Altermagnets have attracted considerable interest for their capacity to generate spin splitting while preserving zero net magnetization. This work proposes a distinct class of antiferromagnetic materials, termed X-type antiferromagnets, which are shown to produce more efficient $\cal T$-odd spin currents than altermagnets along specific crystallographic directions due to their unique Fermi surface geometry. The spin current polarization is controlled by the Néel vector orientation. In the (110)-oriented $\beta-\mathrm{Fe}_2\mathrm{PO}_5$, the Fermi surface exhibits a $d$-wave altermagnetic-like characteristic and becomes compressed into an approximately X-shaped $d$-wave configuration, yielding highly efficient $\cal T$-odd spin currents with a charge-to-spin conversion efficiency reaching 90%. Moreover, when the Néel vector is tilted via unit cell selection or external means, the system generates out-of-plane spin-polarized currents with efficiencies substantially exceeding those of known ferromagnets, altermagnets, noncollinear antiferromagnets, and low-symmetry materials. The highly efficient charge-spin conversion in X-type antiferromagnets provides a novel and highly effective spin source system for the development of low-power spintronic devices.

Decoding the internal structure of the proton is a fundamental challenge in physics. Historically, any new discovery about the proton has fuelled advances in several scientific fields. We have reported that gluons inside the proton accumulate near the critical momentum due to chaotic phenomena, forming gluon condensation. Surprisingly, the pion distribution predicted by this gluon distribution for the production of high-energy proton collisions could answer two puzzles in astronomy and high-energy physics. We find that during ultrahigh-energy cosmic ray collisions, gluon condensation may abruptly produce a large number of low-momentum pions, whose electromagnetic decays have the typical breakout properties appearing in various cosmic gamma-ray spectra. On the other hand, the Large Hadron Collider (LHC), which is well below the cosmic ray energy scale, also shows weak but recognisable signs of gluon condensation, which had been mistaken for BEC pions. The connection between these two phenomena, which occur at different scales in the Universe, supports the existence of a new structure within the proton-gluon condensation.

We present the design and test results of two optical data transmission ASICs for the High-Luminosity LHC (HL-LHC) experiments. These ASICs include a two-channel serializer (LOCs2) and a single-channel Vertical Cavity Surface Emitting Laser (VCSEL) driver (LOCld1V2). Both ASICs are fabricated in a commercial 0.25-um Silicon-on-Sapphire (SoS) CMOS technology and operate at a data rate up to 8 Gbps per channel. The power consumption of LOCs2 and LOCld1V2 are 1.25 W and 0.27 W at 8-Gbps data rate, respectively. LOCld1V2 has been verified meeting the radiation-tolerance requirements for HL-LHC experiments.

Layered perovskites $A_3M_2$O$_7$ are known to exhibit the so-called hybrid improper ferroelectricity. Despite experimentally confirmed cases (e.g. nonmagnetic $M$=Ti and Sn), the ferroelectricity in magnetic Ca$_3$Mn$_2$O$_7$ remains a puzzle. Here, the structural, ferroelectric, magnetoelectric, and optical properties of Ca$_3$Mn$_2$O$_7$ are systematically investigated. Switchable polarization is directly measured, demonstrating its ferroelectricity. In addition, magnetoelectric response is also evidenced, implying the coupling between magnetism and ferroelectricity. Furthermore, strong visible light absorption is observed, which can be understood from its electronic structure. Its direct and appropriate band gap, as well as wide conducting bands, makes Ca$_3$Mn$_2$O$_7$ a potential candidate for ferroelectric photoelectric applications.

In which we propose neural network architecture (dune neural network) for recognizing general noisy image without adding any artificial noise in the training data. By representing each free parameter of the network as an uncertainty interval, and applying a linear transformation to each input element, we show that the resulting architecture achieves decent noise robustness when faced with input data with white noise. We apply simple dune neural networks for MNIST dataset and demonstrate that even for very noisy input images which are hard for human to recognize, our approach achieved better test set accuracy than human without dataset augmentation. We also find that our method is robust for many other examples with various background patterns added.

We study the light right-handed slepton bulk regions for dark matter from the Generalized Minimal Supergravity (GmSUGRA) in the Minimal Supersymmetric Standard Model (MSSM). In our comprehensive numerical studies, we show that $\mathcal{R_{\tilde{\phi}}}\gtrsim10\%$ is a conservative criteria to formulate bulk region, where $\mathcal{R_{\tilde{\phi}}}\equiv({m_{\tilde{\phi}}-m_{\tilde{\chi}_1^0}})/{m_{\tilde{\chi}_1^0}}$. For right-handed stau as the Next to the Lightest Supersymmetric Partcile (NLSP), we find a large viable parameter space, consistent with the current LHC constraints, Planck2018 dark matter relic density bounds, and direct bounds on neutralino-nucleons scattering cross-section that naturally supports the right-handed stau bulk regions for dark matter. In particular, the upper bounds on the masses of the Lightest Supersymmetric Particle (LSP) neutralino and right-handed stau are about 120.4 GeV and 138 GeV, respectively. This bulk region may be beyond the current LHC reach and could be probed at LUX-ZEPLIN, a next-generation dark matter direct detection experiment, the Future Circular Collider (FCC-ee) at CERN, and the Circular Electron Positron Collider (CEPC). However, the scenario with the right-handed selectron as the NLSP is excluded by the LHC supersymmetry searches.

We investigate both analytically and numerically the buildup of antiferromagnetic (AF) correlation in the dynamically tuned Ising model with various geometries by using the Rydberg atomic system. It is shown that Magnus expansion up to second order for the local lattice geometries can describe quantitatively the creation of the AF correlation for different lattice arrays, e.g., $2 \times n$ lattice, cyclic lattice with star, and triangular lattice. We find that the magnitude of AF correlation for the same Manhattan distance is the algebraic sum of the correlations contributed by all shortest paths -- a typical superposition law. Such a law is independent of nonequivalent paths, lattice geometries, and quench style.

Ghost imaging via sparsity constraints (GISC) spectral camera modulates the three-dimensional (3D) hyperspectral image into a two-dimensional (2D) compressive image with speckles in a single shot. It obtains a 3D hyperspectral image (HSI) by reconstruction algorithms. The rapid development of deep learning has provided a new method for 3D HSI reconstruction. Moreover, the imaging performance of the GISC spectral camera can be improved by optimizing the speckle modulation. In this paper, we propose an end-to-end GISCnet with super-Rayleigh speckle modulation to improve the imaging quality of the GISC spectral camera. The structure of GISCnet is very simple but effective, and we can easily adjust the network structure parameters to improve the image reconstruction quality. Relative to Rayleigh speckles, our super-Rayleigh speckles modulation exhibits a wealth of detail in reconstructing 3D HSIs. After evaluating 648 3D HSIs, it was found that the average peak signal-to-noise ratio increased from 27 dB to 31 dB. Overall, the proposed GISCnet with super-Rayleigh speckle modulation can effectively improve the imaging quality of the GISC spectral camera by taking advantage of both optimized super-Rayleigh modulation and deep-learning image reconstruction, inspiring joint optimization of light-field modulation and image reconstruction to improve ghost imaging performance.

We analyse the shifted hybrid inflation in a no-scale SU(5) model with supersymmetry, which naturally circumvents the monopole problem. The no-scale framework is derivable as the effective field theory of the supersymmetric (SUSY) compactifications of string theory, and it yields a flat potential with no anti-de Sitter vacua, resolving the $\eta$ problem. The model predicts a scalar spectral tilt $n_s$ compatible with the most recent measurements by the Planck satellite, while also accommodating observable values of the tensor-to-scalar ratio $r$ ($\sim 0.0015$), potentially measurable by the near-future experiments. Moreover, the proton decay lifetime in the presence of the dimension-5 operators is found to lie above the current limit imposed by the Super-Kamiokande experiment. A realistic scenario of reheating and non-thermal leptogenesis is invoked, wherein the reheating temperature $T_r$ lies in the $\{2 \times 10^6 \lesssim T_r \lesssim 2 \times 10^9\}$ GeV range, and at the same time the gravitino makes a viable dark matter (DM) candidate.

Automatically extracting useful information from electronic medical records along with conducting disease diagnoses is a promising task for both clinical decision support(CDS) and neural language processing(NLP). Most of the existing systems are based on artificially constructed knowledge bases, and then auxiliary diagnosis is done by rule matching. In this study, we present a clinical intelligent decision approach based on Convolutional Neural Networks(CNN), which can automatically extract high-level semantic information of electronic medical records and then perform automatic diagnosis without artificial construction of rules or knowledge bases. We use collected 18,590 copies of the real-world clinical electronic medical records to train and test the proposed model. Experimental results show that the proposed model can achieve 98.67\% accuracy and 96.02\% recall, which strongly supports that using convolutional neural network to automatically learn high-level semantic features of electronic medical records and then conduct assist diagnosis is feasible and effective.

The existence of adversarial images has seriously affected the task of image recognition and practical application of deep learning, it is also a key scientific problem that deep learning urgently needs to solve. By far the most effective approach is to train the neural network with a large number of adversarial examples. However, this adversarial training method requires a huge amount of computing resources when applied to ImageNet, and has not yet achieved satisfactory results for high-intensity adversarial attacks. In this paper, we construct an interference neural network by applying additional background images and corresponding labels, and use pre-trained ResNet-152 to efficiently complete the training. Compared with the state-of-the-art results under the PGD attack, it has a better defense effect with much smaller computing resources. This work provides new ideas for academic research and practical applications of effective defense against adversarial attacks.

Path integral Monte Carlo (PIMC) and path integral molecular dynamics (PIMD) provide the golden standard for the ab initio simulations of identical particles. In this work, we achieved significant GPU acceleration based on PIMD, which is equivalent to PIMC in the ab initio simulations, and developed an open-source PIMD code repository that does not rely on any other third party library. Numerical experiments show that for a system of 1600 interacting identical bosons in a harmonic trap, using a single GPU and a single CPU, it only takes two hours to achieve satisfactory simulation accuracy. With the increase of the number of identical particles, the advantage of GPU acceleration over CPU becomes more obvious, making it possible to simulate tens of thousands of identical particles from first principles using a single GPU. For example, for a system of 10000 non-interacting bosons, numerical experiments show that it takes 23 hours to obtain a simulation that is highly consistent with the exact results. Our study shows that GPU acceleration can lay a solid foundation for the wide application of PIMD simulations for extremely large-scale identical particle quantum systems with more than 10,000 particles. Numerical experiments show that a 24GB GPU can simulate up to 40000 identical particles from first principles, and the GPU acceleration leads to a roughly linear relationship between the computation time and the number of identical particles. In addition, we have also successfully implemented simulations for fictitious identical particle thermodynamics using GPU to overcome the Fermion sign problem, which makes it promising to efficiently and accurately simulate tens of thousands of fermions based on GPU.

We present a realistic supersymmetric $\mu$-hybrid inflation model within the framework of $SU(4)_C \times SU(2)_L \times U(1)_R$ gauge symmetry, wherein the symmetry breaking $SU(4)_C \times SU(2)_L \times U(1)_R\rightarrow SU(3)_C\times SU(2)_L \times U(1)_{B-L}\times U(1)_R$ occurs before observable inflation, effectively eliminating topologically stable primordial monopoles. Subsequent breaking of $U(1)_{B-L} \times U(1)_R \rightarrow U(1)_Y$ after inflation leads to the formation of superheavy metastable cosmic strings (CSs), capable of producing a stochastic gravitational wave background (SGWB) consistent with the recent PTA data. Moreover, the scalar spectral index $n_s$ and the tensor-to-scalar ratio $r$ align with Planck 2018 observations. A consistent scenario for reheating and non-thermal leptogenesis is employed to explain the observed matter content of the universe. Finally, the embedding of $G_{421}$ into the Pati-Salam gauge symmetry $G_{422}$ is briefly discussed, predicting potentially observable proton decay rates detectable at facilities such as Hyper Kamiokande and DUNE.

We study the cosmological implications of an effective field theory model derived within a configuration of D7 brane stacks in the framework of type-IIB string theory. We consider a suitable geometric setup where the K\"ahler moduli fields are stabilized and the parametric space is constrained so that a de Sitter vacuum is ensured. In addition to the moduli fields we also take into account the usual Higgs and matter fields included in the effective field theory. In this background, we implement the standard hybrid inflation scenario with a singlet scalar field acting as the inflaton and the Higgs states serving as waterfall fields. Radiative corrections and soft supersymmetry breaking terms play an essential role in the realization of a successful inflationary scenario consistent with the present cosmological data. Small tensor-to-scalar ratio values are predicted, which can be probed in future planned experiments. Further constraints on the model's parameters are derived from bounds on dark radiation which is measured as a contribution to the effective number of neutrino species $N_{\rm eff}$. In particular, we find an excess of $\Delta N_{\rm eff}\leq{0.95}$at$2\sigma$ confidence level with natural values of the involved couplings.

This study introduces a movement-prediction-adjusted naïve forecast for time series exhibiting symmetric random walk characteristics, which is applicable after accurate movement predictions are available. Specifically, the original naïve forecast is adjusted by a weighted movement prediction term, where the weights are determined via two parameters derived from the in-sample data: one based on directional accuracy of the movement prediction and the other on the mean absolute increment of the target series. Simulation experiments were conducted across four types of synthetic symmetric random walk series, each with different variance structures. For each time series, diverse movement predictions with predefined directional accuracies were randomly generated, and the resulting forecasts were evaluated via the RMSE, MAE, MAPE, and sMAPE metrics. The results demonstrated a clear monotonic improvement in the forecast performance as the directional accuracy increased. Notably, the adjusted naïve forecast achieved statistically significant improvements even at relatively low directional accuracy levels slightly above 0.50. These findings imply that the movement-prediction-adjusted naïve forecast can serve as an effective second-stage method for forecasting symmetric random walk time series when consistent and accurate movement predictions are provided.

We investigate the possibility that primordial black holes (PBHs) can be formed from large curvature perturbations generated during the waterfall phase transition in a hybrid inflation model driven by an axion-like particle (ALP) $\phi$. The model predicts a spectral index $n_s \simeq 0.964$ and a tensor-to-scalar ratio $r \simeq 0.003$, in agreement with Planck data and potentially testable by next generation cosmic microwave background (CMB) experiments such as CMB-S4 and LiteBIRD. We find that the PBH mass and the peak of the associated scalar-induced gravitational wave (SIGW) spectrum are correlated with the ALP mass. In particular, PBHs in the mass range $10^{-13}\, M_\odot$ can constitute either the entire dark matter (DM) content of the universe or a significant fraction of it. The predicted second-order GWs from this mechanism are within the sensitivity reach of future observatories like LISA and ET. The typical reheating temperature in the model is around $10^6 - 10^7$ GeV is consistent with Big Bang Nucleosynthesis (BBN) constraints.

This paper presents several component prototypes towards a low-latency, small-form-factor optical link designed for the ATLAS Liquid Argon Calorimeter Phase-I trigger upgrade. A prototype of the custom-made dual-channel optical transmitter module, the Miniature optical Transmitter (MTx), with separate transmitter optical sub-assemblies (TOSAs) has been demonstrated at data rates up to 8 Gbps per channel. A Vertical-Cavity Surface-Emitting Laser (VCSEL) driver ASIC has been developed and is used in the current MTx prototypes. A serializer ASIC prototype, operating at up to 8 Gbps per channel, has been designed and tested. A low-latency, low-overhead encoder ASIC prototype has been designed and tested. The latency of the whole link, including the transmitter latency and the receiver latency but not the latency of the fiber, is estimated to be less than 57.9 ns. The size of the MTx is 45 mm x 15 mm x 6 mm.

We show that the behavior of the cosmic ray electron spectrum in the TeV energy band near the Earth is dominated by gluon condensation and anomalous electron/positron pair-production in Cygnus X.

We investigate both the $Z$ and $H$ poles solutions for the Higgsino mass parameter \mu>0 and \mu<0 for the neutralino dark matter in light of the LHC supersymmetry searches and the direct detection dark matter experiments, LUX-ZEPLIN (LZ), in the Generalized Minimal Supergravity (GmSUGRA). Our study indicates that the latest experimental constraints from the LHC and LZ Collaborations exclude the light Higgsinos in the $Z$ and $H$ pole regions for the \mu>0 case. Interestingly, for the \mu < 0 case, a very light Higgsinos can still be consistent with the current constraints from the electroweakino searches and LZ experiment in the $Z$ and $H$ poles. Consequently, the \mu < 0 case appears more promising and thus requires the dedicated efforts to make definitive conclusions about their current status from the experimental Collaborations. In this framework, our findings indicate a deviation of up to $2\sigma$ from the central value of $a_\mu \equiv (g-2)_\mu/2$, resonating with the experimental results reported by CMD and BDM.