Anti-shadowing Effect on Charmonium Production at a Fixed-target Experiment Using LHC Beams

We investigate charmonium production in Pb+Pb collisions at LHC beam energy $E_{\text {lab}}$=2.76 A TeV at fixed-target experiment ($\sqrt {s_{\text{NN}}}$=72 GeV). In the frame of a transport approach including cold and hot nuclear matter effects on charmonium evolution, we focus on the anti-shadowing effect on the nuclear modification factors $R_{AA}$ and $r_{AA}$ for the $J/\psi$ yield and transverse momentum. The yield is more suppressed at less forward rapidity ($y_\text{lab}\simeq$2) than that at very forward rapidity ($y_\text{lab}\simeq$4) due to the shadowing and anti-shadowing in different rapidity bins.Comment: 7 pages, 3 figures; submitted to Advances in High Energy Physics. arXiv admin note: text overlap with arXiv:1409.555

Thermal Charm and Charmonium Production in Quark Gluon Plasma

We study the effect of thermal charm production on charmonium regeneration in high energy nuclear collisions. By solving the kinetic equations for charm quark and charmonium distributions in Pb+Pb collisions, we calculate the global and differential nuclear modification factors $R_{AA}(N_{part})$ and $R{AA}(p_t)$ for $J/\Psi$s. Due to the thermal charm production in hot medium, the charmonium production source changes from the initially created charm quarks at SPS, RHIC and LHC to the thermally produced charm quarks at Future Circular Collider (FCC), and the $J/\Psi$ suppression ($R_{AA}<1$) observed so far will be replaced by a strong enhancement ($R_{AA}>1$) at FCC at low transverse momentum.Comment: 6 pages, 3 figure

Deep recurrent spiking neural networks capture both static and dynamic representations of the visual cortex under movie stimuli

In the real world, visual stimuli received by the biological visual system are predominantly dynamic rather than static. A better understanding of how the visual cortex represents movie stimuli could provide deeper insight into the information processing mechanisms of the visual system. Although some progress has been made in modeling neural responses to natural movies with deep neural networks, the visual representations of static and dynamic information under such time-series visual stimuli remain to be further explored. In this work, considering abundant recurrent connections in the mouse visual system, we design a recurrent module based on the hierarchy of the mouse cortex and add it into Deep Spiking Neural Networks, which have been demonstrated to be a more compelling computational model for the visual cortex. Using Time-Series Representational Similarity Analysis, we measure the representational similarity between networks and mouse cortical regions under natural movie stimuli. Subsequently, we conduct a comparison of the representational similarity across recurrent/feedforward networks and image/video training tasks. Trained on the video action recognition task, recurrent SNN achieves the highest representational similarity and significantly outperforms feedforward SNN trained on the same task by 15% and the recurrent SNN trained on the image classification task by 8%. We investigate how static and dynamic representations of SNNs influence the similarity, as a way to explain the importance of these two forms of representations in biological neural coding. Taken together, our work is the first to apply deep recurrent SNNs to model the mouse visual cortex under movie stimuli and we establish that these networks are competent to capture both static and dynamic representations and make contributions to understanding the movie information processing mechanisms of the visual cortex

Deep Spiking Neural Networks with High Representation Similarity Model Visual Pathways of Macaque and Mouse

Deep artificial neural networks (ANNs) play a major role in modeling the visual pathways of primate and rodent. However, they highly simplify the computational properties of neurons compared to their biological counterparts. Instead, Spiking Neural Networks (SNNs) are more biologically plausible models since spiking neurons encode information with time sequences of spikes, just like biological neurons do. However, there is a lack of studies on visual pathways with deep SNNs models. In this study, we model the visual cortex with deep SNNs for the first time, and also with a wide range of state-of-the-art deep CNNs and ViTs for comparison. Using three similarity metrics, we conduct neural representation similarity experiments on three neural datasets collected from two species under three types of stimuli. Based on extensive similarity analyses, we further investigate the functional hierarchy and mechanisms across species. Almost all similarity scores of SNNs are higher than their counterparts of CNNs with an average of 6.6%. Depths of the layers with the highest similarity scores exhibit little differences across mouse cortical regions, but vary significantly across macaque regions, suggesting that the visual processing structure of mice is more regionally homogeneous than that of macaques. Besides, the multi-branch structures observed in some top mouse brain-like neural networks provide computational evidence of parallel processing streams in mice, and the different performance in fitting macaque neural representations under different stimuli exhibits the functional specialization of information processing in macaques. Taken together, our study demonstrates that SNNs could serve as promising candidates to better model and explain the functional hierarchy and mechanisms of the visual system.Comment: Accepted by Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI-23

Wireless Powered Sensor Networks for Internet of Things: Maximum Throughput and Optimal Power Allocation

This paper investigates a wireless powered sensor network (WPSN), where multiple sensor nodes are deployed to monitor a certain external environment. A multi-antenna power station (PS) provides the power to these sensor nodes during wireless energy transfer (WET) phase, and consequently the sensor nodes employ the harvested energy to transmit their own monitoring information to a fusion center (FC) during wireless information transfer (WIT) phase. The goal is to maximize the system sum throughput of the sensor network, where two different scenarios are considered, i.e., PS and the sensor nodes belong to the same or different service operator(s). For the first scenario, we propose a global optimal solution to jointly design the energy beamforming and time allocation. We further develop a closed-form solution for the proposed sum throughput maximization. For the second scenario in which the PS and the sensor nodes belong to different service operators, energy incentives are required for the PS to assist the sensor network. Specifically, the sensor network needs to pay in order to purchase the energy services released from the PS to support WIT. In this case, the paper exploits this hierarchical energy interaction, which is known as energy trading. We propose a quadratic energy trading based Stackelberg game, linear energy trading based Stackelberg game, and social welfare scheme, in which we derive the Stackelberg equilibrium for the formulated games, and the optimal solution for the social welfare scheme. Finally, numerical results are provided to validate the performance of our proposed schemes

A theoretical investigation on the parametric instability excited by X-mode polarized electromagnetic wave at Tromsø

Recent ionospheric modification experiments performed at Tromsø, Norway, have indicated that X-mode pump wave is capable of stimulating high-frequency enhanced plasma lines, which manifests the excitation of parametric instability. This paper investigates theoretically how the observation can be explained by the excitation of parametric instability driven by X-mode pump wave. The threshold of the parametric instability has been calculated for several recent experimental observations at Tromsø, illustrating that our derived equations for the excitation of parametric instability for X-mode heating can explain the experimental observations. According to our theoretical calculation, a minimum fraction of pump wave electric field needs to be directed along the geomagnetic field direction in order for the parametric instability threshold to be met. A full-wave finite difference time domain simulation has been performed to demonstrate that a small parallel component of pump wave electric field can be achieved during X-mode heating in the presence of inhomogeneous plasma

Robust Design for Intelligent Reflecting Surface-Assisted Secrecy SWIPT Network

This paper investigates the robust beamforming design in a secrecy multiple-input single-output (MISO) network aided by the intelligent reflecting surface (IRS) with simultaneous wireless information and power transfer (SWIPT). Specifically, by considering that the energy receivers (ERs) are potential eavesdroppers (Eves) and imperfect channel state information (CSI) of the direct and cascaded channels can be obtained, we investigate the max-min fairness robust secrecy design. The objective is to maximize the minimum robust information rate among the legitimate information receivers (IRs). To solve the formulated non-convex design problem in bounded and probabilistic CSI error models, we utilize the alternating optimization (AO) and successive convex approximation (SCA) methods to obtain an approximate problem. Then, an iteration-based algorithm framework was proposed, where the unit modulus constraint (UMC) of the IRS is handled by the penalty dual decomposition (PDD) method. Moreover, a stochastic SCA method is proposed to handle the outage constrained design with statistical CSI. Finally, simulation results validate the promising performance of the proposed design

The Mechanism of Intragranular Acicular Ferrite Nucleation Induced by Mg-Al-O Inclusions

The features of inclusion and microstructure for carbon structural steel containing Mg-Al-O inclusions were studied through the scanning electron microscope (SEM) and Energy Dispersive Spectrometer (EDS). It can be seen that, in Mg-Al-O inclusions, the elements of Mn, Si, and S coexist, and their central mole ratio of Mg/Al varies in a wide range. This value for most inclusions is larger than 0.5, which suggests the formation of solid solution between MgAl2O4 and MgO. After etching, the typical microstructure of intragranular acicular ferrites is observed, which is due to the nucleation effect induced by Mg-Al-O inclusions. From the SEM-EDS mapping images, it is found that the element of sulfur accumulates on the periphery of nucleation inclusion. Moreover, line EDS analysis hints that Mn-depletion zone (MDZ) exists in steel matrix, which is adjacent to the complex inclusion. Combined with the theoretical analysis, this phenomenon can be explained by the absorption of Mn due to the magnesium vacancy in MgAl2O4, and this MDZ promotes the nucleation of intragranular acicular ferrite. Through statistical analysis of SEM images for microstructure, the probabilistic nature of inducing nucleation effect is revealed. These results may be helpful to clarify the nature of oxide metallurgy