PACIAE 2.0: An updated parton and hadron cascade model (program) for the relativistic nuclear collisions

We have updated the parton and hadron cascade model PACIAE for the relativistic nuclear collisions, from based on JETSET 6.4 and PYTHIA 5.7 to based on PYTHIA 6.4, and renamed as PACIAE 2.0. The main physics concerning the stages of the parton initiation, parton rescattering, hadronization, and hadron rescattering were discussed. The structures of the programs were briefly explained. In addition, some calculated examples were compared with the experimental data. It turns out that this model (program) works well.Comment: 23 pages, 7 figure

Investigation of a non-Hermitian edge burst with time-dependent perturbation theory

Edge burst is a phenomenon in non-Hermitian quantum dynamics discovered by a recent numerical study [W.-T. Xue, et al, Phys. Rev. Lett 2, 128.120401(2022)]. It finds that a large proportion of particle loss occurs at the system boundary in a class of non-Hermitian quantum walk. In this paper, we investigate the evolution of real-space wave functions for this lattice system. We find the wave function of the edge site is distinct from the bulk sites. Using time-dependent perturbation theory, we derive the analytical expression of the real-space wave functions and find that the different evolution behaviors between the edge and bulk sites are due to their different nearest-neighbor site configurations. We also find the edge wave function primarily results from the transition of the two nearest-neighbor non-decay sites. Besides, the numerical diagonalization shows the edge wave function is mainly propagated by a group of eigen-modes with a relatively large imaginary part. Our work provides an analytical method for studying non-Hermitian quantum dynamical problems.Comment: 11 pages, 7 figure

Δ\Delta-scaling and heat capacity in relativistic ion collisions

The $\Delta$-scaling method has been applied to the total multiplicity distribution of the relativistic ion collisions of p+p, C+C and Pb+Pb which were simulated by a Monte Carlo package, LUCIAE 3.0. It is found that the $\Delta$-scaling parameter decreases with the increasing of the system size. Moreover, the heat capacities of different mesons and baryons have been extracted from the event-by-event temperature fluctuation in the region of low transverse mass and they show the dropping trend with the increasing of impact parameter.Comment: version 2: major change: 4 pages, 3 figures; Proceeding of International Conference on "Strangeness in Quark Matter" (SQM2004), Cape Town, South Africa, Spet. 2004 (Submitted to J. Phys. G.

Imaginary Stark Skin Effect

The non-Hermitian skin effect (NHSE) is a unique phenomenon in non-Hermitian systems. However, studies on NHSE in systems without translational symmetry remain largely unexplored. Here, we unveil a new class of NHSE, dubbed "imaginary Stark skin effect" (ISSE), in a one-dimensional lossy lattice with a spatially increasing loss rate. The energy spectrum of this model exhibits a T-shaped feature, with approximately half of the eigenstates localized at the left boundary. These skin modes exhibit peculiar behaviors, expressed as a single stable exponential decay wave within the bulk region. We use the transfer matrix method to analyze the formation of the ISSE in this model. According to the eigen-decomposition of the transfer matrix, the wave function is divided into two parts, one of which dominates the behavior of the skin modes in the bulk. Our findings provide insights into the NHSE in systems without translational symmetry and contribute to the understanding of non-Hermitian systems in general.Comment: 5+9 pages 4+6 figure

Instance-specific and Model-adaptive Supervision for Semi-supervised Semantic Segmentation

Recently, semi-supervised semantic segmentation has achieved promising performance with a small fraction of labeled data. However, most existing studies treat all unlabeled data equally and barely consider the differences and training difficulties among unlabeled instances. Differentiating unlabeled instances can promote instance-specific supervision to adapt to the model's evolution dynamically. In this paper, we emphasize the cruciality of instance differences and propose an instance-specific and model-adaptive supervision for semi-supervised semantic segmentation, named iMAS. Relying on the model's performance, iMAS employs a class-weighted symmetric intersection-over-union to evaluate quantitative hardness of each unlabeled instance and supervises the training on unlabeled data in a model-adaptive manner. Specifically, iMAS learns from unlabeled instances progressively by weighing their corresponding consistency losses based on the evaluated hardness. Besides, iMAS dynamically adjusts the augmentation for each instance such that the distortion degree of augmented instances is adapted to the model's generalization capability across the training course. Not integrating additional losses and training procedures, iMAS can obtain remarkable performance gains against current state-of-the-art approaches on segmentation benchmarks under different semi-supervised partition protocols

Beyond Attentive Tokens: Incorporating Token Importance and Diversity for Efficient Vision Transformers

Vision transformers have achieved significant improvements on various vision tasks but their quadratic interactions between tokens significantly reduce computational efficiency. Many pruning methods have been proposed to remove redundant tokens for efficient vision transformers recently. However, existing studies mainly focus on the token importance to preserve local attentive tokens but completely ignore the global token diversity. In this paper, we emphasize the cruciality of diverse global semantics and propose an efficient token decoupling and merging method that can jointly consider the token importance and diversity for token pruning. According to the class token attention, we decouple the attentive and inattentive tokens. In addition to preserving the most discriminative local tokens, we merge similar inattentive tokens and match homogeneous attentive tokens to maximize the token diversity. Despite its simplicity, our method obtains a promising trade-off between model complexity and classification accuracy. On DeiT-S, our method reduces the FLOPs by 35% with only a 0.2% accuracy drop. Notably, benefiting from maintaining the token diversity, our method can even improve the accuracy of DeiT-T by 0.1% after reducing its FLOPs by 40%

Autism as a disorder of neural information processing: directions for research and targets for therapy

The broad variation in phenotypes and severities within autism spectrum disorders suggests the involvement of multiple predisposing factors, interacting in complex ways with normal developmental courses and gradients. Identification of these factors, and the common developmental path into which theyfeed, is hampered bythe large degrees of convergence from causal factors to altered brain development, and divergence from abnormal brain development into altered cognition and behaviour. Genetic, neurochemical, neuroimaging and behavioural findings on autism, as well as studies of normal development and of genetic syndromes that share symptoms with autism, offer hypotheses as to the nature of causal factors and their possible effects on the structure and dynamics of neural systems. Such alterations in neural properties may in turn perturb activity-dependent development, giving rise to a complex behavioural syndrome many steps removed from the root causes. Animal models based on genetic, neurochemical, neurophysiological, and behavioural manipulations offer the possibility of exploring these developmental processes in detail, as do human studies addressing endophenotypes beyond the diagnosis itself