-up multi-messenger observations of the next galactic or nearby extragalactic CCSN., Comment: 24 pages, 9 figures, accepted for the publication at JCAP

Maps are essential to mobile robotics tasks like localization and planning. We propose the open street map (osm) XML based Area Graph file format to store hierarchical, topometric semantic multi-floor maps of indoor and outdoor environments, since currently no such format is popular within the robotics community. Building on-top of osm we leverage the available open source editing tools and libraries of osm, while adding the needed mobile robotics aspect with building-level obstacle representation yet very compact, topometric data that facilitates planning algorithms. Through the use of common osm keys as well as custom ones we leverage the power of semantic annotation to enable various applications. For example, we support planning based on robot capabilities, to take the locomotion mode and attributes in conjunction with the environment information into account. The provided C++ library is integrated into ROS. We evaluate the performance of osmAG using real data in a global path planning application on a very big osmAG map, demonstrating its convenience and effectiveness for mobile robots., Comment: 7 pages

In this study, we develop an active reconfigurable intelligent surface (RIS)-assisted multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) prototype compliant with the 5G New Radio standard at 3.5~GHz. The experimental results clearly indicate that active RIS plays a vital role in enhancing MIMO performance, surpassing passive RIS. Furthermore, when considering factors such as complexity, energy consumption, and performance, the comparative evaluation between passive RIS and active RIS reinforces the critical role of active RIS in MIMO systems. These findings underscore the practical significance of active RIS in improving MIMO gain in 5G scenarios., Comment: 5 pages, 5 figures, 1 table, accepted by IEEE Communications Letters, for demo video see: https://www.youtube.com/watch?v=3R6eZXizwns

This study conducts a theoretical investigation into the signal amplification arising from multiple Rabi sideband coherence within a one-dimensional waveguide quantum electrodynamics system. We utilize a semi-infinite waveguide to drive an anharmonic multi-level transmon with a strong coherent microwave field, examining the scattering behavior by introducing a probe signal. Our findings reveal signal amplification under specific resonant conditions, presenting spectra that reveal finer details than previously documented in the literature. To elucidate the mechanisms behind this amplification, we develop a model that explicitly accounts for multiple dressed sidebands in the presence of a strong driving field. From this model, we derive the reflection amplitude of the probe signal. Notably, our results indicate that amplification can occur due to either population inversion or, in some instances, through the constructive interference of multiple sidebands even in the absence of population inversion. Additionally, we explore how qubit dephasing impacts the amplification process.

A typical benchmark dataset for recommender system (RecSys) evaluation consists of user-item interactions generated on a platform within a time period. The interaction generation mechanism partially explains why a user interacts with (e.g., like, purchase, rate) an item, and the context of when a particular interaction happened. In this study, we conduct a meticulous analysis of the MovieLens dataset and explain the potential impact of using the dataset for evaluating recommendation algorithms. We make a few main findings from our analysis. First, there are significant differences in user interactions at the different stages when a user interacts with the MovieLens platform. The early interactions largely define the user portrait which affects the subsequent interactions. Second, user interactions are highly affected by the candidate movies that are recommended by the platform's internal recommendation algorithm(s). Third, changing the order of user interactions makes it more difficult for sequential algorithms to capture the progressive interaction process. We further discuss the discrepancy between the interaction generation mechanism that is employed by the MovieLens system and that of typical real-world recommendation scenarios. In summary, the MovieLens platform demonstrates an efficient and effective way of collecting user preferences to address cold-starts. However, models that achieve excellent recommendation accuracy on the MovieLens dataset may not demonstrate superior performance in practice, for at least two kinds of differences: (i) the differences in the contexts of user-item interaction generation, and (ii) the differences in user knowledge about the item collections. While results on MovieLens can be useful as a reference, they should not be solely relied upon as the primary justification for the effectiveness of a recommendation system model.

Small Object Detection (SOD) is an important machine vision topic because (i) a variety of real-world applications require object detection for distant objects and (ii) SOD is a challenging task due to the noisy, blurred, and less-informative image appearances of small objects. This paper proposes a new SOD dataset consisting of 39,070 images including 137,121 bird instances, which is called the Small Object Detection for Spotting Birds (SOD4SB) dataset. The detail of the challenge with the SOD4SB dataset is introduced in this paper. In total, 223 participants joined this challenge. This paper briefly introduces the award-winning methods. The dataset, the baseline code, and the website for evaluation on the public testset are publicly available., Comment: This paper is included in the proceedings of the 18th International Conference on Machine Vision Applications (MVA2023). It will be officially published at a later date. Project page : https://www.mva-org.jp/mva2023/challenge

In this work, we present an efficient and quantization-aware panoptic driving perception model (Q- YOLOP) for object detection, drivable area segmentation, and lane line segmentation, in the context of autonomous driving. Our model employs the Efficient Layer Aggregation Network (ELAN) as its backbone and task-specific heads for each task. We employ a four-stage training process that includes pretraining on the BDD100K dataset, finetuning on both the BDD100K and iVS datasets, and quantization-aware training (QAT) on BDD100K. During the training process, we use powerful data augmentation techniques, such as random perspective and mosaic, and train the model on a combination of the BDD100K and iVS datasets. Both strategies enhance the model's generalization capabilities. The proposed model achieves state-of-the-art performance with an mAP@0.5 of 0.622 for object detection and an mIoU of 0.612 for segmentation, while maintaining low computational and memory requirements.

The potential of the muon colliders open up new possibilities for the exploration of new physics beyond the Standard Model. It is worthwhile to investigate whether muon colliders are suitable for studying gluonic quartic gauge couplings~(gQGCs), which can be contributed by dimension-8 operators in the framework of the Standard Model effective field theory, and are intensively studied recently. In this paper, we study the sensitivity of the process $\mu^+\mu^-\to j j \nu\bar{\nu}$ to gQGCs. Our result indicate that the muon colliders with c.m. energies larger than $4\;{\rm TeV}$ can be more sensitive to gQGCs than the Large Hadron Collider., Comment: 20 pages, 5 figures

We propose a universal spin superconducting diode effect (SDE) induced by spin-orbit coupling (SOC), where the critical spin supercurrents in opposite directions are unequal. By analysis from both the Ginzburg-Landau theory and energy band analysis, we show that the spin-$\uparrow \uparrow$ and spin-$\downarrow \downarrow$ Cooper pairs possess opposite phase gradients and opposite momenta from the SOC, which leads to the spin SDE. Two superconductors with SOC, a $p$-wave superconductor as a toy model and a practical superconducting nanowire, are numerically studied and they both exhibit spin SDE. In addition, our theory also provides a unified picture for both spin and charge SDEs. Besides, we propose spin-polarized detection and nonlocal spin transport, as mature experimental technologies, to confirm the spin SDE in superconducting nanowires., Comment: 6 pages, 4 figures (+ supplementary materials 4 pages, 1 figure)

We discuss JUNO sensitivity to the annihilation of MeV dark matter in the galactic halo via detecting inverse beta decay reactions of electron anti-neutrinos resulting from the annihilation. We study possible backgrounds to the signature, including the reactor neutrinos, diffuse supernova neutrino background, charged- and neutral-current interactions of atmospheric neutrinos, backgrounds from muon-induced fast neutrons and cosmogenic isotopes. A fiducial volume cut, as well as the pulse shape discrimination and the muon veto are applied to suppress the above backgrounds. It is shown that JUNO sensitivity to the thermally averaged dark matter annihilation rate in 10 years of exposure would be significantly better than the present-day best limit set by Super-Kamiokande and would be comparable to that expected by Hyper-Kamiokande., Comment: 25 pages, 9 figures, matches the publised version

Large-scale pre-trained language models such as BERT are popular solutions for text classification. Due to the superior performance of these advanced methods, nowadays, people often directly train them for a few epochs and deploy the obtained model. In this opinion paper, we point out that this way may only sometimes get satisfactory results. We argue the importance of running a simple baseline like linear classifiers on bag-of-words features along with advanced methods. First, for many text data, linear methods show competitive performance, high efficiency, and robustness. Second, advanced models such as BERT may only achieve the best results if properly applied. Simple baselines help to confirm whether the results of advanced models are acceptable. Our experimental results fully support these points., Comment: Accepted by ACL 2023

The "search for extraterrestrial intelligence" (SETI) commensal surveys aim to scan the sky to find possible technosignatures from the extraterrestrial intelligence (ETI). The mitigation of radio frequency interference (RFI) is an important step, especially for the most sensitive Five-hundred-meter Aperture Spherical radio Telescope (FAST), which can detect more weak RFI. In this paper, we propose several new techniques for RFI mitigation, and use our procedure to search for ETI signals from the archival data of FAST's first SETI commensal survey. We detect the persistent narrowband RFI by setting a threshold of the signals' sky separation, and detect the drifting RFI (and potentially other types of RFI) using the Hough transform. We also use the clustering algorithms to remove more RFI and select candidates. The results of our procedure are compared to the earlier work on the same FAST data. We find that our methods, though relatively simpler in computation, remove more RFI (99.9912% compared to 99.9063% in the earlier work), but preserve the simulated ETI signals except those (5.1%) severely affected by the RFI. We also report more interesting candidate signals, about a dozen of which are new candidates that are not previously reported. In addition, we find that the proposed Hough transform method, with suitable parameters, also has the potential to remove the broadband RFI. We conclude that our methods can effectively remove the vast majority of the RFI while preserving and finding the candidate signals that we are interested in., Comment: 14 pages, 10 figures. AJ accepted

Cosmology constraints serve as a crucial criterion in discriminating cosmological models. The traditional combined method to constrain the cosmological parameters designates the corresponding theoretical value and observational data as functions of redshift, however, sometimes the redshift cannot be measured directly, or the measurement error is large, or the definition of redshift is controversial. In this paper, we propose a novel joint method to constrain parameters that eliminates the redshift $z$ and makes full use of the multiple observables $\left\lbrace \mathcal{F}_{1,\mathrm{obs}},\mathcal{F}_{2,\mathrm{obs}},\cdots,\mathcal{F}_{M,\mathrm{obs}}\right\rbrace$ spanning in $M$-dimensional joint observables space. Considering the generality of the mathematical form of the cosmological models and the guidance from low to high dimensions, we firstly validate our method in a three-dimensional joint observables space spanned by $H(z)$, $f\sigma_{8}(z)$ and $D_{A}(z)$, where the three coordinates can be considered redshift-free measurements of the same celestial body (or shared-redshift data reconstructed model independently). Our results are consistent with the traditional combined method but with lower errors, yielding $H_0=68.7\pm0.1\mathrm{~km} \mathrm{~s}^{-1}\mathrm{~Mpc}^{-1}$, $\Omega_{m0}=0.289\pm0.003$, $\sigma_{8}=0.82\pm0.01$ and showing alleviated parametric degeneracies to some extent. In principle, our joint constraint method allows an extended form keeping the redshift information as an independent coordinate and can also be readily degraded to the form of a traditional combined method to constrain parameters., Comment: 20 pages, 15 figures, 4 tables. Accepted for publication in ApJS. Corresponds to version published in ApJS. We propose a new approach to discriminate among concurring cosmological models by adopting classes of cosmological constraints that can maximize the use of the observable space and can allow to eliminate the redshift from the analysis

We present a python-based program for phenomenological investigations in particle physics using machine learning algorithms, called \verb"MLAnalysis". The program is able to convert LHE and LHCO files generated by \verb"MadGraph5_aMC@NLO" into data sets for machine learning algorithms, which can analyze the information of the events. At present, it contains three machine learning (ML) algorithms: isolation forest (IF) algorithm, nested isolation forest (NIF) algorithm, kmeans anomaly detection (KMAD), and some basic functionality to analyze the kinematic features of a data set. Users can use this program to improve the efficiency of searching for new physics signals., Comment: 21 pages, 4 figures