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In this paper, we study the single production of doubly charged Higgs $H^{\pm\pm}$ in the type-II seesaw at the high energy muon collider. Compared with the pair production channel $\mu^+\mu^-\to H^{++} H^{--}$, the single production channel $\mu^+\mu^-\to \mu^\mp\ell^\mp H^{\pm\pm}$ in principle could probe the mass region above the threshold $m_{H^{\pm\pm}}>\sqrt{s}/2$. The single production channel depends on the Yukawa coupling $h$, which is related to the neutrino oscillation parameters. We show that the Majorana phases $\phi_1$ and $\phi_2$ have great impact on the individual cross section of the single production. We find that the same sign dilepton signature from $H^{\pm\pm}\to \ell^\pm\ell^\pm$ could probe $m_{H^{\pm\pm}}\lesssim2.6(7.1)$ TeV at the 3 (10) TeV muon collider when the triplet VEV $v_\Delta\lesssim3$ eV., Comment: 19 pages, 11 figures

This paper presents a probabilistic approach to represent and quantify model-form uncertainties in the reduced-order modeling of complex systems using operator inference techniques. Such uncertainties can arise in the selection of an appropriate state-space representation, in the projection step that underlies many reduced-order modeling methods, or as a byproduct of considerations made during training, to name a few. Following previous works in the literature, the proposed method captures these uncertainties by expanding the approximation space through the randomization of the projection matrix. This is achieved by combining Riemannian projection and retraction operators - acting on a subset of the Stiefel manifold - with an information-theoretic formulation. The efficacy of the approach is assessed on canonical problems in fluid mechanics by identifying and quantifying the impact of model-form uncertainties on the inferred operators.

The $DDK$ system has gain increasing attention in recent research due to its potential to contain a three-hadron bound state. This article utilizes an extension of the Non-Relativistic Effective Field Theory (NREFT) and the finite volume particle-dimer framework to derive Lorentz-invariant quantization conditions for the $DDK$ three-body system. Using current model input conditions, the finite volume energy spectrum of the $DDK$ three-body system was calculated. This new calculation incorporates relativistic kinematics, allowing it to be applicable across a broader energy range starting from the threshold. In this work, we present a comprehensive \( O(p^{2}) \) calculation. The spurious pole is effectively subtracted within the framework of relativistic kinematics. The spectra in the moving frame are also obtained. These analyses provide a broader testing ground for future lattice simulations. They are expected to reveal more detailed properties of the $DDK$ system and other three-hadron systems., Comment: 20 pages, 7 figures

Designing spectral convolutional networks is a formidable task in graph learning. In traditional spectral graph neural networks (GNNs), polynomial-based methods are commonly used to design filters via the Laplacian matrix. In practical applications, however, these polynomial methods encounter inherent limitations, which primarily arise from the the low-order truncation of polynomial filters and the lack of overall modeling of the graph spectrum. This leads to poor performance of existing spectral approaches on real-world graph data, especially when the spectrum is highly concentrated or contains many numerically identical values, as they tend to apply the exact same modulation to signals with the same frequencies. To overcome these issues, in this paper, we propose Graph State Space Network (GrassNet), a novel graph neural network with theoretical support that provides a simple yet effective scheme for designing and learning arbitrary graph spectral filters. In particular, our GrassNet introduces structured state space models (SSMs) to model the correlations of graph signals at different frequencies and derives a unique rectification for each frequency in the graph spectrum. To the best of our knowledge, our work is the first to employ SSMs for the design of GNN spectral filters, and it theoretically offers greater expressive power compared with polynomial filters. Extensive experiments on nine public benchmarks reveal that GrassNet achieves superior performance in real-world graph modeling tasks.

Images captured in hazy weather generally suffer from quality degradation, and many dehazing methods have been developed to solve this problem. However, single image dehazing problem is still challenging due to its ill-posed nature. In this paper, we propose a depth order guided single image dehazing method, which utilizes depth order in hazy images to guide the dehazing process to achieve a similar depth perception in corresponding dehazing results. The consistency of depth perception ensures that the regions that look farther or closer in hazy images also appear farther or closer in the corresponding dehazing results, and thus effectively avoid the undesired visual effects. To achieve this goal, a simple yet effective strategy is proposed to extract the depth order in hazy images, which offers a reference for depth perception in hazy weather. Additionally, a depth order embedded transformation model is devised, which performs transmission estimation under the guidance of depth order to realize an unchanged depth order in the dehazing results. The extracted depth order provides a powerful global constraint for the dehazing process, which contributes to the efficient utilization of global information, thereby bringing an overall improvement in restoration quality. Extensive experiments demonstrate that the proposed method can better recover potential structure and vivid color with higher computational efficiency than the state-of-the-art dehazing methods., Comment: 14 pages, 15 figures

Superconducting qubits, a promising candidate for universal quantum computing, currently face limitations in chip size due to reproducibility, wiring complexity, and packaging modes. Distributed quantum modules offer a viable strategy for constructing larger quantum information processing systems, though universal quantum gate operations between remote qubits have yet to be realized. Here, we demonstrate high-fidelity perfect entanglers between two remote superconducting quantum devices over 30 cm distance, leveraging the standing-wave modes in the coaxial cable connecting them. We achieve cross-entropy benchmarking (XEB) fidelities of $(99.15 \pm 0.02)\%$ and $(98.04 \pm 0.04)\%$ for CNOT and CZ gates, respectively, which are more efficient and universal than existing state transfer or feedback-based protocols. This advancement significantly enhances the feasibility of universal distributed quantum information processing, essential for the future development of large-scale quantum systems.

In this paper, we investigate the common parameter space of dark matter and leptogenesis in the $U(1)_{B-L}$ symmetry. This model involves a complex scalar $\phi$, sterile neutrinos $N$, and Majorana dark matter $\chi$, where only dark matter $\chi$ is charged under the $Z_2$ symmetry. Masses of $N$ and $\chi$ are generated via the Yukawa interactions to $\phi$ after breaking of the $U(1)_{B-L}$ symmetry. TeV scale sterile neutrinos $N$ are responsible for the generation of baryon asymmetry through the resonance leptogenesis mechanism. The new particles in the $U(1)_{B-L}$ have a significant impact on the dilution of $N$, thus on leptogenesis. Meanwhile, the annihilation processes of dark matter $\chi$ are almost identical to that of $N$, which indicates that both leptogenesis and dark matter are closely related to satisfying the observed results simultaneously. Under various theoretical and experimental constraints, the viable common parameter space of dark matter and leptogenesis is obtained for both global and local $U(1)_{B-L}$ symmetry., Comment: 38 pages, 15 figures

$P_c(4457)$ has been discovered over five years, but the parity of this particle remains undetermined. In this letter we propose a new interpretation for $P_c(4457)$, which is the state generated from the coupled-channel $\bar{D}^0\Lambda_c^{+}(2595)$ and $\pi^0 P_c(4312)$ since they can exchange an almost on-shell $\Sigma_c^+$. In this scenario, the parity of $P_c(4457)$ will be positive, which is different from the candidate of the bound state of $\bar{D}^*\Sigma_c$. The main decay channel of $P_c(4457)$ in this model is $P_c(4312)\pi$. We propose three processes $\Lambda_b^0 \to J/\psi K_s p \pi^-$, $\Lambda_b^0 \to J/\psi K^- p \pi^0$, and $\Lambda_b^0 \to J/\psi p \pi^- \pi^+ K^-$ to verify $P_c(4457)\to P_c(4312)\pi$., Comment: 6 Pages, 2 figures

Heavy neutral leptons $N$ are introduced to explain the tiny neutrino masses via the seesaw mechanism. For proper small mixing parameter $V_{\ell N}$, the heavy neutral leptons $N$ become long-lived, which leads to the displaced vertex signature at colliders. In this paper, we consider the displaced heavy neutral lepton from the neutrinophilic Higgs doublet $\Phi_\nu$ decay. The new Higgs doublet with MeV scale VEV can naturally explain the tiny neutrino masses with TeV scale $N$. Different from current experimental searches via the $W^\pm\to \ell^\pm N$ decay, the new decays as $H^\pm\to \ell^\pm N$ are not suppressed by the small mixing parameter $V_{\ell N}$. Therefore, a larger parameter space is expected to be detected at colliders. We then investigate the promising region at the 14 TeV HL-LHC and the 3 TeV CLIC. According to our simulation, the DV signature could probe $|V_{\ell N}|^2\gtrsim10^{-19}$ with $m_N

Heat shuttling phenomenon is characterized by the presence of a non-zero heat flow between two bodies without net thermal bias on average. It was initially predicted in the context of nonlinear heat conduction within atomic lattices coupled to two time-oscillating thermostats. Recent theoretical works revealed an analog of this effect for heat exchanges mediated by thermal photons between two solids having a temperature dependent emissivity. In this paper, we present the experimental proof of this effect using systems made with composite materials based on phase change materials. By periodically modulating the temperature of one of two solids we report that the system akin to heat pumping with a controllable heat flow direction. Additionally, we demonstrate the effectiveness of a simultaneous modulation of two temperatures to control both the strength and direction of heat shuttling by exploiting the phase delay between these temperatures. These results show that this effect is promising for an active thermal management of solid-state technology, to cool down solids, to insulate them from their background or to amplify heat exchanges.

Developing thermal analogues of field-effect transistor could open the door to a low-power and even zero-power communication technology working with heat rather than electricity. These solid-sate devices could also find many applications in the field of active thermal management in numerous technologies (microelectronic, building science, energy harvesting,conversion,...). Recent theoretical works has suggested that a photonic transistor made with three terminals can in principle be used to switch, modulate, and even amplify heat flux through exchange of thermal photons. Here, we report an experimental demonstration of thermal transistor effect using a non-contact system composed by a temperature-controlled metal-insulator-based material interacting in far-field regime with two blackbodies held at two different temperatures. We demonstrate that, with a tiny change in the temperature of the active layer, the heat flux received by the cold blackbody can be drastically modified. An amplification parameter of heat flux over 20 is reported.

We present STAR, a text-to-image model that employs scale-wise auto-regressive paradigm. Unlike VAR, which is limited to class-conditioned synthesis within a fixed set of predetermined categories, our STAR enables text-driven open-set generation through three key designs: To boost diversity and generalizability with unseen combinations of objects and concepts, we introduce a pre-trained text encoder to extract representations for textual constraints, which we then use as guidance. To improve the interactions between generated images and fine-grained textual guidance, making results more controllable, additional cross-attention layers are incorporated at each scale. Given the natural structure correlation across different scales, we leverage 2D Rotary Positional Encoding (RoPE) and tweak it into a normalized version. This ensures consistent interpretation of relative positions across token maps at different scales and stabilizes the training process. Extensive experiments demonstrate that STAR surpasses existing benchmarks in terms of fidelity,image text consistency, and aesthetic quality. Our findings emphasize the potential of auto-regressive methods in the field of high-quality image synthesis, offering promising new directions for the T2I field currently dominated by diffusion methods., Comment: 12 pages, 6 figures

The involvement of evanescent waves in the near-field regime could greatly enhance the spontaneous thermal radiation, offering a unique opportunity to study nanoscale photon-phonon interaction. However, accurately characterizing this subtle phenomenon is very challenging. This paper proposes a transient all-optical method for rapidly characterizing near-field radiative heat transfer (NFRHT) between macroscopic objects, using the first law of thermodynamics. Significantly, a full measurement at a fixed gap distance is completed within tens of seconds. By simplifying the configuration, the transient all-optical method achieves high measurement accuracy and reliable reproducibility. The proposed method can effectively analyze the NFRHT in various material systems, including SiO2, SiC, and Si, which involve different phonon or plasmon polaritons. Experimental observations demonstrate significant super-Planckian radiation, which arises from the near-field coupling of bounded surface modes. Furthermore, the method achieves excellent agreement with theory, with a minimal discrepancy of less than 2.7% across a wide temperature range. This wireless method could accurately characterize the NFRHT for objects with different sizes or optical properties, enabling the exploration of both fundamental interests and practical applications.

Motion-based controllable video generation offers the potential for creating captivating visual content. Existing methods typically necessitate model training to encode particular motion cues or incorporate fine-tuning to inject certain motion patterns, resulting in limited flexibility and generalization. In this work, we propose MotionClone, a training-free framework that enables motion cloning from reference videos to versatile motion-controlled video generation, including text-to-video and image-to-video. Based on the observation that the dominant components in temporal-attention maps drive motion synthesis, while the rest mainly capture noisy or very subtle motions, MotionClone utilizes sparse temporal attention weights as motion representations for motion guidance, facilitating diverse motion transfer across varying scenarios. Meanwhile, MotionClone allows for the direct extraction of motion representation through a single denoising step, bypassing the cumbersome inversion processes and thus promoting both efficiency and flexibility. Extensive experiments demonstrate that MotionClone exhibits proficiency in both global camera motion and local object motion, with notable superiority in terms of motion fidelity, textual alignment, and temporal consistency., Comment: 18 pages, 14 figures, https://bujiazi.github.io/motionclone.github.io/

Large language models (LLMs), including both proprietary and open-source models, have showcased remarkable capabilities in addressing a wide range of downstream tasks. Nonetheless, when it comes to practical Chinese legal tasks, these models fail to meet the actual requirements. Proprietary models do not ensure data privacy for sensitive legal cases, while open-source models demonstrate unsatisfactory performance due to their lack of legal knowledge. To address this problem, we introduce LawGPT, the first open-source model specifically designed for Chinese legal applications. LawGPT comprises two key components: legal-oriented pre-training and legal supervised fine-tuning. Specifically, we employ large-scale Chinese legal documents for legal-oriented pre-training to incorporate legal domain knowledge. To further improve the model's performance on downstream legal tasks, we create a knowledge-driven instruction dataset for legal supervised fine-tuning. Our experimental results demonstrate that LawGPT outperforms the open-source LLaMA 7B model. Our code and resources are publicly available at https://github.com/pengxiao-song/LaWGPT and have received 5.7K stars on GitHub., Comment: Technical Report

Graph neural networks are recognized for their strong performance across various applications, with the backpropagation algorithm playing a central role in the development of most GNN models. However, despite its effectiveness, BP has limitations that challenge its biological plausibility and affect the efficiency, scalability and parallelism of training neural networks for graph-based tasks. While several non-BP training algorithms, such as the direct feedback alignment, have been successfully applied to fully-connected and convolutional network components for handling Euclidean data, directly adapting these non-BP frameworks to manage non-Euclidean graph data in GNN models presents significant challenges. These challenges primarily arise from the violation of the i.i.d. assumption in graph data and the difficulty in accessing prediction errors for all samples (nodes) within the graph. To overcome these obstacles, in this paper we propose DFA-GNN, a novel forward learning framework tailored for GNNs with a case study of semi-supervised learning. The proposed method breaks the limitations of BP by using a dedicated forward training mechanism. Specifically, DFA-GNN extends the principles of DFA to adapt to graph data and unique architecture of GNNs, which incorporates the information of graph topology into the feedback links to accommodate the non-Euclidean characteristics of graph data. Additionally, for semi-supervised graph learning tasks, we developed a pseudo error generator that spreads residual errors from training data to create a pseudo error for each unlabeled node. These pseudo errors are then utilized to train GNNs using DFA. Extensive experiments on 10 public benchmarks reveal that our learning framework outperforms not only previous non-BP methods but also the standard BP methods, and it exhibits excellent robustness against various types of noise and attacks.

This paper introduces a novel benchmark as part of the AIS 2024 Real-Time Image Super-Resolution (RTSR) Challenge, which aims to upscale compressed images from 540p to 4K resolution (4x factor) in real-time on commercial GPUs. For this, we use a diverse test set containing a variety of 4K images ranging from digital art to gaming and photography. The images are compressed using the modern AVIF codec, instead of JPEG. All the proposed methods improve PSNR fidelity over Lanczos interpolation, and process images under 10ms. Out of the 160 participants, 25 teams submitted their code and models. The solutions present novel designs tailored for memory-efficiency and runtime on edge devices. This survey describes the best solutions for real-time SR of compressed high-resolution images., Comment: CVPR 2024, AI for Streaming (AIS) Workshop

The process of drawing electoral district boundaries is known as political redistricting. Within this context, gerrymandering is the practice of drawing these boundaries such that they unfairly favor a particular political party, often leading to unequal representation and skewed electoral outcomes. One of the few ways to detect gerrymandering is by algorithmically sampling redistricting plans. Previous methods mainly focus on sampling from some neighborhood of ``realistic' districting plans, rather than a uniform sample of the entire space. We present a deterministic subexponential time algorithm to uniformly sample from the space of all possible $ k $-partitions of a bounded degree planar graph, and with this construct a sample of the entire space of redistricting plans. We also give a way to restrict this sample space to plans that match certain compactness and population constraints at the cost of added complexity. The algorithm runs in $ 2^{O(\sqrt{n}\log n)} $ time, although we only give a heuristic implementation. Our method generalizes an algorithm to count self-avoiding walks on a square to count paths that split general planar graphs into $ k $ regions, and uses this to sample from the space of all $ k $-partitions of a planar graph., Comment: 17 Pages

We derive the modified L\"uscher equation in the presence of the long-range force caused by the exchange of a light particle. It is shown that the use of this equation enables one to circumvent the problems related to the strong partial-wave mixing and the t-channel sub-threshold singularities. It is also demonstrated that the present method is intrinsically linked to the so-called modified effective-range expansion (MERE) in the infinite volume. A detailed comparison with the two recently proposed alternative approaches is provided., Comment: 28 pages, 4 figures

In this work, we observe that the generators, which are pre-trained on massive natural images, inherently hold the promising potential for superior low-light image enhancement against varying scenarios.Specifically, we embed a pre-trained generator to Retinex model to produce reflectance maps with enhanced detail and vividness, thereby recovering features degraded by low-light conditions.Taking one step further, we introduce a novel optimization strategy, which backpropagates the gradients to the input seeds rather than the parameters of the low-light enhancement model, thus intactly retaining the generative knowledge learned from natural images and achieving faster convergence speed. Benefiting from the pre-trained knowledge and seed-optimization strategy, the low-light enhancement model can significantly regularize the realness and fidelity of the enhanced result, thus rapidly generating high-quality images without training on any low-light dataset. Extensive experiments on various benchmarks demonstrate the superiority of the proposed method over numerous state-of-the-art methods qualitatively and quantitatively.

Correlated errors can significantly impact the quantum error correction, which challenges the assumption that errors occur in different qubits independently in both space and time. Superconducting qubits have been found to suffer correlated errors across multiple qubits, which could be attributable to ionizing radiations and cosmic rays. Nevertheless, the direct evidence and a quantitative understanding of this relationship are currently lacking. In this work, we propose to continuously monitor multi-qubit simultaneous charge-parity jumps to detect correlated errors and find that occur more frequently than multi-qubit simultaneous bit flips. Then, we propose to position two cosmic-ray muon detectors directly beneath the sample box in a dilution refrigerator and successfully observe the correlated errors in a superconducting qubit array triggered by muons. By introducing a lead shielding layer on the refrigerator, we also reveal that the majority of other correlated errors are primarily induced by gamma rays. Furthermore, we find the superconducting film with a higher recombination rate of quasiparticles used in the qubits is helpful in reducing the duration of correlated errors. Our results provide experimental evidence of the impact of gamma rays and muons on superconducting quantum computation and offer practical insights into mitigation strategies for quantum error correction. In addition, we observe the average occurrence rate of muon-induced correlated errors in our processor is approximately 0.40 min$^{-1}$cm$^{-2}$, which is comparable to the muon event rate detected by the muon detector with 0.506 min$^{-1}$cm$^{-2}$. This demonstrates the potential applications of superconducting qubit arrays as low-energy threshold sensors in the field of high-energy physics., Comment: 7 pages and 5 figures for the main text, 20 pages and 20 figures for the supplementary materials

We study the spin alignment of neutral rho mesons in a pion gas using spin kinetic or Boltzmann equations. The $\rho\pi\pi$ coupling is given by the chiral effective theory. The collision terms at the leading and next-to-leading order in spin Boltzmann equations are derived. The evolution of the spin density matrix of the neutral rho meson is simulated with different initial conditions. The numerical results show that the interaction of pions and neutral rho mesons creates very small spin alignment in the central rapidity region if there is no rho meson in the system at the initial time. Such a small spin alignment in the central rapidity region will decay rapidly toward zero in later time. If there are rho mesons with a sizable spin alignment at the initial time the spin alignment will also decrease rapidly. We also considered the effect on $\rho_{00}$ from the elliptic flow of pions in the blast wave model. With vanishing spin alignment at the initial time, the deviation of $\rho_{00}$ from 1/3 is positive but very small., Comment: RevTex 4, 17 pages, 12 figures

In this work, we observe that the model, which is trained on vast general images using masking strategy, has been naturally embedded with the distribution knowledge regarding natural images, and thus spontaneously attains the underlying potential for strong image denoising. Based on this observation, we propose a novel zero-shot denoising paradigm, i.e., Masked Pre-train then Iterative fill (MPI). MPI pre-trains a model with masking and fine-tunes it for denoising of a single image with unseen noise degradation. Concretely, the proposed MPI comprises two key procedures: 1) Masked Pre-training involves training a model on multiple natural images with random masks to gather generalizable representations, allowing for practical applications in varying noise degradation and even in distinct image types. 2) Iterative filling is devised to efficiently fuse pre-trained knowledge for denoising. Similar to but distinct from pre-training, random masking is retained to bridge the gap, but only the predicted parts covered by masks are assembled for efficiency, which enables high-quality denoising within a limited number of iterations. Comprehensive experiments across various noisy scenarios underscore the notable advances of proposed MPI over previous approaches with a marked reduction in inference time. Code is available at https://github.com/krennic999/MPI.git., Comment: 11 pages, 9 figures

An important aspect of solar energetic particle (SEP) events is their source populations. Elemental abundance enhancements of impulsive SEP events, originating in presumed coronal reconnection episodes, can be fitted to steep power laws of A/Q, where A and Q are the atomic mass and ionic charge. Since thermal electron energies are enhanced and nonthermal electron distributions arise in the reconnection process, we might expect that ionic charge states Q would be increased through ionization interactions with those electron populations during the acceleration process. The temperature estimated from the SEPs corresponds to the charge state during the acceleration process, while the actual charge state measured in situ may be modified as the SEPs pass through the corona. We examine whether the temperature estimation from the A/Q would differ with various kappa values in a kappa function representing high-energy tail deviating from a Maxwellian velocity distribution. We find that the differences in the A/Q between a Maxwellian and an extreme kappa distribution are about 10-30%. We fit power-law enhancement of element abundances as a function of their A/Q with various kappa values. Then, we find that the derived source region temperature is not significantly affected by whether or not the electron velocity distribution deviates from a Maxwellian, i.e., thermal, distribution. Assuming that electrons are heated in the acceleration region, the agreement of the SEP charge state during acceleration with typical active region temperatures suggests that SEPs are accelerated and leave the acceleration region in a shorter time than the ionization time scale., Comment: 14 pages, 10 figures

In this paper, we first assess and harness various Vision Foundation Models (VFMs) in the context of Domain Generalized Semantic Segmentation (DGSS). Driven by the motivation that Leveraging Stronger pre-trained models and Fewer trainable parameters for Superior generalizability, we introduce a robust fine-tuning approach, namely Rein, to parameter-efficiently harness VFMs for DGSS. Built upon a set of trainable tokens, each linked to distinct instances, Rein precisely refines and forwards the feature maps from each layer to the next layer within the backbone. This process produces diverse refinements for different categories within a single image. With fewer trainable parameters, Rein efficiently fine-tunes VFMs for DGSS tasks, surprisingly surpassing full parameter fine-tuning. Extensive experiments across various settings demonstrate that Rein significantly outperforms state-of-the-art methods. Remarkably, with just an extra 1% of trainable parameters within the frozen backbone, Rein achieves a mIoU of 78.4% on the Cityscapes, without accessing any real urban-scene datasets.Code is available at https://github.com/w1oves/Rein.git.

We derive an explicit expression for the Lellouch-L\"uscher (LL) factor in the $K\to 3\pi$ decays at leading order (without derivative couplings). Several important technical details are addressed, like a proper decomposition into the isospin amplitudes, the choice of a minimal set of effective couplings and the renormalization, as well as the algorithm for the solution of the pertinent Faddeev equations in the infinite volume which is based on the contour deformation method. Most importantly, our numerical results demonstrate that the three-body force contributes very little to the LL factor. This result paves the way for the study of the $K\to 3\pi$ decays on the lattice., Comment: 41 pages, 14 figures

The neutrinophilic two Higgs doublet model is one of the simplest models to explain the origin of tiny Dirac neutrino masses. This model introduces a new Higgs doublet with eV scale VEV to naturally generate the tiny neutrino masses. Depending on the same Yukawa coupling, the neutrino oscillation patterns can be probed with the dilepton signature from the decay of charged scalar $H^\pm$. For example, the normal hierarchy predicts BR$(H^+\to e^+\nu)\ll$ BR$(H^+\to \mu^+\nu)\approx$ BR$(H^+\to \tau^+\nu)\simeq0.5$ when the lightest neutrino mass is below 0.01 eV, while the inverted hierarchy predicts BR$(H^+\to e^+\nu)/2\simeq$ BR$(H^+\to \mu^+\nu)\simeq$ BR$(H^+\to \tau^+\nu)\simeq0.25$. By precise measurement of BR$(H^+\to \ell^+\nu)$, we are hopefully to probe the lightest neutrino mass and the atmospheric mixing angle $\theta_{23}$. Through the detailed simulation of the dilepton signature and corresponding backgrounds, we find that the 3 TeV CLIC could discover $M_{H^+}\lesssim1220$ GeV for NH and $M_{H^+}\lesssim1280$ GeV for IH. Meanwhile, the future 100 TeV FCC-hh collider could probe $M_{H^+}\lesssim1810$ GeV for NH and $M_{H^+}\lesssim2060$ GeV for IH., Comment: 18 pages, 9 figures

JIT (Just-in-Time) technology has garnered significant attention for improving the efficiency of database execution. It offers higher performance by eliminating interpretation overhead compared to traditional execution engines. LLVM serves as the primary JIT architecture, which was implemented in PostgreSQL since version 11. However, recent advancements in WASM-based databases, such as Mutable, present an alternative JIT approach. This approach minimizes the extensive engineering efforts associated with the execution engine and focuses on optimizing supported operators for lower latency and higher throughput. In this paper, we perform comprehensive experiments on these two representative open-source databases to gain deeper insights into the effectiveness of different JIT architectures., Comment: A peer-reviewed version of this paper has been published at the Australasian Database Conference (ADC) 2023

Transmission line state assessment and prediction are of great significance for the rational formulation of operation and maintenance strategy and improvement of operation and maintenance level. Aiming at the problem that existing models cannot take into account the robustness and data demand, this paper proposes a state prediction method based on semi-supervised learning. Firstly, for the expanded feature vector, the regular matrix is used to fill in the missing data, and the sparse coding problem is solved by representation learning. Then, with the help of a small number of labelled samples to initially determine the category centers of line segments in different defective states. Finally, the estimated parameters of the model are corrected using unlabeled samples. Example analysis shows that this method can improve the recognition accuracy and use data more efficiently than the existing models.

The near-field radiative heat transfer (NFRHT) between one-dimensional metamaterials comprising phonon dielectric multilayers was experimented. Large sized (1cm x 1cm) near-field samples were fabricated using SiC, SiO2 and Ge layers at a certain gap distance, and the effect of layer stacking order and phonon resonance quality on the NFRHT was examined. The measured results show good agreement with those obtained theoretically employing the transmission matrix method. Super-Planckian blackbody radiation was observed between the emitters and receivers with identical structures. Measurements demonstrate failure of the effective medium theory (EMT) in predicting the near-field heat flux especially in the presence of bounded surface modes, such as the epsilon-near-zero (ENZ) mode. Analyses also indicate that, in certain cases, the EMT can provide reasonable physical insight into the underlying coupling process from the perspective of homogenized media. The conditions to apply the EMT in the near-field regime was also touched upon., Comment: 18 pages,5 figures