张量网络与神经网络在物理学中的应用和交融

基于张量网络的数值重正化群方法,被广泛地应用到物理学的研究中,已经成为量子多体计算方法大家庭的重要一员。近年来,基于神经网络的机器学习方法也逐渐渗透到物理学领域,并被成功应用在量子多体等问题的研究中。文章简要综述了近年来张量网络和神经网络在凝聚态物理和统计物理学的应用,并讨论了两者的相互交叉和结合。     

A review of high-energy strong-interaction physics

La Rivista del Nuovo Cimento (1971-1977) -     

Spin physics in high-energy hardon interaction

Data on single-spin asymmetry (A_N) in high-energy hadron-hadron collisions are discussed. The data are classified according to the beamand target types. The single-spin asymmetry A_N is considered as a function of kinematical variables and the sort of participant particles. Data from the PROZA and FODS facilities operating in beams of the IHEP accelerator are presented. The origin of a large single-spin asymmetry is briefly considered.     

Probing new physics scale with high-energy neutrinos

Motivated by the new physics beyond the Standard Model through the scale at which neutrino mass has origin, we investigate its possible manifestation at low energy within the corresponding propagation of the energetic neutrino. Then, we consider the SN1987A and base on the recorded neutrino data to explore the scale range of the new physics.     

Properties of certain collective variables used in high-energy physics

The properties of the collective variables _m (m=1, 2), T, and P, which are used extensively in high-energy physics, are investigated. It is shown that the variables _m are intimately related to the dipole (m=1) and quadrupole (m=2) moments. A general equation is given for calculating the variance of _m for azimuth angles having any probability density function; well-known special cases are deduced from this equation. Two collective variables, the oblateness T and the prolateness P, have the property of stability when the momentum distribution of particles in the ground state is symmetric about some axis. This property should be useful in searches for events of the jet and vortex type.Deceased.A. R. Beruni State Technical University, Tashkent. Translated from Izvestiya Vysshikh Uchebnykh Zavedenii, Fizika, No. 3, pp. 79–83, March, 1993.     

Thouless-anderson-palmer equations for neural networks

Previous derivation of the Thouless-Anderson-Palmer (TAP) equations for the Hopfield model by the cavity method yielded results that were inconsistent with those of the perturbation theory as well as the results derived by the replica theory of the model. Here we present a derivation of the TAP equation for the Hopfield model by the cavity method and show that it agrees with the form derived by perturbation theory. We also use the cavity method to derive TAP equations for the pseudoinverse neural network model. These equations are consistent with the results of the replica theory of these models.     

Estimating perturbative coefficients in high-energy physics and condensed matter theory

Using our method to estimate perturbative coefficients in quantum field theory (QFT), we consider several examples in high-energy physics and condensed matter theory. The results, in all cases, are remarkably good for the known terms. We also predict the values of as yet unknown terms. Moreover, we consider the general convergence properties of asymptotic series in QFT.     

LAA: a project using dedicated funding to develop technology for high-energy physics experiments

The European Physical Journal H - In the mid-1980s, the cost of investment in infrastructure for particle accelerators and colliders at the highest energy had risen to such level that the host...     

Sparse coding for layered neural networks

We investigate storage capacity of two types of fully connected layered neural networks with sparse coding when binary patterns are embedded into the networks by a Hebbian learning rule. One of them is a layered network, in which a transfer function of even layers is different from that of odd layers. The other is a layered network with intra-layer connections, in which the transfer function of inter-layer is different from that of intra-layer, and inter-layered neurons and intra-layered neurons are updated alternately. We derive recursion relations for order parameters by means of the signal-to-noise ratio method, and then apply the self-control threshold method proposed by Dominguez and Bollé to both layered networks with monotonic transfer functions. We find that a critical value _C of storage capacity is about 0.11|a ln a|⁻¹ (a1) for both layered networks, where a is a neuronal activity. It turns out that the basin of attraction is larger for both layered networks when the self-control threshold method is applied.     

Computing for ongoing experiments on high-energy physics at Laboratory of Particle Physics at JINR

A computer infrastructure constructed at JINR’s Laboratory of Particle Physics, purposed for the effective participation of JINR’s experts in ongoing experiments in particle and nuclear physics is presented. The principles of the design and construction of the PC farm are outlined, and the computer and information services used for effective application of distributed computer and information resources are described.     

非线性物理破解神经编码机制

信息在神经系统中以脉冲点序列的形式传输和处理，神经系统如何通过脉冲点序列对所表达的信息进行编码，一直是一个谜．人们曾普遍认为，神经可能通过发放率（单位时间发放脉冲出现的次数）对信息编码，也有人猜测，神经可通过脉冲点序列的时序编码．由于神经发放的随机性，这就使得任何编码机制都面临着被表达信息的确定性与表达该信息的信号的随机性的矛盾．通过神经非线性随机动力学模型，文章作者发现，神经点序列的发放率对点序列的时序信息传输的影响，揭示了神经点序列时序信息在神经非线性传输中的随机共振特征．由此预期，并进一步通过听觉心理物理实验观察到，在一定条件下噪声对听觉的增强作用．从而通过非线性物理揭示了听觉时序编码机制的存在以及在时序编码中随机性噪声的积极作用．     

非线性物理破解神经编码机制

信息在神经系统中以脉冲点序列的形式传输和处理,神经系统如何通过脉冲点序列对所表达的信息进行编码,一直是一个谜.人们曾普遍认为,神经可能通过发放率（单位时间发放脉冲出现的次数）对信息编码,也有人猜测,神经可通过脉冲点序列的时序编码.由于神经发放的随机性,这就使得任何编码机制都面临着被表达信息的确定性与表达该信息的信号的随机性的矛盾.通过神经非线性随机动力学模型,文章作者发现,神经点序列的发放率对点序列的时序信息传输的影响,揭示了神经点序列时序信息在神经非线性传输中的随机共振特征.由此预期,并进一步通过听觉心