Rapidity dependent transverse momentum spectra of heavy quarkonia produced in small collision systems at the LHC

The rapidity dependent transverse momentum spectra of heavy quarkonia (J/psi and Upsilon mesons) produced in small collision systems such as proton-proton (pp) and proton-lead (p-Pb) collisions at center-of-mass energy (per nucleon pair) 5-13 TeV are described by a two-component statistical model which is based on the Tsallis statistics and inverse power-law. The experimental data measured by the LHCb Collaboration at the Large Hadron Collider (LHC) are well fitted by the model results. The related parameters are obtained and the dependences of parameters on rapidity are analyzed.Comment: 17 pages, 11 figures. Advances in High Energy Physics, accepte

Unsupervised Ranking of Multi-Attribute Objects Based on Principal Curves

Unsupervised ranking faces one critical challenge in evaluation applications, that is, no ground truth is available. When PageRank and its variants show a good solution in related subjects, they are applicable only for ranking from link-structure data. In this work, we focus on unsupervised ranking from multi-attribute data which is also common in evaluation tasks. To overcome the challenge, we propose five essential meta-rules for the design and assessment of unsupervised ranking approaches: scale and translation invariance, strict monotonicity, linear/nonlinear capacities, smoothness, and explicitness of parameter size. These meta-rules are regarded as high level knowledge for unsupervised ranking tasks. Inspired by the works in [8] and [14], we propose a ranking principal curve (RPC) model, which learns a one-dimensional manifold function to perform unsupervised ranking tasks on multi-attribute observations. Furthermore, the RPC is modeled to be a cubic B\'ezier curve with control points restricted in the interior of a hypercube, thereby complying with all the five meta-rules to infer a reasonable ranking list. With control points as the model parameters, one is able to understand the learned manifold and to interpret the ranking list semantically. Numerical experiments of the presented RPC model are conducted on two open datasets of different ranking applications. In comparison with the state-of-the-art approaches, the new model is able to show more reasonable ranking lists.Comment: This paper has 14 pages and 9 figures. The paper has submitted to IEEE Transactions on Knowledge and Data Engineering (TKDE

Formulation of transverse mass distributions in Au-Au collisions at 200 GeV/nucleon

The transverse mass spectra of light mesons produced in Au-Au collisions at 200 GeV/nucleon are analyzed in Tsallis statistics. In high energy collisions, it has been found that the spectra follow a generalized scaling law. We applied Tsallis statistics to the description of different particles using the scaling properties. The calculated results are in agreement with experimental data of PHENIX Collaboration. And, the temperature of emission sources is extracted consistently.Comment: 8 pages, 11 figure

Dependence of elliptic flow on transverse momentum in 200 GeV Au-Au and 2.76 TeV Pb-Pb collisions

We investigate the dependence of elliptic flows $v_2$ on transverse momentum $P_T$ for charged hadrons produced in nucleus-nucleus collisions at high energy by using a multi-source ideal gas model which includes the interaction contribution of the emission sources. Our calculated results are approximately in agreement with the experimental data over a wider $P_T$ range from the STAR and ALICE Collaborations. It is found that the expansion factor increases linearly with the impact parameter from most central (0-5%) to mid-peripheral (35-40%) collisions.Comment: 8 pages, 4 figure

Kinetic freeze-out temperatures in central and peripheral collisions: Which one is larger?

The kinetic freeze-out temperatures, $T_0$, in nucleus-nucleus collisions at the Relativistic Heavy Ion Collider (RHIC) and Large Hadron Collider (LHC) energies are extracted by four methods: i) the Blast-Wave model with Boltzmann-Gibbs statistics (the BGBW model), ii) the Blast-Wave model with Tsallis statistics (the TBW model), iii) the Tsallis distribution with flow effect (the improved Tsallis distribution), and iv) the intercept in $T=T_0+am_0$ (the alternative method), where $m_0$ denotes the rest mass and $T$ denotes the effective temperature which can be obtained by different distribution functions. It is found that the relative sizes of $T_0$ in central and peripheral collisions obtained by the conventional BGBW model which uses a zero or nearly zero transverse flow velocity, $\beta_T$, are contradictory in tendency with other methods. With a re-examination for $\beta_T$ in the first method in which $\beta_T$ is taken to be $\sim(0.40\pm0.07)c$, a recalculation presents a consistent result with others. Finally, our results show that the kinetic freeze-out temperature in central collisions is larger than that in peripheral collisions.Comment: 22 pages, 11 figures. Nuclear Science and Techniques, accepte

Self-induced spontaneous transport of water molecules through a symmetrical nanochannel by ratchetlike mechanism

Water molecules, confined in a carbon nanotube, were monitored using molecular dynamics simulation. Spontaneous directional transportation during a long timescale was observed in the symmetrical nanochannel by a ratchet-like mechanism. This ratchet-like system was without any asymmetrical structure or external field, while the asymmetric ratchet-like potential solely resulted from the transported water molecules that formed hydrogen-bonded chains. Remarkably, the resulting net water fluxes reached the level of the biological channel and the average duration for spontaneous directional transportation reached the timescale of many biomolecular functions. This is the first report that heat energy from the surroundings can be used to drive molecules uni-directionally during a long timescale in a nanochannel system. This effect is ascribed to the unique structure of the water molecule.Comment: 9 pages and two figure

First-principles based analysis of thermal transport in metallic nanostructures: size effect and Wiedemann-Franz law

Metallic nanostructures (the nanofilms and nanowires) are widely used in electronic devices, and their thermal transport properties are crucial for heat dissipation. However, there are still gaps in understanding thermal transport in metallic nanostructures, especially regarding the size effect and validity of the Wiedemann-Franz law. In this work, we perform mode-by-mode first-principles calculations combining the Boltzmann transport equation to understand thermal transport in metallic nanostructures. We take the gold (Au) and tungsten (W) nanostructures as prototypes. It is found that when the size of nanostructures is on the order of several tens of nanometers, the electronic/phonon thermal conductivity is smaller than the bulk value and decreases with size. The phonon contribution increases in nanostructures for those metals with small bulk phonon thermal conductivity (like Au), while the phonon contribution may increase or be suppressed in nanostructures for those metals with large bulk phonon thermal conductivity (like W). By assuming that the grain boundary does not induce inelastic electron-phonon scattering, the Wiedemann-Franz law works well in both Au and W nanostructures if the Lorentz ratio is estimated using electronic thermal conductivity. The Wiedemann-Franz law also works well in Au nanostructures when the Lorentz ratio is estimated by total thermal conductivity.Comment: 22 pages, 9 figure

LGM-Net: Learning to Generate Matching Networks for Few-Shot Learning

In this work, we propose a novel meta-learning approach for few-shot classification, which learns transferable prior knowledge across tasks and directly produces network parameters for similar unseen tasks with training samples. Our approach, called LGM-Net, includes two key modules, namely, TargetNet and MetaNet. The TargetNet module is a neural network for solving a specific task and the MetaNet module aims at learning to generate functional weights for TargetNet by observing training samples. We also present an intertask normalization strategy for the training process to leverage common information shared across different tasks. The experimental results on Omniglot and miniImageNet datasets demonstrate that LGM-Net can effectively adapt to similar unseen tasks and achieve competitive performance, and the results on synthetic datasets show that transferable prior knowledge is learned by the MetaNet module via mapping training data to functional weights. LGM-Net enables fast learning and adaptation since no further tuning steps are required compared to other meta-learning approaches.Comment: To appear in ICML201

Incremental Concept Learning via Online Generative Memory Recall

The ability to learn more and more concepts over time from incrementally arriving data is essential for the development of a life-long learning system. However, deep neural networks often suffer from forgetting previously learned concepts when continually learning new concepts, which is known as catastrophic forgetting problem. The main reason for catastrophic forgetting is that the past concept data is not available and neural weights are changed during incrementally learning new concepts. In this paper, we propose a pseudo-rehearsal based class incremental learning approach to make neural networks capable of continually learning new concepts. We use a conditional generative adversarial network to consolidate old concepts memory and recall pseudo samples during learning new concepts and a balanced online memory recall strategy is to maximally maintain old memories. And we design a comprehensible incremental concept learning network as well as a concept contrastive loss to alleviate the magnitude of neural weights change. We evaluate the proposed approach on MNIST, Fashion-MNIST and SVHN datasets and compare with other rehearsal based approaches. The extensive experiments demonstrate the effectiveness of our approach

Examining the model dependence of the determination of kinetic freeze-out temperature and transverse flow velocity in small collision system

The transverse momentum distributions of the identified particles produced in small collision systems at the Relativistic Heavy Ion Collider (RHIC) and Large Hadron Collider (LHC) have been analyzed by four models. The first two models utilize the blast-wave model with different statistics. The last two models employ certain linear correspondences based on different distributions. The four models describe the experimental data measured by the Pioneering High Energy Nuclear Interaction eXperiment (PHENIX), Solenoidal Tracker at RHIC (STAR), and A Large Ion Collider Experiment (ALICE) cCollaborations equally well. It is found that both the kinetic freeze-out temperature and transverse flow velocity in the central collisions are comparable with those in the peripheral collisions. With the increase of collision energy from that of the RHIC to that of the LHC, the considered quantities typically do not decrease. Comparing with the central collisions, the proton-proton collisions are closer to the peripheral collisions.Comment: 17 pages, 9 figures. Nuclear Science and Techniques, Accepted. arXiv admin note: text overlap with arXiv:1703.0494