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Laryngo-pharyngeal cancer (LPC) is a highly fatal malignant disease affecting the head and neck region. Previous studies on endoscopic tumor detection, particularly those leveraging dual-branch network architectures, have shown significant advancements in tumor detection. These studies highlight the potential of dual-branch networks in improving diagnostic accuracy by effectively integrating global and local (lesion) feature extraction. However, they are still limited in their capabilities to accurately locate the lesion region and capture the discriminative feature information between the global and local branches. To address these issues, we propose a novel SAM-guided fusion network (SAM-FNet), a dual-branch network for laryngo-pharyngeal tumor detection. By leveraging the powerful object segmentation capabilities of the Segment Anything Model (SAM), we introduce the SAM into the SAM-FNet to accurately segment the lesion region. Furthermore, we propose a GAN-like feature optimization (GFO) module to capture the discriminative features between the global and local branches, enhancing the fusion feature complementarity. Additionally, we collect two LPC datasets from the First Affiliated Hospital (FAHSYSU) and the Sixth Affiliated Hospital (SAHSYSU) of Sun Yat-sen University. The FAHSYSU dataset is used as the internal dataset for training the model, while the SAHSYSU dataset is used as the external dataset for evaluating the model's performance. Extensive experiments on both datasets of FAHSYSU and SAHSYSU demonstrate that the SAM-FNet can achieve competitive results, outperforming the state-of-the-art counterparts. The source code of SAM-FNet is available at the URL of https://github.com/VVJia/SAM-FNet.

Silicon strip detectors have been widely utilized in space experiments for gamma-ray and cosmic-ray detections thanks to their high spatial resolution and stable performance. For a silicon micro-strip detector, the Monte Carlo simulation is recognized as a practical and cost-effective approach to verify the detector performance. In this study, a technique for the simulation of the silicon micro-strip detector with the $\rm Allpix^{2}$ framework is developed. By incorporating the electric field into the particle transport simulation based on Geant4, this framework could precisely emulate the carrier drift in the silicon micro-strip detector. The simulation results are validated using the beam test data as well as the flight data of the DAMPE experiment, which suggests that the $\rm Allpix^{2}$ framework is a powerful tool to obtain the performance of the silicon micro-strip detector.

In this paper we investigate the shear viscoelasticity and the hydrodynamic modes in a holographic solid model with several sets of axions that all break the translations spontaneously on boundary. Comparing with the single-axion model, the shear modulus is enhanced at high temperatures and the shear viscosity is always suppressed in the presence of additional axions. However, the different sets of axions exhibit competitive relationship in determining the shear modulus at low temperatures. Furthermore, by calculating the black hole quasi-normal modes, it is found that adding more axions only increases the amount of diffusive modes. The number of the sound modes always remains unchanged., Comment: 25pages, 20 figures

Parameterised quantum circuits (PQCs) hold great promise for demonstrating quantum advantages in practical applications of quantum computation. Examples of successful applications include the variational quantum eigensolver, the quantum approximate optimisation algorithm, and quantum machine learning. However, before executing PQCs on real quantum devices, they undergo compilation and optimisation procedures. Given the inherent error-proneness of these processes, it becomes crucial to verify the equivalence between the original PQC and its compiled or optimised version. Unfortunately, most existing quantum circuit verifiers cannot directly handle parameterised quantum circuits; instead, they require parameter substitution to perform verification. In this paper, we address the critical challenge of equivalence checking for PQCs. We propose a novel compact representation for PQCs based on tensor decision diagrams. Leveraging this representation, we present an algorithm for verifying PQC equivalence without the need for instantiation. Our approach ensures both effectiveness and efficiency, as confirmed by experimental evaluations. The decision-diagram representations offer a powerful tool for analysing and verifying parameterised quantum circuits, bridging the gap between theoretical models and practical implementations.

Most existing data preprocessing is done at the CPU. Although some studies use techniques such as multi-processing and double buffering to accelerate CPU preprocessing, CPU computational speed and storage bandwidth still limit the processing speed. Other studies try to use intelligent data storage devices, such as computational storage devices, to complete data preprocessing instead of CPUs. The current studies use only one device to complete data preprocessing operations, which cannot fully overlap data preprocessing and accelerator computation time. To fully exploit the independence and high bandwidth of the novel CSD, this paper proposes an advanced, highly parallel dual-pronged data preprocessing algorithm (DDLP) that significantly improves the execution efficiency and computational overlap between heterogeneous devices. DDLP enables the CPU and CSD to start data preprocessing operations from both ends of the dataset separately. Meanwhile, we propose two adaptive dynamic selection strategies to make DDLP control the GPU to automatically read data from different sources. We achieve sufficient computational overlap between CSD data preprocessing and CPU preprocessing, GPU computation, and GPU data reading. In addition, DDLP leverages GPU Direct Storage technology to enable efficient SSD-to-GPU data transfer. DDLP reduces the usage of expensive CPU and DRAM resources, reduces the number of SSD-to-GPU data transfers, and improves the energy efficiency of preprocessing while reducing the overall preprocessing and training time. Extensive experimental results show that DDLP can improve learning speed by up to 23.5% on ImageNet Dataset while reducing energy consumption by 19.7% and CPU and DRAM usage by 37.6%. DDLP also improve learning speed by up to 27.6% on Cifar-10 Dataset.

Multi-modal brain images from MRI scans are widely used in clinical diagnosis to provide complementary information from different modalities. However, obtaining fully paired multi-modal images in practice is challenging due to various factors, such as time, cost, and artifacts, resulting in modality-missing brain images. To address this problem, unsupervised multi-modal brain image translation has been extensively studied. Existing methods suffer from the problem of brain tumor deformation during translation, as they fail to focus on the tumor areas when translating the whole images. In this paper, we propose an unsupervised tumor-aware distillation teacher-student network called UTAD-Net, which is capable of perceiving and translating tumor areas precisely. Specifically, our model consists of two parts: a teacher network and a student network. The teacher network learns an end-to-end mapping from source to target modality using unpaired images and corresponding tumor masks first. Then, the translation knowledge is distilled into the student network, enabling it to generate more realistic tumor areas and whole images without masks. Experiments show that our model achieves competitive performance on both quantitative and qualitative evaluations of image quality compared with state-of-the-art methods. Furthermore, we demonstrate the effectiveness of the generated images on downstream segmentation tasks. Our code is available at https://github.com/scut-HC/UTAD-Net., Comment: 8 pages, 5 figures. It has been provisionally accepted for IJCNN 2024

Bagging has achieved great success in the field of machine learning by integrating multiple base classifiers to build a single strong classifier to reduce model variance. The performance improvement of bagging mainly relies on the number and diversity of base classifiers. However, traditional deep learning model training methods are expensive to train individually and difficult to train multiple models with low similarity in a restricted dataset. Recently, diffusion models, which have been tremendously successful in the fields of imaging and vision, have been found to be effective in generating neural network model weights and biases with diversity. We creatively propose a Bagging deep learning training algorithm based on Efficient Neural network Diffusion (BEND). The originality of BEND comes from the first use of a neural network diffusion model to efficiently build base classifiers for bagging. Our approach is simple but effective, first using multiple trained model weights and biases as inputs to train autoencoder and latent diffusion model to realize a diffusion model from noise to valid neural network parameters. Subsequently, we generate several base classifiers using the trained diffusion model. Finally, we integrate these ba se classifiers for various inference tasks using the Bagging method. Resulting experiments on multiple models and datasets show that our proposed BEND algorithm can consistently outperform the mean and median accuracies of both the original trained model and the diffused model. At the same time, new models diffused using the diffusion model have higher diversity and lower cost than multiple models trained using traditional methods. The BEND approach successfully introduces diffusion models into the new deep learning training domain and provides a new paradigm for future deep learning training and inference.

Recently, in studies of holographic models and hydrodynamics with spontaneous breaking of approximate symmetries, it has been proposed that the damping of pseudo-Goldstone modes at finite temperatures is universally constrained in the way that $\Omega_{\varphi}/m_{\varphi}^2\simeq D_{\varphi}$ in the broken phase, where $\Omega_{\varphi}$ and $m_{\varphi} $ are the relaxation rate at zero wavenumber and the mass of pseudo-Goldstones, $D_{\varphi}$ is the Goldstone diffusivity in the limit of purely spontaneous breaking. In this paper, we investigate the pseudo-Goldstone damping in a purely relaxational O($N$) model by performing the functional renormalization group calculations at the full quantum and stochastic level within the Schwinger-Keldysh formalism. We find that, away from the critical temperature, the proposed relation is always valid. When the temperature is very close to the critical value such that the mass of the Higgs mode is comparable to the mass of the pseudo-Goldstone modes, the pseudo-Goldstone damping displays a novel scaling behavior that follows $\Omega_\varphi/m_\varphi^2\propto m_{\varphi}^{\Delta_\eta}$ with a correction $\Delta_\eta$ controlled by the critical universalities. Moreover, we study how the correction depends on the value of $N$ and show that $\Delta_\eta \rightarrow 0$ when fluctuations are infinitely suppressed in the large $N$ limit. In this case, the proposed relation works even in the critical region. Finally, we match our results to the dissipative sector of the pion dynamics near the chiral phase transition., Comment: V2:minor revision, references added, discussion on the pure SSB case has been moved to the supplement

The Silicon Charge Detector (SCD) is a subdetector of the High Energy Cosmic Radiation Detection payload. The dynamic range of the silicon microstrip detector can be extended by the capacitive coupling effect, which is related to the interstrip capacitance and the coupling capacitance. A detector prototype with several sets of parameters was designed and tested in the ion beams at the CERN Super Proton Synchrotron. The capacitive coupling fractions with readout strip and floating strip incidences were studied using the beam test data and SPICE simulation.

The pulse widths of fast radio bursts are always broadened due to the scattering of the plasma medium through which the electromagnetic wave passes. The recorded pulse width will be further affected by the radio telescopes since the sampling time and the bandwidth cannot be infinitely small. In this study, we focus on the pulse widths of the 3287 bursts detected from FRB 20121102A as of October 2023. Various effects such as the scattering broadening, the redshift broadening and the instrumental broadening are examined. It is found that the instrumental broadening only contributes a fraction of $10^{-3}$--$10^{-1}$ to the observed pulse width. The scattering broadening is even smaller, which constitutes a tiny fraction of $10^{-5}$--$10^{-2}$ in the observed pulse width. After correcting for these broadenings, the intrinsic pulse width is derived for each burst. The maximum and minimum pulse widths at different frequencies are highlighted. Interestingly, both the mean value and the dispersion range of intrinsic pulse width are found to be inversely proportional to the square of the central frequency. The intrinsic widths of most bursts are in a narrow range of 1--10 ms, which leads to a quasi-linear correlation between the fluence and the peak flux.

We study the linear stability of holographic homogeneous solids (HHS) at finite temperature and in presence of a background shear strain by means of a large scale quasi-normal mode analysis which extends beyond the hydrodynamic limit. We find that mechanical instability can arise either as a result of a complex speed of sound -- gradient instability -- or of a negative diffusion constant. Surprisingly, the simplest HHS models are linearly stable for arbitrarily large values of the background strain. For more complex HHS, the onset of the diffusive instability always precedes that of the gradient instability, which becomes the dominant destabilizing process only above a critical value of the background shear strain. Finally, we observe that the critical strains for the two instabilities approach each other at low temperatures. We conclude by presenting a phase diagram for HHS as a function of temperature and background shear strain which shows interesting similarities with the physics of superfluids in presence of background superfluid velocity., Comment: v2: manuscript revised and improved

With the rapid development of quantum computing, automatic verification of quantum circuits becomes more and more important. While several decision diagrams (DDs) have been introduced in quantum circuit simulation and verification, none of them supports symbolic computation. Algorithmic manipulations of symbolic objects, however, have been identified as crucial, if not indispensable, for several verification tasks. This paper proposes the first decision-diagram approach for operating symbolic objects and verifying quantum circuits with symbolic terms. As a notable example, our symbolic tensor decision diagrams (symbolic TDD) could verify the functionality of the 160-qubit quantum Fourier transform circuit within three minutes. Moreover, as demonstrated on Bernstein-Vazirani algorithm, Grover's algorithm, and the bit-flip error correction code, the symbolic TDD enables efficient verification of quantum circuits with user-supplied oracles and/or classical controls.