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Symmetry breaking in a layered superconductor with Ising spin-orbit coupling has offered an opportunity to realize unconventional superconductivity. To be more specific, orbital Fulde-Ferrell-Larkin-Ovchinnikov (FFLO) state, exhibiting layer-dependent finite-momentum pairing, may emerge in transition metal dichalcogenides materials (TMDC) in the presence of an in-plane magnetic field. Orbital FFLO state can be more robust against the magnetic field than the conventional superconducting state with zero-momentum pairing. This feature renders its potential in field resilient superconducting functionality. Although, orbital FFLO state has been reported in NbSe2 and MoS2, it is not yet clear if orbital FFLO state can be a general feature of TMDC superconductor. Here, we report the observation of orbital FFLO state in 2H-NbS2 flakes and its dependence on the thickness of flake. We conclude that the relatively weak interlayer coupling is instrumental in stabilizing orbital FFLO state at higher temperature with respect to the critical temperature and lower magnetic field with respect to paramagnetic limit in NbS2 in comparison to its NbSe2 counterpart.

Monocular depth estimation has shown promise in general imaging tasks, aiding in localization and 3D reconstruction. While effective in various domains, its application to bronchoscopic images is hindered by the lack of labeled data, challenging the use of supervised learning methods. In this work, we propose a transfer learning framework that leverages synthetic data with depth labels for training and adapts domain knowledge for accurate depth estimation in real bronchoscope data. Our network demonstrates improved depth prediction on real footage using domain adaptation compared to training solely on synthetic data, validating our approach.

Deep neural networks (DNNs) have recently achieved impressive success across a wide range of real-world vision and language processing tasks, spanning from image classification to many other downstream vision tasks, such as object detection, tracking, and segmentation. However, previous well-established DNNs, despite being able to maintain superior accuracy, have also been evolving to be deeper and wider and thus inevitably necessitate prohibitive computational resources for both training and inference. This trend further enlarges the computational gap between computation-intensive DNNs and resource-constrained embedded computing systems, making it challenging to deploy powerful DNNs upon real-world embedded computing systems towards ubiquitous embedded intelligence. To alleviate the above computational gap and enable ubiquitous embedded intelligence, we, in this survey, focus on discussing recent efficient deep learning infrastructures for embedded computing systems, spanning from training to inference, from manual to automated, from convolutional neural networks to transformers, from transformers to vision transformers, from vision models to large language models, from software to hardware, and from algorithms to applications. Specifically, we discuss recent efficient deep learning infrastructures for embedded computing systems from the lens of (1) efficient manual network design for embedded computing systems, (2) efficient automated network design for embedded computing systems, (3) efficient network compression for embedded computing systems, (4) efficient on-device learning for embedded computing systems, (5) efficient large language models for embedded computing systems, (6) efficient deep learning software and hardware for embedded computing systems, and (7) efficient intelligent applications for embedded computing systems., Comment: ACM Transactions on Embedded Computing Systems (TECS) 2024

We derived here the factor $\Upsilon$, which quantifies how the gravitational wave spectrum generated by sound waves in the radiation sector grows over time, in a universe with a generic expanding rate set by another dominant energy content. When the dominant energy density satisfies $\rho \propto a^{-3(1+w)}$, we found that $\Upsilon$ has a compact analytical expression: $\Upsilon =\frac{2[1-y^{3(w-1)/2}]}{3(1-w)}$, where $y = a(t)/a(t_s)$ which is the ratio of the scale factor at a later time $t$ to that at $t_s$ when gravitational wave production from sound waves starts. This generic result reduces to that derived previously for radiation-dominated and matter-dominated cases, thus generalizing previous formulas to more general cosmological contexts and providing more accurate results. The derivation relies solely on a stationary source, implying that this generic result of $\Upsilon$ serves as an universal factor in describing the growth of the gravitational wave production and can appear beyond cosmological phase transitions., Comment: 9 pages, 3 figures

Accurate and complete segmentation of airways in chest CT images is essential for the quantitative assessment of lung diseases and the facilitation of pulmonary interventional procedures. Although deep learning has led to significant advancements in medical image segmentation, maintaining airway continuity remains particularly challenging. This difficulty arises primarily from the small and dispersed nature of airway structures, as well as class imbalance in CT scans. To address these challenges, we designed a Multi-scale Nested Residual U-Net (MNR-UNet), incorporating multi-scale inputs and Residual Multi-scale Modules (RMM) into a nested residual framework to enhance information flow, effectively capturing the intricate details of small airways and mitigating gradient vanishing. Building on this, we developed a three-stage segmentation pipeline to optimize the training of the MNR-UNet. The first two stages prioritize high accuracy and sensitivity, while the third stage focuses on repairing airway breakages to balance topological completeness and correctness. To further address class imbalance, we introduced a weighted Breakage-Aware Loss (wBAL) to heighten focus on challenging samples, penalizing breakages and thereby extending the length of the airway tree. Additionally, we proposed a hierarchical evaluation framework to offer more clinically meaningful analysis. Validation on both in-house and public datasets demonstrates that our approach achieves superior performance in detecting more accurate airway voxels and identifying additional branches, significantly improving airway topological completeness. The code will be released publicly following the publication of the paper.

Altermagnets represent a new class of magnetic phases without net magnetization that are invariant under a combination of rotation and time reversal. Unlike conventional collinear antiferromagnets (AFM), altermagnets could lead to new correlated states and important material properties deriving from their non-relativistic spin-split band structure. Indeed, they are the magnetic analogue of unconventional superconductors and can yield spin polarized electrical currents in the absence of external magnetic fields, making them promising candidates for next-generation spintronics. Here, we report altermagnetism in the correlated insulator, magnetically-ordered tetragonal oxychalcogenide, La$_2$O$_3$Mn$_2$Se$_2$. Symmetry analysis reveals a $\mathit{d}_{x^2 - y^2}$-wave type spin momentum locking, which is supported by density functional theory (DFT) calculations. Magnetic measurements confirm the AFM transition below $\sim$166 K while neutron pair distribution function analysis reveals a 2D short-range magnetic order that persists above the N\'eel temperature. Single crystals are grown and characterized using X-ray diffraction, optical and electron microscopy, and microRaman spectroscopy to confirm the crystal structure, stoichiometry, and uniformity.

The permanent ionization damage of semiconductor devices in harsh radiation environments stems from $E'_\gamma$ defect centers generation in the $a$-SiO$_2$ dielectric or isolation layers, but the long-standing "hole transport-trapping" generation mechanism encounters dilemmas to explain recent experiments. In this work, we propose a new "hole capture-structural relaxation" (HCSR) mechanism, based on spin-polarized first-principles calculations of oxygen vacancies ($V_{\rm O}$'s) in $a$-SiO$_2$. It is found that due to an electronic metastability caused by the localization of defect electronic states, the previously suggested puckered precursor, $V_{O\gamma}$, cannot exist in $a$-SiO$_2$, and the $E'_\gamma$ centers can arise from a structural relaxation of dimer $V_{O\delta}$ after nonradiative capture of irradiation-induced valence band holes. We demonstrate that, such an HCSR mechanism can consistently explain the basic but puzzling temperature and electric-field dependences in recent experiments. Moreover, by using reaction rate theory, we derive an analytical formula to uniformly describe the sublinear experimental data over a wide dose and temperature range. This work not only provides a rationale for defect generation in ionizing-irradiated $a$-SiO$_2$, but also offer a general approach to understanding the irradiation physics in alternative dielectrics and wide-band gap semiconductors with intrinsic electronic metastability., Comment: 5 pages, 3 figures

Employing aberration-corrected scanning transmission electron microscopy (STEM), we meticulously investigated the intrinsic chemical heterogeneity of Fe1+yTe, Fe1+yTe0.8Se0.2, and Fe1+yTe0.5Se0.5. Comprehensive analysis reveals the presence of interstitial iron atoms (Feint) across all samples, pre-dominantly occupying the 2c site of the P4/nmm space group. Moreover, a superstructure phase characterized by a wave vector q = 2/5a + 1/2c, originating from the ordering of Feint, is distinctly observable in the parent compound Fe1+yTe. In this scenario, the Feint atoms interact with adjacent Fe atoms, forming iron polycomplex and leading to an evident distortion of the FeTe4 tetrahedral. Experimental results further demonstrate effective suppression of Feint concentration and ordering through appropriate Se substitution; notably, Fe1+yTe0.5Se0.5 manifests the lowest concentration of Feint atoms. Our findings additionally indicate that Se substitution is random, and nanoscale phase separation induced by Te/Se chemical heterogeneity is commonly observed within Fe1+yTe1-xSex crystals., Comment: 14 pages, 5 figures

The advent of large integral field spectroscopic surveys has found that elliptical galaxies (EGs) can be classified into two classes: the fast rotators (whose kinematics are dominated by rotation) and the slow rotators (which exhibit slow or no rotation pattern). It is often suggested that while the slow rotators typically have boxy isophotal shapes, have a high $\alpha$-to-iron abundance ratio, and are quite massive, the fast rotators often exhibit the opposite properties (that is, having disky isophotes, lower $\alpha$-to-iron ratio, and of typical masses). Whether the EGs consist of two distinct populations (i.e., a dichotomy exists), remains an unsolved issue. To examine the existence of the dichotomy, we used a sample of 1,895 EGs from the SDSS-IV MaNGA survey, and measured robustly the stellar kinematics, isophotal shapes, and [Mg/Fe] ratio. We confirmed the previous finding that the bulk of the EGs are disky (65%) and fast rotators (67%), but found no evidence supporting a dichotomy, based on a principal component analysis. The different classes (boxy/disky and slow/fast rotators) of EGs occupy slightly different loci in the principal component space. This may explain the observed trends that led to the premature support of a dichotomy based on small samples of galaxies., Comment: 27 pages, 19 figures, 5 tables. Submitted to ApJ. Comments are welcome!

The evolution of superconducting quantum processors is driven by the need to reduce errors and scale for fault-tolerant computation. Reducing physical qubit error rates requires further advances in the microscopic modeling and control of decoherence mechanisms in superconducting qubits. Piezoelectric interactions contribute to decoherence by mediating energy exchange between microwave photons and acoustic phonons. Centrosymmetric materials like silicon and sapphire do not display piezoelectricity and are the preferred substrates for superconducting qubits. However, the broken centrosymmetry at material interfaces may lead to piezoelectric losses in qubits. While this loss mechanism was predicted two decades ago, interface piezoelectricity has not been experimentally observed in superconducting devices. Here, we report the observation of interface piezoelectricity at an aluminum-silicon junction and show that it constitutes an important loss channel for superconducting devices. We fabricate aluminum interdigital surface acoustic wave transducers on silicon and demonstrate piezoelectric transduction from room temperature to millikelvin temperatures. We find an effective electromechanical coupling factor of $K^2\approx 2 \times 10^{-5}\%$ comparable to weakly piezoelectric substrates. We model the impact of the measured interface piezoelectric response on superconducting qubits and find that the piezoelectric surface loss channel limits qubit quality factors to $Q\sim10^4-10^8$ for designs with different surface participation ratios and electromechanical mode matching. These results identify electromechanical surface losses as a significant dissipation channel for superconducting qubits, and show the need for heterostructure and phononic engineering to minimize errors in next-generation superconducting qubits.

Data literacy is essential in today's data-driven world, emphasizing individuals' abilities to effectively manage data and extract meaningful insights. However, traditional classroom-based educational approaches often struggle to fully address the multifaceted nature of data literacy. As education undergoes digital transformation, innovative technologies such as Virtual Reality (VR) offer promising avenues for immersive and engaging learning experiences. This paper introduces DataliVR, a pioneering VR application aimed at enhancing the data literacy skills of university students within a contextual and gamified virtual learning environment. By integrating Large Language Models (LLMs) like ChatGPT as a conversational artificial intelligence (AI) chatbot embodied within a virtual avatar, DataliVR provides personalized learning assistance, enriching user learning experiences. Our study employed an experimental approach, with chatbot availability as the independent variable, analyzing learning experiences and outcomes as dependent variables with a sample of thirty participants. Our approach underscores the effectiveness and user-friendliness of ChatGPT-powered DataliVR in fostering data literacy skills. Moreover, our study examines the impact of the ChatGPT-based AI chatbot on users' learning, revealing significant effects on both learning experiences and outcomes. Our study presents a robust tool for fostering data literacy skills, contributing significantly to the digital advancement of data literacy education through cutting-edge VR and AI technologies. Moreover, our research provides valuable insights and implications for future research endeavors aiming to integrate LLMs (e.g., ChatGPT) into educational VR platforms., Comment: 10 pages, this paper was accepted to ISMAR2024

Single-image depth estimation is essential for endoscopy tasks such as localization, reconstruction, and augmented reality. Most existing methods in surgical scenes focus on in-domain depth estimation, limiting their real-world applicability. This constraint stems from the scarcity and inferior labeling quality of medical data for training. In this work, we present EndoOmni, the first foundation model for zero-shot cross-domain depth estimation for endoscopy. To harness the potential of diverse training data, we refine the advanced self-learning paradigm that employs a teacher model to generate pseudo-labels, guiding a student model trained on large-scale labeled and unlabeled data. To address training disturbance caused by inherent noise in depth labels, we propose a robust training framework that leverages both depth labels and estimated confidence from the teacher model to jointly guide the student model training. Moreover, we propose a weighted scale-and-shift invariant loss to adaptively adjust learning weights based on label confidence, thus imposing learning bias towards cleaner label pixels while reducing the influence of highly noisy pixels. Experiments on zero-shot relative depth estimation show that our EndoOmni improves state-of-the-art methods in medical imaging for 41\% and existing foundation models for 25\% in terms of absolute relative error on specific dataset. Furthermore, our model provides strong initialization for fine-tuning to metric depth estimation, maintaining superior performance in both in-domain and out-of-domain scenarios. The source code will be publicly available.

In comparison to common quadrotors, the shape change of morphing quadrotors endows it with a more better flight performance but also results in more complex flight dynamics. Generally, it is extremely difficult or even impossible for morphing quadrotors to establish an accurate mathematical model describing their complex flight dynamics. To figure out the issue of flight control design for morphing quadrotors, this paper resorts to a combination of model-free control techniques (e.g., deep reinforcement learning, DRL) and convex combination (CC) technique, and proposes a convex-combined-DRL (cc-DRL) flight control algorithm for position and attitude of a class of morphing quadrotors, where the shape change is realized by the length variation of four arm rods. In the proposed cc-DRL flight control algorithm, proximal policy optimization algorithm that is a model-free DRL algorithm is utilized to off-line train the corresponding optimal flight control laws for some selected representative arm length modes and hereby a cc-DRL flight control scheme is constructed by the convex combination technique. Finally, simulation results are presented to show the effectiveness and merit of the proposed flight control algorithm.

A quantum network provides an infrastructure connecting quantum devices with revolutionary computing, sensing, and communication capabilities. As the best-known application of a quantum network, quantum key distribution (QKD) shares secure keys guaranteed by the laws of quantum mechanics. A quantum satellite constellation offers a solution to facilitate the quantum network on a global scale. The Micius satellite has verified the feasibility of satellite quantum communications, however, scaling up quantum satellite constellations is challenging, requiring small lightweight satellites, portable ground stations and real-time secure key exchange. Here we tackle these challenges and report the development of a quantum microsatellite capable of performing space-to-ground QKD using portable ground stations. The quantum microsatellite features a payload weighing approximately 23 kg, while the portable ground station weighs about 100 kg. These weights represent reductions by more than an order and two orders of magnitude, respectively, compared to the Micius satellite. Additionally, we multiplex bidirectional satellite-ground optical communication with quantum communication, enabling key distillation and secure communication in real-time. Using the microsatellite and the portable ground stations, we demonstrate satellite-based QKD with multiple ground stations and achieve the sharing of up to 0.59 million bits of secure keys during a single satellite pass. The compact quantum payload can be readily assembled on existing space stations or small satellites, paving the way for a satellite-constellation-based quantum and classical network for widespread real-life applications., Comment: 40 pages, 8 figures

Hybrid MKNF Knowledge Bases (HMKNF-KBs) constitute a formalism for tightly integrated reasoning over closed-world rules and open-world ontologies. This approach allows for accurate modeling of real-world systems, which often rely on both categorical and normative reasoning. Conflict-driven solving is the leading approach for computationally hard problems, such as satisfiability (SAT) and answer set programming (ASP), in which MKNF is rooted. This paper investigates the theoretical underpinnings required for a conflict-driven solver of HMKNF-KBs. The approach defines a set of completion and loop formulas, whose satisfaction characterizes MKNF models. This forms the basis for a set of nogoods, which in turn can be used as the backbone for a conflict-driven solver.

One of the most significant vulnerabilities in the source unit of quantum key distribution (QKD) is the correlation between quantum states after modulation, which shall be characterized and evaluated for its practical security performance. In this work, we propose a methodology to characterize the intensity correlation according to the single-photon detection results in the measurement unit without modifying the configuration of the QKD system. In contrast to the previous research that employs extra classical optical detector to measure the correlation, our method can directly analyse the detection data generated during the raw key exchange, enabling to characterize the feature of correlation in real-time system operation. The basic method is applied to a BB84 QKD system and the characterized correlation decreases the secure key rate shown by the security proof. Furthermore, the method is extended and applied to characterize the correlation from the result of Bell-state measurement, which demonstrates its applicability to a running full-scheme MDI QKD system. This study provides an approach for standard certification of a QKD system.

The entangled photons are crucial resources for quantum communications and networking. Here, we present an ultra-bright polarization-entangled photon source based on a periodically poled lithium niobate waveguide designed for practical quantum communication networks. Using a 780 nm pump laser, the source achieves a pair generation rate of 2.4 $\times 10^{10}$ pairs/s/mW. This work has achieved a directly measured power of 17.9 nW in entangled photon generation with a 3.2 mW pump power. Based on this, we demonstrate the practicality of the source by conducting quantum key distribution experiments over long-distance fiber links, achieving the applicable secure key rates of up to 440.80 bits/s over 200 km with 62 dB loss and reaching a maximum secure key generation distance of 404 km. These results demonstrate the potential of wavelength-multiplexed polarization-entangled photon sources for high-speed, long-distance quantum communication, positioning them as key components for future large-scale quantum networks., Comment: 18 pages, 6 figures

Microwave-optics entanglement plays a crucial role in building hybrid quantum networks with quantum nodes working in the microwave and optical frequency bands. However, there are limited efficient ways to produce such entanglement due to the large frequency mismatch between the two regimes. Here, we present a new mechanism to prepare microwave-optics entanglement based on a hybrid system of two coupled opto- and magnomechanical microspheres, i.e., a YIG sphere and a silica sphere. The YIG sphere holds a magnon mode and a vibration mode induced by magnetostriction, while the silica sphere supports an optical whispering-gallery mode and a mechanical mode coupled via an optomechanical interaction. The two mechanical modes are close in frequency and directly coupled via physical contact of the two microspheres. We show that by simultaneously activating the magnomechanical (optomechanical) Stokes (anti-Stokes) scattering, stationary entanglement can be established between the magnon and optical modes via mechanics-mechanics coupling. This leads to stationary microwave-optics entanglement by further coupling the YIG sphere to a microwave cavity and utilizing the magnon-microwave state swapping. Our protocol is within reach of current technology and may become a promising new approach for preparing microwave-optics entanglement, which finds unique applications in hybrid quantum networks and quantum information processing with hybrid quantum systems.

The emergence of quantum mechanics and general relativity has transformed our understanding of the natural world significantly. However, integrating these two theories presents immense challenges, and their interplay remains untested. Recent theoretical studies suggest that the single-photon interference covering huge space can effectively probe the interface between quantum mechanics and general relativity. We developed an alternative design using unbalanced Michelson interferometers to address this and validated its feasibility over an 8.4 km free-space channel. Using a high-brightness single-photon source based on quantum dots, we demonstrated single-photon interference along this long-distance baseline. We achieved a phase measurement precision of 16.2 mrad, which satisfied the measurement requirements for a gravitational redshift at the geosynchronous orbit by five times the standard deviation. Our results confirm the feasibility of the single-photon version of the Colella-Overhauser-Werner experiment for testing the quantum effects in curved spacetime., Comment: 22 pages, 6 figures

The kink structure in band dispersion usually refers to a certain electron-boson interaction, which is crucial in understanding the pairing in unconventional superconductors. Here we report the evidence of the observation of a kink structure in Fe-based superconductor CsCa$_2$Fe$_4$As$_4$F$_2$ using angle-resolved photoemission spectroscopy. The kink shows an orbital selective and momentum dependent behavior, which is located at 15 meV below Fermi level along the Gamma-M direction at the band with dxz orbital character and vanishes when approaching the Gamma-X direction, correlated with a slight decrease of the superconducting gap. Most importantly, this kink structure disappears when the superconducting gap closes, indicating that the corresponding bosonic mode (9 meV) is closely related to superconductivity. However, the origin of this mode remains unidentified, since it cannot be related to phonons or the spin resonance mode (15 meV) observed by inelastic neutron scattering. The behavior of this mode is rather unique and challenges our present understanding of the superconducting paring mechanism of the bilayer FeAs-based superconductors., Comment: 14 pages, 4 figures

We introduce a visual analysis method for multiple causal graphs with different outcome variables, namely, multi-outcome causal graphs. Multi-outcome causal graphs are important in healthcare for understanding multimorbidity and comorbidity. To support the visual analysis, we collaborated with medical experts to devise two comparative visualization techniques at different stages of the analysis process. First, a progressive visualization method is proposed for comparing multiple state-of-the-art causal discovery algorithms. The method can handle mixed-type datasets comprising both continuous and categorical variables and assist in the creation of a fine-tuned causal graph of a single outcome. Second, a comparative graph layout technique and specialized visual encodings are devised for the quick comparison of multiple causal graphs. In our visual analysis approach, analysts start by building individual causal graphs for each outcome variable, and then, multi-outcome causal graphs are generated and visualized with our comparative technique for analyzing differences and commonalities of these causal graphs. Evaluation includes quantitative measurements on benchmark datasets, a case study with a medical expert, and expert user studies with real-world health research data.

Generative models have surged in popularity recently due to their ability to produce high-quality images and video. However, steering these models to produce images with specific attributes and precise control remains challenging. Humans, particularly their faces, are central to content generation due to their ability to convey rich expressions and intent. Current generative models mostly generate flat neutral expressions and characterless smiles without authenticity. Other basic expressions like anger are possible, but are limited to the stereotypical expression, while other unconventional facial expressions like doubtful are difficult to reliably generate. In this work, we propose the use of AUs (action units) for facial expression control in face generation. AUs describe individual facial muscle movements based on facial anatomy, allowing precise and localized control over the intensity of facial movements. By combining different action units, we unlock the ability to create unconventional facial expressions that go beyond typical emotional models, enabling nuanced and authentic reactions reflective of real-world expressions. The proposed method can be seamlessly integrated with both text and image prompts using adapters, offering precise and intuitive control of the generated results. Code and dataset are available in {https://github.com/tvaranka/fineface}.

Silicon nanomechanical resonators display ultra-long lifetimes at cryogenic temperatures and microwave frequencies. Achieving quantum control of single-phonons in these devices has so far relied on nonlinearities enabled by coupling to ancillary qubits. In this work, we propose using atomic forces to realize a silicon nanomechanical qubit without coupling to an ancillary qubit. The proposed qubit operates at 60 MHz with a single-phonon level anharmonicity of 5 MHz. We present a circuit quantum acoustodynamics architecture where electromechanical resonators enable dispersive state readout and multi-qubit operations. The combination of strong anharmonicity, ultrahigh mechanical quality factors, and small footprints achievable in this platform could enable quantum-nonlinear phononics for quantum information processing and transduction.

Based on the beyond-mean-field Skyrme-Hartree-Fock model, we investigate the shape coexistence in Ne isotopes and the effect of $\la$ hyperon on the energy level structure in the nuclei. The up-to-date Skyrme-type $N\la$ interaction SLL4 and the $NN$ interaction SGII are employed. Low-lying energy spectra of $^{20,22,24,26,28,30,32,34}$Ne, including the low-lying states with $J\leq 6$, are predicted, discussed in detail, and found in good agreement with experimental results. The electric quadrupole transition rate is also examined. The coexistences of a ground state rotational band and a $\be$ vibrational band are revealed in $^{20,22,24}$Ne. Unlike the previously discovered shrinkage effect of $\la_{s}$ on the ground state nuclei, it is found that the $\la_{s}$ may alter the excitation mode of the second band by affecting the distribution of the collective wave function, thereby causing the $\be$ vibrational band transitions to a vibrational band with equidistant energy levels., Comment: 12 pages, 6 figures

We introduce an open source high-quality Mandarin TTS dataset MSceneSpeech (Multiple Scene Speech Dataset), which is intended to provide resources for expressive speech synthesis. MSceneSpeech comprises numerous audio recordings and texts performed and recorded according to daily life scenarios. Each scenario includes multiple speakers and a diverse range of prosodic styles, making it suitable for speech synthesis that entails multi-speaker style and prosody modeling. We have established a robust baseline, through the prompting mechanism, that can effectively synthesize speech characterized by both user-specific timbre and scene-specific prosody with arbitrary text input. The open source MSceneSpeech Dataset and audio samples of our baseline are available at https://speechai-demo.github.io/MSceneSpeech/., Comment: Accepted by INTERSPEECH 2024

4D radars are increasingly favored for odometry and mapping of autonomous systems due to their robustness in harsh weather and dynamic environments. Existing datasets, however, often cover limited areas and are typically captured using a single platform. To address this gap, we present a diverse large-scale dataset specifically designed for 4D radar-based localization and mapping. This dataset was gathered using three different platforms: a handheld device, an e-bike, and an SUV, under a variety of environmental conditions, including clear days, nighttime, and heavy rain. The data collection occurred from September 2023 to February 2024, encompassing diverse settings such as roads in a vegetated campus and tunnels on highways. Each route was traversed multiple times to facilitate place recognition evaluations. The sensor suite included a 3D lidar, 4D radars, stereo cameras, consumer-grade IMUs, and a GNSS/INS system. Sensor data packets were synchronized to GNSS time using a two-step process: a convex hull algorithm was applied to smooth host time jitter, and then odometry and correlation algorithms were used to correct constant time offsets. Extrinsic calibration between sensors was achieved through manual measurements and subsequent nonlinear optimization. The reference motion for the platforms was generated by registering lidar scans to a terrestrial laser scanner (TLS) point cloud map using a lidar inertial odometry (LIO) method in localization mode. Additionally, a data reversion technique was introduced to enable backward LIO processing. We believe this dataset will boost research in radar-based point cloud registration, odometry, mapping, and place recognition., Comment: 11 pages, 4 figures, 5 tables

The proton drip line of (hyper)nuclei is examined within the framework of the deformed Skyrme-Hartree Fock approach by adjusting the nuclear force parameters to exactly reproduce the core binding energies. The impact of adding a {\Lambda} hyperon in a s or p state is studied, and it is found that in some cases the deformation effect facilitates the extension of the drip line by an added p-state hyperon. However, no extension of the drip line is found for s-state hypernuclei., Comment: 7 pages, 2 Figures

We show how to prepare magnonic squeezed states in an optomagnomechanical system, in which magnetostriction induced mechanical displacement couples to an optical cavity via radiation pressure. We discuss two scenarios depending on whether the magnomechanical coupling is linear or dispersive. We show that in both cases the strong mechanical squeezing obtained via two-tone driving of the optical cavity can be efficiently transferred to the magnon mode. In the linear coupling case, stationary magnon squeezing is achieved; while in the dispersive coupling case, a transient magnonic squeezed state is prepared in a two-step protocol. The proposed magnonic squeezed states find promising applications in quantum information processing and quantum sensing using magnons., Comment: Special Topic on "Brillouin Scattering and Optomechanics" in APL Photonics

Accurate bronchoscope localization is essential for pulmonary interventions, by providing six degrees of freedom (DOF) in airway navigation. However, the robustness of current vision-based methods is often compromised in clinical practice, and they struggle to perform in real-time and to generalize across cases unseen during training. To overcome these challenges, we propose a novel Probabilistic Airway Navigation System (PANS), leveraging Monte-Carlo method with pose hypotheses and likelihoods to achieve robust and real-time bronchoscope localization. Specifically, our PANS incorporates diverse visual representations (\textit{e.g.}, odometry and landmarks) by leveraging two key modules, including the Depth-based Motion Inference (DMI) and the Bronchial Semantic Analysis (BSA). To generate the pose hypotheses of bronchoscope for PANS, we devise the DMI to accurately propagate the estimation of pose hypotheses over time. Moreover, to estimate the accurate pose likelihood, we devise the BSA module by effectively distinguishing between similar bronchial regions in endoscopic images, along with a novel metric to assess the congruence between estimated depth maps and the segmented airway structure. Under this probabilistic formulation, our PANS is capable of achieving the 6-DOF bronchoscope localization with superior accuracy and robustness. Extensive experiments on the collected pulmonary intervention dataset comprising 10 clinical cases confirm the advantage of our PANS over state-of-the-arts, in terms of both robustness and generalization in localizing deeper airway branches and the efficiency of real-time inference. The proposed PANS reveals its potential to be a reliable tool in the operating room, promising to enhance the quality and safety of pulmonary interventions.

The growing success of deep learning in various domains has prompted investigations into its application to tabular data, where deep models have shown promising results compared to traditional tree-based methods. In this paper, we revisit Neighborhood Component Analysis (NCA), a classic tabular prediction method introduced in 2004, designed to learn a linear projection that captures semantic similarities between instances. We find that minor modifications, such as adjustments to the learning objectives and the integration of deep learning architectures, significantly enhance NCA's performance, enabling it to surpass most modern deep tabular models. Additionally, we introduce a stochastic neighbor sampling strategy that improves both the efficiency and predictive accuracy of our proposed ModernNCA -- sampling only a subset of neighbors during training, while utilizing the entire neighborhood during inference. Extensive experiments demonstrate that our ModernNCA achieves state-of-the-art results in both classification and regression tasks across various tabular datasets, outperforming both tree-based and other deep tabular models, while also reducing training time and model size.

This paper presents FedType, a simple yet pioneering framework designed to fill research gaps in heterogeneous model aggregation within federated learning (FL). FedType introduces small identical proxy models for clients, serving as agents for information exchange, ensuring model security, and achieving efficient communication simultaneously. To transfer knowledge between large private and small proxy models on clients, we propose a novel uncertainty-based asymmetrical reciprocity learning method, eliminating the need for any public data. Comprehensive experiments conducted on benchmark datasets demonstrate the efficacy and generalization ability of FedType across diverse settings. Our approach redefines federated learning paradigms by bridging model heterogeneity, eliminating reliance on public data, prioritizing client privacy, and reducing communication costs., Comment: This paper has been accepted by ICML 2024

Obtaining a precise form for the predicted gravitational wave (GW) spectrum from a phase transition is a topic of great relevance for beyond Standard Model (BSM) physicists. Currently, the most sophisticated semi-analytic framework for estimating the dominant contribution to the spectrum is the sound shell model; however, full calculations within this framework can be computationally expensive, especially for large-scale scans. The community therefore generally manages with fit functions to the GW spectrum, the most widely used of which is a single broken power law. We provide a more precise fit function based on the sound shell model: our fit function features a double broken power law with two frequency breaks corresponding to the two characteristic length scales of the problem -- inter-bubble spacing and thickness of sound shells, the second of which is neglected in the single broken power law fit. Compared to previously proposed fits, we demonstrate that our fit function more faithfully captures the GW spectrum coming from a full calculation of the sound shell model, over most of the space of the thermodynamic parameters governing the phase transition. The physical origins of the fit parameters and their dependence on the thermodynamic parameters are studied in the underlying sound shell model: in particular, we perform a series of detailed scans for these quantities over the plane of the strength of the phase transition ($\alpha$) and the bubble wall velocity ($v_w$). Wherever possible, we comment on the physical interpretations of these scans. The result of our study can be used to generate accurate GW spectra with our fit function, given initial inputs of $\alpha$, $v_w$, $\beta/H$ (nucleation rate parameter) and $T_n$ (nucleation temperature) for the relevant BSM scenario., Comment: 31 pages, 9 figures

Kagome metal CsV$_3$Sb$_5$ has attracted much recent attention due to the coexistence of multiple exotic orders and the associated proposals to mimic unconventional high temperature superconductors. Nevertheless, magnetism and strong electronic correlations -- two essential ingredients for unconventional superconductivity, are absent in this V-based Kagome metal. CsCr$_3$Sb$_5$ is a newly discovered Cr-based parallel of CsV$_3$Sb$_5$, in which magnetism appears with charge density wave and superconductivity at different temperature and pressure regions. Enhanced electronic correlations are also suggested by theoretical proposals due to the calculated flat bands. Here, we report angle-resolved photoemission measurements and first-principles calculations on this new material system. Electron energy bands and the associated orbitals are resolved. Flat bands are observed near the Fermi level. Doping dependent measurements on Cs(Cr$_x$V$_{1-x}$)$_3$Sb$_5$ reveal a gradually enhanced band renormalization from CsV$_3$Sb$_5$ to CsCr$_3$Sb$_5$, accompanied by distinct spatial symmetry breaking states in the phase diagram.

Multi-view hand mesh reconstruction is a critical task for applications in virtual reality and human-computer interaction, but it remains a formidable challenge. Although existing multi-view hand reconstruction methods achieve remarkable accuracy, they typically come with an intensive computational burden that hinders real-time inference. To this end, we propose MLPHand, a novel method designed for real-time multi-view single hand reconstruction. MLP Hand consists of two primary modules: (1) a lightweight MLP-based Skeleton2Mesh model that efficiently recovers hand meshes from hand skeletons, and (2) a multi-view geometry feature fusion prediction module that enhances the Skeleton2Mesh model with detailed geometric information from multiple views. Experiments on three widely used datasets demonstrate that MLPHand can reduce computational complexity by 90% while achieving comparable reconstruction accuracy to existing state-of-the-art baselines.

Synthesizing electronic health records (EHR) data has become a preferred strategy to address data scarcity, improve data quality, and model fairness in healthcare. However, existing approaches for EHR data generation predominantly rely on state-of-the-art generative techniques like generative adversarial networks, variational autoencoders, and language models. These methods typically replicate input visits, resulting in inadequate modeling of temporal dependencies between visits and overlooking the generation of time information, a crucial element in EHR data. Moreover, their ability to learn visit representations is limited due to simple linear mapping functions, thus compromising generation quality. To address these limitations, we propose a novel EHR data generation model called EHRPD. It is a diffusion-based model designed to predict the next visit based on the current one while also incorporating time interval estimation. To enhance generation quality and diversity, we introduce a novel time-aware visit embedding module and a pioneering predictive denoising diffusion probabilistic model (PDDPM). Additionally, we devise a predictive U-Net (PU-Net) to optimize P-DDPM.We conduct experiments on two public datasets and evaluate EHRPD from fidelity, privacy, and utility perspectives. The experimental results demonstrate the efficacy and utility of the proposed EHRPD in addressing the aforementioned limitations and advancing EHR data generation.

We simulate quench dynamics of the Su-Schrieffer-Heeger (SSH) chain on the IBM quantum computers, calculating the R\'enyi entanglement entropy, the twist order parameter and the Berry phase. The latter two quantities can be deduced from a slow-twist operator defined in the Lieb-Schultz-Mattis theorem. The R\'enyi entropy is obtained using a recently developed randomized measurement scheme. The twist order parameter and the Berry phase are measured without the need for additional gates or ancilla qubits. We consider quench protocols in which a trivial initial state evolves dynamically in time under the topological SSH Hamiltonian in the fully dimerized limit (the flat-band limit). During these quenches, there are persistent and periodic oscillations in the time evolution of both entanglement entropy and twist order parameter. Through the implementation of error mitigation techniques using a global depolarizing ansatz and postselection, our simulations on the IBM devices yield results that closely match exact solutions., Comment: 9 pages, 11 figures

We explore the generation of topological defects in the course of a dynamical phase transition in a ring with a weak link, i.e., a SSS Josephson junction, from the AdS/CFT correspondence. By setting different parameters of the junction (width, steepness, depth) and the final temperature of the quench, the configurations of the charge density and condensate of the order parameters of the dual field theory are presented. Meanwhile, we observe that in the final equilibrium state, variations in parameters of the junctions only affect the configurations of the charge density and condensate of the order parameters, without altering their values outside the junction. However, variations in the final temperature will directly affect the values of the charge density and condensate of the order parameters outside of the junction. Moreover, in the final equilibrium state, we propose an analytic relation between the gauge-invariant velocity in the two superconducting states in the SSS Josephson junction, which agrees well with the numerical results., Comment: 16 pages, 8 figures

In the extended thermodynamics of black holes, there exists a thermodynamical pressure and its conjugate volume. Extensive studies have been conducted on the phase structure of numerous black holes, which have demonstrated striking similarities to the phase structure of various ordinary matter systems. A comparison of the thermodynamic properties of spherically symmetric AdS black holes with those of ordinary thermodynamic systems reveals that the isovolumetric heat capacity of the former is zero, whereas that of the latter is non-zero. It is a subject of interest for the intrinsic reason for this discrepancy. The effective thermodynamic quantities, based on the context of the boundary between the black hole horizon and the cosmological horizon in dS spacetime, as well as the interaction between the two horizons, are presented. The heat capacity in the Reissner-N\"{o}rdstrom-de Sitter (RN-dS) spacetime is then investigated, and it is demonstrated that the behavior of the heat capacity in the RN-dS spacetime is analogous to that of Schottky specific heat. Treating the two horizon interfaces in the RN-dS spacetime as two distinct energy levels in a two-energy system, and the thermodynamic properties in the RN-dS spacetime are discussed with the method of studying the thermodynamic properties in an ordinary two-energy system, to elucidate the intrinsic reasons for the occurrence of Schottky specific heat in the RN-dS spacetime. The heat capacity observed in the RN-dS spacetime is not only consistent with that of the Schottky specific heat described by the effective thermodynamic quantities in the RN-dS spacetime, but also with that of the heat capacity in an ordinary two-energy-level system. The quantum properties in the RN-dS spacetime are reflected, thereby providing a new avenue for further in-depth study of the quantum properties of black holes and dS spacetime.

Metamorphic testing has become one mainstream technique to address the notorious oracle problem in software testing, thanks to its great successes in revealing real-life bugs in a wide variety of software systems. Metamorphic relations, the core component of metamorphic testing, have continuously attracted research interests from both academia and industry. In the last decade, a rapidly increasing number of studies have been conducted to systematically generate metamorphic relations from various sources and for different application domains. In this article, based on the systematic review on the state of the art for metamorphic relations' generation, we summarize and highlight visions for further advancing the theory and techniques for identifying and constructing metamorphic relations, and discuss potential research trends in related areas., Comment: Accepted by International Workshop on Software Engineering in 2030

We study the SM-like Higgs boson decays $h\rightarrow MZ$ in the Triplet extended NMSSM (TNMSSM),where M is a vector meson ($\rho$, $\omega$, $\phi$, $J/\Psi$, $\Upsilon$). Compared to the minimal supersymmetric standard model (MSSM), the TNMSSM includes two new SU(2) triplets with hypercharge $\pm 1$ and a SM gauge singlet which are coupled to each other. The indirect contributions to the decays $h \rightarrow MZ$ are produced from the effective $h\gamma Z$ vertex, and they are more important than the direct contributions. The results of this work would encourage a detection on $h \rightarrow Z\gamma$ at the future high energy colliders for exploring new physics beyond the SM., Comment: Accepted for published in Chinese Physics C